Graeme Birchall (until Version 9.7, 2011)
Version 1.0, Rodney Krick, November 2019
Contributors: Andres Gomez Casanova

Creative Commons License
Db2 SQL Cookbook by Graeme Birchall is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://github.com/rodneykrick/db2-sql-cookbook.
Permissions beyond the scope of this license may be available at https://github.com/rodneykrick/db2-sql-cookbook

Introduction

I started using Db2 in 1991. Five years later, Graeme Birchall released the first version of his "Db2 SQL Cookbook". This book helped me to learn SQL and I used it a lot of times to learn new stuff and to check how he writes similar queries (the first time I can remember was when I took my first Db2 Certification. "IBM Certified Solutions Expert — DB2 UDB V5 Application Development", in October 2000 at IDUG in Geneva). The last version of the book I am aware of was published in 16 August 2011, based on version 9.7 of Db2 LUW. In the past years I’ve googled sometimes to check if there was a new version of the book and if someone decided to maintain it. I didn’t find any new version and, as far as I know, Graeme Birchall deleted everything he had (his homepage, where the book was published and every links that he maintained).

Because I’ve learned a lot and I found the book very good to help people that are starting with SQL, I decided to take the contents of the book as they were in the last published version and use it to initiate a new version of the book. The idea behind it is to show how SQL works and enrich it with examples of the daily work with SQL. I use SQL almost everyday in my work. As I’ve heard in one of the many conferences I’ve visited in the last 30 years, "SQL is a very easy language, when you formulate very easy queries". You will find easy queries for simple use cases and complex queries for complex use cases. This is not necessarily a rule: I’ve seen many complex queries for simple use cases and great simple queries for complex use cases. The best SQL statements follow the old good KISS rule: Keep It Simple and Stupid.

Somethings that I decided to change in this book, comparing to the original version:

  • I’ve migrated the text to asciidoc and opened a project in GitHub (https://github.com/rodneykrick/db2-sql-cookbook). As the people at Asciidoc use to say: "It’s just text, mate!" I believe with this format the community will be able to contribute and improve the content.

  • Graeme tried to keep syntax diagrams in his book. I’ll try to follow a more practical approach in this version of the book. You won’t find syntax diagrams, just sample code that was tested in Db2 (some will work in other RDBMS, it would be great and helpful if we manage to check and document this). If you want to learn more about the statement and the syntax, you should use the SQL reference book of your RDBMS, in the version you are using.

  • To publish the result (the last version of the book) I reserved a new domain (http://db2-sql-cookbook.org). I’ll try to generate a HTML Version of the book and upload it as frequently as possible. The PDF version can be found here: http://db2-sql-cookbook.org/pdf/Db2_SQL_Cookbook.pdf. It will be generated together with the HTML version.

Some notes:

  • I will manage the project in the first phase. I hope, the experts outside will join it, enrich and update the content (this is what I also intend to do). Maybe we get more and more people involved and keep this book up to date.

  • If someone knows Graeme Birchall please let him know that we are trying to keep his work going on. I only decided to "clone" his book because of his statements regarding the distribution of the book (see Why Free). I really searched for him in the www, but wasn’t successful. As I finished the first version I’ve put a message on the Db2 Listserver (hosted at http://www.idug.org) and got an answer from Ian Bjorhovde. He had the same idea in the past and contacted Graeme. Graeme gave him a copy of his book and wrote in his answer: "So think of this as one of those copyleft situations where I am putting the document in the public domain". Andres Gomez Casanova suggested to put the work under the Creative Commons Attribution-ShareAlike 4.0 International license with the following arguments: "With a specific license the original work from Graeme and any improvement will remain with the same rights, and prevent from any unfair use". I think this is a good thing!

  • And just another point: if you never worked with SQL, I recommend you to start with the SQL Tutorial in w3schools. It is a great site to start learing it! [https://www.w3schools.com/sql/default.asp]

Have fun!

Rodney Krick
rk@aformatik.de

1. Document history

(The history of the original book can bee seen at Graeme Birchall Book Editions Upload Dates)

Version Date Content

1.0

9.10.2019

Started new project in GitHub. Text transformed to asciidoc.
Syntax diagrams were removed.
Cross references updated.

1.1

24.11.2019

Added license terms

1.2

06.12.2019

Update functions to Db2 version 11.1

1.3

18.01.2020

Added Time Travel chapter.

2. Quick find

2.1. Index of Concepts

2.1.1. Join rows

To combine matching rows in multiple tables, use a join (see Joins).

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

EMP_JB

ID JOB

10

Sales

20

Clerk

Join example
SELECT nm.id
     , nm.name
	 , jb.job
FROM emp_nm nm
   , emp_jb jb
WHERE nm.id = jb.id
ORDER BY 1;

ANSWER

ID NAME JOB

10

Sanders

Sales

20

Pernal

Clerk

2.1.2. Outer Join

To get all of the rows from one table, plus the matching rows from another table (if there are any), use an outer join (see Join Types).

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

EMP_JB

ID JOB

10

Sales

20

Clerk

Left-outer-join example
SELECT nm.id ,nm.name ,jb.job
FROM emp_nm nm
LEFT OUTER JOIN emp_jb jb
ON nm.id = jb.id
ORDER BY nm.id;

ANSWER

ID NAME JOB

10

Sanders

Sales

20

Pernal

Clerk

50

Hanes

-

To get rows from either side of the join, regardless of whether they match (the join) or not, use a full outer join (see Full Outer Joins).

2.1.3. Null Values & Replace

Use the COALESCE function (see COALESCE) to replace a null value (e.g. generated in an outer join) with a non-null value.

2.1.4. Select Where No Match

To get the set of the matching rows from one table where something is true or false in another table (e.g. no corresponding row), use a sub-query (see Sub-Query).

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

EMP_JB

ID JOB

10

Sales

20

Clerk

Sub-query example
SELECT * FROM emp_nm nm
WHERE NOT EXISTS
  (SELECT * FROM emp_jb jb
   WHERE nm.id = jb.id)
ORDER BY id;

ANSWER

ID NAME

50

Hanes

2.1.5. Append Rows

To add (append) one set of rows to another set of rows, use a union (see Union, Intersect, and Except).

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

EMP_JB

ID JOB

10

Sales

20

Clerk

Union example
SELECT *
FROM emp_nm
WHERE emp_nm name < 'S'
  UNION
SELECT *
FROM emp_jb
ORDER BY 1, 2;

ANSWER

ID 2

10

Sales

20

Clerk

20

Pernal

50

Hanes

2.1.6. Assign Output Numbers

To assign line numbers to SQL output, use the ROW_NUMBER function (see ROW_NUMBER).

EMP_JB

ID JOB

10

Sales

20

Clerk

Assign row-numbers example
SELECT id
     , job
     , ROW_NUMBER() OVER(ORDER BY job) AS R
FROM emp_jb
ORDER BY job;

ANSWER

ID JOB R

20

Clerk

1

10

Sales

2

2.1.7. Assign Unique Key Numbers

To make each row inserted into a table automatically get a unique key value, use an identity column, or a sequence, when creating the table (see Identity Columns and Sequences).

2.1.8. If-Then-Else Logic

To include if-then-else logical constructs in SQL stmts, use the CASE phrase (see CASE Expression).

EMP_JB

ID JOB

10

Sales

20

Clerk

Case stmt example
SELECT id
     , job
     , CASE
         WHEN job = 'Sales' THEN 'Fire'
         ELSE 'Demote'
       END AS STATUS
 FROM emp_jb;

ANSWER

ID JOB STATUS

10

Sales

Fire

20

Clerk

Demote

2.1.9. Get Dependents

To get all of the dependents of some object, regardless of the degree of separation from the parent to the child, use recursion (see Recursive SQL).

FAMILY

PARNT CHILD

GrDad

Dad

Dad

Dghtr

Dghtr

GrSon

Dghtr

GrDtr

Recursion example
WITH temp (persn, lvl)
AS (SELECT parnt, 1
    FROM family
    WHERE parnt = 'Dad'
      UNION ALL
    SELECT child, Lvl + 1
    FROM temp, family
    WHERE persn = parnt)
SELECT * FROM temp;

ANSWER

PERSN LVL

Dad

1

Dghtr

2

GrSon

3

GrDtr

3

2.1.10. Convert String to Rows

To convert a (potentially large) set of values in a string (character field) into separate rows (e.g. one row per word), use recursion (see Recursive SQL).

INPUT DATA "Some silly text" Use Recursive SQL

ANSWER

Table 1. Convert string to rows
TEXT LINE#

Some

1

silly

2

text

3

Be warned - in many cases, the code is not pretty.

2.1.11. Convert Rows to String

To convert a (potentially large) set of values that are in multiple rows into a single combined field, use recursion (see Recursive SQL).

INPUT DATA

TEXT LINE#

Some

1

silly

2

text

3

Use Recursive SQL

ANSWER: "Some silly text"

2.1.12. Fetch First "n" Rows

To fetch the first "n" matching rows, use the FETCH FIRST notation (see FETCH FIRST Clause).

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

Fetch first "n" rows example
SELECT * FROM
emp_nm
ORDER BY id DESC
FETCH FIRST 2 ROWS ONLY;

ANSWER

ID NAME

50

Hanes

20

Pernal

Another way to do the same thing is to assign row numbers to the output, and then fetch those rows where the row-number is less than "n" (see Selecting "n" Rows).

2.1.13. Fetch Subsequent "n" Rows

To the fetch the "n" through "n + m" rows, first use the ROW_NUMBER function to assign output numbers, then put the result in a nested-table-expression, and then fetch the rows with desired numbers.

2.1.14. Fetch Uncommitted Data

To retrieve data that may have been changed by another user, but which they have yet to commit, use the WITH UR (Uncommitted Read) notation.

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

Fetch WITH UR example
SELECT *
FROM emp_nm
WHERE name like 'S%'
WITH UR;

ANSWER

ID NAME

10

Sanders

Using this option can result in one fetching data that is subsequently rolled back, and so was never valid. Use with extreme care.

2.1.15. Summarize Column Contents

Use a column function (see Column Functions or Aggregate Functions) to summarize the contents of a column.

EMP_NM

ID NAME

10

Sanders

20

Pernal

50

Hanes

Column Functions example
SELECT AVG(id)AS avg
      ,MAX(name) AS maxn
      ,COUNT(*) AS #rows
FROM emp_nm;

ANSWER

AVG MAXN #ROWS

26

Sanders

3

2.1.16. Subtotals and Grand Totals

To obtain subtotals and grand-totals, use the ROLLUP or CUBE statements (see ROLLUP Statement).

Subtotal and Grand-total example
SELECT job
      ,dept
      ,SUM(salary) AS sum_sal
      ,COUNT(*) AS #emps
FROM staff
WHERE dept < 30
AND salary < 90000
AND job < 'S'
GROUP BY ROLLUP(job, dept)
ORDER BY job, dept;

ANSWER

JOB DEPT SUM_SAL #EMPS

Clerk

15

84766.70

2

Clerk

20

77757.35

2

Clerk

-

162524.05

4

Mgr

10

243453.45

3

Mgr

15

80659.80

1

Mgr

-

324113.25

4

-

-

486637.30

8

2.1.17. Enforcing Data Integrity

When a table is created, various Db2 features can be used to ensure that the data entered in the table is always correct:

  • Uniqueness (of values) can be enforced by creating unique indexes.

  • Check constraints can be defined to limit the values that a column can have.

  • Default values (for a column) can be defined - to be used when no value is provided.

  • Identity columns (see Identity Columns and Sequences), can be defined to automatically generate unique numeric values (e.g. invoice numbers) for all of the rows in a table. Sequences can do the same thing over multiple tables.

  • Referential integrity rules can be created to enforce key relationships between tables.

  • Triggers can be defined to enforce more complex integrity rules, and also to do things (e.g. populate an audit trail) whenever data is changed.

See the Db2 manuals for documentation or Protecting Your Data for more information about the above.

2.1.18. Hide Complex SQL

One can create a view (see View) to hide complex SQL that is run repetitively. Be warned however that doing so can make it significantly harder to tune the SQL - because some of the logic will be in the user code, and some in the view definition.

2.2. Summary Table

Some queries that use a GROUP BY can be made to run much faster by defining a summary table (see Materialized Query Tables) that Db2 automatically maintains. Subsequently, when the user writes the original GROUP BY against the source-data table, the optimizer substitutes with a much simpler (and faster) query against the summary table.

3. Introduction to SQL

This chapter contains a basic introduction to Db2 SQL. It also has numerous examples illustrating how to use this language to answer particular business problems. However, it is not meant to be a definitive guide to the language. Please refer to the relevant IBM manuals for a more detailed description.

3.1. Syntax Diagram

The original book has lots of syntax diagrams in it. We decided not to put them here anymore. Syntax diagrams can be found in the SQL Reference of Db2 (or your prefered RDBMS). We only give you lots of examples how to use SQL. When you master this stuff, you should learn more and improve your knowledge using the "real" manuals.

3.2. SQL Comments

A comment in a SQL statement starts with two dashes and goes to the end of the line:

SQL Comment example
SELECT name
FROM staff
ORDER BY id;
-- this is a comment.
-- this is another comment.

Some Db2 command processors (e.g. db2batch on the PC, or SPUFI on the mainframe) can process intelligent comments. These begin the line with a --#SET phrase, and then identify the value to be set. In the following example, the statement delimiter is changed using an intelligent comment:

Set Delimiter example
--#SET DELIMITER !
SELECT name
FROM staff
WHERE id = 10!
--#SET DELIMITER ;
SELECT name
FROM staff
WHERE id = 20;

When using the Db2 Command Processor (batch) script, the default statement terminator can be set using the "-tdx" option, where "x" is the value have chosen.

See the section titled Special Character Usage for notes on how to refer to the statement delimiter in the SQL text.

3.3. Statement Delimiter

Db2 SQL does not come with a designated statement delimiter (terminator), though a semicolon is often used. A semi-colon cannot be used when writing a compound SQL statement (see Compound SQL) because that character is used to terminate the various subcomponents of the statement.

3.4. SQL Components

3.4.1. Db2 Objects

Db2 is a relational database that supports a variety of object types. In this section we shall overview those items which one can obtain data from using SQL.

Table

A table is an organized set of columns and rows. The number, type, and relative position, of the various columns in the table is recorded in the Db2 catalogue. The number of rows in the table will fluctuate as data is inserted and deleted. The CREATE TABLE statement is used to define a table. The following example will define the EMPLOYEE table, which is found in the Db2 sample database.

Db2 sample table – EMPLOYEE
CREATE TABLE employee
( empno     CHARACTER(6) NOT NULL
, firstnme  VARCHAR(12)  NOT NULL
, midinit   CHARACTER(1) NOT NULL
, lastname  VARCHAR(15)  NOT NULL
, workdept  CHARACTER(3)
, phoneno   CHARACTER(4)
, hiredate  DATE
, job       CHARACTER(8)
, edlevel   SMALLINT     NOT NULL
, sex       CHARACTER(1)
, birthdate DATE
, salary    DECIMAL(9,02)
, bonus     DECIMAL(9,02)
, comm      DECIMAL(9,02)
);
View

A view is another way to look at the data in one or more tables (or other views). For example, a user of the following view will only see those rows (and certain columns) in the EMPLOYEE table where the salary of a particular employee is greater than or equal to the average salary for their particular department.

Db2 sample view – EMPLOYEE_VIEW
CREATE VIEW employee_view AS
SELECT a.empno
     , a.firstnme
     , a.salary
     , a.workdept
FROM employee a
WHERE a.salary >=
(SELECT AVG(b.salary)
 FROM employee b
 WHERE a.workdept = b.workdept
);

A view need not always refer to an actual table. It may instead contain a list of values:

Define a view using a VALUES clause
CREATE VIEW silly (c1, c2, c3)
AS VALUES
 (11, 'AAA', SMALLINT(22))
,(12, 'BBB', SMALLINT(33))
,(13, 'CCC', NULL);

Selecting from the above view works the same as selecting from a table:

SELECT from a view that has its own data
SELECT c1, c2, c3
FROM silly
ORDER BY c1 ASC;

ANSWER

C1 C2 C3

11

AAA

22

12

BBB

33

13

CCC

-

We can go one step further and define a view that begins with a single value that is then manipulated using SQL to make many other values. For example, the following view, when selected from, will return 10,000 rows. Note however that these rows are not stored anywhere in the database - they are instead created on the fly when the view is queried.

Define a view that creates data on the fly
CREATE VIEW test_data AS
WITH temp1 (num1) AS
 (VALUES (1)
  UNION ALL
 SELECT num1 + 1
 FROM temp1
 WHERE num1 < 10000)
SELECT *
FROM temp1;
Alias

An alias is an alternate name for a table or a view. Unlike a view, an alias can not contain any processing logic. No authorization is required to use an alias other than that needed to access to the underlying table or view.

Define three aliases, the latter on the earlier
CREATE ALIAS employee_al1 FOR employee;
COMMIT;

CREATE ALIAS employee_al2 FOR employee_al1;
COMMIT;

CREATE ALIAS employee_al3 FOR employee_al2;
COMMIT;

Neither a view, nor an alias, can be linked in a recursive manner (e.g. V1 points to V2, which points back to V1). Also, both views and aliases still exist after a source object (e.g. a table) has been dropped. In such cases, a view, but not an alias, is marked invalid.

Nickname

A nickname is the name that one provides to Db2 for either a remote table, or a non-relational object that one wants to query as if it were a table.

Define a nickname
CREATE NICKNAME emp FOR unixserver.production.employee;
Tablesample

Use of the optional TABLESAMPLE reference enables one to randomly select (sample) some fraction of the rows in the underlying base table:

TABLESAMPLE example
SELECT *
FROM staff
TABLESAMPLE BERNOULLI(10);

See Randomly Sample Data for information on using the TABLESAMPLE feature.

3.4.2. Db2 Data Types

Db2 comes with the following standard data types:

  • SMALLINT, INT, and BIGINT (i.e. integer numbers).

  • FLOAT, REAL, and DOUBLE (i.e. floating point numbers).

  • DECIMAL and NUMERIC (i.e. decimal numbers).

  • DECFLOAT (i.e. decimal floating-point numbers).

  • CHAR, VARCHAR, and LONG VARCHAR (i.e. character values).

  • GRAPHIC, VARGRAPHIC, and LONG VARGRAPHIC (i.e. graphical values).

  • BLOB, CLOB, and DBCLOB (i.e. binary and character long object values).

  • DATE, TIME, and TIMESTAMP (i.e. date/time values).

  • DATALINK (i.e. link to external object).

  • XML (i.e. contains well formed XML data).

Below is a simple table definition that uses some of the above data types:

Sample table definition
CREATE TABLE sales_record
(sales#         INTEGER       NOT NULL
                GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1)
, sale_ts       TIMESTAMP     NOT NULL
, num_items     SMALLINT      NOT NULL
, payment_type  CHAR(2)       NOT NULL
, sale_value    DECIMAL(12,2) NOT NULL
, sales_tax     DECIMAL(12,2)
, employee#     INTEGER       NOT NULL
, CONSTRAINT sales1 CHECK (payment_type IN ('CS','CR'))
, CONSTRAINT sales2 CHECK (sale_value > 0)
, CONSTRAINT sales3 CHECK (num_items > 0)
, CONSTRAINT sales4 FOREIGN KEY (employee#)
  REFERENCES staff (id) ON DELETE RESTRICT
, PRIMARY KEY (sales#)
);

In the above table, we have listed the relevant columns, and added various checks to ensure that the data is always correct. In particular, we have included the following:

  • The sales# is automatically generated (see Identity Columns and Sequences for details). It is also the primary key of the table, and so must always be unique.

  • The payment-type must be one of two possible values.

  • Both the sales-value and the num-items must be greater than zero.

  • The employee# must already exist in the staff table. Furthermore, once a row has been inserted into this table, any attempt to delete the related row from the staff table will fail.

Default Lengths

The following table has two columns:

Table with default column lengths
CREATE TABLE default_values
(c1 CHAR    NOT NULL
,d1 DECIMAL NOT NULL);

The length has not been provided for either of the above columns. In this case, Db2 defaults to CHAR(1) for the first column and DECIMAL(5,0) for the second column.

Data Type Usage

In general, use the standard Db2 data types as follows:

  • Always store monetary data in a decimal field.

  • Store non-fractional numbers in one of the integer field types.

  • Use floating-point when absolute precision is not necessary.

A Db2 data type is not just a place to hold data. It also defines what rules are applied when the data in manipulated. For example, storing monetary data in a Db2 floating-point field is a no-no, in part because the data-type is not precise, but also because a floating-point number is not manipulated (e.g. during division) according to internationally accepted accounting rules.

DECFLOAT Arithmetic

DECFLOAT numbers have quite different processing characteristics from the other number types. For a start, they support more values:

  • Zero.

  • Negative and positive numbers (e.g. -1234.56).

  • Negative and positive infinity.

  • Negative and positive NaN (i.e. Not a Number).

  • Negative and positive sNaN (i.e. signaling Not a Number).

NaN Usage

The value NaN represents the result of an arithmetic operation that does not return a number (e.g. the square root of a negative number), but is also not infinity. For example, the expression 0/0 returns NaN, while 1/0 returns infinity.

The value NaN propagates through any arithmetic expression. Thus the final result is always either positive or negative NaN, as the following query illustrates:

NaN arithmetic usage
SELECT    DECFLOAT(+1.23)        +  NaN  AS "  NaN"
        , DECFLOAT(-1.23)        +  NaN  AS "  NaN"
        , DECFLOAT(-1.23)        + -NaN  AS " -NaN"
        , DECFLOAT(+infinity)    +  NaN  AS "  NaN"
        , DECFLOAT(+sNaN)        +  NaN  AS "  NaN"
        , DECFLOAT(-sNaN)        +  NaN  AS " -NaN"
        , DECFLOAT(+NaN)         +  NaN  AS "  NaN"
        , DECFLOAT(-NaN)         +  NaN  AS " -NaN"
FROM sysibm.sysdummy1;
Any reference to a signaling NaN value in a statement (as above) will result in a warning message being generated.
Infinity Usage

The value infinity works similar to NaN. Its reference in an arithmetic expression almost always returns either positive or negative infinity (assuming NaN is not also present). The one exception is division by infinity, which returns a really small, but still finite, number:

Infinity arithmetic usage
SELECT  DECFLOAT(1) / +infinity AS      " 0E-6176"
    ,   DECFLOAT(1) * +infinity AS      " Infinity"
    ,   DECFLOAT(1) + +infinity AS      " Infinity"
    ,   DECFLOAT(1) - +infinity AS      "-Infinity"
    ,   DECFLOAT(1) / -infinity AS      " -0E-6176"
    ,   DECFLOAT(1) * -infinity AS      "-Infinity"
    ,   DECFLOAT(1) + -infinity AS      "-Infinity"
    ,   DECFLOAT(1) - -infinity AS      " Infinity"
FROM sysibm.sysdummy1;

The next query shows some situations where either infinity or NaN is returned:

DECFLOAT arithmetic results
SELECT  DECFLOAT(+1.23) / 0            AS " Infinity"
    ,   DECFLOAT(-1.23) / 0            AS "-Infinity"
    ,   DECFLOAT(+1.23) + infinity     AS " Infinity"
    ,   DECFLOAT(0)     / 0            AS "NaN"
    ,   DECFLOAT(infinity) + -infinity AS "NaN"
    ,   LOG(DECFLOAT(0))               AS "-Infinity"
    ,   LOG(DECFLOAT(-123))            AS "NaN"
    ,   SQRT(DECFLOAT(-123))           AS "NaN"
FROM sysibm.sysdummy1;
DECFLOAT Value Order

The DECFLOAT values have the following order, from low to high:

DECFLOAT value order
-NaN -sNan -infinity -1.2 -1.20 0 1.20 1.2 infinity sNaN NaN

Please note that the numbers 1.2 and 1.200 are "equal", but they will be stored as different values, and will have a different value order. The TOTALORDER function can be used to illustrate this. It returns one of three values:

  • Zero if the two values have the same order.

  • +1 if the first value has a higher order (even if it is equal).

  • -1 if the first value has a lower order (even if it is equal).

Equal values that may have different orders
WITH temp1 (d1, d2) AS
(VALUES (DECFLOAT(+1.0), DECFLOAT(+1.00))
       ,(DECFLOAT(-1.0), DECFLOAT(-1.00))
       ,(DECFLOAT(+0.0), DECFLOAT(+0.00))
       ,(DECFLOAT(-0.0), DECFLOAT(-0.00))
       ,(DECFLOAT(+0), DECFLOAT(-0)) )
SELECT TOTALORDER(d1,d2) AS TOTALORDER
FROM temp1;

ANSWER

TOTALORDER

1

-1

1

1

0

The NORMALIZE_DECFLOAT scalar function can be used to strip trailing zeros from a DECFLOAT value:

Remove trailing zeros
WITH temp1 (d1) AS
(VALUES (DECFLOAT(+0 ,16))
       ,(DECFLOAT(+0.0 ,16))
       ,(DECFLOAT(+0.00 ,16))
       ,(DECFLOAT(+0.000 ,16))
)
SELECT d1
     , HEX(d1)                     AS hex_d1
     , NORMALIZE_DECFLOAT(d1)      AS d2
     , HEX(NORMALIZE_DECFLOAT(d1)) AS hex_d2
FROM temp1;

ANSWER

D1 HEX_D1 D2 HEX_D2

0

0000000000003822

0

0000000000003822

0.0

0000000000003422

0

0000000000003822

0.00

0000000000003022

0

0000000000003822

0.000

0000000000002C22

0

0000000000003822

DECFLOAT Scalar Functions

The following scalar functions support the DECFLOAT data type:

  • COMPARE_DECFLOAT: Compares order of two DECFLOAT values.

  • DECFLOAT: Converts input value to DECFLOAT.

  • NORMALIZE_DECFLOAT: Removes trailing blanks from DECFLOAT value.

  • QUANTIZE: Converts number to DECFLOAT, using mask to define precision.

  • TOTALORDER: Compares order of two DECFLOAT values.

Date/Time Arithmetic

Manipulating date/time values can sometimes give unexpected results. What follows is a brief introduction to the subject. The basic rules are:

  • Multiplication and division is not allowed.

  • Subtraction is allowed using date/time values, date/time durations, or labeled durations.

  • Addition is allowed using date/time durations, or labeled durations.

The valid labeled durations are listed below:

Table 2. Labeled Durations and Date/Time Types
LABELED DURATIONS WORKS WITH DATE/TIME

SINGULAR

PLURAL

ITEM FIXED SIZE

DATE

TIME

TIMESTAMP

YEAR

YEARS

N

Y

-

Y

MONTH

MONTHS

N

Y

-

Y

DAY

DAYS

Y

Y

-

Y

HOUR

HOURS

Y

-

Y

Y

MINUTE

MINUTES

Y

-

Y

Y

SECOND

SECONDS

Y

-

Y

Y

MICROSECOND

MICROSECONDS

Y

-

Y

Y

Usage Notes

  • It doesn’t matter if one uses singular or plural. One can add "4 day" to a date.

  • Some months and years are longer than others. So when one adds "2 months" to a date the result is determined, in part, by the date that you began with. More on this below.

  • One cannot add "minutes" to a date, or "days" to a time, etc.

  • One cannot combine labeled durations in parenthesis: "date - (1 day + 2 months)" will fail. One should instead say: "date - 1 day - 2 months".

  • Adding too many hours, minutes or seconds to a time will cause it to wrap around. The overflow will be lost.

  • Adding 24 hours to the time '00.00.00' will get '24.00.00'. Adding 24 hours to any other time will return the original value.

  • When a decimal value is used (e.g. 4.5 days) the fractional part is discarded. So to add (to a timestamp value) 4.5 days, add 4 days and 12 hours.

Now for some examples:

Example, Labeled Duration usage
SELECT     sales_date
    ,      sales_date - 10 DAY     AS d1
    ,      sales_date + -1 MONTH   AS d2
    ,      sales_date + 99 YEARS   AS d3
    ,      sales_date + 55 DAYS
                      - 22 MONTHS  AS d4
    ,      sales_date + (4+6) DAYS AS d5
FROM sales
WHERE sales_person = 'GOUNOT'
AND  sales_date = '1995-12-31';

ANSWER

sales_date d1 d2 d3 d4 d5

1995-12-31

1995-12-21

1995-11-30

2094-12-31

1994-04-24

1996-01-10

Adding or subtracting months or years can give somewhat odd results when the month of the beginning date is longer than the month of the ending date. For example, adding 1 month to '2004-01-31' gives '2004-02-29', which is not the same as adding 31 days, and is not the same result that one will get in 2005. Likewise, adding 1 month, and then a second 1 month to '2004-01-31' gives '2004-03-29', which is not the same as adding 2 months. Below are some examples of this issue:

Adding Months - Varying Results
SELECT sales_date
     , sales_date + 2 MONTH           AS d1
     , sales_date + 3 MONTHS          AS d2
     , sales_date + 2 MONTH + 1 MONTH AS d3
     , sales_date + (2+1) MONTHS      AS d4
FROM sales
WHERE sales_person = 'GOUNOT'
AND sales_date = '1995-12-31';

ANSWER

sales_date d1 d2 d3 d4

1995-12-31

1996-02-29

1996-03-31

1996-03-29

1996-03-31

Date/Time Duration Usage

When one date/time value is subtracted from another date/time value the result is a date, time,or timestamp duration. This decimal value expresses the difference thus:

Table 3. Date/Time Durations
DURATION-TYPE FORMAT NUMBER-REPRESENTS USE-WITH-D-TYPE

DATE

DECIMAL(8,0)

yyyymmdd

TIMESTAMP, DATE

TIME

DECIMAL(6,0)

hhmmss

TIMESTAMP, TIME

TIMESTAMP

DECIMAL(20,6)

yyyymmddhhmmss.zzzzzz

TIMESTAMP

Below is an example of date duration generation:

Date Duration Generation
SELECT empno
     , hiredate
     , birthdate
     , hiredate - birthdate
FROM employee
WHERE workdept = 'D11'
AND lastname < 'L'
ORDER BY empno;

ANSWER

EMPNO HIREDATE BIRTHDATE -

000150

1972-02-12

1947-05-17

240826

000200

1966-03-03

1941-05-29

240905

000210

1979-04-11

1953-02-23

260116

A date/time duration can be added to or subtracted from a date/time value, but it does not make for very pretty code:

Subtracting a Date Duration
SELECT hiredate
     , hiredate - 12345678
     , hiredate - 1234 years
                - 56 months
                - 78 days
FROM employee
WHERE empno = '000150';

ANSWER

HIREDATE - -

1972-02-12

0733-03-26

0733-03-26

Date/Time Subtraction

One date/time can be subtracted (only) from another valid date/time value. The result is a date/time duration value. Date Duration Generation above has an example.

Db2 Special Registers

A special register is a Db2 variable that contains information about the state of the system. The complete list follows:

Table 4. Db2 Special Registers
Special Register Updatable Data type

CURRENT CLIENT_ACCTNG

no

VARCHAR(255)

CURRENT CLIENT_APPLNAME

no

VARCHAR(255)

CURRENT CLIENT_USERID

no

VARCHAR(255)

CURRENT CLIENT_WRKSTNNAME

no

VARCHAR(255)

CURRENT DATE

no

DATE

CURRENT DBPARTITIONNUM

no

INTEGER

CURRENT DECFLOAT ROUNDING MODE

no

VARCHAR(128)

CURRENT DEFAULT TRANSFORM GROUP

yes

VARCHAR(18)

CURRENT DEGREE

yes

CHAR(5)

CURRENT EXPLAIN MODE

yes

VARCHAR(254)

CURRENT EXPLAIN SNAPSHOT

yes

CHAR(8)

CURRENT FEDERATED ASYNCHRONY

yes

INTEGER

CURRENT IMPLICIT XMLPARSE OPTION

yes

VARCHAR(19)

CURRENT ISOLATION

yes

CHAR(2)

CURRENT LOCK TIMEOUT

yes

INTEGER

CURRENT MAINTAINED TABLE TYPES FOR OPTIMIZATION

yes

VARCHAR(254)

CURRENT MDC ROLLOUT MODE

yes

VARCHAR(9)

CURRENT OPTIMIZATION PROFILE

yes

VARCHAR(261)

CURRENT PACKAGE PATH

yes

VARCHAR(4096)

CURRENT PATH

yes

VARCHAR(2048)

CURRENT QUERY OPTIMIZATION

yes

INTEGER

CURRENT REFRESH AGE

yes

DECIMAL(20,6)

CURRENT SCHEMA

yes

VARCHAR(128)

CURRENT SERVER

no

VARCHAR(128)

CURRENT TIME

no

TIME

CURRENT TIMESTAMP

no

TIMESTAMP

CURRENT TIMEZONE

no

DECIMAL(6,0)

CURRENT USER

no

VARCHAR(128)

SESSION_USER

yes

VARCHAR(128)

SYSTEM_USER

no

VARCHAR(128)

USER

yes

VARCHAR(128)

Usage Notes

  • Some special registers can be referenced using an underscore instead of a blank in the name - as in: CURRENT_DATE.

  • Some special registers can be updated using the SET command (see list above).

  • All special registers can be queried using the SET command. They can also be referenced in ordinary SQL statements.

  • Those special registers that automatically change over time (e.g. current timestamp) are always the same for the duration of a given SQL statement. So if one inserts a thousand rows in a single insert, all will get the same current timestamp.

  • One can reference the current timestamp in an insert or update, to record in the target table when the row was changed. To see the value assigned, query the DML statement. See Select DML Changes for details.

Refer to the Db2 SQL Reference Volume 1 for a detailed description of each register.

Sample SQL
Using Special Registers
SET CURRENT ISOLATION = RR;
SET CURRENT SCHEMA = 'ABC';
SELECT CURRENT TIME      AS cur_TIME
     , CURRENT ISOLATION AS cur_ISO
     , CURRENT SCHEMA    AS cur_ID
FROM sysibm.sysdummy1;

ANSWER

CUR_TIME CUR_ISO CUR_ID

12:15:16

RR

ABC

Distinct Types

A distinct data type is a field type that is derived from one of the base Db2 field types. It is used when one wants to prevent users from combining two separate columns that should never be manipulated together (e.g. adding US dollars to Japanese Yen).

The following source type do not support distinct types: XML, Array.

The creation of a distinct type, under the covers, results in the creation of two implied functions that can be used to convert data to and from the source type and the distinct type. Support for the basic comparison operators ( =, <>, <, < =, >, and > = ) is also provided. Below is a typical create and drop statement:

Create and drop distinct type
CREATE DISTINCT TYPE JAP_YEN AS DECIMAL(15,2) WITH COMPARISONS;
DROP DISTINCT TYPE JAP_YEN;
A distinct type cannot be dropped if it is currently being used in a table.

Usage Example

Imagine that we had the following customer table:

Sample table, without distinct types
CREATE TABLE customer
( id             INTEGER      NOT NULL
, fname          VARCHAR(10)  NOT NULL WITH DEFAULT ''
, lname          VARCHAR(15)  NOT NULL WITH DEFAULT ''
, date_of_birth  DATE
, citizenship    CHAR(3) NOT NULL WITH DEFAULT ''
, usa_sales      DECIMAL(9,2)
, eur_sales      DECIMAL(9,2)
, sales_office#  SMALLINT
, last_updated   TIMESTAMP
, PRIMARY KEY(id));

One problem with the above table is that the user can add the American and European sales values, which if they are expressed in dollars and euros respectively, is silly:

Silly query, but works
SELECT id
     , usa_sales + eur_sales AS tot_sales
FROM customer;

To prevent the above, we can create two distinct types:

Create Distinct Type examples
CREATE DISTINCT TYPE USA_DOLLARS AS DECIMAL(9,2) WITH COMPARISONS;
CREATE DISTINCT TYPE EUROS       AS DECIMAL(9,2) WITH COMPARISONS;

Now we can define the customer table thus:

Sample table, with distinct types
CREATE TABLE customer
( id            INTEGER     NOT NULL
, fname         VARCHAR(10) NOT NULL WITH DEFAULT ''
, lname         VARCHAR(15) NOT NULL WITH DEFAULT ''
, date_of_birth DATE
, citizenship   CHAR(3)
, usa_sales     USA_DOLLARS
, eur_sales     EUROS
, sales_office# SMALLINT
, last_updated  TIMESTAMP
, PRIMARY KEY(id));

Now, when we attempt to run the following, it will fail:

Silly query, now fails
SELECT id
     , usa_sales + eur_sales AS tot_sales
FROM customer;

The creation of a distinct type, under the covers, results in the creation two implied functions that can be used to convert data to and from the source type and the distinct type. In the next example, the two monetary values are converted to their common decimal source type, and then added together:

Silly query, works again
SELECT id
    , DECIMAL(usa_sales) + DECIMAL(eur_sales) AS tot_sales
FROM customer;
Fullselect, Subselect, & Common Table Expression

It is not the purpose of this book to give you detailed description of SQL terminology, but there are a few words that you should know. For example, the following diagram illustrates the various components of a query:

WITH get_matching_rows AS
     (SELECT id
           , name
           , salary
      FROM staff
      WHERE id < 50
      UNION ALL
      SELECT id
           , name
           , salary
      FROM staff
      WHERE id = 100
     )
     SELECT *
     FROM get_matching_rows
     ORDER BY id
     FETCH FIRST 10 ROWS ONLY
     FOR FETCH ONLY
     WITH UR;
Query components
  • The structure from WITH until the last parenthesis is a COMMON TABLE EXPRESSION.

  • Each select block is called a SUBSELECT.

  • The block inside the WITH with two SUBSELECTS is a FULLSELECT.

3.4.3. Query Components

  • SUBSELECT: A query that selects zero or more rows from one or more tables.

  • FULLSELECT: One or more subselects or VALUES clauses, connected using a UNION, INTERSECT, or EXCEPT, all enclosed in parenthesis.

  • COMMON TABLE EXPRESSION: A named fullselect that can be referenced one more times in another subselect. See Common Table Expression for a more complete definition.

3.5. SELECT Statement

A SELECT statement is used to query the database. It has the following components, not all of which need be used in any particular query:

  • SELECT clause. One of these is required, and it must return at least one item, be it a column, a literal, the result of a function, or something else. One must also access at least one table, be that a true table, a temporary table, a view, an alias or a table function.

  • WITH clause. This clause is optional. Use this phrase to include independent SELECT statements that are subsequently accessed in a final SELECT (see Common Table Expression).

  • ORDER BY clause. Optionally, order the final output (see Order By, Group By, and Having).

  • FETCH FIRST clause. Optionally, stop the query after "n" rows (see FETCH FIRST Clause). If an optimize-for value is also provided, both values are used independently by the optimizer.

  • READ-ONLY clause. Optionally, state that the query is read-only. Some queries are inherently read-only, in which case this option has no effect.

  • FOR UPDATE clause. Optionally, state that the query will be used to update certain columns that are returned during fetch processing.

  • OPTIMIZE FOR n ROWS clause. Optionally, tell the optimizer to tune the query assuming that not all of the matching rows will be retrieved. If a first-fetch value is also provided, both values are used independently by the optimizer.

Refer to the IBM manuals for a complete description of all of the above. Some of the more interesting options are described below.

SELECT Clause

Every query must have at least one SELECT statement, and it must return at least one item, and access at least one object.

3.5.1. SELECT Items

  • Column: A column in one of the table being selected from.

  • Literal: A literal value (e.g. "ABC"). Use the AS expression to name the literal.

  • Special Register: A special register (e.g. CURRENT TIME).

  • Expression: An expression result (e.g. MAX(COL1*10)).

  • Full Select: An embedded SELECT statement that returns a single row.

3.5.2. FROM Objects

  • Table: Either a permanent or temporary Db2 table.

  • View: A standard Db2 view.

  • Alias: A Db2 alias that points to a table, view, or another alias.

  • Full Select: An embedded SELECT statement that returns a set of rows.

  • Table function: A kind of function that returns a table.

Sample SQL

Sample SELECT statement
SELECT deptno
     , admrdept
     , 'ABC' AS abc
FROM department
WHERE deptname LIKE '%ING%'
ORDER BY 1;

ANSWER

DEPTNO ADMRDEPT ABC

B01

A00

ABC

D11

D01

ABC

To select all of the columns in a table (or tables) one can use the "*" notation:

Use "*" to select all columns in table
SELECT *
FROM department
WHERE deptname LIKE '%ING%'
ORDER BY 1;

ANSWER (part of)

DEPTNO etc…​

B01

PLANNING

D11

MANUFACTU

To select both individual columns, and all of the columns (using the "" notation), in a single SELECT statement, one can still use the "", but it must fully-qualified using either the object name, or a correlation name:

Select an individual column, and all columns
SELECT deptno
     , department.*
FROM department
WHERE deptname LIKE '%ING%'
ORDER BY 1;

ANSWER (part of)

DEPTNO DEPTNO etc…​

B01

B01

PLANNING

D11

D11

MANUFACTU

Use the following notation to select all the fields in a table twice:

Select all columns twice
SELECT department.*
     , department.*
FROM department
WHERE eptname LIKE '%NING%'
ORDER BY 1;

ANSWER (part of)

DEPTNO etc…​ …​ DEPTNO etc…​ …​

B01

PLANNING

…​

B01

PLANNING

…​

D11

MANUFACTU

…​

D11

MANUFACTU

…​

3.5.3. FETCH FIRST Clause

The fetch first clause limits the cursor to retrieving "n" rows. If the clause is specified and no number is provided, the query will stop after the first fetch. If this clause is used, and there is no ORDER BY, then the query will simply return a random set of matching rows, where the randomness is a function of the access path used and/or the physical location of the rows in the table:

FETCH FIRST without ORDER BY, gets random rows
SELECT years
     , name
     , id
FROM staff
FETCH FIRST 3 ROWS ONLY;

ANSWER

YEARS NAME ID

7

Sanders

10

8

Pernal

20

5

Marenghi

30

Using the FETCH FIRST clause to get the first "n" rows can sometimes return an answer that is not what the user really intended. See below for details.

If an ORDER BY is provided, then the FETCH FIRST clause can be used to stop the query after a certain number of what are, perhaps, the most desirable rows have been returned. However, the phrase should only be used in this manner when the related ORDER BY uniquely identifies each row returned. To illustrate what can go wrong, imagine that we wanted to query the STAFF table in order to get the names of those three employees that have worked for the firm the longest - in order to give them a little reward (or possibly to fire them). The following query could be run:

FETCH FIRST with ORDER BY, gets wrong answer
SELECT years
     , name
     , id
FROM staff
WHERE years IS NOT NULL
ORDER BY years DESC
FETCH FIRST 3 ROWS ONLY;

ANSWER

YEARS NAME ID

13

Graham

310

12

Jones

260

10

Hanes

50

The above query answers the question correctly, but the question was wrong, and so the answer is wrong. The problem is that there are two employees that have worked for the firm for ten years, but only one of them shows, and the one that does show was picked at random by the query processor. This is almost certainly not what the business user intended. The next query is similar to the previous, but now the ORDER ID uniquely identifies each row returned (presumably as per the end-user’s instructions):

FETCH FIRST with ORDER BY, gets right answer
SELECT years
     , name
     , id
FROM staff
WHERE years IS NOT NULL
ORDER BY years DESC
       , id DESC
FETCH FIRST 3 ROWS ONLY;

ANSWER

YEARS NAME ID

13

Graham

310

12

Jones

260

10

Quill

290

Getting the first "n" rows from a query is actually quite a complicated problem. Refer to Selecting "n" or more Rows for a more complete discussion.

3.5.4. Correlation Name

The correlation name is defined in the FROM clause and relates to the preceding object name. In some cases, it is used to provide a short form of the related object name. In other situations, it is required in order to uniquely identify logical tables when a single physical table is referred to twice in the same query. Some sample SQL follows:

Correlation Name usage example
SELECT a.empno
     , a.lastname
, (SELECT MAX(empno)AS empno
   FROM employee) AS b
FROM employee a
WHERE a.empno = b.empno;

ANSWER

EMPNO LASTNAME

000340

GOUNOT

Correlation name usage example
SELECT a.empno
     , a.lastname
     , b.deptno AS dept
FROM employee   a
   , department b
WHERE a.workdept = b.deptno
AND a.job <> 'SALESREP'
AND b.deptname = 'OPERATIONS'
AND a.sex IN ('M','F')
AND b.location IS NULL
ORDER BY 1;

ANSWER

EMPNO LASTNAME DEPT

000090

HENDERSON

E11

000280

SCHNEIDER

E11

000290

PARKER

E11

000300

SMITH

E11

000310

SETRIGHT

E11

3.5.5. Renaming Fields

The AS phrase can be used in a SELECT list to give a field a different name. If the new name is an invalid field name (e.g. contains embedded blanks), then place the name in quotes:

Renaming fields using AS
SELECT empno    AS e_num
     , midinit  AS "m int"
     , phoneno  AS "..."
FROM employee
WHERE empno < '000030'
ORDER BY 1;

ANSWER

E_NUM M INT …​

000010

I

3978

000020

L

3476

The new field name must not be qualified (e.g. A.C1), but need not be unique. Subsequent usage of the new name is limited as follows:

  • It can be used in an order by clause.

  • It cannot be used in other part of the select (where-clause, group-by, or having).

  • It cannot be used in an update clause.

  • It is known outside of the fullselect of nested table expressions, common table expressions, and in a view definition.

View field names defined using AS
CREATE view emp2
AS SELECT empno AS e_num
        , midinit AS "m int"
        , phoneno AS "..."
FROM employee;

SELECT * FROM emp2 WHERE "..." = '3978';

ANSWER

E_NUM M INT …​

000010

I

3978

3.5.6. Working with Nulls

In SQL something can be true, false, or NULL. This three-way logic has to always be considered when accessing data. To illustrate, if we first select all the rows in the STAFF table where the SALARY is < $10,000, then all the rows where the SALARY is >= $10,000, we have not necessarily found all the rows in the table because we have yet to select those rows where the SALARY is null. The presence of null values in a table can also impact the various column functions. For example, the AVG function ignores null values when calculating the average of a set of rows. This means that a user-calculated average may give a different result from a Db2 calculated equivalent:

AVG of data containing null values
SELECT AVG(comm)            AS a1
     , SUM(comm) / COUNT(*) AS a2
FROM staff
WHERE id < 100;

ANSWER

A1 A2

796.025

530.68

Null values can also pop in columns that are defined as NOT NULL. This happens when a field is processed using a column function and there are no rows that match the search criteria:

Getting a NULL value from a field defined NOT NULL
SELECT COUNT(*)      AS num
     , MAX(lastname) AS max
FROM employee
WHERE firstnme = 'FRED';

ANSWER

NUM MAX

0

-

Why Null Exist

NULL values can represent two kinds of data. In first case, the value is unknown (e.g. we do not know the name of the person’s spouse). Alternatively, the value is not relevant to the situation (e.g. the person does not have a spouse). Many people prefer not to have to bother with nulls, so they use instead a special value when necessary (e.g. an unknown employee name is blank). This trick works OK with character data, but it can lead to problems when used on numeric values (e.g. an unknown salary is set to zero).

Locating Null Values

One can not use an equal predicate to locate those values that are null because a null value does not actually equal anything, not even null, it is simply null. The IS NULL or IS NOT NULL phrases are used instead. The following example gets the average commission of only those rows that are not null. Note that the second result differs from the first due to rounding loss.

AVG of those rows that are not null
SELECT AVG(comm)            AS a1
     , SUM(comm) / COUNT(*) AS a2
FROM staff
WHERE id < 100
AND comm IS NOT NULL;

ANSWER

A1 A2

796.025

796.02

3.5.7. Quotes and Double-quotes

To write a string, put it in quotes. If the string contains quotes, each quote is represented by a pair of quotes:

Quote usage
SELECT 'JOHN'        AS J1
     , 'JOHN''S'     AS J2
     , '''JOHN''S''' AS J3
     , '"JOHN''S"'   AS J4
FROM staff
WHERE id = 10;

ANSWER

J1 J2 J3 J4

JOHN

JOHN’S

'JOHN’S'

"JOHN’S"

Double quotes can be used to give a name to an output field that would otherwise not be valid. To put a double quote in the name, use a pair of quotes:

Double-quote usage
SELECT id    AS "USER ID"
     , dept  AS "D#"
     , years AS "#Y"
     , 'ABC' AS "'TXT'"
     , '"'   AS """quote"" fld"
FROM staff s
WHERE id < 40
ORDER BY "USER ID";

ANSWER

USER ID D# #Y 'TXT' "quote" fld

10

20

7

ABC

"

20

20

8

ABC

"

30

38

5

ABC

"

3.6. SQL Predicates

A predicate is used in either the WHERE or HAVING clauses of a SQL statement. It specifies a condition that true, false, or unknown about a row or a group.

3.6.1. Predicate Precedence

As a rule, a query will return the same result regardless of the sequence in which the various predicates are specified. However, note the following:

  • Predicates separated by an OR may need parenthesis - see AND/OR Precedence.

  • Checks specified in a CASE statement are done in the order written - see CASE Expression.

Basic Predicate

A basic predicate compares two values. If either value is null, the result is unknown. Otherwise the result is either true or false.

Basic Predicate examples
SELECT id, job, dept
FROM staff
WHERE job = 'Mgr'
AND NOT job <> 'Mgr'
AND NOT job = 'Sales'
AND id <> 100
AND id >= 0
AND id <= 150
AND NOT dept = 50
ORDER BY id;

ANSWER

ID JOB DEPT

10

Mgr

20

30

Mgr

38

50

Mgr

15

140

Mgr

51

A variation of this predicate type can be used to compare sets of columns/values. Everything on both sides must equal in order for the expressions to match:

Basic Predicate example, multi-value check
SELECT id, dept, job
FROM staff
WHERE (id,dept) = (30,28)
OR (id,years) = (90, 7)
OR (dept,job) = (38,'Mgr')
ORDER BY 1;

ANSWER

ID DEPT JOB

30

38

Mgr

Below is the same query written the old fashioned way:

Same query as prior, using individual predicates
SELECT id, dept, job
FROM staff
WHERE (id   = 30 AND dept = 28)
OR    (id   = 90 AND years = 7)
OR    (dept = 38 AND job = 'Mgr')
ORDER BY 1;

ANSWER

ID DEPT JOB

30

38

Mgr

3.6.2. Quantified Predicate

A quantified predicate compares one or more values with a collection of values.

Quantified Predicate example, two single-value sub-queries
SELECT id, job
FROM staff
WHERE job = ANY (SELECT job FROM staff)
AND id <= ALL (SELECT id FROM staff)
ORDER BY id;

ANSWER

ID JOB

10

Mgr

Quantified Predicate example, multi-value sub-query
SELECT id, dept, job
FROM staff
WHERE (id,dept) = ANY
(SELECT dept, id
 FROM staff
)
ORDER BY 1;

ANSWER

ID DEPT JOB

20

20

Sales

See the sub-query chapter on Sub-Query for more data on this predicate type.

3.6.3. BETWEEN Predicate

The BETWEEN predicate compares a value within a range of values.

The between check always assumes that the first value in the expression is the low value and the second value is the high value. For example, BETWEEN 10 AND 12 may find data, but BETWEEN 12 AND 10 never will.

BETWEEN Predicate examples
SELECT id, job
FROM staff
WHERE   id     BETWEEN 10 AND 30
AND     id NOT BETWEEN 30 AND 10
AND NOT id NOT BETWEEN 10 AND 30
ORDER BY id;

ANSWER

ID JOB

10

Mgr

20

Sales

30

Mgr

3.6.4. EXISTS Predicate

An EXISTS predicate tests for the existence of matching rows.

EXISTS Predicate example
SELECT id, job
FROM staff a
WHERE EXISTS
(SELECT *
 FROM staff b
 WHERE b.id = a.id
 AND b.id < 50
)
ORDER BY id;

ANSWER

ID JOB

10

Mgr

20

Sales

30

Mgr

40

Sales

See the sub-query chapter on Sub-Query for more data on this predicate type.

3.6.5. IN Predicate

The IN predicate compares one or more values with a list of values.

The list of values being compared in the IN statement can either be a set of in-line expressions (e.g. ID in (10,20,30)), or a set rows returned from a sub-query. Either way, Db2 simply goes through the list until it finds a match.

IN Predicate examples, single values
SELECT id, job
FROM staff a
WHERE id IN (10,20,30)
AND id IN
    (SELECT id
     FROM staff
    )
AND id NOT IN 99
ORDER BY id;

ANSWER

ID JOB

10

Mgr

20

Sales

30

Mgr

The IN statement can also be used to compare multiple fields against a set of rows returned from a sub-query. A match exists when all fields equal. This type of statement is especially useful when doing a search against a table with a multi-columns key.

Be careful when using the NOT IN expression against a sub-query result. If any one row in the sub-query returns null, the result will be no match. See Sub-Query for more details.
IN Predicate example, multi-value
SELECT empno, lastname
FROM employee
WHERE (empno, 'AD3113') IN
    (SELECT empno, projno
     FROM emp_act
     WHERE emptime > 0.5
    )
ORDER BY 1;

ANSWER

EMPNO LASTNAME

000260

JOHNSON

000270

PEREZ

See the sub-query chapter on Sub-Query for more data on this statement type.

3.6.6. LIKE Predicate

The LIKE predicate does partial checks on character strings.

The percent and underscore characters have special meanings. The first means skip a string of any length (including zero) and the second means skip one byte. For example:

  • LIKE 'AB_D%' Finds 'ABCD' and 'ABCDE', but not 'ABD', nor 'ABCCD'.

  • LIKE '_X' Finds 'XX' and 'DX', but not 'X', nor 'ABX', nor 'AXB'.

  • LIKE '%X' Finds 'AX', 'X', and 'AAX', but not 'XA'.

LIKE Predicate examples
SELECT id
     , name
FROM staff
WHERE name LIKE 'S%n'
   OR name LIKE '_a_a%'
   OR name LIKE '%r_%a'
ORDER BY id;

ANSWER

ID NAME

130

Yamaguchi

200

Scoutten

The ESCAPE Phrase

The escape character in a LIKE statement enables one to check for percent signs and/or underscores in the search string. When used, it precedes the '%' or '_' in the search string indicating that it is the actual value and not the special character which is to be checked for. When processing the LIKE pattern, Db2 works thus: Any pair of escape characters is treated as the literal value (e.g. "+" means the string ""). Any single occurrence of an escape character followed by either a "%" or a "\_" means the literal "%" or "_" (e.g. "+%" means the string "%"). Any other "%" or "\_" is used as in a normal LIKE pattern.

Table 5. LIKE and ESCAPE examples
LIKE STATEMENT TEXT WHAT VALUES MATCH

LIKE 'AB%'

Finds AB, any string

LIKE 'AB%' ESCAPE '+'

Finds AB, any string

LIKE 'AB+%' ESCAPE '+'

Finds AB%

LIKE 'AB+' ESCAPE ''

Finds AB+

LIKE 'AB+%%' ESCAPE '+'

Finds AB%, any string

LIKE 'AB+%' ESCAPE ''

Finds AB+, any string

LIKE 'AB++%' ESCAPE ''

Finds AB+%

LIKE 'AB++%%' ESCAPE ''

Finds AB+%, any string

LIKE 'AB+%%%' ESCAPE ''

Finds AB%%, any string

LIKE 'AB' ESCAPE '+'

Finds AB++

LIKE 'AB%' ESCAPE ''

Finds AB++%

LIKE 'AB%' ESCAPE '+'

Finds AB++, any string

LIKE 'AB+%+%' ESCAPE ''

Finds AB%+, any string

Now for sample SQL:

LIKE and ESCAPE examples
SELECT id
FROM staff
WHERE id = 10
AND 'ABC' LIKE 'AB%'
AND 'A%C' LIKE 'A/%C' ESCAPE '/'
AND 'A_C' LIKE 'A\_C' ESCAPE '\'
AND 'A_$' LIKE 'A$_$$' ESCAPE '$';

ANSWER

ID

10

3.6.7. LIKE_COLUMN Function

The LIKE predicate cannot be used to compare one column against another. One may need to do this when joining structured to unstructured data. For example, imagine that one had a list of SQL statements (in a table) and a list of view names in a second table. One might want to scan the SQL text (using a LIKE predicate) to find those statements that referenced the views. The LOCATE function can be used to do a simple equality check. The LIKE predicate allows a more sophisticated search. The following code creates a scalar function and dependent procedure that can compare one column against another (by converting both column values into input variables). The function is just a stub. It passes the two input values down to the procedure where they are compared using a LIKE predicate. If there is a match, the function returns one, else zero.

These examples use an "!" as the stmt delimiter.
Create LIKE_COLUMN procedure
--#SET DELIMITER !
CREATE PROCEDURE LIKE_COLUMN
 ( IN instr1 VARCHAR(4000)
 , IN instr2 VARCHAR(4000)
 , OUT outval SMALLINT)
LANGUAGE SQL
CONTAINS SQL
DETERMINISTIC
NO EXTERNAL ACTION
BEGIN
    SET outval =
        CASE
            WHEN instr1 LIKE instr2
                THEN 1
            ELSE 0
        END;
    RETURN;
END!
Create LIKE_COLUMN function
CREATE FUNCTION LIKE_COLUMN
( instr1 VARCHAR(4000)
, instr2 VARCHAR(4000))
RETURNS SMALLINT
LANGUAGE SQL
CONTAINS SQL
DETERMINISTIC
NO EXTERNAL ACTION
BEGIN ATOMIC
    DECLARE outval SMALLINT;
    CALL LIKE_COLUMN(instr1, instr2, outval);
    RETURN outval;
END!

Below is an example of the above function being used to compare to the contents of one column against another:

Use LIKE_COLUMN function
WITH temp1 (jtest) AS
(VALUES ('_gr%')
      , ('S_le%')
)
SELECT
  s.id
, s.name
, s.job
, t.jtest
FROM staff s
   , temp1 t
WHERE LIKE_COLUMN(s.job , t.jtest) = 1
AND s.id < 70
ORDER BY s.id;

ANSWER

ID NAME JOB JTEST

10

Sanders

Mgr

_gr%

20

Pernal

Sales

S_le%

30

Marenghi

Mgr

_gr%

40

O’Brien

Sales

S_le%

50

Hanes

Mgr

_gr%

60

Quigley

Sales

S_le%

3.6.8. NULL Predicate

The NULL predicate checks for null values. The result of this predicate cannot be unknown. If the value of the expression is null, the result is true. If the value of the expression is not null, the result is false.

NULL predicate examples
SELECT id, comm
FROM staff
WHERE id < 100
AND id IS NOT NULL
AND comm IS NULL
AND NOT comm IS NOT NULL
ORDER BY id;

ANSWER

ID COMM

10

-

30

-

50

-

Use the COALESCE function to convert null values into something else.

3.6.9. Special Character Usage

To refer to a special character in a predicate, or anywhere else in a SQL statement, use the "X" notation to substitute with the ASCII hex value. For example, the following query will list all names in the STAFF table that have an "a" followed by a semicolon:

Refer to semi-colon in SQL text
SELECT id
     , name
FROM staff
WHERE name LIKE '%a' || X'3B' || '%'
ORDER BY id;

3.6.10. Precedence Rules

Expressions within parentheses are done first, then prefix operators (e.g. -1), then multiplication and division, then addition and subtraction. When two operations of equal precedence are together (e.g. 1 * 5 / 4) they are done from left to right.

Precedence rules example
Example:

555 +     -22  /  (12 - 3) * 66
    ^     ^    ^      ^    ^
    5th  2nd  3rd    1st  4th

ANSWER: 423

Be aware that the result that you get depends very much on whether you are doing integer or decimal arithmetic. Below is the above done using integer numbers:

Precedence rules, integer example
SELECT               (12 - 3)      AS int1
       ,       -22 / (12 - 3)      AS int2
       ,       -22 / (12 - 3) * 66 AS int3
       , 555 + -22 / (12 - 3) * 66 AS int4
FROM sysibm.sysdummy1;

ANSWER

INT1 INT2 INT3 INT4

9

-2

-132

423

Db2 truncates, not rounds, when doing integer arithmetic.

Here is the same done using decimal numbers:

Precedence rules, decimal example
SELECT               (12.0 - 3)      AS dec1
       ,       -22 / (12.0 - 3)      AS dec2
       ,       -22 / (12.0 - 3) * 66 AS dec3
       , 555 + -22 / (12.0 - 3) * 66 AS dec4
FROM sysibm.sysdummy1;

ANSWER

DEC1 DEC2 DEC3 DEC4

9.0

-2.4

-161.3

393.6

3.6.11. AND/OR Precedence

AND operations are done before OR operations. This means that one side of an OR is fully processed before the other side is begun. To illustrate:

TABLE1

col1 col2

A

AA

B

BB

C

CC

SELECT *
FROM table1
WHERE col1 =  'C'
AND   col1 >= 'A'
OR    col2 >= 'AA'
ORDER BY col1;

ANSWER

COL1 COL2

A

AA

B

BB

C

CC

SELECT *
FROM table1
WHERE (col1 = 'C'
AND    col1 >= 'A')
OR     col2 >= 'AA'
ORDER BY col1;

ANSWER

COL1 COL2

A

AA

B

BB

C

CC

Use of OR and parenthesis
SELECT *
FROM table1
WHERE col1 = 'C'
AND  (col1 >= 'A'
OR    col2 >= 'AA')
ORDER BY col1;

ANSWER

COL1 COL2

C

CC

The omission of necessary parenthesis surrounding OR operators is a very common mistake. The result is usually the wrong answer. One symptom of this problem is that many more rows are returned (or updated) than anticipated.

3.6.12. Processing Sequence

The various parts of a SQL statement are always executed in a specific sequence in order to avoid semantic ambiguity:

  • FROM clause.

  • JOIN ON clause.

  • WHERE clause.

  • GROUP BY and aggregate.

  • HAVING clause.

  • SELECT list.

  • ORDER BY clause.

  • FETCH FIRST.

Observe that ON predicates (e.g. in an outer join) are always processed before any WHERE predicates (in the same join) are applied. Ignoring this processing sequence can cause what looks like an outer join to run as an inner join (see ON and WHERE Usage). Likewise, a function that is referenced in the SELECT section of a query (e.g. row-number) is applied after the set of matching rows has been identified, but before the data has been ordered.

3.7. CAST Expression

The CAST expression is used to convert one data type to another. It is similar to the various field-type functions (e.g. CHAR, SMALLINT) except that it can also handle null values and host-variable parameter markers.

3.7.1. Input vs. Output Rules

  • EXPRESSION: If the input is neither null, nor a parameter marker, the input data-type is converted to the output data-type. Truncation and/or padding with blanks occur as required. An error is generated if the conversion is illegal.

  • NULL: If the input is null, the output is a null value of the specified type.

  • PARAMETER MAKER: This option is only used in programs and need not concern us here. See the Db2 SQL Reference for details.

Examples

Use the CAST expression to convert the SALARY field from decimal to integer:

Use CAST expression to convert Decimal to Integer
SELECT id
     , salary
     , CAST(salary AS INTEGER) AS sal2
FROM staff
WHERE id < 30
ORDER BY id;

ANSWER

ID SALARY SAL2

10

98357.50

98357

20

78171.25

78171

Use the CAST expression to truncate the JOB field. A warning message will be generated for the second line of output because non-blank truncation is being done.

Use CAST expression to truncate Char field
SELECT id
     , job
     , CAST(job AS CHAR(3)) AS job2
FROM staff
WHERE id < 30
ORDER BY id;

ANSWER

ID JOB JOB2

10

Mgr

Mgr

20

Sales

Sal

Use the CAST expression to make a derived field called JUNK of type SMALLINT where all of the values are null.

Use CAST expression to define SMALLINT field with null values
SELECT id
     , CAST(NULL AS SMALLINT) AS junk
FROM staff
WHERE id < 30
ORDER BY id;

ANSWER

ID JUNK

10

-

20

-

The CAST expression can also be used in a join, where the field types being matched differ:

CAST expression in join
SELECT stf.id
     , emp.empno
FROM staff stf
LEFT OUTER JOIN employee emp
ON stf.id = CAST(emp.empno AS INTEGER)
AND emp.job = 'MANAGER'
WHERE stf.id < 60
ORDER BY stf.id;

ANSWER

ID EMPNO

10

-

20

000020

30

000030

40

-

50

000050

Of course, the same join can be written using the raw function:

Function usage in join
SELECT stf.id
     , emp.empno
FROM staff stf
LEFT OUTER JOIN employee emp
ON stf.id = INTEGER(emp.empno)
AND emp.job = 'MANAGER'
WHERE stf.id < 60
ORDER BY stf.id;

ANSWER

ID EMPNO

10

-

20

000020

30

000030

40

-

50

000050

3.8. VALUES Statement

The VALUES clause is used to define a set of rows and columns with explicit values. The clause is commonly used in temporary tables, but can also be used in view definitions. Once defined in a table or view, the output of the VALUES clause can be grouped by, joined to, and otherwise used as if it is an ordinary table - except that it can not be updated.

Each column defined is separated from the next using a comma. Multiple rows (which may also contain multiple columns) are separated from each other using parenthesis and a comma. When multiple rows are specified, all must share a common data type. Some examples follow:

VALUES usage examples
VALUES 6                        <== 1 row, 1 column
VALUES(6)                       <== 1 row, 1 column
VALUES 6, 7, 8                  <== 1 row, 3 columns
VALUES (6), (7), (8)            <== 3 rows, 1 column
VALUES (6,66), (7,77), (8,NULL) <== 3 rows, 2 column

3.8.1. Sample SQL

The VALUES clause can be used by itself as a very primitive substitute for the SELECT statement. One key difference is that output columns cannot be named. But they can be ordered, and fetched, and even named externally, as the next example illustrates:

PLAIN VALUES
Logically equivalent VALUES statements
VALUES
  (1,2)
, (2,3)
, (3,4)
ORDER BY 2 DESC;
VALUES + WITH
WITH temp (c1,c2) AS
( VALUES (1,2)
       , (2,3)
       , (3,4)
)
SELECT  *
FROM temp
ORDER BY 2 DESC;
VALUES + SELECT
SELECT *
FROM (VALUES (1,2)
           , (2,3)
           , (3,4)
) temp (c1,c2)
ORDER BY 2 DESC;

ANSWER

1 2

3

4

2

3

1

2

The VALUES clause can encapsulate several independent queries:

VALUES running selects
VALUES
(
  (SELECT COUNT(*) FROM employee)
, (SELECT AVG(salary) FROM staff)
, (SELECT MAX(deptno) FROM department)
)
FOR FETCH ONLY
WITH UR;

ANSWER

1 2 3

42

67932.78

J22

The next statement defines a temporary table containing two columns and three rows. The first column defaults to type integer and the second to type varchar.

Use VALUES to define a temporary table (1 of 4)
WITH temp1 (col1, col2) AS
(VALUES
  (0, 'AA')
, (1, 'BB')
, (2, NULL)
)
SELECT *
FROM temp1;

ANSWER

COL1 COL2

0

AA

1

BB

2

-

If we wish to explicitly control the output field types we can define them using the appropriate function. This trick does not work if even a single value in the target column is null.

Use VALUES to define a temporary table (2 of 4)
WITH temp1 (col1, col2) AS
(VALUES
  (DECIMAL(0 ,3, 1), 'AA')
, (DECIMAL(1 ,3, 1), 'BB')
, (DECIMAL(2 ,3, 1), NULL)
)
SELECT *
FROM temp1;

ANSWER

COL1 COL2

0.0

AA

1.0

BB

2.0

-

If any one of the values in the column that we wish to explicitly define has a null value, we have to use the CAST expression to set the output field type:

Use VALUES to define a temporary table (3 of 4)
WITH temp1 (col1,col2) AS
(VALUES
  (0, CAST('AA' AS CHAR(1)))
, (1, CAST('BB' AS CHAR(1)))
, (2, CAST(NULL AS CHAR(1)))
)
SELECT *
FROM temp1;

ANSWER

COL1 COL2

0

A

1

B

2

-

Alternatively, we can set the output type for all of the not-null rows in the column. Db2 will then use these rows as a guide for defining the whole column:

Use VALUES to define a temporary table (4 of 4)
WITH temp1 (col1,col2) AS
(VALUES
  (0, CHAR('AA', 1))
, (1, CHAR('BB', 1))
, (2, NULL)
)
SELECT *
FROM temp1;

ANSWER

COL1 COL2

0

A

1

B

2

-

3.8.2. More Sample SQL

Temporary tables, or (permanent) views, defined using the VALUES expression can be used much like a Db2 table. They can be joined, unioned, and selected from. They can not, however, be updated, or have indexes defined on them. Temporary tables can not be used in a sub-query.

Derive one temporary table from another
WITH temp1 (col1, col2,col3) AS
(VALUES
  (0, 'AA', 0.00)
, (1, 'BB', 1.11)
, (2, 'CC', 2.22)
)
, temp2 (col1b, colx) AS
(SELECT col1
      , col1 + col3
FROM temp1
)
SELECT *
FROM temp2;

ANSWER

COL1B COLX

0

0.00

1

2.11

2

4.22

Define a view using a VALUES clause
CREATE VIEW silly (c1, c2, c3)
AS VALUES
  (11, 'AAA', SMALLINT(22))
, (12, 'BBB', SMALLINT(33))
, (13, 'CCC', NULL);
COMMIT;
Use VALUES defined data to seed a recursive SQL statement
WITH temp1 (col1) AS
(VALUES 0
 UNION ALL
 SELECT col1 + 1
 FROM temp1
 WHERE col1 + 1 < 100
)
SELECT *
FROM temp1;

ANSWER

COL1

0

1

2

3

etc

All of the above examples have matched a VALUES statement up with a prior WITH expression, so as to name the generated columns. One doesn’t have to use the latter, but if you don’t, you get a table with unnamed columns, which is pretty useless:

Generate table with unnamed columns
SELECT *
FROM
(VALUES
       (123, 'ABC')
     , (234, 'DEF')
) AS ttt
ORDER BY 1 DESC;

ANSWER

- -

234

DEF

123

ABC

Combine Columns

The VALUES statement can be used inside a TABLE function to combine separate columns into one. In the following example, three columns in the STAFF table are combined into a single column – with one row per item:

Combine columns example
SELECT id
, salary AS sal
, comm   AS com
, combo
, typ
FROM staff
, TABLE( VALUES(salary , 'SAL')
             , (comm   , 'COM')
 ) AS tab(combo, typ)
WHERE id < 40
ORDER BY id
       , typ;

ANSWER

ID SAL COM COMBO TYP

10

98357.50

-

COM

10

98357.50

-

98357.50

SAL

20

78171.25

612.45

612.45

COM

20

78171.25

612.45

78171.25

SAL

30

77506.75

-

COM

30

77506.75

-

77506.75

SAL

The above query works as follows:

  • The set of matching rows are obtained from the STAFF table.

  • For each matching row, the TABLE function creates two rows, the first with the salary value, and the second with the commission.

  • Each new row as gets a second literal column – indicating the data source.

  • Finally, the "AS" expression assigns a correlation name to the table output, and also defines two column names.

The TABLE function is resolved row-by-row, with the result being joined to the current row in the STAFF table. This explains why we do not get a Cartesian product, even though no join criteria are provided.

The keyword LATERAL can be used instead of TABLE in the above query.

3.9. CASE Expression

CASE expressions enable one to do if-then-else type processing inside of SQL statements.

The sequence of the CASE conditions can affect the answer. The first WHEN check that matches is the one used.

3.9.1. CASE Syntax Styles

There are two general flavors of the CASE expression. In the first kind, each WHEN statement does its own independent check. In the second kind, all of the WHEN conditions do similar "equal" checks against a common reference expression.

Use CASE (1st type) to expand a value
SELECT Lastname
, sex AS sx
, CASE sex
   WHEN 'F' THEN 'FEMALE'
   WHEN 'M' THEN 'MALE'
   ELSE NULL
  END AS sexx
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME SX SEXX

JEFFERSON

M

MALE

JOHN

F

FEMALE

JOHNSON

F

FEMALE

JONES

M

MALE

Use CASE (2nd type) to expand a value
SELECT lastname
     , sex AS sx
     , CASE WHEN sex = 'F' THEN 'FEMALE'
            WHEN sex = 'M' THEN 'MALE'
            ELSE NULL
       END AS sexx
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME SX SEXX

JEFFERSON

M

MALE

JOHN

F

FEMALE

JOHNSON

F

FEMALE

JONES

M

MALE

Notes & Restrictions

  • If more than one WHEN condition is true, the first one processed that matches is used.

  • If no WHEN matches, the value in the ELSE clause applies. If no WHEN matches and there is no ELSE clause, the result is NULL.

  • There must be at least one non-null result in a CASE statement. Failing that, one of the NULL results must be inside of a CAST expression.

  • All result values must be of the same type.

  • Functions that have an external action (e.g. RAND) can not be used in the expression part of a CASE statement.

3.9.2. Sample SQL

Use CASE to display the higher of two values
SELECT lastname
     , midinit AS mi
     , sex AS sx
     , CASE WHEN midinit > SEX THEN midinit
            ELSE sex
       END AS mx
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME MI SX MX

JEFFERSON

J

M

M

JOHN

K

K

K

JOHNSON

P

F

P

JONES

T

M

T

Use CASE to get multiple counts in one pass
SELECT COUNT(*) AS tot
     , SUM(CASE sex WHEN 'F' THEN 1 ELSE 0 END) AS #f
     , SUM(CASE sex WHEN 'M' THEN 1 ELSE 0 END) AS #m
FROM employee
WHERE lastname LIKE 'J%';

ANSWER

TOT #F #M

4

2

2

Use CASE inside a function
SELECT lastname
     , LENGTH(RTRIM(lastname)) AS len
     , SUBSTR(lastname , 1 ,
                CASE WHEN LENGTH(RTRIM(lastname)) > 6 THEN 6
                ELSE LENGTH(RTRIM(lastname))
                END
             ) AS lastnm
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME LEN LASTNM

JEFFERSON

9

JEFFER

JOHN

4

JOHN

JOHNSON

7

JOHNSO

JONES

5

JONES

The CASE expression can also be used in an UPDATE statement to do any one of several alternative updates to a particular field in a single pass of the data:

UPDATE statement with nested CASE expressions
UPDATE staff
SET comm =
CASE dept
    WHEN 15 THEN comm * 1.1
    WHEN 20 THEN comm * 1.2
    WHEN 38 THEN
        CASE
            WHEN years < 5 THEN comm * 1.3
            WHEN years >= 5 THEN comm * 1.4
            ELSE NULL
        END
    ELSE comm
END
WHERE comm IS NOT NULL
AND dept < 50;

In the next example a CASE expression is used to avoid a divide-by-zero error:

Use CASE to avoid divide by zero
WITH temp1 (c1, c2) AS
(VALUES
(88, 9),(44, 3),(22, 0),(0, 1))
SELECT c1
     , c2
     , CASE c2
         WHEN 0 THEN NULL
         ELSE c1/c2
       END AS c3
FROM temp1;

ANSWER

C1 C2 C3

88

9

9

44

3

14

22

0

0

1

0

-

At least one of the results in a CASE expression must be a value (i.e. not null). This is so that Db2 will know what output type to make the result.

3.9.3. Problematic CASE Statements

The case WHEN checks are always processed in the order that they are found. The first one that matches is the one used. This means that the answer returned by the query can be affected by the sequence on the WHEN checks. To illustrate this, the next statement uses the SEX field (which is always either "F" or "M") to create a new field called SXX. In this particular example, the SQL works as intended.

Use CASE to derive a value (correct)
SELECT lastname
     , sex
     , CASE
        WHEN sex >= 'M' THEN 'MAL'
        WHEN sex >= 'F' THEN 'FEM'
       END AS sxx
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME SX SXX

JEFFERSON

M

MAL

JOHN

F

FEM

JOHNSON

F

FEM

JONES

M

MAL

In the example below all of the values in SXX field are "FEM". This is not the same as what happened above, yet the only difference is in the order of the CASE checks.

Use CASE to derive a value (incorrect)
SELECT lastname
     , sex
     , CASE
        WHEN sex >= 'F' THEN 'FEM'
        WHEN sex >= 'M' THEN 'MAL'
       END AS sxx
FROM employee
WHERE lastname LIKE 'J%'
ORDER BY 1;

ANSWER

LASTNAME SX SXX

JEFFERSON

M

FEM

JOHN

F

FEM

JOHNSON

F

FEM

JONES

M

FEM

In the prior statement the two WHEN checks overlap each other in terms of the values that they include. Because the first check includes all values that also match the second, the latter never gets invoked. Note that this problem can not occur when all of the WHEN expressions are equality checks.

3.9.4. CASE in Predicate

The result of a CASE expression can be referenced in a predicate:

Use CASE in a predicate
SELECT id
     , dept
     , salary
     , comm
FROM staff
WHERE CASE
        WHEN comm < 70                      THEN 'A'
        WHEN name LIKE 'W%'                 THEN 'B'
        WHEN salary < 11000                 THEN 'C'
        WHEN salary < 18500 AND dept <> 33  THEN 'D'
        WHEN salary < 19000                 THEN 'E'
END IN ('A','C','E')
ORDER BY id;

ANSWER

ID DEPT SALARY COMM

130

42

10505.90

75.60

270

66

18555.50

330

66

10988.00

55.50

The above query is arguably more complex than it seems at first glance, because unlike in an ordinary query, the CASE checks are applied in the sequence they are defined. So a row will only match "B" if it has not already matched "A". In order to rewrite the above query using standard AND/OR predicates, we have to reproduce the CASE processing sequence. To this end, the three predicates in the next example that look for matching rows also apply any predicates that preceded them in the CASE statement:

Same stmt as prior, without CASE predicate
SELECT id
     , name
     , salary
     , comm
FROM staff
WHERE (comm < 70)
   OR (salary < 11000 AND NOT name LIKE 'W%')
   OR (salary < 19000 AND NOT (name LIKE 'W%'
                           OR (salary < 18500 AND dept <> 33)
    )
   )
ORDER BY id;

ANSWER

ID DEPT SALARY COMM

130

42

10505.90

75.60

270

66

18555.50

330

66

10988.00

55.50

3.10. Miscellaneous SQL Statements

This section will briefly discuss several miscellaneous SQL statements. See the Db2 manuals for more details.

3.10.1. Cursor

A cursor is used in an application program to retrieve and process individual rows from a result set. To use a cursor, one has to do the following:

  • DECLARE the cursor. The declare statement has the SQL text that the cursor will run. If the cursor is declared "with hold", it will remain open after a commit, otherwise it will be closed at commit time.

The declare cursor statement is not actually executed when the program is run. It simply defines the query that will be run.
  • OPEN the cursor. This is when the contents of on any host variables referenced by the cursor (in the predicate part of the query) are transferred to Db2.

  • FETCH rows from the cursor. One does as many fetches as is needed. If no row is found, the SQLCODE from the fetch will be 100.

  • CLOSE the cursor.

Syntax Notes

  • The cursor-name must be unique with the application program.

  • The WITH HOLD phrase indicates that the cursor will remain open if the unit of work ends with a commit. The cursor will be closed if a rollback occurs.

  • The WITH RETURN phrase is used when the cursor will generate the result set returned by a stored procedure. If the cursor is open when the stored procedure ends the result set will be return either to the calling procedure, or directly to the client application.

  • The FOR phrase can either refer to a select statement, the text for which will follow, or to the name of a statement has been previously prepared.

Usage notes

  • Cursors that require a sort (e.g. to order the output) will obtain the set of matching rows at open time, and then store them in an internal temporary table. Subsequent fetches will be from the temporary table.

  • Cursors that do not require a sort are resolved as each row is fetched from the data table.

  • All references to the current date, time, and timestamp will return the same value (i.e. as of when the cursor was opened) for all fetches in a given cursor invocation.

  • One does not have to close a cursor, but one cannot reopen it until it is closed. All open cursors are automatically closed when the thread terminates, or when a rollback occurs, or when a commit is done - except if the cursor is defined "with hold".

  • One can both update and delete "where current of cursor". In both cases, the row most recently fetched is updated or deleted. An update can only be used when the cursor being referenced is declared "for update of".

Examples

Sample cursor
DECLARE fred CURSOR FOR
WITH RETURN TO CALLER
SELECT id
     , name
     , salary
     , comm
FROM staff
WHERE id < :id-var
AND   salary > 1000
ORDER BY id ASC
FETCH FIRST 10 ROWS ONLY
OPTIMIZE FOR 10 ROWS
FOR FETCH ONLY
WITH UR
Use cursor in program
DECLARE fred CURSOR WITH HOLD FOR
SELECT name
     , salary
FROM staff
WHERE id > :id-var
FOR UPDDATE OF salary, comm

OPEN fred

DO UNTIL SQLCODE = 100
    FETCH fred INTO :name-var
                  , :salary-var
    IF salary < 1000 THEN
    DO
        UPDATE staff
        SET salary = :new-salary-var
        WHERE CURRENT OF fred
    END-IF
END-DO

CLOSE fred

3.10.2. Select Into

A SELECT-INTO statement is used in an application program to retrieve a single row. If more than one row matches, an error is returned. The statement text is the same as any ordinary query, except that there is an INTO section (listing the output variables) between the SELECT list and the FROM section.

Example

Singleton select
SELECT name
     , salary
INTO :name-var
   , :salary-var
FROM staff
WHERE id = :id-var

3.10.3. Prepare

The PREPARE statement is used in an application program to dynamically prepare a SQL statement for subsequent execution.

Syntax Notes

  • The statement name names the statement. If the name is already in use, it is overridden.

  • The OUTPUT descriptor will contain information about the output parameter markers.

  • The DESCRIBE statement may be used instead of this clause.

  • The INPUT descriptor will contain information about the input parameter markers.

  • The FROM phrase points to the host-variable which contains the SQL statement text.

Prepared statement can be used by the following:

Table 6. What statements can use prepared statement
STATEMENT CAN BE USED BY STATEMENT TYPE

DESCRIBE

Any statement

DECLARE CURSOR

Must be SELECT

EXECUTE

Must not be SELECT

3.10.4. Describe

The DESCRIBE statement is typically used in an application program to get information about a prepared statement. It can also be used in the Db2 command processor (but not in Db2BATCH) to get a description of a table, or the output columns in a SQL statement:

Below are some examples of using the statement:

DESCRIBE the output columns in a select statement
DESCRIBE OUTPUT SELECT * FROM staff
SQLDA Information
sqldaid : SQLDA sqldabc: 896  sqln: 20 sqld: 7
Column Information



sqltype           sqllen sqlname.data              sqlname.length
----------------- -----  ------------------------- -------------
500 SMALLINT      2      ID                        2
449 VARCHAR       9      NAME                      4
501 SMALLINT      2      DEPT                      4
453 CHARACTER     5      JOB                       3
501 SMALLINT      2      YEARS                     5
485 DECIMAL       7, 2   SALARY                    6
485 DECIMAL       7, 2   COMM                      4
DESCRIBE the columns in a table
DESCRIBE TABLE staff

Column name             Type schema Type name    Length Scale Nulls
----------------------  -------     -----------  ------ ----  ----
ID                      SYSIBM      SMALLINT     2      0     No
NAME                    SYSIBM      VARCHAR      9      0     Yes
DEPT                    SYSIBM      SMALLINT     2      0     Yes
JOB                     SYSIBM      CHARACTER    5      0     Yes
YEARS                   SYSIBM      SMALLINT     2      0     Yes
SALARY                  SYSIBM      DECIMAL      7      2     Yes
COMM                    SYSIBM      DECIMAL      7      2     Yes

3.10.5. Execute

The EXECUTE statement is used in an application program to execute a prepared statement. The statement can not be a select.

3.10.6. Execute Immediate

The EXECUTE IMMEDIATE statement is used in an application program to prepare and execute a statement. Only certain kinds of statement (e.g. insert, update, delete, commit) can be run this way. The statement can not be a select.

3.10.7. Set Variable

The SET statement is used in an application program to set one or more program variables to values that are returned by Db2.

Examples

SET single host-variable
SET :host-var = CURRENT TIMESTAMP
SET multiple host-variables
SET :host-v1 = CURRENT TIME
  , :host-v2 = CURRENT DEGREE
  , :host-v3 = NULL

The SET statement can also be used to get the result of a select, as long as the select only returns a single row:

SET using row-fullselect
SET
( :hv1
, :hv2
, :hv3) =
(SELECT id
      , name
      , salary
FROM staff
WHERE   id = :id-var)

3.10.8. Set Db2 Control Structures

In addition to setting a host-variable, one can also set various Db2 control structures:

Other SET statements
SET CONNECTION
SET CURRENT DEFAULT TRANSFORM GROUP
SET CURRENT DEGREE
SET CURRENT EXPLAIN MODE
SET CURRENT EXPLAIN SNAPSHOT
SET CURRENT ISOLATION
SET CURRENT LOCK TIMEOUT
SET CURRENT MAINTAINED TABLE TYPES FOR OPTIMIZATION
SET CURRENT PACKAGE PATH
SET CURRENT PACKAGESET
SET CURRENT QUERY OPTIMIZATION
SET CURRENT REFRESH AGE
SET ENCRYPTION PASSWORD
SET EVENT MONITOR STATE
SET INTEGRITY SET PASSTHRU
SET PATH
SET SCHEMA
SET SERVER OPTION
SET SESSION AUTHORIZATION

3.11. Unit-of-Work Processing

No changes that you make are deemed to be permanent until they are committed. This section briefly lists the commands one can use to commit or rollback changes.

3.11.1. Commit

The COMMIT statement is used to commit whatever changes have been made. Locks that were taken as a result of those changes are freed. If no commit is specified, an implicit one is done when the thread terminates.

3.11.2. Savepoint

The SAVEPOINT statement is used in an application program to set a savepoint within a unit of work. Subsequently, the program can be rolled back to the savepoint, as opposed to rolling back to the start of the unit of work.

Notes

  • If the savepoint name is the same as a savepoint that already exists within the same level, it overrides the prior savepoint - unless the latter was defined a being unique, in which case an error is returned.

  • The RETAIN CURSORS phrase tells Db2 to, if possible, keep open any active cursors.

  • The RETAIN LOCKS phrase tells Db2 to retain any locks that were obtained subsequent to the savepoint. In other words, the changes are rolled back, but the locks that came with those changes remain.

Savepoint Levels

Savepoints exist within a particular savepoint level, which can be nested within another level. A new level is created whenever one of the following occurs:

  • A new unit of work starts.

  • A procedure defined with NEW SAVEPOINT LEVEL is called.

  • An atomic compound SQL statement starts.

A savepoint level ends when the process that caused its creation finishes. When a savepoint level ends, all of the savepoints created within it are released. The following rules apply to savepoint usage:

  • Savepoints can only be referenced from within the savepoint level in which they were created. Active savepoints in prior levels are not accessible.

  • The uniqueness of savepoint names is only enforced within a given savepoint level. The same name can exist in multiple active savepoint levels.

Example

Savepoints are especially useful when one has multiple SQL statements that one wants to run or rollback as a whole, without affecting other statements in the same transaction. For example, imagine that one is transferring customer funds from one account to another. Two updates will be required - and if one should fail, both should fail:

Example of savepoint usage
INSERT INTO transaction_audit_table;
SAVEPOINT before_updates ON ROLLBACK RETAIN CURSORS;
UPDATE savings_account
    SET balance = balance - 100
    WHERE cust# = 1234;
IF SQLCODE <> 0 THEN
    ROLLBACK TO SAVEPOINT before_updates;
ELSE
    UPDATE checking_account
        SET balance = balance + 100
        WHERE cust# = 1234;
    IF SQLCODE <> 0 THEN
        ROLLBACK TO SAVEPOINT before_updates;
    END
END
COMMIT;

In the above example, if either of the update statements fail, the transaction is rolled back to the predefined savepoint. And regardless of what happens, there will still be a row inserted into the transaction-audit table.

3.11.3. Savepoints vs. Commits

Savepoints differ from commits in the following respects:

  • One cannot rollback changes that have been committed.

  • Only a commit guarantees that the changes are stored in the database. If the program subsequently fails, the data will still be there.

  • Once a commit is done, other users can see the changed data. After a savepoint, the data is still not visible to other users.

3.11.4. Release Savepoint

The RELEASE SAVEPOINT statement will remove the named savepoint. Any savepoints nested within the named savepoint are also released. Once run, the application can no longer rollback to any of the released savepoints.

3.11.5. Rollback

The ROLLBACK statement is used to rollback any database changes since the beginning of the unit of work, or since the named savepoint - if one is specified.

4. Data Manipulation Language

The chapter has a very basic introduction to the DML (Data Manipulation Language) statements. See the Db2 manuals for more details.

4.1. Insert

The INSERT statement is used to insert rows into a table, view, or fullselect. To illustrate how it is used, this section will use a copy of the EMP_ACT sample table:

EMP_ACT_COPY sample table - DDL
CREATE TABLE emp_act_copy
( empno    CHARACTER (00006) NOT NULL
, projno   CHARACTER (00006) NOT NULL
, actno    SMALLINT          NOT NULL
, emptime  DECIMAL (05,02)
, emstdate DATE
, emendate DATE);

4.1.1. Target Objects

One can insert into a table, view, nickname, or SQL expression. For views and SQL expressions, the following rules apply:

  • The list of columns selected cannot include a column function (e.g. MIN).

  • There must be no GROUP BY or HAVING acting on the select list.

  • The list of columns selected must include all those needed to insert a new row.

  • The list of columns selected cannot include one defined from a constant, expression, or a scalar function.

  • Sub-queries, and other predicates, are fine, but are ignored (see Insert into a fullselect).

  • The query cannot be a join, nor (plain) union.

  • A "union all" is permitted - as long as the underlying tables on either side of the union have check constraints such that a row being inserted is valid for one, and only one, of the tables in the union.

All bets are off if the insert is going to a table that has an INSTEAD OF trigger defined.

Usage Notes

  • One has to provide a list of the columns (to be inserted) if the set of values provided does not equal the complete set of columns in the target table, or are not in the same order as the columns are defined in the target table.

  • The columns in the INCLUDE list are not inserted. They are intended to be referenced in a SELECT statement that encompasses the INSERT (see Select DML Changes).

  • The input data can either be explicitly defined using the VALUES statement, or retrieved from some other table using a fullselect.

4.1.2. Direct Insert

To insert a single row, where all of the columns are populated, one lists the input the values in the same order as the columns are defined in the table:

Single row insert
INSERT INTO emp_act_copy VALUES
('100000' ,'ABC' ,10 ,1.4 ,'2003-10-22', '2003-11-24');

To insert multiple rows in one statement, separate the row values using a comma:

Multi row insert
INSERT INTO emp_act_copy VALUES
    ('200000' ,'ABC' ,10 ,1.4 ,'2003-10-22', '2003-11-24')
,   ('200000' ,'DEF' ,10 ,1.4 ,'2003-10-22', '2003-11-24')
,   ('200000' ,'IJK' ,10 ,1.4 ,'2003-10-22', '2003-11-24');
If multiple rows are inserted in one statement, and one of them violates a unique index check, all of the rows are rejected.

The NULL and DEFAULT keywords can be used to assign these values to columns. One can also refer to special registers, like the current date and current time:

Using null and default values
INSERT INTO emp_act_copy VALUES
('400000' ,'ABC' ,10 ,NULL ,DEFAULT, CURRENT DATE);

To leave some columns out of the insert statement, one has to explicitly list the columns that are included. When this is done, one can refer to the columns used in any order:

Explicitly listing columns being populated during insert
INSERT INTO emp_act_copy (projno, emendate, actno, empno)
VALUES
('ABC' ,DATE(CURRENT TIMESTAMP) ,123 ,'500000');

4.1.3. Insert into Full-Select

The next statement inserts a row into a fullselect that just happens to have a predicate which, if used in a subsequent query, would not find the row inserted. The predicate has no impact on the insert itself:

Insert into a fullselect
INSERT INTO
(SELECT *
 FROM emp_act_copy
 WHERE empno < '1'
)
VALUES
('510000' ,'ABC' ,10 ,1.4 ,'2003-10-22', '2003-11-24');
One can insert rows into a view (with predicates in the definition) that are outside the bounds of the predicates. To prevent this, define the view WITH CHECK OPTION.

4.1.4. Insert from Select

One can insert a set of rows that is the result of a query using the following notation:

Insert result of select statement
INSERT INTO emp_act_copy
SELECT LTRIM(CHAR(id + 600000))
     , SUBSTR(UCASE(name),1,6)
     , salary / 229
     , 123
     , CURRENT DATE
     , '2003-11-11'
FROM staff
WHERE id < 50;
In the above example, the fractional part of the SALARY value is eliminated when the data is inserted into the ACTNO field, which only supports integer values.

If only some columns are inserted using the query, they need to be explicitly listed:

Insert result of select - specified columns only
INSERT INTO emp_act_copy (empno, actno, projno)
SELECT LTRIM(CHAR(id + 700000))
     , MINUTE(CURRENT TIME)
     , 'DEF'
FROM staff
WHERE id < 40;

One reason why tables should always have unique indexes is to stop stupid SQL statements like the following, which will double the number of rows in the table:

Stupid - insert - doubles rows
INSERT INTO emp_act_copy
SELECT *
FROM emp_act_copy;

The select statement using the insert can be as complex as one likes. In the next example, it contains the union of two queries:

Inserting result of union
INSERT INTO emp_act_copy (empno, actno, projno)
SELECT LTRIM(CHAR(id + 800000))
     , 77
     , 'XYZ'
FROM staff
WHERE id < 40
UNION
SELECT LTRIM(CHAR(id + 900000))
     , SALARY / 100
     , 'DEF'
FROM staff
WHERE id < 50;

The select can also refer to a common table expression. In the following example, six values are first generated, each in a separate row. These rows are then selected during the insert:

Insert from common table expression
INSERT INTO emp_act_copy (empno, actno, projno, emptime)
WITH temp1 (col1) AS
( VALUES (1),(2),(3),(4),(5),(6))
SELECT LTRIM(CHAR(col1 + 910000))
     , col1
     , CHAR(col1)
     , col1 / 2
FROM temp1;

The next example inserts multiple rows - all with an EMPNO beginning "92". Three rows are found in the STAFF table, and all three are inserted, even though the sub-query should get upset once the first row has been inserted. This doesn’t happen because all of the matching rows in the STAFF table are retrieved and placed in a work-file before the first insert is done:

Insert with irrelevant sub-query
INSERT INTO emp_act_copy (empno, actno, projno)
SELECT LTRIM(CHAR(id + 920000))
     , id
     , 'ABC'
FROM staff
WHERE id < 40
AND NOT EXISTS
( SELECT *
 FROM emp_act_copy
 WHERE empno LIKE '92%'
);

4.1.5. Insert into Multiple Tables

Below are two tables that hold data for US and international customers respectively:

Customer tables - for insert usage
CREATE TABLE us_customer
( cust#     INTEGER     NOT NULL
, cname     CHAR(10)    NOT NULL
, country   CHAR(03)    NOT NULL
, CHECK (country = 'USA')
, PRIMARY KEY (cust#));

CREATE TABLE intl_customer
( cust#     INTEGER  NOT NULL
, cname     CHAR(10) NOT NULL
, country   CHAR(03) NOT NULL
, CHECK (country <> 'USA')
, PRIMARY KEY (cust#));

One can use a single insert statement to insert into both of the above tables because they have mutually exclusive check constraints. This means that a new row will go to one table or the other, but not both, and not neither. To do so one must refer to the two tables using a "union all" phrase - either in a view, or a query, as is shown below:

Insert into multiple tables
INSERT INTO
(SELECT *
 FROM us_customer
 UNION ALL
 SELECT *
 FROM intl_customer
)
VALUES
 (111,'Fred','USA')
,(222,'Dave','USA')
,(333,'Juan','MEX');

The above statement will insert two rows into the table for US customers, and one row into the table for international customers.

4.2. Update

The UPDATE statement is used to change one or more columns/rows in a table, view, or fullselect. Each column that is to be updated has to specified. Here is an example:

Single row update
UPDATE emp_act_copy
SET emptime  = NULL
  , emendate = DEFAULT
  , emstdate = CURRENT DATE + 2 DAYS
  , actno    = ACTNO / 2
  , projno   = 'ABC'
WHERE empno = '100000';

Usage Notes

  • One can update rows in a table, view, or fullselect. If the object is not a table, then it must be updateable (i.e. refer to a single table, not have any column functions, etc).

  • The correlation name is optional, and is only needed if there is an expression or predicate that references another table.

  • The columns in the INCLUDE list are not updated. They are intended to be referenced in a SELECT statement that encompasses the UPDATE (see Select DML Changes).

  • The SET statement lists the columns to be updated, and the new values they will get.

  • Predicates are optional. If none are provided, all rows in the table are updated.

  • Usually, all matching rows are updated. To update some fraction of the matching rows, use a fullselect (see Use Full-Select).

Update Examples

To update all rows in a table, leave off all predicates:

Mass update
UPDATE emp_act_copy
SET actno = actno / 2;

In the next example, both target columns get the same values. This happens because the result for both columns is calculated before the first column is updated:

Two columns get same value
UPDATE emp_act_copy ac1
SET actno   = actno * 2
  , emptime = actno * 2
WHERE empno LIKE '910%';

One can also have an update refer to the output of a select statement - as long as the result of the select is a single row:

Update using select
UPDATE emp_act_copy
SET actno =
    ( SELECT MAX(salary) / 10
      FROM staff)
WHERE empno = '200000';

The following notation lets one update multiple columns using a single select:

Multi-row update using select
UPDATE emp_act_copy
SET (actno, emstdate, projno) =
    ( SELECT MAX(salary) / 10
    , CURRENT DATE + 2 DAYS
    , MIN(CHAR(id))
    FROM staff
    WHERE id <> 33
    )
WHERE empno LIKE '600%';

Multiple rows can be updated using multiple different values, as long as there is a one-to-one relationship between the result of the select, and each row to be updated.

Multi-row update using correlated select
UPDATE emp_act_copy ac1
SET (actno, emptime) =
    (SELECT ac2.actno + 1
           , ac1.emptime / 2
      FROM emp_act_copy ac2
      WHERE ac2.empno LIKE '60%'
      AND SUBSTR(ac2.empno,3) = SUBSTR(ac1.empno,3)
     )
WHERE EMPNO LIKE '700%';

4.2.1. Use Full-Select

An update statement can be run against a table, a view, or a fullselect. In the next example, the table is referred to directly:

Direct update of table
UPDATE emp_act_copy
SET   emptime = 10
WHERE empno   = '000010'
AND   projno  = 'MA2100';

Below is a logically equivalent update that pushes the predicates up into a fullselect:

Update of fullselect
UPDATE
(SELECT *
 FROM  emp_act_copy
 WHERE empno  = '000010'
 AND   projno = 'MA2100'
) AS ea
SET emptime = 20;

4.2.2. Update First "n" Rows

An update normally changes all matching rows. To update only the first "n" matching rows do the following:

  • In a fullselect, retrieve the first "n" rows that you want to update.

  • Update all rows returned by the fullselect.

In the next example, the first five staff with the highest salary get a nice fat commission:

Update first "n" rows
UPDATE
(SELECT *
 FROM staff
 ORDER BY salary DESC
 FETCH FIRST 5 ROWS ONLY
) AS xxx
SET comm = 10000;
The above statement may update five random rows – if there is more than one row with the ordering value. To prevent this, ensure that the list of columns provided in the ORDER BY identify each matching row uniquely.

4.2.3. Use OLAP Function

Imagine that we want to set the employee-time for a particular row in the EMP_ACT table to the MAX time for that employee. Below is one way to do it:

Set employee-time in row to MAX - for given employee
UPDATE emp_act_copy ea1
SET emptime =
    (SELECT MAX(emptime)
     FROM emp_act_copy ea2
     WHERE ea1.empno = ea2.empno
    )
WHERE empno  = '000010'
AND   projno = 'MA2100';

The same result can be achieved by calling an OLAP function in a fullselect, and then updating the result. In next example, the MAX employee-time per employee is calculated (for each row), and placed in a new column. This column is then used to do the final update:

Use OLAP function to get max-time, then apply (correct)
UPDATE
    (SELECT ea1.*
          , MAX(emptime) OVER(PARTITION BY empno) AS maxtime
     FROM emp_act_copy ea1
    ) AS ea2
SET   emptime = maxtime
WHERE empno   = '000010'
AND   projno  = 'MA2100';

The above statement has the advantage of only accessing the EMP_ACT table once. If there were many rows per employee, and no suitable index (i.e. on EMPNO and EMPTIME), it would be much faster than the prior update. The next update is similar to the prior - but it does the wrong update! In this case, the scope of the OLAP function is constrained by the predicate on PROJNO, so it no longer gets the MAX time for the employee:

Use OLAP function to get max-time, then apply (wrong)
UPDATE emp_act_copy
SET    emptime = MAX(emptime) OVER(PARTITION BY empno)
WHERE  empno   = '000010'
AND    projno  = 'MA2100';

4.2.4. Correlated and Uncorrelated Update

In the next example, regardless of the number of rows updated, the ACTNO will always come out as one. This is because the sub-query that calculates the row-number is correlated, which means that it is resolved again for each row to be updated in the "AC1" table. At most, one "AC2" row will match, so the row-number must always equal one:

Update with correlated query
UPDATE emp_act_copy ac1
SET (actno, emptime)
    = (SELECT ROW_NUMBER() OVER()
            , ac1.emptime / 2
       FROM emp_act_copy ac2
       WHERE ac2.empno LIKE '60%'
       AND SUBSTR(ac2.empno,3) = SUBSTR(ac1.empno,3)
      )
WHERE EMPNO LIKE '800%';

In the next example, the ACTNO will be updated to be values 1, 2, 3, etc, in order that the rows are updated. In this example, the sub-query that calculates the row-number is uncorrelated, so all of the matching rows are first resolved, and then referred to in the next, correlated, step:

Update with uncorrelated query
UPDATE emp_act_copy ac1
SET (actno, emptime) =
    (SELECT c1
          , c2
     FROM (SELECT ROW_NUMBER() OVER() AS c1
                , actno / 100         AS c2
                , empno
           FROM emp_act_copy
           WHERE empno LIKE '60%'
          ) AS ac2
     WHERE SUBSTR(ac2.empno,3) = SUBSTR(ac1.empno,3)
    )
WHERE empno LIKE '900%';

4.3. Delete

The DELETE statement is used to remove rows from a table, view, or fullselect. The set of rows deleted depends on the scope of the predicates used. The following example would delete a single row from the EMP_ACT sample table:

Single-row delete
DELETE
FROM emp_act_copy
WHERE empno  = '000010'
AND   projno = 'MA2100'
AND   actno  = 10;

Usage Notes

  • One can delete rows from a table, view, or fullselect. If the object is not a table, then it must be deletable (i.e. refer to a single table, not have any column functions, etc).

  • The correlation name is optional, and is only needed if there is a predicate that references another table.

  • The columns in the INCLUDE list are not updated. They are intended to be referenced in a SELECT statement that encompasses the DELETE (see Select DML Changes).

  • Predicates are optional. If none are provided, all rows are deleted.

  • Usually, all matching rows are deleted. To delete some fraction of the matching rows, use a fullselect (see Use Full-Select).

4.3.1. Basic Delete

This statement would delete all rows in the EMP_ACT table:

Mass delete
DELETE
FROM emp_act_copy;

This statement would delete all the matching rows in the EMP_ACT:

Selective delete
DELETE
FROM emp_act_copy
WHERE empno  LIKE '00%'
AND   projno >=   'MA';

4.3.2. Correlated Delete

The next example deletes all the rows in the STAFF table - except those that have the highest ID in their respective department:

Correlated delete (1 of 2)
DELETE
FROM staff s1
WHERE id NOT IN
    (SELECT MAX(id)
     FROM staff s2
     WHERE s1.dept = s2.dept
    );

Here is another way to write the same:

Correlated delete (2 of 2)
DELETE
FROM staff s1
WHERE EXISTS
    (SELECT *
     FROM staff s2
     WHERE s2.dept = s1.dept
     AND   s2.id > s1.id
    );

The next query is logically equivalent to the prior two, but it works quite differently. It uses a fullselect and an OLAP function to get, for each row, the ID, and also the highest ID value in the current department. All rows where these two values do not match are then deleted:

Delete using fullselect and OLAP function
DELETE
FROM
    (SELECT id
          , MAX(id) OVER(PARTITION BY dept) AS max_id
     FROM staff
    ) AS ss
WHERE id <> max_id;

4.3.3. Delete First "n" Rows

A delete removes all encompassing rows. Sometimes this is not desirable - usually because an unknown, and possibly undesirably large, number rows is deleted. One can write a delete that stops after "n" rows using the following logic:

  • In a fullselect, retrieve the first "n" rows that you want to delete.

  • Delete all rows returned by the fullselect.

In the following example, those five employees with the highest salary are deleted:

Delete first "n" rows
DELETE
FROM
    (SELECT *
     FROM staff
     ORDER BY salary DESC
     FETCH FIRST 5 ROWS ONLY
) AS xxx;
The above statement may delete five random rows – if there is more than one row with the same salary. To prevent this, ensure that the list of columns provided in the ORDER BY identify each matching row uniquely.

4.4. Select DML Changes

A special kind of SELECT statement (see Select DML Changes) can encompass an INSERT, UPDATE, or DELETE statement to get the before or after image of whatever rows were changed (e.g. select the list of rows deleted). This kind of SELECT can be very useful when the DML statement is internally generating a value that one needs to know (e.g. an INSERT automatically creates a new invoice number using a sequence column), or get the set of rows that were removed by a delete. All of this can be done by coding a special kind of select.

4.4.1. Table Types

  • OLD: Returns the state of the data prior to the statement being run. This is allowed for an update and a delete.

  • NEW: Returns the state of the data prior to the application of any AFTER triggers or referential constraints. Data in the table will not equal what is returned if it is subsequently changed by AFTER triggers or R.I. This is allowed for an insert and an update.

  • FINAL: Returns the final state of the data. If there AFTER triggers that alter the target table after running of the statement, an error is returned. Ditto for a view that is defined with an INSTEAD OF trigger. This is allowed for an insert and an update.

Usage Notes

  • Only one of the above tables can be listed in the FROM statement.

  • The table listed in the FROM statement cannot be given a correlation name.

  • No other table can be listed (i.e. joined to) in the FROM statement. One can reference another table in the SELECT list (see example Join result to another table), or by using a sub-query in the predicate section of the statement.

  • The SELECT statement cannot be embedded in a nested-table expression.

  • The SELECT statement cannot be embedded in an insert statement.

  • To retrieve (generated) columns that are not in the target table, list them in an INCLUDE phrase in the DML statement. This technique can be used to, for example, assign row numbers to the set of rows entered during an insert.

  • Predicates (on the select) are optional. They have no impact on the underlying DML.

  • The INPUT SEQUENCE phrase can be used in the ORDER BY to retrieve the rows in the same sequence as they were inserted. It is not valid in an update or delete.

  • The usual scalar functions, OLAP functions, and column functions, plus the GROUP BY phrase, can be applied to the output - as desired.

4.4.2. Insert Examples

The example below selects from the final result of the insert:

Select rows inserted
SELECT empno
, projno AS prj
, actno  AS act
FROM FINAL TABLE
(INSERT INTO emp_act_copy
 VALUES
  ('200000', 'ABC', 10, 1, '2003-10-22', '2003-11-24')
, ('200000', 'DEF', 10, 1, '2003-10-22', '2003-11-24')
)
ORDER BY 1,2,3;

ANSWER

EMPNO PRJ ACT

200000

ABC

10

200000

DEF

10

One way to retrieve the new rows in the order that they were inserted is to include a column in the insert statement that is a sequence number:

Include column to get insert sequence
SELECT empno
, projno AS prj
, actno AS act
, row#
AS r#
FROM FINAL TABLE
    (INSERT INTO emp_act_copy (empno, projno, actno)
    INCLUDE (row# SMALLINT)
    VALUES
     ('300000','ZZZ',999,1)
    ,('300000','VVV',111,2)
   )
ORDER BY row#;

ANSWER

EMPNO PRJ ACT R#

300000

ZZZ

999

1

300000

VVV

111

2

The next example uses the INPUT SEQUENCE phrase to select the new rows in the order that they were inserted. Row numbers are assigned to the output:

Select rows in insert order
SELECT empno
     , projno AS prj
     , actno AS act
     , ROW_NUMBER() OVER() AS r#
FROM FINAL TABLE
    (INSERT INTO emp_act_copy (empno, projno, actno)
     VALUES
       ('400000','ZZZ',999)
     , ('400000','VVV',111)
    )
ORDER BY INPUT SEQUENCE;

ANSWER

EMPNO PRJ ACT R#

400000

ZZZ

999

1

400000

VVV

111

2

The INPUT SEQUENCE phrase only works in an insert statement. It can be listed in the ORDER BY part of the statement, but not in the SELECT part. The only way to display the row number of each row inserted is to explicitly assign row numbers.

In the next example, the only way to know for sure what the insert has done is to select from the result. This is because the select statement (in the insert) has the following unknowns:

  • We do not, or may not, know what ID values were selected, and thus inserted.

  • The project-number is derived from the current-time special register.

  • The action-number is generated using the RAND function.

Now for the insert:

Select from an insert that has unknown values
SELECT empno
,projno AS prj
,actno AS act
,ROW_NUMBER() OVER() AS r#
FROM NEW TABLE
    (INSERT INTO emp_act_copy (empno, actno, projno)
        SELECT LTRIM(CHAR(id + 600000))
             , SECOND(CURRENT TIME)
             , CHAR(SMALLINT(RAND(1) * 1000))
        FROM staff
        WHERE id < 40
    )
ORDER BY INPUT SEQUENCE;

ANSWER

EMPNO PRJ ACT R#

600010

1

59

1

600020

563

59

2

600030

193

59

3

4.4.3. Update Examples

The statement below updates the matching rows by a fixed amount. The select statement gets the old EMPTIME values:

Select values - from before update
SELECT empno
     , projno  AS prj
     , emptime AS etime
FROM OLD TABLE
    (UPDATE emp_act_copy
     SET emptime = emptime * 2
     WHERE empno = '200000')
ORDER BY projno;

ANSWER

EMPNO PRJ ETIME

200000

ABC

1.00

200000

DEF

1.00

The next statement updates the matching EMPTIME values by random amount. To find out exactly what the update did, we need to get both the old and new values. The new values are obtained by selecting from the NEW table, while the old values are obtained by including a column in the update which is set to them, and then subsequently selected:

Select values - before and after update
SELECT projno  AS prj
     , old_t   AS old_t
     , emptime AS new_t
FROM NEW TABLE
    (UPDATE emp_act_copy
     INCLUDE (old_t DECIMAL(5,2))
     SET    emptime = emptime * RAND(1) * 10
          , old_t   = emptime
     WHERE empno    = '200000'
    )
ORDER BY 1;

ANSWER

PRJ OLD_T NEW_T

ABC

2.00

0.02

DEF

2.00

11.27

4.4.4. Delete Examples

The following example lists the rows that were deleted:

List deleted rows
SELECT projno AS prj
     , actno  AS act
FROM OLD TABLE
    (DELETE
    FROM emp_act_copy
    WHERE empno = '300000'
   )
ORDER BY 1,2;

ANSWER

PRJ ACT

VVV

111

ZZZ

999

The next query deletes a set of rows, and assigns row-numbers (to the included field) as the rows are deleted. The subsequent query selects every second row:

Assign row numbers to deleted rows
SELECT empno
     , projno
     , actno AS act
     , row#  AS r#
FROM OLD TABLE
    (DELETE
     FROM emp_act_copy
     INCLUDE (row# SMALLINT)
     SET    row# = ROW_NUMBER() OVER()
     WHERE empno = '000260'
    )
WHERE row# = row# / 2 * 2
ORDER BY 1, 2, 3;

ANSWER

EMPNO PROJNO ACT R#

000260

AD3113

70

2

000260

AD3113

80

4

000260

AD3113

180

6

Predicates (in the select result phrase) have no impact on the range of rows changed by the underlying DML, which is determined by its own predicates.

One cannot join the table generated by a DML statement to another table, nor include it in a nested table expression, but one can join in the SELECT phrase. The following delete illustrates this concept by joining to the EMPLOYEE table:

Join result to another table
SELECT empno
    , (SELECT lastname
       FROM
        (SELECT empno AS e#
              , lastname
         FROM employee
        ) AS xxx
       WHERE empno = e#)
    , projno AS projno
    , actno AS act
FROM OLD TABLE
    (DELETE
     FROM emp_act_copy
     WHERE empno < '0001')
ORDER BY 1, 2, 3
FETCH FIRST 5 ROWS ONLY;

ANSWER

EMPNO LASTNAME PROJNO ACT

000010

HAAS

AD3100

10

000010

HAAS

MA2100

10

000010

HAAS

MA2110

10

000020

THOMPSON

PL2100

30

000030

KWAN

IF1000

10

Observe above that the EMPNO field in the EMPLOYEE table was be renamed (before doing the join) using a nested table expression. This was necessary because one cannot join on two fields that have the same name, without using correlation names. A correlation name cannot be used on the OLD TABLE, so we had to rename the field to get around this problem.

4.5. Merge

The MERGE statement is a combination insert and update, or delete, statement on steroids. It can be used to take the data from a source table, and combine it with the data in a target table.

The qualifying rows in the source and target tables are first matched by unique key value, and then evaluated:

  • If the source row is already in the target, the latter can be either updated or deleted.

  • If the source row is not in the target, it can be inserted.

  • If desired, a SQL error can also be generated.

Usage Rules

The following rules apply to the merge statement:

  • Correlation names are optional, but are required if the field names are not unique.

  • If the target of the merge is a fullselect or a view, it must allow updates, inserts, and deletes - as if it were an ordinary table.

  • At least one ON condition must be provided.

  • The ON conditions must uniquely identify the matching rows in the target table.

  • Each individual WHEN check can only invoke a single modification statement.

  • When a MATCHED search condition is true, the matching target row can be updated, deleted, or an error can be flagged.

  • When a NOT MATCHED search condition is true, the source row can be inserted into the target table, or an error can be flagged.

  • When more than one MATCHED or NOT MATCHED search condition is true, the first one that matches (for each type) is applied. This prevents any target row from being updated or deleted more than once. Ditto for any source row being inserted.

  • The ELSE IGNORE phrase specifies that no action be taken if no WHEN check evaluates to true.

  • If an error is encountered, all changes are rolled back.

  • Row-level triggers are activated for each row merged, depending on the type of modification that is made. So if the merge initiates an insert, all insert triggers are invoked. If the same input initiates an update, all update triggers are invoked.

  • Statement-level triggers are activated, even if no rows are processed. So if a merge does either an insert, or an update, both types of statement triggers are invoked, even if all of the input is inserted.

4.5.1. Sample Tables

To illustrate the merge statement, the following test tables were created and populated:

Sample tables for merge
CREATE TABLE old_staff AS
(SELECT id
      , job
      , salary
 FROM staff
)
WITH NO DATA;

CREATE TABLE new_staff AS
(SELECT id
      , salary
 FROM staff
)
WITH NO DATA;

INSERT INTO old_staff
    SELECT id
         , job
         , salary
    FROM staff
    WHERE id BETWEEN 20 and 40;

OLD_STAFF

ID JOB SALARY

20

Sales

78171.25

30

Mgr

77506.75

40

Sales

78006.00

INSERT INTO new_staff
SELECT id, salary / 10
FROM staff
WHERE id BETWEEN 30 and 50;

NEW_STAFF

ID SALARY

30

7750.67

40

7800.60

50

8065.98

4.5.2. Update or Insert Merge

The next statement merges the new staff table into the old, using the following rules:

  • The two tables are matched on common ID columns.

  • If a row matches, the salary is updated with the new value.

  • If there is no matching row, a new row is inserted.

Now for the code:

Merge - do update or insert
MERGE INTO old_staff oo
USING new_staff nn
ON oo.id = nn.id
WHEN MATCHED THEN
    UPDATE
    SET oo.salary = nn.salary
WHEN NOT MATCHED THEN
    INSERT
    VALUES (nn.id,'?',nn.salary);

OLD_STAFF

ID JOB SALARY

20

Sales

78171.25

30

Mgr

77506.75

40

Sales

78006.00

NEW_STAFF

ID SALARY

30

7750.67

40

7800.60

50

8065.98

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

30

Mgr

7750.67

40

Sales

7800.60

50

?

8065.98

4.5.3. Delete-only Merge

The next statement deletes all matching rows:

Merge - delete if match
MERGE INTO old_staff oo
USING new_staff nn
ON oo.id = nn.id
WHEN MATCHED THEN
    DELETE;

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

4.5.4. Complex Merge

The next statement has the following options:

  • The two tables are matched on common ID columns.

  • If a row matches, and the old salary is < 18,000, it is updated.

  • If a row matches, and the old salary is > 18,000, it is deleted.

  • If no row matches, and the new ID is > 10, the new row is inserted.

  • If no row matches, and (by implication) the new ID is ⇐ 10, an error is flagged.

Now for the code:

Merge with multiple options
MERGE INTO old_staff oo
USING      new_staff nn
ON oo.id = nn.id
WHEN MATCHED
AND oo.salary < 78000 THEN
    UPDATE
    SET oo.salary = nn.salary
WHEN MATCHED
AND oo.salary > 78000 THEN
    DELETE
WHEN NOT MATCHED
AND nn.id > 10 THEN
    INSERT
    VALUES (nn.id,'?',nn.salary)
WHEN NOT MATCHED THEN
    SIGNAL SQLSTATE '70001'
    SET MESSAGE_TEXT = 'New ID <= 10';

OLD_STAFF

ID JOB SALARY

20

Sales

78171.25

30

Mgr

77506.75

40

Sales

78006.00

NEW_STAFF

ID SALARY

30

7750.67

40

7800.60

50

8065.98

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

30

Mgr

7750.67

50

?

8065.98

The merge statement is like the case statement (see CASE Expression) in that the sequence in which one writes the WHEN checks determines the processing logic. In the above example, if the last check was written before the prior, any non-match would generate an error.

4.5.5. Using a Fullselect

The following merge generates an input table (i.e. fullselect) that has a single row containing the MAX value of every field in the relevant table. This row is then inserted into the table:

Merge MAX row into table
MERGE INTO old_staff
USING
    (SELECT MAX(id) + 1 AS max_id
          , MAX(job)    AS max_job
          , MAX(salary) AS max_sal
     FROM old_staff
    ) AS mx
ON id = max_id
WHEN NOT MATCHED THEN
    INSERT
    VALUES (max_id, max_job, max_sal);

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

30

Mgr

77506.75

40

Sales

78006.00

41

Sales

78171.25

Here is the same thing written as a plain on insert:

Merge logic - done using insert
INSERT INTO old_staff
SELECT MAX(id) + 1 AS max_id
     , MAX(job)    AS max_job
     , MAX(salary) AS max_sal
FROM old_staff;

Use a fullselect on the target and/or source table to limit the set of rows that are processed during the merge:

Merge using two fullselects
MERGE INTO
    (SELECT *
     FROM old_staff
     WHERE id < 40
    ) AS oo
USING
    (SELECT *
     FROM new_staff
     WHERE id < 50
    ) AS nn
ON oo.id = nn.id
WHEN MATCHED THEN
    DELETE
WHEN NOT MATCHED THEN
    INSERT
    VALUES (nn.id,'?',nn.salary);

OLD_STAFF

ID JOB SALARY

20

Sales

78171.25

30

Mgr

77506.75

40

Sales

78006.00

NEW_STAFF

ID SALARY

30

7750.67

40

7800.60

50

8065.98

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

40

?

7800.60

40

Sales

78006.00

Observe that the above merge did the following:

  • The target row with an ID of 30 was deleted - because it matched.

  • The target row with an ID of 40 was not deleted, because it was excluded in the fullselect that was done before the merge.

  • The source row with an ID of 40 was inserted, because it was not found in the target fullselect. This is why the base table now has two rows with an ID of 40.

  • The source row with an ID of 50 was not inserted, because it was excluded in the fullselect that was done before the merge.

4.5.6. Listing Columns

The next example explicitly lists the target fields in the insert statement - so they correspond to those listed in the following values phrase:

Listing columns and values in insert
MERGE INTO old_staff oo
USING new_staff nn
ON oo.id = nn.id
WHEN MATCHED THEN
    UPDATE
    SET (salary, job) = (1234, '?')
WHEN NOT MATCHED THEN
    INSERT (id,salary,job)
    VALUES (id,5678.9,'?');

AFTER-MERGE

ID JOB SALARY

20

Sales

78171.25

30

?

1234.00

40

?

1234.00

50

?

5678.90

5. Compound SQL

A compound statement groups multiple independent SQL statements into a single executable. In addition, simple processing logic can be included to create what is, in effect, a very basic program. Such statements can be embedded in triggers, SQL functions, SQL methods, and dynamic SQL statements.

5.1. Introduction

A compound SQL statement begins with an (optional) name, followed by the variable declarations, followed by the procedural logic.

Below is a compound statement that reads a set of rows from the STAFF table and, for each row fetched, updates the COMM field to equal the current fetch number.

Sample Compound SQL statement
BEGIN ATOMIC
    DECLARE cntr SMALLINT DEFAULT 1;
    FOR V1 AS
        SELECT id as idval
        FROM staff
        WHERE id < 80
        ORDER BY id
    DO
        UPDATE staff
            SET comm = cntr
            WHERE id = idval;
        SET cntr = cntr + 1;
    END FOR;
END

5.1.1. Statement Delimiter

Db2 SQL does not come with a designated statement delimiter (terminator), though a semicolon is typically used. However, a semi-colon cannot be used in a compound SQL statement because that character is used to differentiate the sub-components of the statement. In Db2BATCH, one can run the SET DELIMITER command (intelligent comment) to use something other than a semi-colon. The following script illustrates this usage:

Set Delimiter example
--#SET DELIMITER !
SELECT NAME FROM STAFF WHERE id = 10!
--#SET DELIMITER ;
SELECT NAME FROM STAFF WHERE id = 20;

In the Db2 command processor one can do the same thing using the terminator keyword:

Set Terminator example
--#SET TERMINATOR !
SELECT NAME FROM STAFF WHERE id = 10!
--#SET TERMINATOR ;
SELECT NAME FROM STAFF WHERE id = 20;

5.1.2. SQL Statement Usage

When used in dynamic SQL, the following control statements can be used:

  • FOR statement

  • GET DIAGNOSTICS statement

  • IF statement

  • ITERATE statement

  • LEAVE statement

  • SIGNAL statement

  • WHILE statement

There are many more PSM (persistent stored modules) control statements than what is shown above. But only these ones can be used in Compound SQL statements.

The following SQL statements can be issued:

  • fullselect

  • UPDATE

  • DELETE

  • INSERT

  • SET variable statement

5.1.3. DECLARE Variables

All variables have to be declared at the start of the compound statement. Each variable must be given a name and a type and, optionally, a default (start) value.

DECLARE examples
BEGIN ATOMIC
    DECLARE aaa, bbb, ccc SMALLINT DEFAULT 1;
    DECLARE ddd CHAR(10) DEFAULT NULL;
    DECLARE eee INTEGER;
    SET eee = aaa + 1;
    UPDATE staff
    SET comm   = aaa
      , salary = bbb
      , years  = eee
    WHERE id = 10;
END

FOR Statement

The FOR statement executes a group of statements for each row fetched from a query.

In the next example one row is fetched per year of service (for selected years) in the STAFF table. That row is then used to do two independent updates to the three matching rows:

FOR statement example
BEGIN ATOMIC
    FOR V1 AS
        SELECT years   AS yr_num
             , max(id) AS max_id
        FROM staff
        WHERE years < 4
        GROUP BY years
        ORDER BY years
    DO
        UPDATE staff
            SET salary = salary / 10
            WHERE id = max_id;
        UPDATE staff
            set comm = 0
            WHERE years = yr_num;
    END FOR;
END

BEFORE

ID SALARY COMM

180

37009.75

236.50

230

83369.80

189.65

330

49988.00

55.50

AFTER

ID SALARY COMM

180

37009.75

0.00

230

8336.98

0.00

330

4998.80

0.00

5.1.4. GET DIAGNOSTICS Statement

The GET DIAGNOSTICS statement returns information about the most recently run SQL statement. One can either get the number of rows processed (i.e. inserted, updated, or deleted), or the return status (for an external procedure call).

In the example below, some number of rows are updated in the STAFF table. Then the count of rows updated is obtained, and used to update a row in the STAFF table:

GET DIAGNOSTICS statement example
BEGIN ATOMIC
    DECLARE numrows INT DEFAULT 0;
    UPDATE staff
    SET salary = 12345
    WHERE id < 100;
    GET DIAGNOSTICS numrows = ROW_COUNT;
    UPDATE staff
        SET salary = numrows
        WHERE id = 10;
END

5.1.5. IF Statement

The IF statement is used to do standard if-then-else branching logic. It always begins with an IF THEN statement and ends with and END IF statement.

The next example uses if-then-else logic to update one of three rows in the STAFF table, depending on the current timestamp value:

IF statement example
BEGIN ATOMIC
    DECLARE cur INT;
    SET cur = MICROSECOND(CURRENT TIMESTAMP);
    IF cur > 600000 THEN
        UPDATE staff
            SET name = CHAR(cur)
            WHERE id = 10;
    ELSEIF cur > 300000 THEN
        UPDATE staff
            SET name = CHAR(cur)
            WHERE id = 20;
    ELSE
        UPDATE staff
            SET name = CHAR(cur)
            WHERE id = 30;
    END IF;
END

5.1.6. ITERATE Statement

The ITERATE statement causes the program to return to the beginning of the labeled loop.

In next example, the second update statement will never get performed because the ITERATE will always return the program to the start of the loop:

ITERATE statement example
BEGIN ATOMIC
    DECLARE cntr INT DEFAULT 0;
    whileloop:
    WHILE cntr < 60 DO
        SET cntr = cntr + 10;
        UPDATE staff
            SET salary = cntr
            WHERE id = cntr;
        ITERATE whileloop;
        UPDATE staff
            SET comm = cntr + 1
            WHERE id = cntr;
    END WHILE;
END

5.1.7. LEAVE Statement

The LEAVE statement exits the labeled loop.

In the next example, the WHILE loop would continue forever, if left to its own devices. But after some random number of iterations, the LEAVE statement will exit the loop:

LEAVE statement example
BEGIN ATOMIC
    DECLARE cntr INT DEFAULT 1;
    whileloop:
    WHILE 1 <> 2 DO
        SET cntr = cntr + 1;
        IF RAND() > 0.99 THEN
            LEAVE whileloop;
        END IF;
    END WHILE;
    UPDATE staff
        SET salary = cntr
        WHERE id = 10;
END

5.1.8. SIGNAL Statement

The SIGNAL statement is used to issue an error or warning message.

The next example loops a random number of times, and then generates an error message using the SIGNAL command, saying how many loops were done:

SIGNAL statement example
BEGIN ATOMIC
    DECLARE cntr INT DEFAULT 1;
    DECLARE emsg CHAR(20);
    whileloop:
    WHILE RAND() < .99 DO
        SET cntr = cntr + 1;
    END WHILE;
    SET emsg = '#loops: ' || CHAR(cntr);
    SIGNAL SQLSTATE '75001' SET MESSAGE_TEXT = emsg;
END

5.1.9. WHILE Statement

The WHILE statement repeats one or more statements while some condition is true.

The next statement has two nested WHILE loops, and then updates the STAFF table:

WHILE statement example
BEGIN ATOMIC
    DECLARE c1, C2 INT DEFAULT 1;
    WHILE c1 < 10 DO
        WHILE c2 < 20 DO
            SET c2 = c2 + 1;
        END WHILE;
        SET c1 = c1 + 1;
    END WHILE;
    UPDATE staff
        SET salary = c1
          , comm = c2
        WHERE id = 10;
END

5.1.10. Other Usage

The following Db2 objects also support the language elements described above:

  • Triggers

  • Stored procedures

  • User-defined functions

  • Embedded compound SQL (in programs).

Some of the above support many more language elements. For example stored procedures that are written in SQL also allow the following: ASSOCIATE, CASE, GOTO, LOOP, REPEAT, RESIGNAL , and RETURN .

5.1.11. Test Query

To illustrate some of the above uses of compound SQL, we are going to get from the STAFF table a complete list of departments, and the number of rows in each department. Here is the basic query, with the related answer:

List departments in STAFF table
SELECT dept
     , count(*) as #rows
FROM staff
GROUP BY dept
ORDER BY dept;

ANSWER

DEPT #ROWS

10

4

15

4

20

4

38

5

42

4

51

5

66

5

84

4

If all you want to get is this list, the above query is the way to go. But we will get the same answer using various other methods, just to show how it can be done using compound SQL statements.

Trigger

One cannot get an answer using a trigger. All one can do is alter what happens during an insert, update, or delete. With this in mind, the following example does the following:

  • Sets the statement delimiter to an "!". Because we are using compound SQL inside the trigger definition, we cannot use the usual semi-colon.

  • Creates a new table (note: triggers are not allowed on temporary tables).

  • Creates an INSERT trigger on the new table. This trigger gets the number of rows per department in the STAFF table - for each row (department) inserted.

  • Inserts a list of departments into the new table.

  • Selects from the new table.

Now for the code:

Trigger with compound SQL
--#SET DELIMITER !
CREATE TABLE dpt
( dept   SMALLINT NOT NULL
, #names SMALLINT
, PRIMARY KEY(dept))!
COMMIT!

CREATE TRIGGER dpt1 AFTER INSERT ON dpt
REFERENCING NEW AS NNN
FOR EACH ROW
MODE Db2SQL
BEGIN ATOMIC
    DECLARE namecnt SMALLINT DEFAULT 0;
    FOR getnames AS
        SELECT COUNT(*) AS #n
        FROM staff
        WHERE dept = nnn.dept
    DO
        SET namecnt = #n;
    END FOR;
    UPDATE dpt
    SET #names = namecnt
    WHERE dept = nnn.dept;
    END!
COMMIT!

INSERT INTO dpt (dept)
    SELECT DISTINCT dept
    FROM staff!
COMMIT!
SELECT *
FROM dpt
ORDER BY dept!
This example uses an "!" as the stmt delimiter.

ANSWER

DEPT #NAMES

10

4

15

4

20

4

38

5

42

4

51

5

66

5

84

4

The above code was designed to be run in Db2BATCH. The "set delimiter" notation will probably not work in other environments.
Scalar Function

One can do something very similar to the above that is almost as stupid using a user-defined scalar function, that calculates the number of rows in a given department. The basic logic will go as follows:

  • Set the statement delimiter to an "!".

  • Create the scalar function.

  • Run a query that first gets a list of distinct departments, then calls the function.

Here is the code:

Scalar Function with compound SQL
--#SET DELIMITER !
CREATE FUNCTION dpt1 (deptin SMALLINT)
RETURNS SMALLINT
BEGIN ATOMIC
    DECLARE num_names SMALLINT;
    FOR getnames AS
        SELECT COUNT(*) AS #n
        FROM staff
        WHERE dept = deptin
    DO
        SET num_names = #n;
    END FOR;
    RETURN num_names;
END!

COMMIT!

SELECT XXX.*
     , dpt1(dept) as #names
FROM
    (SELECT dept
     FROM staff
     GROUP BY dept
    ) AS XXX
ORDER BY dept!
This example uses an "!" as the stmt delimiter.

ANSWER

DEPT #NAMES

10

4

15

4

20

4

38

5

42

4

51

5

66

5

84

4

Because the query used in the above function will only ever return one row, we can greatly simplify the function definition thus:

Scalar Function with compound SQL
--#SET DELIMITER !
CREATE FUNCTION dpt1 (deptin SMALLINT)
RETURNS SMALLINT
BEGIN ATOMIC
    RETURN
        SELECT COUNT(*)
        FROM staff
        WHERE dept = deptin;
END!
COMMIT!
This example uses an "!" as the stmt delimiter.
SELECT XXX.*
     , dpt1(dept) as #names
FROM
    (SELECT dept
     FROM staff
     GROUP BY dept
) AS XXX
ORDER BY dept!

In the above example, the RETURN statement is directly finding the one matching row, and then returning it to the calling statement.

Table Function

Below is almost exactly the same logic, this time using a table function:

Table Function with compound SQL

--#SET DELIMITER !
CREATE FUNCTION dpt2 ()
RETURNS TABLE ( dept    SMALLINT
              , #names  SMALLINT)
BEGIN ATOMIC
    RETURN
        SELECT dept
             , count(*)
        FROM staff
        GROUP BY dept
        ORDER BY dept;
END!

COMMIT!

--#SET DELIMITER ;
SELECT *
FROM TABLE(dpt2()) T1
ORDER BY dept;
This example uses an "!" as the stmt delimiter.

ANSWER

DEPT #NAMES

10

4

15

4

20

4

38

5

42

4

51

5

66

5

84

4

Sample Compound SQL statement
BEGIN ATOMIC
  DECLARE cntr SMALLINT DEFAULT 1;
  FOR V1 AS
    SELECT id as idval
    FROM staff
    WHERE id < 80
    ORDER BY id
  DO
    UPDATE staff SET comm = cntr
    WHERE id = idval;
    SET cntr = cntr + 1;
  END FOR;
END

6. Column Functions or Aggregate Functions

By themselves, column functions work on the complete set of matching rows. One can use a GROUP BY expression to limit them to a subset of matching rows. One can also use them in an OLAP function to treat individual rows differently.

Be very careful when using either a column function, or the DISTINCT clause, in a join. If the join is incorrectly coded, and does some form of Cartesian Product, the column function may get rid of the all the extra (wrong) rows so that it becomes very hard to confirm that the answer is incorrect. Likewise, be appropriately suspicious whenever you see that someone (else) has used a DISTINCT statement in a join. Sometimes, users add the DISTINCT clause to get rid of duplicate rows that they didn’t anticipate and don’t understand.

6.1. Column Functions, Definitions

6.1.1. ARRAY_AGG

Aggregate the set of elements in an array. If an ORDER BY is provided, it determines the order in which the elements are entered into the array.

6.1.2. AVG

Get the average (mean) value of a set of non-null rows. The columns(s) must be numeric. ALL is the default. If DISTINCT is used duplicate values are ignored. If no rows match, the null value is returned.

AVG function examples
SELECT AVG(dept)          AS a1
     , AVG(ALL dept)      AS a2
     , AVG(DISTINCT dept) AS a3
     , AVG(dept/10)       AS a4
     , AVG(dept)/10       AS a5
FROM staff
HAVING AVG(dept) > 40;

ANSWER

A1 A2 A3 A4 A5

41

41

40

3

4

Observe columns A4 and A5 above. Column A4 has the average of each value divided by 10. Column A5 has the average of all of the values divided by 10. In the former case, precision has been lost due to rounding of the original integer value and the result is arguably incorrect. This problem also occurs when using the SUM function.
Averaging Null and Not-Null Values

Some database designers have an intense and irrational dislike of using nullable fields. What they do instead is define all columns as not-null and then set the individual fields to zero (for numbers) or blank (for characters) when the value is unknown. This solution is reasonable in some situations, but it can cause the AVG function to give what is arguably the wrong answer. One solution to this problem is some form of counseling or group therapy to overcome the phobia. Alternatively, one can use the CASE expression to put null values back into the answer-set being processed by the AVG function. The following SQL statement uses a modified version of the IBM sample STAFF table (all null COMM values were changed to zero) to illustrate the technique:

Convert zero to null before doing AVG
UPDATE staff
SET comm = 0
WHERE comm IS NULL;

SELECT AVG(salary) AS salary
     , AVG(comm)   AS comm1
     , AVG(CASE comm
              WHEN 0 THEN NULL
              ELSE comm
           END) AS comm2
FROM staff;

ANSWER

SALARY COMM1 COMM2

67932.78

351.98

513.31

UPDATE staff
SET comm = NULL
WHERE comm = 0;

The COMM2 field above is the correct average. The COMM1 field is incorrect because it has factored in the zero rows with really represent null values. Note that, in this particular query, one cannot use a WHERE to exclude the "zero" COMM rows because it would affect the average salary value.

Dealing with Null Output

The AVG, MIN, MAX, and SUM functions almost always return a null value when there are no matching rows (see No Rows Match for exceptions). One can use the COALESCE function, or a CASE expression, to convert the null value into a suitable substitute. Both methodologies are illustrated below:

Convert null output (from AVG) to zero
SELECT COUNT(*)                AS c1
     , AVG(salary)             AS a1
     , COALESCE(AVG(salary),0) AS a2
     , CASE
         WHEN AVG(salary) IS NULL THEN 0
         ELSE AVG(salary)
      END                      AS a3
FROM staff
WHERE id < 10;

ANSWER

C1 A1 A2 A3

0

-

0

0

AVG Date/Time Values

The AVG function only accepts numeric input. However, one can, with a bit of trickery, also use the AVG function on a date field. First convert the date to the number of days since the start of the Current Era, then get the average, then convert the result back to a date. Please be aware that, in many cases, the average of a date does not really make good business sense. Having said that, the following SQL gets the average birth-date of all employees:

AVG of date column
SELECT AVG(DAYS(birthdate))
     , DATE(AVG(DAYS(birthdate)))
FROM employee;

ANSWER

1 2

721092

1975-04-14

Time data can be manipulated in a similar manner using the MIDNIGHT_SECONDS function. If one is really desperate (or silly), the average of a character field can also be obtained using the ASCII and CHR functions.

Average of an Average

In some cases, getting the average of an average gives an overflow error. Inasmuch as you shouldn’t do this anyway, it is no big deal:

Select average of average
SELECT AVG(avg_sal) AS avg_avg
FROM (SELECT dept
           , AVG(salary) AS avg_sal
     FROM staff
     GROUP BY dept
    ) AS xxx;

ANSWER: Overflow error

6.1.3. CORRELATION

I don’t know a thing about statistics, so I haven’t a clue what this function does. But I do know that the SQL Reference is wrong - because it says the value returned will be between 0 and 1. I found that it is between -1 and +1 (see below). The output type is float.

CORRELATION function examples
WITH temp1(col1, col2, col3, col4) AS
(VALUES (0, 0, 0, RAND(1))
 UNION ALL
 SELECT col1 + 1
      , col2 - 1
      , RAND()
      , RAND()
 FROM temp1
 WHERE col1 < = 1000
 )
SELECT DEC(CORRELATION(col1, col1), 5, 3) AS cor11
     , DEC(CORRELATION(col1, col2), 5, 3) AS cor12
     , DEC(CORRELATION(col2, col3), 5, 3) AS cor23
     , DEC(CORRELATION(col3, col4), 5, 3) AS cor34
FROM temp1;

ANSWER

COR11 COR12 COR23 COR34

1.000

-1.000

-0.017

-0.005

6.1.4. COUNT

Get the number of values in a set of rows. The result is an integer. The value returned depends upon the options used:

  • COUNT(*) gets a count of matching rows.

  • COUNT(expression) gets a count of rows with a non-null expression value.

  • COUNT(ALL expression) is the same as the COUNT(expression) statement.

  • COUNT(DISTINCT expression) gets a count of distinct non-null expression values.

COUNT function examples
SELECT COUNT(*)                     AS c1
     , COUNT(INT(comm/10))          AS c2
     , COUNT(ALL INT(comm/10))      AS c3
     , COUNT(DISTINCT INT(comm/10)) AS c4
     , COUNT(DISTINCT INT(comm))    AS c5
     , COUNT(DISTINCT INT(comm))/10 AS c6
FROM staff;

ANSWER

C1 C2 C3 C4 C5 C6

35

24

24

19

24

2

There are 35 rows in the STAFF table (see C1 above), but only 24 of them have non-null commission values (see C2 above). If no rows match, the COUNT returns zero - except when the SQL statement also contains a GROUP BY. In this latter case, the result is no row.

COUNT function with and without GROUP BY
SELECT 'NO GP-BY' AS c1
     , COUNT(*)   AS c2
FROM staff
WHERE id = -1
UNION
SELECT 'GROUP-BY' AS c1
     , COUNT(*)   AS c2
FROM staff
WHERE id = -1
GROUP BY dept;

ANSWER

C1 C2

NO GP-BY

0

6.1.5. COUNT_BIG

Get the number of rows or distinct values in a set of rows. Use this function if the result is too large for the COUNT function. The result is of type decimal 31. If the DISTINCT option is used both duplicate and null values are eliminated. If no rows match, the result is zero.

COUNT_BIG function examples
SELECT COUNT_BIG(*)                AS c1
     , COUNT_BIG(dept)             AS c2
     , COUNT_BIG(DISTINCT dept)    AS c3
     , COUNT_BIG(DISTINCT dept/10) AS c4
     , COUNT_BIG(DISTINCT dept)/10 AS c5
FROM STAFF;

ANSWER

C1 C2 C3 C4 C5

35.

35.

8.

7.

0.

6.1.6. COVARIANCE

Returns the covariance of a set of number pairs. The output type is float.

COVARIANCE function examples
WITH temp1(c1, c2, c3, c4) AS
(VALUES (0 , 0 , 0 , RAND(1))
 UNION ALL
 SELECT c1 + 1
      , c2 - 1
      , RAND()
      , RAND()
FROM temp1
WHERE c1 <= 1000
)
SELECT DEC(COVARIANCE(c1,c1),6,0) AS cov11
     , DEC(COVARIANCE(c1,c2),6,0) AS cov12
     , DEC(COVARIANCE(c2,c3),6,4) AS cov23
     , DEC(COVARIANCE(c3,c4),6,4) AS cov34
FROM temp1;

ANSWER

COV11 COV12 COV23 COV34

83666.

-83666.

-1.4689

-0.0004

6.1.7. COVARIANCE_SAMP

Returns the sample covariance of a set of number pairs.

6.1.8. CUME_DIST

Returns the cumulative distribution of a row that is hypothetically inserted into a group of rows.

6.1.9. GROUPING

The GROUPING function is used in CUBE, ROLLUP, and GROUPING SETS statements to identify what rows come from which particular GROUPING SET. A value of 1 indicates that the corresponding data field is null because the row is from of a GROUPING SET that does not involve this row. Otherwise, the value is zero.

GROUPING function example
SELECT dept
     , AVG(salary)    AS salary
     , GROUPING(dept) AS df
FROM staff
GROUP BY ROLLUP(dept)
ORDER BY dept;

ANSWER

DEPT SALARY DF

10

83365.86

0

15

60482.33

0

20

63571.52

0

38

60457.11

0

42

49592.26

0

51

83218.16

0

66

73015.24

0

84

66536.75

0

-

67932.78

1

See the section titled "Group By and Having" for more information on this function.

6.1.10. LISTAGG

Aggregates a set of string elements into one string by concatenating the strings. Optionally, a separator string can be provided which is inserted between contiguous input strings.

6.1.11. MAX

Get the maximum value of a set of rows. The use of the DISTINCT option has no affect. If no rows match, the null value is returned.

MAX function examples
SELECT MAX(dept)
     , MAX(ALL dept)
     , MAX(DISTINCT dept)
     , MAX(DISTINCT dept/10)
FROM staff;

ANSWER

1 2 3 4

84

84

84

8

MAX and MIN usage with Scalar Functions

Several Db2 scalar functions convert a value from one format to another, for example from numeric to character. The function output format will not always shave the same ordering sequence as the input. This difference can affect MIN, MAX, and ORDER BY processing.

MAX function with dates
SELECT MAX(hiredate)
     , CHAR(MAX(hiredate),USA)
     , MAX(CHAR(hiredate,USA))
FROM employee;

ANSWER

1 2 3

2006-12-15

12/15/2006

12/15/2006

In the above the SQL, the second field gets the MAX before doing the conversion to character whereas the third field works the other way round. In most cases, the later is wrong. In the next example, the MAX function is used on a small integer value that has been converted to character. If the CHAR function is used for the conversion, the output is left justified, which results in an incorrect answer. The DIGITS output is correct (in this example).

MAX function with numbers, 1 of 2
SELECT MAX(id)         AS id
     , MAX(CHAR(id))   AS chr
     , MAX(DIGITS(id)) AS dig
FROM staff;

ANSWER

ID CHR DIG

350

90

00350

The DIGITS function can also give the wrong answer - if the input data is part positive and part negative. This is because this function does not put a sign indicator in the output.

MAX function with numbers, 2 of 2
SELECT MAX(id - 250)         AS id
     , MAX(CHAR(id - 250))   AS chr
     , MAX(DIGITS(id - 250)) AS dig
FROM staff;

ANSWER

D CHR DIG

100

90

0000000240

Be careful when using a column function on a field that has been converted from number to character, or from date/time to character. The result may not be what you intended.

6.1.12. MEDIAN

Returns the median value in a set of values.

6.1.13. MIN

Get the minimum value of a set of rows. The use of the DISTINCT option has no affect. If no rows match, the null value is returned.

MIN function examples
SELECT MIN(dept)
     , MIN(ALL dept)
     , MIN(DISTINCT dept)
     , MIN(DISTINCT dept/10)
FROM staff;

ANSWER

1 2 3 4

10

10

10

1

6.1.14. PERCENTILE_CONT

Returns the value that corresponds to the specified percentile given a sort specification by using a continuous distribution model.

6.1.15. PERCENTILE_DISC

Returns the value that corresponds to the specified percentile given a sort specification by using a discrete distribution model.

6.1.16. PERCENT_RANK

Returns the relative percentile rank of a row that is hypothetically inserted into a group of rows.

6.1.17. Regression Functions

The various regression functions support the fitting of an ordinary-least-squares regression line of the form y = a * x + b to a set of number pairs.

REGR_AVGX returns a quantity that than can be used to compute the validity of the regression model. The output is of type float.

REGR_AVGY (see REGR_AVGX).

REGR_COUNT returns the number of matching non-null pairs. The output is integer.

REGR_INTERCEPT returns the y-intercept of the regression line.

REGR_R2 returns the coefficient of determination for the regression.

REGR_SLOPE returns the slope of the line.

REGR_SXX (see REGR_AVGX).

REGR_SXY (see REGR_AVGX).

REGR_SYY (see REGR_AVGX).

See the IBM SQL Reference for more details on the above functions.

REGRESSION functions examples
SELECT DEC(REGR_SLOPE(bonus,salary),7,5)     AS r_slope
     , DEC(REGR_INTERCEPT(bonus,salary),7,3) AS r_icpt
     , INT(REGR_COUNT(bonus,salary))         AS r_count
     , INT(REGR_AVGX(bonus,salary))          AS r_avgx
     , INT(REGR_AVGY(bonus,salary))          AS r_avgy
     , DEC(REGR_SXX(bonus,salary),10)        AS r_sxx
     , INT(REGR_SXY(bonus,salary))           AS r_sxy
     , INT(REGR_SYY(bonus,salary))           AS r_syy
FROM employee
WHERE workdept = 'A00';

ANSWERS

r_slope r_icpt r_count r_avgx r_avgy r_sxx r_sxy r_syy

0.00247

644.862

5

70850

820

8784575000

21715000

168000

6.1.18. STDDEV

Get the standard deviation of a set of numeric values. If DISTINCT is used, duplicate values are ignored. If no rows match, the result is null. The output format is double.

STDDEV function examples
SELECT AVG(dept)                AS a1
,STDDEV(dept)                   AS s1
,DEC(STDDEV(dept),3,1)          AS s2
,DEC(STDDEV(ALL dept),3,1)      AS s3
,DEC(STDDEV(DISTINCT dept),3,1) AS s4
FROM staff;

ANSWER

A1 S1 S2 S3 S4

41

+2.3522355E+1

23.5

23.5

24.1

6.1.19. STDDEV_SAMP

The STDDEV_SAMP function returns the sample standard deviation (division by [n-1]) of a set of numbers.

6.1.20. SUM

Get the sum of a set of numeric values. If DISTINCT is used, duplicate values are ignored. Null values are always ignored. If no rows match, the result is null.

SUM function examples
SELECT SUM(dept)          AS s1
     , SUM(ALL dept)      AS s2
     , SUM(DISTINCT dept) AS s3
     , SUM(dept/10)       AS s4
     , SUM(dept)/10       AS s5

FROM staff;

ANSWER

S1 S2 S3 S4 S5

1459

1459

326

134

145

The answers S4 and S5 above are different. This is because the division is done before the SUM in column S4, and after in column S5. In the former case, precision has been lost due to rounding of the original integer value and the result is arguably incorrect. When in doubt, use the S5 notation.

6.1.21. VAR or VARIANCE

Get the variance of a set of numeric values. If DISTINCT is used, duplicate values are ignored. If no rows match, the result is null. The output format is double.

VARIANCE function examples
SELECT AVG(dept)                        AS a1
     , VARIANCE(dept)                   AS s1
     , DEC(VARIANCE(dept),4,1)          AS s2
     , DEC(VARIANCE(ALL dept),4,1)      AS s3
     , DEC(VARIANCE(DISTINCT dept),4,1) AS s4
FROM staff;

ANSWER

A1 V1 V2 V3 V4

41

+5.533012244E+2

553

553

582

6.1.22. VARIANCE_SAMP

Returns the sample variance (division by [n-1]) of a set of numbers.

6.1.23. XMLAGG

Returns an XML sequence containing an item for each non-null value in a set of XML values.

6.1.24. XMLGROUP

The XMLGROUP function returns an XML value with a single XQuery document node containing one top-level element node. This is an aggregate expression that will return a single-rooted XML document from a group of rows where each row is mapped to a row subelement.

7. OLAP Functions

=== Introduction

Online Analytical Processing (OLAP) functions enable one to sequence and rank query rows. They are especially useful when the calling program is very simple.

7.1. The Bad Old Days

To really appreciate the value of the OLAP functions, one should try to do some seemingly trivial task without them. To illustrate this point, consider the following query:

Select rows from STAFF table
SELECT s1.job
     , s1.id
     , s1.salary
FROM staff s1
WHERE s1.name LIKE '%s%'
AND   s1.id   <    90
ORDER BY s1.job
       , s1.id;

ANSWER

JOB ID SALARY

Clerk

80

43504.60

Mgr

10

98357.50

Mgr

50

80659.80

Let us now add two fields to this query:

  • A running sum of the salaries selected.

  • A running count of the rows retrieved.

Adding these fields is easy - when using OLAP functions:

Using OLAP functions to get additional fields
SELECT s1.job
     , s1.id
     , s1.salary
     , SUM(salary) OVER(ORDER BY job, id)  AS sumsal
     , ROW_NUMBER() OVER(ORDER BY job, id) AS r
FROM staff s1
WHERE s1.name LIKE '%s%'
AND   s1.id   <    90
ORDER BY s1.job
       , s1.id;

ANSWER

JOB ID SALARY SUMSAL R

Clerk

80

43504.60

43504.60

1

Mgr

10

98357.50

141862.10

2

Mgr

50

80659.80

222521.90

3

7.1.1. Write Query without OLAP Functions

If one does not have OLAP functions, one can still get the required answer, but the code is quite tricky. The problem is that this seemingly simple query contains two nasty tricks:

  • Not all of the rows in the table are selected.

  • The output is ordered on two fields, the first of which is not unique.

Below is the arguably the most elegant way to write the above query without using OLAP functions. There query has the following basic characteristics:

  • Define a common-table-expression with the set of matching rows.

  • Query from this common-table-expression.

  • For each row fetched, do two nested select statements. The first gets a running sum of the salaries, and the second gets a running count of the rows retrieved.

Now for the code:

Running counts without OLAP functions
WITH temp1 AS
(SELECT *
 FROM staff s1
 WHERE s1.name LIKE '%s%'
 AND   s1.id   <    90
)
SELECT s1.job
     , s1.id
     , s1.salary
     , (SELECT SUM(s2.salary)
        FROM temp1 s2
        WHERE (s2.job < s1.job)
        OR    (s2.job = s1.job AND s2.id <= s1.id)
       ) AS sumsal
    , (SELECT COUNT(*)
       FROM temp1 s2
       WHERE (s2.job < s1.job)
       OR    (s2.job = s1.job AND s2.id <= s1.id)
      ) AS r
FROM temp1 s1
ORDER BY s1.job
       , s1.id;

ANSWER

JOB ID SALARY SUMSAL R

Clerk

80

43504.60

43504.60

1

Mgr

10

98357.50

141862.10

2

Mgr

50

80659.80

222521.90

3

7.1.2. Concepts

Below are some of the basic characteristics of OLAP functions:

  • OLAP functions are column functions that work (only) on the set of rows that match the predicates of the query.

  • Unlike ordinarily column functions, (e.g. SUM), OLAP functions do not require that the whole answer-set be summarized. In fact, OLAP functions never change the number of rows returned by the query.

  • OLAP functions work on sets of values, but the result is always a single value.

  • OLAP functions are used to return individual rows from a table (e.g. about each staff member), along with related summary data (e.g. average salary in department).

  • OLAP functions are often applied on some set (i.e. of a moving window) of rows that is defined relative to the current row being processed. These matching rows are classified using an ORDER BY as being one of three types:

    • Preceding rows are those that have already been processed.

    • Following rows are those that have yet to be processed.

    • Current row is the one currently being processed.

  • The ORDER BY used in an OLAP function is not related to the ORDER BY expression used to define the output order of the final answer set.

  • OLAP functions can summarize the matching rows by a subset (i.e. partition). When this is done, it is similar to the use of a GROUP BY in an ordinary column function.

Below is a query that illustrates these concepts. It gets some individual rows from the STAFF table, while using an OLAP function to calculate a running average salary within the DEPT of the current row. The average is calculated using one preceding row (in ID order), the current row, and two following rows:

Sample OLAP query
SELECT dept
     , id
     , salary
     , DEC(AVG(salary) OVER(PARTITION BY dept
                            ORDER BY id
                            ROWS BETWEEN 1 PRECEDING
                                 AND     2 FOLLOWING)
           , 8, 2) AS avb_sal
FROM staff
WHERE dept IN (20, 38)
ORDER BY dept
       , id;

ANSWER

DEPT ID SALARY AVG_SAL

20

10

98357.50

73344.45

20

20

78171.25

63571.52

20

80

43504.60

51976.20

20

190

34252.75

38878.67

38

30

77506.75

74107.01

38

40

78006.00

66318.95

38

60

66808.30

56194.70

38

120

42954.75

48924.26

38

180

37009.75

39982.25

TABLE

DEPT ID SALARY Matching? Partition Relation to row [38 60 66808.30 56194.70]

15

110

42508.20

N

-

-

15

170

42258.50

N

-

-

20

10

98357.50

Y

1

-

20

20

78171.25

Y

1

-

20

80

43504.60

Y

1

-

20

190

34252.75

Y

1

-

38

30

77506.75

Y

2

Preceding row

38

40

78006.00

Y

2

Preceding row

38

60

66808.30

Y

2

Current row

38

120

42954.75

Y

2

Following row

38

180

37009.75

Y

2

Following row

42

90

38001.75

N

-

-

42

100

78352.80

N

-

-

42

130

40505.90

N

-

-

Below is another query that calculates various running averages:

Sample OLAP query
SELECT dept
     , id
     , salary
     , DEC(AVG(salary) OVER() ,8,2)                  AS avg1
     , DEC(AVG(salary) OVER(PARTITION BY dept) ,8,2) AS avg2
     , DEC(AVG(salary) OVER(PARTITION BY dept
                            ORDER BY id
                            ROWS UNBOUNDED PRECEDING)
          , 8, 2)                                    AS avg3
     , DEC(AVG(salary) OVER(PARTITION BY dept
                            ORDER BY id
                            ROWS BETWEEN 1 PRECEDING AND 2 FOLLOWING)
          , 8, 2)                                    AS avg4
FROM staff
WHERE dept IN (15,20)
AND   id   >  20
ORDER BY dept
       , id;

ANSWER

DEPT ID SALARY AVG1 AVG2 AVG3 AVG4

15

50

80659.80

53281.11

60482.33

80659.80

66556.94

15

70

76502.83

53281.11

60482.33

78581.31

60482.33

15

110

42508.20

53281.11

60482.33

66556.94

53756.51

15

170

42258.50

53281.11

60482.33

60482.33

42383.35

20

80

43504.60

53281.11

38878.67

43504.60

38878.67

20

190

34252.75

53281.11

38878.67

38878.67

38878.67

  • AVG1: An average of all matching rows

  • AVG2: An average of all matching rows within a department.

  • AVG3: An average of matching rows within a department, from the first matching row (ordered by ID), up to and including the current row.

  • AVG4: An average of matching rows within a department, starting with one preceding row (i.e. the highest, ordered by ID), the current row, and the next two following rows.

7.1.3. PARTITION Expression

The PARTITION BY expression, which is optional, defines the set of rows that are used in each OLAP function calculation.

Below is a query that uses different partitions to average sets of rows:

PARTITION BY examples
SELECT id
     , dept
     , job
     , years
     , salary
     , DEC(AVG(salary) OVER(PARTITION BY dept) ,7,2)      AS dpt_avg
     , DEC(AVG(salary) OVER(PARTITION BY job) ,7,2)       AS job_avg
     , DEC(AVG(salary) OVER(PARTITION BY years/2) ,7,2)   AS yr2_avg
     , DEC(AVG(salary) OVER(PARTITION BY dept, job) ,7,2) AS d_j_avg
FROM staff
WHERE dept IN (15,20)
AND   id   >  20
ORDER BY id;

ANSWER

ID DEPT JOB YEARS SALARY DPT_AVG JOB_AVG YR2_AVG D_J_AVG

50

15

Mgr

10

80659.80

60482.33

80659.80

80659.80

80659.80

70

15

Sales

7

76502.83

60482.33

76502.83

76502.83

76502.83

80

20

Clerk

-

43504.60

38878.67

40631.01

43504.60

38878.67

110

15

Clerk

5

42508.20

60482.33

40631.01

42383.35

42383.35

170

15

Clerk

4

42258.50

60482.33

40631.01

42383.35

42383.35

190

20

Clerk

8

34252.75

38878.67

40631.01

34252.75

38878.67

7.1.4. PARTITION vs. GROUP BY

The PARTITION clause, when used by itself, returns a very similar result to a GROUP BY, except that like all OLAP functions, it does not remove the duplicate rows. To illustrate, below is a simple query that does a GROUP BY:

Sample query using GROUP BY
SELECT dept
     , SUM(years) AS sum
     , AVG(years) AS avg
     , COUNT(*)   AS row
FROM staff
WHERE id    BETWEEN 40 AND 120
AND   years IS NOT  NULL
GROUP BY dept;

ANSWER

DEPT SUM AVG ROW

15

22

7

3

38

6

6

1

42

13

6

2

Below is a similar query that uses a PARTITION phrase. Observe that each value calculated is the same, but duplicate rows have not been removed:

Sample query using PARTITION
SELECT dept
     , SUM(years) OVER(PARTITION BY dept) AS sum
     , AVG(years) OVER(PARTITION BY dept) AS avg
     , COUNT(*)   OVER(PARTITION BY dept) AS row
FROM staff
WHERE id    BETWEEN 40 AND 120
AND   years IS NOT  NULL
ORDER BY dept;

ANSWER

DEPT SUM AVG ROW

15

22

7

3

15

22

7

3

15

22

7

3

38

6

6

1

42

13

6

2

42

13

6

2

Below is a similar query that uses the PARTITION phrase, and then uses a DISTINCT clause to remove the duplicate rows:

Sample query using PARTITION and DISTINCT
SELECT DISTINCT dept
     , SUM(years) OVER(PARTITION BY dept) AS sum
     , AVG(years) OVER(PARTITION BY dept) AS avg
     , COUNT(*)   OVER(PARTITION BY dept) AS row
FROM staff
WHERE id    BETWEEN 40 AND 120
AND   years IS NOT  NULL
ORDER BY dept;

ANSWER

DEPT SUM AVG ROW

15

22

7

3

38

6

6

1

42

13

6

2

Even though the above statement gives the same answer as the prior GROUP BY example, it is not the same internally. Nor is it (probably) as efficient, and it is certainly not as easy to understand. Therefore, when in doubt, use the GROUP BY syntax.

7.1.5. Window Definition

An OLAP function works on a "window" of matching rows. This window can be defined as:

  • All matching rows.

  • All matching rows within a partition.

  • Some moving subset of the matching rows (within a partition, if defined).

A moving window has to have an ORDER BY clause so that the set of matching rows can be determined.

Window Size Partitions
  • UNBOUNDED PRECEDING: All of the preceding rows.

  • Number PRECEDING: The "n" preceding rows.

  • UNBOUNDED FOLLOWING: All of the following rows.

  • Number FOLLOWING: The "n" following rows.

  • CURRENT ROW: Only the current row.

Defaults

  • No ORDER BY: UNBOUNDED PRECEDING to UNBOUNDED FOLLOWING.

  • ORDER BY only: UNBOUNDED PRECEDING to CURRENT ROW.

  • No BETWEEN: CURRENT ROW to "n" preceding/following row or rank.

  • BETWEEN stmt: From "n" to "n" preceding/following row or rank. The end-point must be greater than or equal to the starting point.

7.1.6. Sample Queries

Below is a query that illustrates some of the above concepts:

Different window sizes_
SELECT id
     , salary
     , DEC(AVG(salary) OVER()                 ,7,2) AS avg_all
     , DEC(AVG(salary) OVER(ORDER BY id)      ,7,2) AS avg_odr
     , DEC(AVG(salary)
        OVER(ORDER BY id
             ROWS BETWEEN UNBOUNDED PRECEDING
             AND UNBOUNDED FOLLOWING)         ,7,2) AS avg_p_f
     , DEC(AVG(salary)
        OVER(ORDER BY id
             ROWS BETWEEN UNBOUNDED PRECEDING
             AND CURRENT ROW)                ,7,2) AS avg_p_c
     , DEC(AVG(salary)
        OVER(ORDER BY id
             ROWS BETWEEN CURRENT ROW
             AND UNBOUNDED FOLLOWING)        ,7,2) AS avg_c_f
     , DEC(AVG(salary)
        OVER(ORDER BY id
             ROWS BETWEEN 2 PRECEDING
             AND 1 FOLLOWING)                ,7,2) AS avg_2_1
FROM staff
WHERE dept IN (15,20)
AND id > 20
ORDER BY id;

ANSWER

ID SALARY AVG_ALL AVG_ODR AVG_P_F AVG_P_C AVG_C_F AVG_2_1

50

80659.80

53281.11

80659.80

53281.11

80659.80

53281.11

78581.31

70

76502.83

53281.11

78581.31

53281.11

78581.31

47805.37

66889.07

80

43504.60

53281.11

66889.07

53281.11

66889.07

40631.01

60793.85

110

42508.20

53281.11

60793.85

53281.11

60793.85

39673.15

51193.53

170

42258.50

53281.11

57086.78

53281.11

57086.78

38255.62

40631.01

190

34252.75

53281.11

53281.11

53281.11

53281.11

34252.75

39673.15

When the BETWEEN syntax is used, the start of the range/rows must be less than or equal to the end of the range/rows.

When no BETWEEN is used, the set of rows to be evaluated goes from the current row up or down to the end value:

Different window sizes
SELECT id
, SUM(id) OVER(ORDER BY id)                          AS sum1
, SUM(id) OVER(ORDER BY id ROWS 1 PRECEDING)         AS sum2
, SUM(id) OVER(ORDER BY id ROWS UNBOUNDED PRECEDING) AS sum3
, SUM(id) OVER(ORDER BY id ROWS CURRENT ROW)         AS sum4
, SUM(id) OVER(ORDER BY id ROWS2 FOLLOWING)          AS sum6
, SUM(id) OVER(ORDER BY id ROWS UNBOUNDED FOLLOWING) AS sum6
FROM staff
WHERE id < 40
ORDER BY id;

ANSWER

ID SUM1 SUM2 SUM3 SUM4 SUM5 SUM6

10

10

10

10

10

60

60

20

30

30

30

20

50

50

30

60

50

60

30

30

30

7.1.7. ROWS vs. RANGE

(OLAP, ROWS A moving window of rows to be evaluated (relative to the current row) can be defined using either the ROW or RANGE expressions. These differ as follows:

  • ROWS: Refers to the "n" rows before and/or after (within the partition), as defined by the ORDER BY.

  • RANGE: Refers to those rows before and/or after (within the partition) that are within an arithmetic range of the current row, as defined by the ORDER BY.

The next query compares the ROW and RANGE expressions:

ROW vs. RANGE example
SELECT id
, SMALLINT(SUM(id)
    OVER(ORDER BY id
         RANGE BETWEEN 10 PRECEDING AND 10 FOLLOWING))        AS rng1
, SMALLINT(SUM(id)
    OVER(ORDER BY id
         ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING))           AS row1
, SMALLINT(SUM(id)
    OVER(ORDER BY id
         RANGE BETWEEN 10 PRECEDING AND CURRENT ROW))         AS rng2
, SMALLINT(SUM(id)
    OVER(ORDER BY id
         ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING))           AS row2
, SMALLINT(SUM(id)
    OVER(ORDER BY id DESC
         ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING))           AS row3
, SMALLINT(SUM(id)
    OVER(ORDER BY id
         RANGE BETWEEN UNBOUNDED PRECEDING AND 20 FOLLOWING)) AS rng3
FROM staff
WHERE id < 60
ORDER BY id;

ANSWER

ID RNG1 ROW1 RNG2 ROW2 ROW3 RNG3

10

30

30

10

-

90

60

20

60

60

30

10

120

100

30

90

90

50

30

90

150

40

120

120

70

60

50

150

50

90

90

90

90

-

150

Usage Notes

  • An ORDER BY statement is required when using either expression.

  • If no RANGE or ROWS expression was provided, the default range (assuming there was an ORDER BY) is all preceding rows – up to the current row.

  • When using the RANGE expression, only one expression can be specified in the ORDER BY, and that expression must be numeric.

7.1.8. ORDER BY Expression

The ORDER BY phrase has several purposes:

  • It defines the set of rows that make up a moving window.

  • It provides a set of values to do aggregations on. Each distinct value gets a new result.

  • It gives a direction to the aggregation function processing (i.e. ASC or DESC).

An ORDER BY expression is required for the RANK and DENSE_RANK functions. It is optional for all others (except of using ROWS or RANGE).

Usage Notes

  • ASC: Sorts the values in ascending order. This is the default.

  • DESC: Sorts the values in descending order.

  • NULLS: Determines whether null values are sorted high or low, relative to the non-null values present. Note that the default option differs for ascending and descending order.

  • Sort Expression: The sort-key expression can be any valid column, or any scalar expression is deterministic, and has no external action.

  • ORDER BY ORDER OF table-designator: The table designator refers to a subselect or fullselect in the query and any ordering defined on columns in that subselect or fullselect (note: if there is no explicit ordering the results are unpredictable). If the subselect or fullselect ORDER BY is changed, the ordering sequence will automatically change to match. Note that the final query may have an ordering that differs from that in the subselect or fullselect.

When the table designator refers to a table in the current subselect or fullselect, as opposed to the results of a nested subselect or fullselect, the values are unpredictable.

7.1.9. Sample Query

In the next query, various aggregations are done on a variety of fields, and on a nested-tableexpression that contains an ORDER BY. Observe that the ascending fields sum or count up, while the descending fields sum down. Also observe that each aggregation field gets a separate result for each new set of rows, as defined in the ORDER BY phrase:

ORDER BY example
SELECT dept
     , name
     , salary
     , DEC(SUM(salary) OVER(ORDER BY dept) ,8,2)           AS sum1
     , DEC(SUM(salary) OVER(ORDER BY dept DESC) ,8,2)      AS sum2
     , DEC(SUM(salary) OVER(ORDER BY ORDER OF s1) ,8,2)    AS sum3
     , SMALLINT(RANK() OVER(ORDER BY salary, name, dept) ) AS r1
     , SMALLINT(RANK() OVER(ORDER BY ORDER OF s1)          AS r2
     , ROW_NUMBER() OVER(ORDER)                            AS w1
     , COUNT(*) OVER(ORDER BY salary)                      AS w2
FROM (SELECT *
      FROM staff
      WHERE id < 60
      ORDER BY dept
             , name
      ) AS s1
ORDER BY 1, 2;

ANSWER

DEPT NAME SALARY SUM1 SUM2 SUM3 R1 R2 W1 W2

15

Hanes

80659.80

80659.80

412701.30

80659.80

4

1

4

4

20

Pernal

78171.25

257188.55

332041.50

158831.05

3

2

3

3

20

Sanders

98357.50

257188.55

332041.50

257188.55

5

3

5

5

38

Marenghi

77506.75

412701.30

155512.75

334695.30

1

4

1

1

38

O’Brien

78006.00

412701.30

155512.75

412701.30

2

5

2

2

There is no relationship between the ORDER BY used in an OLAP function, and the final ordering of the answer. Both are calculated independently.

7.1.10. Table Designator

The next two queries illustrate referencing a table designator in a subselect. Observe that as the ORDER BY in the subselect changes, the ordering sequence changes. Note that the final query output order does match that of the subselect:

SELECT id
     , name
     , ROW_NUMBER()
       OVER(ORDER BY ORDER OF s) od
FROM (SELECT *
      FROM staff
      WHERE id < 50
      ORDER BY name ASC
     ) AS s
ORDER BY id ASC;

ANSWER

ID NAME OD

10

Sanders

4

20

Pernal

3

30

Marenghi

1

40

O’Brien

2

ORDER BY table designator examples
SELECT id
     , name
     , ROW_NUMBER()
       OVER(ORDER BY ORDER OF s) od
FROM (SELECT *
      FROM staff
      WHERE id < 50
      ORDER BY name DESC
     ) AS s
ORDER BY id ASC;

ANSWER

ID NAME OD

10

Sanders

1

20

Pernal

2

30

Marenghi

4

40

O’Brien

3

7.1.11. Nulls Processing

When writing the ORDER BY, one can optionally specify whether or not null values should be counted as high or low. The default, for an ascending field is that they are counted as high (i.e. come last), and for a descending field, that they are counted as low:

Overriding the default null ordering sequence
SELECT id
     , years                                              AS yr
     , salary
     , DENSE_RANK() OVER(ORDER BY years ASC)              AS a
     , DENSE_RANK() OVER(ORDER BY years ASC NULLS FIRST)  AS af
     , DENSE_RANK() OVER(ORDER BY years ASC NULLS LAST )  AS al
     , DENSE_RANK() OVER(ORDER BY years DESC)             AS d
     , DENSE_RANK() OVER(ORDER BY years DESC NULLS FIRST) AS df
     , DENSE_RANK() OVER(ORDER BY years DESC NULLS LAST ) AS dl
FROM staff
WHERE id < 100
ORDER BY years
       , salary;

ANSWER

ID YR SALARY A AF AL D DF DL

30

5

77506.75

1

2

1

6

6

5

90

6

38001.75

2

3

2

5

5

4

40

6

78006.00

2

3

2

5

5

4

70

7

76502.83

3

4

3

4

4

3

10

7

98357.50

3

4

3

4

4

3

20

8

78171.25

4

5

4

3

3

2

50

10

80659.80

5

6

5

2

2

1

80

-

43504.60

6

1

6

1

1

6

60

-

66808.30

6

1

6

1

1

6

In general, one null value does not equal another null value. But, as is illustrated above, for purposes of assigning rank, all null values are considered equal.

7.1.12. Counting Nulls

The DENSE RANK and RANK functions include null values when calculating rankings. By contrast the COUNT DISTINCT statement excludes null values when counting values. Thus, as is illustrated below, the two methods will differ (by one) when they are used get a count of distinct values - if there are nulls in the target data:

Counting distinct values - comparison
SELECT COUNT(DISTINCT years) AS y#1
     , MAX(y#)               AS y#2
FROM (SELECT years
           , DENSE_RANK() OVER(ORDER BY years) AS y#
      FROM staff
      WHERE id < 100
     ) AS xxx
ORDER BY 1;

ANSWER

Y#1 Y#2

5

6

7.1.13. OLAP Functions

RANK and DENSE_RANK

The RANK and DENSE_RANK functions enable one to rank the rows returned by a query. The result is of type BIGINT.

The ORDER BY phrase, which is required, is used to both sequence the values, and to tell Db2 when to generate a new value.
RANK vs. DENSE_RANK

The two functions differ in how they handle multiple rows with the same value:

  • The RANK function returns the number of proceeding rows, plus one. If multiple rows have equal values, they all get the same rank, while subsequent rows get a ranking that counts all of the prior rows. Thus, there may be gaps in the ranking sequence.

  • The DENSE_RANK function returns the number of proceeding distinct values, plus one. If multiple rows have equal values, they all get the same rank. Each change in data value causes the ranking number to be incremented by one.

Usage Notes

  • The ORDER BY expression is mandatory.

  • The PARTITION BY expression is optional.

Compare Functions

The following query illustrates the use of the two functions:

Ranking functions example
SELECT id
     , years
     , salary
     , RANK()
        OVER(ORDER BY years) AS rank#
     , DENSE_RANK()
        OVER(ORDER BY years) AS dense#
     , ROW_NUMBER()
        OVER(ORDER BY years) AS row#
FROM staff
WHERE id    < 100
AND   years < 10
ORDER BY years;

ANSWER

ID YEARS SALARY RANK# DENSE# ROW#

30

5

77506.75

1

1

1

40

6

78006.00

2

2

2

90

6

38001.75

2

2

3

10

7

98357.50

4

3

4

70

7

76502.83

4

3

5

20

8

78171.25

6

4

6

7.1.14. ORDER BY Usage

The mandatory ORDER BY phrase gives a sequence to the ranking, and also tells Db2 when to start a new rank value. The following query illustrates both uses:

ORDER BY usage
SELECT job                                                 AS job
     , years                                               AS yr
     , id                                                  AS id
     , name                                                AS name
     , RANK() OVER(ORDER BY job ASC )                      AS a1
     , RANK() OVER(ORDER BY job ASC, years ASC)            AS a2
     , RANK() OVER(ORDER BY job ASC, years ASC ,id ASC )   AS a3
     , RANK() OVER(ORDER BY job DESC)                      AS d1
     , RANK() OVER(ORDER BY job DESC, years DESC)          AS d2
     , RANK() OVER(ORDER BY job DESC, years DESC, id DESC) AS d3
     , RANK() OVER(ORDER BY job ASC, years DESC, id ASC )  AS m1
     , RANK() OVER(ORDER BY job DESC, years ASC, id DESC)  AS m2
FROM staff
WHERE id    <   150
AND   years IN (6,7)
AND   job   >  'L'
ORDER BY job
       , years
       , id;

ANSWER

JOB YR ID NAME A1 A2 A3 D1 D2 D3 M1 M2

Mgr

6

140

Fraye

1

1

1

4

6

6

3

4

Mgr

7

10

Sanders

1

2

2

4

4

5

1

6

Mgr

7

100

Plotz

1

2

3

4

4

4

2

5

Sales

6

40

O’Brien

4

4

4

1

2

3

5

2

Sales

6

90

Koonitz

4

4

5

1

2

2

6

1

Sales

7

70

Rothman

4

6

6

1

1

1

4

3

Observe above that adding more fields to the ORDER BY phrase resulted in more ranking values being generated.

7.1.15. PARTITION Usage

The optional PARTITION phrase lets one rank the data by subsets of the rows returned. In the following example, the rows are ranked by salary within year:

Values ranked by subset of rows
SELECT id
     , years AS yr
     , salary
     , RANK() OVER(PARTITION BY years
                   ORDER BY salary)  AS r1
FROM staff
WHERE id     < 80
AND   years IS NOT NULL
ORDER BY years
       , salary;

ANSWER

ID YR SALARY R1

30

5

77506.75

1

40

6

78006.00

1

70

7

76502.83

1

10

7

98357.50

2

20

8

78171.25

1

50

0

80659.80

1

7.1.16. Multiple Rankings

One can do multiple independent rankings in the same query:

Multiple rankings in same query
SELECT id
     , years
     , salary
     , SMALLINT(RANK() OVER(ORDER BY years ASC))  AS rank_a
     , SMALLINT(RANK() OVER(ORDER BY years DESC)) AS rank_d
     , SMALLINT(RANK() OVER(ORDER BY id, years))  AS rank_iy
FROM staff
WHERE id  <  100
AND years IS NOT NULL
ORDER BY years;

7.1.17. Dumb Rankings

If one wants to, one can do some really dumb rankings. All of the examples below are fairly stupid, but arguably the dumbest of the lot is the last. In this case, the "ORDER BY 1" phrase ranks the rows returned by the constant "one", so every row gets the same rank. By contrast the "ORDER BY 1" phrase at the bottom of the query sequences the rows, and so has valid business meaning:

Dumb rankings, SQL
SELECT id
     , years
     , name
     , salary
     , SMALLINT(RANK() OVER(ORDER BY SUBSTR(name,3,2))) AS dumb1
     , SMALLINT(RANK() OVER(ORDER BY salary / 1000))    AS dumb2
     , SMALLINT(RANK() OVER(ORDER BY years * ID))       AS dumb3
     , SMALLINT(RANK() OVER(ORDER BY 1))                AS dumb4
FROM staff
WHERE id < 40
AND years IS NOT NULL
ORDER BY 1;
ID YEARS NAME SALARY DUMB1 DUMB2 DUMB3 DUMB4

10

7

Sanders

98357.50

1

3

1

1

20

8

Pernal

78171.25

3

2

3

1

30

5

Marenghi

77506.75

2

1

2

1

7.1.18. Subsequent Processing

The ranking function gets the rank of the value as of when the function was applied. Subsequent processing may mean that the rank no longer makes sense. To illustrate this point, the following query ranks the same field twice. Between the two ranking calls, some rows were removed from the answer set, which has caused the ranking results to differ:

Subsequent processing of ranked data
SELECT xxx.*
     , RANK()OVER(ORDER BY id) AS r2
FROM (SELECT id
           , name
           , RANK() OVER(ORDER BY id) AS r1
      FROM staff
      WHERE id     < 100
      AND   years IS NOT NULL
    ) AS xxx
WHERE id > 30
ORDER BY id;

ANSWER

ID NAME R1 R2

40

O’Brien

4

1

50

Hanes

5

2

70

Rothman

6

3

90

Koonitz

7

4

7.1.19. Ordering Rows by Rank

One can order the rows based on the output of a ranking function. This can let one sequence the data in ways that might be quite difficult to do using ordinary SQL. For example, in the following query the matching rows are ordered so that all those staff with the highest salary in their respective department come first, followed by those with the second highest salary, and so on. Within each ranking value, the person with the highest overall salary is listed first:

Ordering rows by rank, using RANK function
SELECT id
     , RANK() OVER(PARTITION BY dept
                   ORDER BY salary DESC) AS r1
     , salary
     , dept AS dp
FROM staff
WHERE id     < 80
AND   years IS NOT NULL
ORDER BY r1     ASC
       , salary DESC;

ANSWER

ID R1 SALARY DP

10

1

98357.50

20

50

1

80659.80

15

40

1

78006.00

38

20

2

78171.25

20

30

2

77506.75

38

70

2

76502.83

15

Here is the same query, written without the ranking function:

Ordering rows by rank, using sub-query
SELECT id
    , (SELECT COUNT(*)
       FROM staff s2
       WHERE s2.id   < 80
       AND s2.years IS NOT NULL
       AND s2.dept = s1.dept
       AND s2.salary >= s1.salary
      ) AS R1
    , salary
    , dept AS dp
FROM staff s1
WHERE id     < 80
AND   years IS NOT NULL
ORDER BY r1     ASC
       , salary DESC;

ANSWER

ID R1 SALARY DP

10

1

98357.50

20

50

1

80659.80

15

40

1

78006.00

38

20

2

78171.25

20

30

2

77506.75

38

70

2

76502.83

15

The above query has all of the failings that were discussed at the beginning of this chapter:

  • The nested table expression has to repeat all of the predicates in the main query, and have predicates that define the ordering sequence. Thus it is hard to read.

  • The nested table expression will (inefficiently) join every matching row to all prior rows.

7.1.20. Selecting the Highest Value

The ranking functions can also be used to retrieve the row with the highest value in a set of rows. To do this, one must first generate the ranking in a nested table expression, and then query the derived field later in the query. The following statement illustrates this concept by getting the person, or persons, in each department with the highest salary:

Get highest salary in each department, use RANK function
SELECT id
     , salary
     , dept AS dp
FROM
   (SELECT s1.*
         , RANK() OVER(PARTITION BY dept
                       ORDER BY salary DESC) AS r1
    FROM staff s1
    WHERE id     < 80
    AND   years IS NOT NULL
   ) AS xxx
WHERE r1 = 1
ORDER BY dp;

ANSWER

ID SALARY DP

50

80659.80

15

10

98357.50

20

40

78006.00

38

Here is the same query, written using a correlated sub-query:

Get highest salary in each department, use correlated sub-query
SELECT id
     , salary
     , dept AS dp
FROM staff s1
WHERE id     < 80
AND   years IS NOT NULL
AND NOT EXISTS
  (SELECT *
   FROM staff s2
   WHERE s2.id     < 80
   AND   s2.years IS NOT NULL
   AND s2.dept = s1.dept
   AND s2.salary > s1.salary)
ORDER BY dp;
ID SALARY DP

50

80659.80

15

10

98357.50

20

40

78006.00

38

Here is the same query, written using an uncorrelated sub-query:

Get highest salary in each department, use uncorrelated sub-query
SELECT id
     , salary
     , dept AS dp
FROM staff
WHERE id     < 80
AND   years IS NOT NULL
AND (dept, salary) IN
  (SELECT dept, MAX(salary)
   FROM staff
   WHERE id     < 80
   AND   years IS NOT NULL
   GROUP BY dept)
ORDER BY dp;

ANSWER

ID SALARY DP

50

80659.80

15

10

98357.50

20

40

78006.00

38

Arguably, the first query above (i.e. the one using the RANK function) is the most elegant of the series because it is the only statement where the basic predicates that define what rows match are written once. With the two sub-query examples, these predicates have to be repeated, which can often lead to errors.

7.1.21. ROW_NUMBER

The ROW_NUMBER function lets one number the rows being returned. The result is of type BIGINT. A syntax diagram follows. Observe that unlike with the ranking functions, the ORDER BY is not required.

ORDER BY Usage

You don’t have to provide an ORDER BY when using the ROW_NUMBER function, but not doing so can be considered to be either brave or foolish, depending on one’s outlook on life. To illustrate this issue, consider the following query:

ORDER BY example, 1 of 3
SELECT id
     , name
     , ROW_NUMBER() OVER()            AS r1
     , ROW_NUMBER() OVER(ORDER BY id) AS r2
FROM staff
WHERE id     < 50
AND   years IS NOT NULL
ORDER BY id;

ANSWER

ID NAME R1 R2

10

Sanders

1

1

20

Pernal

2

2

30

Marenghi

3

3

40

O’Brien

4

4

In the above example, both ROW_NUMBER functions return the same set of values, which happen to correspond to the sequence in which the rows are returned. In the next query, the second ROW_NUMBER function purposely uses another sequence:

ORDER BY example, 2 of 3
SELECT id
     , name
     , ROW_NUMBER() OVER()              AS r1
     , ROW_NUMBER() OVER(ORDER BY name) AS r2
FROM staff
WHERE id     < 50
AND   years IS NOT NULL
ORDER BY id;

ANSWER

ID NAME R1 R2

10

Sanders

4

4

20

Pernal

3

3

30

Marenghi

1

1

40

O’Brien

2

2

Observe that changing the second function has had an impact on the first. Now lets see what happens when we add another ROW_NUMBER function:

ORDER BY example, 3 of 3
SELECT id
,name
,ROW_NUMBER() OVER()              AS r1
,ROW_NUMBER() OVER(ORDER BY ID)   AS r2
,ROW_NUMBER() OVER(ORDER BY NAME) AS r3
FROM staff
WHERE id     < 50
AND   years IS NOT NULL
ORDER BY id;

ANSWER

ID NAME R1 R2 R3

10

Sanders

1

1

4

20

Pernal

2

2

3

30

Marenghi

3

3

1

40

O’Brien

4

4

2

Observe that now the first function has reverted back to the original sequence.

When not given an explicit ORDER BY, the ROW_NUMBER function, may create a value in any odd order. Usually, the sequence will reflect the order in which the rows are returned - but not always.

7.1.22. PARTITION Usage

The PARTITION phrase lets one number the matching rows by subsets of the rows returned. In the following example, the rows are both ranked and numbered within each JOB:

Use of PARTITION phrase
SELECT job
     , years
     , id
     , name
     , ROW_NUMBER() OVER(PARTITION BY job ORDER BY years) AS row#
     , RANK()       OVER(PARTITION BY job ORDER BY years) AS rn1#
     , DENSE_RANK() OVER(PARTITION BY job ORDER BY years) AS rn2#
FROM staff
WHERE id     < 150
AND   years IN (6,7)
AND job > 'L'
ORDER BY job,years;

ANSWER

JOB YEARS ID NAME ROW# RN1# RN2#

Mgr

6

140

Fraye

1

1

1

Mgr

7

10

Sanders

2

2

2

Mgr

7

100

Plotz

3

2

2

Sales

6

40

O’Brien

1

1

1

Sales

6

90

Koonitz

2

1

1

Sales

7

70

Rothman

3

3

2

One problem with the above query is that the final ORDER BY that sequences the rows does not identify a unique field (e.g. ID). Consequently, the rows can be returned in any sequence within a given JOB and YEAR. Because the ORDER BY in the ROW_NUMBER function also fails to identify a unique row, this means that there is no guarantee that a particular row will always give the same row number. For consistent results, ensure that both the ORDER BY phrase in the function call, and at the end of the query, identify a unique row. And to always get the rows returned in the desired row-number sequence, these phrases must be equal.

Selecting "n" Rows

To query the output of the ROW_NUMBER function, one has to make a nested temporary table that contains the function expression. In the following example, this technique is used to limit the query to the first three matching rows:

Select first 3 rows, using ROW_NUMBER function
SELECT *
FROM
    (SELECT id
          , name
          , ROW_NUMBER() OVER(ORDER BY id) AS r
    FROM staff
    WHERE id     < 100
    AND   years IS NOT NULL
   ) AS xxx
WHERE r <= 3
ORDER BY id;

ANSWER

ID NAME R

10

Sanders

1

20

Pernal

2

30

Marenghi

3

In the next query, the FETCH FIRST "n" ROWS notation is used to achieve the same result:

Select first 3 rows, using FETCH FIRST notation
SELECT id
     , name
     , ROW_NUMBER() OVER(ORDER BY id) AS r
FROM staff
WHERE id     < 100
AND   years IS NOT NULL
ORDER BY id
FETCH FIRST 3 ROWS ONLY;

ANSWER

ID NAME R

10

Sanders

1

20

Pernal

2

30

Marenghi

3

So far, the ROW_NUMBER and the FETCH FIRST notations seem to be about the same. But the former is much more flexible. To illustrate, the next query gets the 3rd through 6th rows:

Select 3rd through 6th rows
SELECT *
FROM
    (SELECT id
          , name
          , ROW_NUMBER() OVER(ORDER BY id) AS r
     FROM staff
     WHERE id     < 200
     AND   years IS NOT NULL
    ) AS xxx
WHERE r BETWEEN 3 AND 6
ORDER BY id;

ANSWER

ID NAME R

30

Marenghi

3

40

O’Brien

4

50

Hanes

5

70

Rothman

6

In the next query we get every 5th matching row - starting with the first:

Select every 5th matching row
SELECT *
FROM
    (SELECT id
          , name
          , ROW_NUMBER() OVER(ORDER BY id) AS r
     FROM staff
     WHERE id     < 200
     AND   years IS NOT NULL
    ) AS xxx
WHERE (r - 1) = ((r - 1) / 5) * 5
ORDER BY id;

ANSWER

ID NAME R

10

Sanders

1

70

Rothman

6

140

Fraye

11

190

Sneider

16

In the next query we get the last two matching rows:

Select last two rows
SELECT *
FROM
    (SELECT id
          , name
          , ROW_NUMBER() OVER(ORDER BY id DESC) AS r
     FROM staff
     WHERE id     < 200
     AND   years IS NOT NULL
    ) AS xxx
WHERE r <= 2
ORDER BY id;

ANSWER

ID NAME R

180

Abrahams

2

190

Sneider

1

7.1.23. Selecting "n" or more Rows

Imagine that one wants to fetch the first "n" rows in a query. This is easy to do, and has been illustrated above. But imagine that one also wants to keep on fetching if the following rows have the same value as the "nth". In the next example, we will get the first three matching rows in the STAFF table, ordered by years of service. However, if the 4th row, or any of the following rows, has the same YEAR as the 3rd row, then we also want to fetch them.

The query logic goes as follows:

  • Select every matching row in the STAFF table, and give them all both a row-number and a ranking value. Both values are assigned according to the order of the final output. Do all of this work in a nested table expression.

  • Select from the nested table expression where the rank is three or less.

The query relies on the fact that the RANK function (see RANK and DENSE_RANK) assigns the lowest common row number to each row with the same ranking:

Select first "n" rows, or more if needed
SELECT *
FROM
    (SELECT years
    , id
    , name
    , RANK() OVER(ORDER BY years)           AS rnk
    , ROW_NUMBER() OVER(ORDER BY years, id) AS row
    FROM staff
    WHERE id     < 200
    AND   years IS NOT NULL
    ) AS xxx
WHERE rnk <= 3
ORDER BY years
       , id;

ANSWER

YEARS ID NAME RNK ROW

3

180

Abrahams

1

1

4

170

Kermisch

2

2

5

30

Marenghi

3

3

5

110

Ngan

3

4

The type of query illustrated above can be extremely useful in certain business situations. To illustrate, imagine that one wants to give a reward to the three employees that have worked for the company the longest. Stopping the query that lists the lucky winners after three rows are fetched can get one into a lot of trouble if it happens that there are more than three employees that have worked for the company for the same number of years.

7.1.24. Selecting "n" Rows - Efficiently

Sometimes, one only wants to fetch the first "n" rows, where "n" is small, but the number of matching rows is extremely large. In this section, we will discus how to obtain these "n" rows efficiently, which means that we will try to fetch just them without having to process any of the many other matching rows. Below is an invoice table. Observe that we have defined the INV# field as the primary key, which means that Db2 will build a unique index on this column:

Performance test table - definition
CREATE TABLE invoice
( inv#       INTEGER      NOT NULL
, customer#  INTEGER      NOT NULL
, sale_date  DATE         NOT NULL
, sale_value DECIMAL(9,2) NOT NULL
, CONSTRAINT ctx1 PRIMARY KEY (inv#)
, CONSTRAINT ctx2 CHECK(inv# >= 0));

The next SQL statement will insert 1,000,000 rows into the above table. After the rows are inserted a REORG and RUNSTATS is run, so the optimizer can choose the best access path.

Performance test table - insert 1,000,000 rows
INSERT INTO invoice
WITH temp (n, m) AS
(VALUES
  (INTEGER(0), RAND(1))
  UNION ALL
  SELECT n+1, RAND()
  FROM temp
  WHERE n+1 < 1000000
)
SELECT n                                AS inv#
     , INT(m * 1000)                    AS customer#
     , DATE('2000-11-01') + (m*40) DAYS AS sale_date
     , DECIMAL((m * m * 100),8,2)       AS sale_value
FROM temp;

Imagine we want to retrieve the first five rows (only) from the above table. Below are several queries that get this result. For each query, the elapsed time, as measured by Db2BATCH, is provided. Below we use the "FETCH FIRST n ROWS" notation to stop the query at the 5th row. The query scans the primary index to get first five matching rows, and thus is cheap:

Fetch first 5 rows - 0.000 elapsed seconds
SELECT s.*
FROM invoice s
ORDER BY inv#
FETCH FIRST 5 ROWS ONLY;

The next query is essentially the same as the prior, but this time we tell Db2 to optimize the query for fetching five rows. Nothing has changed, and all is good:

Fetch first 5 rows - 0.000 elapsed seconds
SELECT s.*
FROM invoice s
ORDER BY inv#
FETCH FIRST 5 ROWS ONLY
OPTIMIZE FOR 5 ROWS;

The next query is the same as the first, except that it invokes the ROW_NUMBER function to passively sequence the output. This query also uses the primary index to identify the first five matching rows, and so is cheap:

Fetch first 5 rows+ number rows - 0.000 elapsed seconds
SELECT s.*
     , ROW_NUMBER() OVER() AS row#
FROM invoice s
ORDER BY inv#
FETCH FIRST 5 ROWS ONLY;

The next query is the same as the previous. It uses a nested-table-expression, but no action is taken subsequently, so this code is ignored:

Fetch first 5 rows+ number rows - 0.000 elapsed seconds
SELECT *
FROM
    (SELECT s.*
          , ROW_NUMBER() OVER() AS row#
     FROM invoice s
    ) xxx
ORDER BY inv#
FETCH FIRST 5 ROWS ONLY;

All of the above queries processed only five matching rows. The next query will process all one million matching rows in order to calculate the ROW_NUMBER value, which is on no particular column. It will cost:

Process and number all rows - 0.049 elapsed seconds
SELECT *
FROM
    (SELECT s.*
          , ROW_NUMBER() OVER() AS row#
     FROM invoice s
    ) xxx
WHERE row# <= 5
ORDER BY inv#;

In the above query the "OVER()" phrase told Db2 to assign row numbers to each row. In the next query we explicitly provide the ROW_NUMBER with a target column, which happens to be the same at the ORDER BY sequence, and is also an indexed column. Db2 can use all this information to confine the query to the first "n" matching rows:

Process and number 5 rows only - 0.000 elapsed seconds
SELECT *
FROM
    (SELECT s.*
          , ROW_NUMBER() OVER(ORDER BY inv#) AS row#
     FROM invoice s
    ) xxx
WHERE row# <= 5
ORDER BY inv#;
Changing the above predicate to: "WHERE row# BETWEEN 1 AND 5" will get the same answer, but use a much less efficient access path.

One can also use recursion to get the first "n" rows. One begins by getting the first matching row, and then uses that row to get the next, and then the next, and so on (in a recursive join), until the required number of rows have been obtained. In the following example, we start by getting the row with the MIN invoice-number. This row is then joined to the row with the next to lowest invoice-number, which is then joined to the next, and so on. After five such joins, the cycle is stopped and the result is selected:

Fetch first 5 rows - 0.000 elapsed seconds
WITH temp (inv#, c#, sd, sv, n) AS
(SELECT inv.*
      , 1
FROM invoice inv
WHERE inv# =
    (SELECT MIN(inv#)
    FROM invoice)
UNION ALL
SELECT new.*
     , n + 1
FROM temp old
, invoice new
WHERE old.inv# < new.inv#
AND   old.n    < 5
AND   new.inv# =
    (SELECT MIN(xxx.inv#)
     FROM invoice xxx
     WHERE xxx.inv# > old.inv#)
)
SELECT *
FROM temp;

The above technique is nice to know, but it has several major disadvantages:

  • It is not exactly easy to understand.

  • It requires that all primary predicates (e.g. get only those rows where the sale-value is greater than $10,000) be repeated four times. In the above example there are none, which is unusual in the real world.

  • It quickly becomes both very complicated and quite inefficient when the sequencing value is made up of multiple fields. In the above example, we sequenced by the INV# column, but imagine if we had used the sale-date, sale-value, and customer-number.

  • It is extremely vulnerable to inefficient access paths. For example, if instead of joining from one (indexed) invoice-number to the next, we joined from one (non-indexed) customer-number to the next, the query would run forever.

In this section we have illustrated how minor changes to the SQL syntax can cause major changes in query performance. But to illustrate this phenomenon, we used a set of queries with 1,000,000 matching rows. In situations where there are far fewer matching rows, one can reasonably assume that this problem is not an issue.

7.1.25. FIRST_VALUE and LAST_VALUE

The FIRST_VALUE and LAST_VALUE functions get first or last value in the (moving) window of matching rows.

Usage Notes

  • An expression value must be provided in the first set of parenthesis. Usually this will be a column name, but any valid scalar expression is acceptable.

  • The PARTITION BY expression is optional.

  • The ORDER BY expression is optional.

  • See Window Definition for notes on how to define a moving-window of rows to process.

  • If no explicit moving-window definition is provided, the default window size is between UNBOUNDED PRECEDING (of the partition and/or range) and the CURRENT ROW. This can sometimes cause logic errors when using the LAST_VALUE function. The last value is often simply the current row. To get the last matching value within the partition and/or range, set the upper bound to UNBOUNDED FOLLOWING.

  • If IGNORE NULLS is specified, null values are ignored, unless all values are null, in which case the result is null. The default is RESPECT NULLS.

Examples

The following query illustrates the basics. The first matching name (in ID order) within each department is obtained:

FIRST_VALUE function example
SELECT dept
     , id
     , name
     , FIRST_VALUE(name) OVER(PARTITION BY dept
                              ORDER BY id)       AS frst
FROM staff
WHERE dept <= 15
AND   id   >  160
ORDER BY dept ,id;

ANSWER

DEPT ID NAME FRST

10

210

Lu

Lu

10

240

Daniels

Lu

10

260

Jones

Lu

15

170

Kermisch

Kermisch

The next uses various ordering schemas and moving-window sizes the get a particular first or last value (within a department):

Function examples
SELECT dept
     , id
     , comm
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm)                            AS first1
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm NULLS FIRST)                AS first2
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm NULLS LAST)                 AS first3
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm NULLS LAST
                              ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS first4
     , LAST_VALUE(comm)  OVER(PARTITION BY dept
                              ORDER BY comm)                            AS last1
     , LAST_VALUE(comm)  OVER(PARTITION BY dept
                              ORDER BY comm NULLS FIRST
                              ROWS UNBOUNDED FOLLOWING)                 AS last2
FROM
staff
WHERE id < 100
AND dept < 30
ORDER BY dept ,comm;

ANSWER

DEPT ID COMM FIRST1 FIRST2 FIRST3 FIRST4 LAST1 LAST2

15

70

1152.00

1152.00

-

1152.00

1152.00

1152.00

1152.00

15

50

-

1152.00

-

1152.00

1152.00

-

1152.00

20

80

128.20

128.20

-

128.20

128.20

128.20

612.45

20

20

612.45

128.20

-

128.20

128.20

612.45

612.45

20

10

-

128.20

-

128.20

612.45

-

612.45

The next query illustrates what happens when one, or all, of the matching values are null:

Null value processing
SELECT dept
     , id
     , comm
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm)                            AS rn_lst
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm NULLS LAST)                 AS rn_ls2
     , FIRST_VALUE(comm) OVER(PARTITION BY dept
                              ORDER BY comm NULLS FIRST)                AS rn_fst
     , FIRST_VALUE(comm,'IGNORE NULLS') OVER(PARTITION BY dept
                                             ORDER BY comm NULLS FIRST) AS in_fst
FROM staff
WHERE id   BETWEEN 20 AND 160
AND   dept <= 20
ORDER BY dept ,comm;

ANSWER

DEPT ID COMM RN_LST RN_LS2 RN_FST IN_FST

10

160

15

110

206.60

206.60

206.60

-

206.60

15

70

1152.00

206.60

206.60

-

206.60

15

50

-

206.60

206.60

20

80

128.20

128.20

128.20

128.20

128.20

20

20

612.45

128.20

128.20

128.20

128.20

7.1.26. LAG and LEAD

The LAG, and LEAD functions get the previous or next value from the (moving) window of matching rows:

  • LAG: Get previous value. Return null if at first value.

  • LEAD: Get next value. Return null if at last value.

Usage Notes

  • An expression value must be provided in the first set of parenthesis. Usually this will be a column name, but any valid scalar expression is acceptable.

  • The PARTITION BY expression is optional.

  • The ORDER BY expression is mandatory.

  • The default OFFSET value is 1. A value of zero refers to the current row. An offset that is outside of the moving-window returns null.

  • If IGNORE NULLS is specified, a default (override) value must also be provided.

Examples

The next query uses the LAG function to illustrate what happens when one messes around with the ORDER BY expression:

LAG and LEAD function Examples
SELECT dept
     , id
     , comm
     , LAG(comm)                     OVER(PARTITION BY dept ORDER BY comm) AS lag1
     , LAG(comm,0)                   OVER(PARTITION BY dept ORDER BY comm) AS lag2
     , LAG(comm,2)                   OVER(PARTITION BY dept ORDER BY comm) AS lag3
     , LAG(comm,1,-1,'IGNORE NULLS') OVER(PARTITION BY dept ORDER BY comm) AS lag4
     , LEAD(comm)                    OVER(PARTITION BY dept ORDER BY comm) AS led1
FROM staff
WHERE id   BETWEEN 20 AND 160
AND   dept <= 20
ORDER BY dept ,comm;

ANSWER

DEPT ID COMM LAG1 LAG2 LAG3 LAG4 LED1

10

160

-

-

-

-

-1.00

-

15

110

206.60

-

206.60

-

-1.00

1152.00

15

70

1152.00

206.60

1152.00

-

206.60

-

15

50

-

1152.00

-

206.60

1152.00

-

20

80

128.20

-

128.20

-

-1.00

612.45

20

20

612.45

128.20

612.45

-

128.20

-

7.1.27. Aggregation

The various aggregation functions let one do cute things like get cumulative totals or running averages. In some ways, they can be considered to be extensions of the existing Db2 column functions. The output type is dependent upon the input type.

Syntax Notes

Guess what - this is a complicated function. Be aware of the following:

  • Any Db2 column function (e.g. AVG, SUM, COUNT), except ARRAY_AGG, can use the aggregation function.

  • The OVER() usage aggregates all of the matching rows. This is equivalent to getting the current row, and also applying a column function (e.g. MAX, SUM) against all of the matching rows.

  • The PARTITION BY expression is optional.

  • The ORDER BY expression is mandatory if the aggregation is confined to a set of rows or range of values. Otherwise it is optional. If a RANGE is specified (see ROWS vs. RANGE for definition), then the ORDER BY expression must be a single value that allows subtraction.

  • If an ORDER BY phrase is provided, but neither a RANGE nor ROWS is specified, then the aggregation is done from the first row to the current row.

  • See Window Definition for notes on how to define a moving-window of rows to process.

Basic Usage

In its simplest form, with just an "OVER()" phrase, an aggregation function works on all of the matching rows, running the column function specified. Thus, one gets both the detailed data, plus the SUM, or AVG, or whatever, of all the matching rows. In the following example, five rows are selected from the STAFF table. Along with various detailed fields, the query also gets sum summary data about the matching rows:

Aggregation function, basic usage
SELECT id
     , name
     , salary
     , SUM(salary) OVER() AS sum_sal
     , AVG(salary) OVER() AS avg_sal
     , MIN(salary) OVER() AS min_sal
     , MAX(salary) OVER() AS max_sal
     , COUNT(*)    OVER() AS #rows
FROM staff
WHERE id < 30
ORDER BY id;

ANSWER

ID NAME SALARY SUM_SAL AVG_SAL MIN_SAL MAX_SAL #ROWS

10

Sanders

98357.50

254035.50

84678.50

77506.75

98357.50

3

20

Pernal

78171.25

254035.50

84678.50

77506.75

98357.50

3

30

Marenghi

77506.75

254035.50

84678.50

77506.75

98357.50

3

An aggregation function with just an "OVER()" phrase is logically equivalent to one that has an ORDER BY on a field that has the same value for all matching rows. To illustrate, in the following query, the four aggregation functions are all logically equivalent:

Logically equivalent aggregation functions
SELECT id
     , name
     , salary
     , SUM(salary) OVER()                                  AS sum1
     , SUM(salary) OVER(ORDER BY id * 0)                   AS sum2
     , SUM(salary) OVER(ORDER BY 'ABC')                    AS sum3
     , SUM(salary) OVER(ORDER BY 'ABC'
                        RANGE BETWEEN UNBOUNDED PRECEDING
                        AND UNBOUNDED FOLLOWING)           AS sum4
FROM staff
WHERE id < 60
ORDER BY id;

ANSWER

ID NAME SALARY SUM1 SUM2 SUM3 SUM4

10

Sanders

98357.50

412701.30

412701.30

412701.30

412701.30

20

Pernal

78171.25

412701.30

412701.30

412701.30

412701.30

30

Marenghi

77506.75

412701.30

412701.30

412701.30

412701.30

40

O’Brien

78006.00

412701.30

412701.30

412701.30

412701.30

50

Hanes

80659.80

412701.30

412701.30

412701.30

412701.30

7.1.28. ORDER BY Usage

The ORDER BY phrase (see Order By, Group By, and Having for syntax) has two main purposes:

  • It provides a set of values to do aggregations on. Each distinct value gets a new result.

  • It gives a direction to the aggregation function processing (i.e. ASC or DESC).

In the next query, various aggregations are run on the DEPT field, which is not unique, and on the DEPT and NAME fields combined, which are unique (for these rows). Both ascending and descending aggregations are illustrated. Observe that the ascending fields sum or count up, while the descending fields sum down. Also observe that each aggregation field gets a separate result for each new set of rows, as defined in the ORDER BY phrase:

Aggregation function, ORDER BY usage
SELECT dept
     , name
     , salary
     , SUM(salary) OVER(ORDER BY dept)                 AS sum1
     , SUM(salary) OVER(ORDER BY dept DESC)            AS sum2
     , SUM(salary) OVER(ORDER BY dept, NAME)           AS sum3
     , SUM(salary) OVER(ORDER BY dept DESC, name DESC) AS sum4
     , COUNT(*)    OVER(ORDER BY dept)                 AS rw1
     , COUNT(*)    OVER(ORDER BY dept, NAME)           AS rw2
FROM staff
WHERE id < 60
ORDER BY dept
, name;

ANSWER

DEPT NAME SALARY SUM1 SUM2 SUM3 SUM4 RW1 RW2

15

Hanes

80659.80

80659.80

412701.30

80659.80

412701.30

1

1

20

Pernal

78171.25

257188.55

332041.50

158831.05

332041.50

3

2

20

Sanders

98357.50

257188.55

332041.50

257188.55

253870.25

3

3

38

Marenghi

77506.75

412701.30

155512.75

334695.30

155512.75

5

4

38

O’Brien

78006.00

412701.30

155512.75

412701.30

78006.00

5

5

7.1.29. ROWS Usage

The ROWS phrase (see Window Definition for syntax) is used to limit the aggregation function to a subset of the matching rows. The set of rows to process are defined thus:

  • No ORDER BY: UNBOUNDED PRECEDING to UNBOUNDED FOLLOWING.

  • ORDER BY only: UNBOUNDED PRECEDING to CURRENT ROW.

  • No BETWEEN: CURRENT ROW to "n" preceding/following row.

  • BETWEEN stmt: From "n" to "n" preceding/following row. The end-point must be greater than or equal to the starting point.

The following query illustrates these concepts:

ROWS usage examples
SELECT id
     , years
     , AVG(years) OVER()                                                    AS "p_f"
     , AVG(years) OVER(ORDER BY id ROWS
                       BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS "p_f"
     , AVG(years) OVER(ORDER BY id)                                         AS "p_c"
     , AVG(years) OVER(ORDER BY id
                       ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)    AS "p_c"
     , AVG(years) OVER(ORDER BY id
                       ROWS UNBOUNDED PRECEDING)                            AS "p_c"
     , AVG(years) OVER(ORDER BY id
                       ROWS UNBOUNDED FOLLOWING)                            AS "c_f"
     , AVG(years) OVER(ORDER BY id
                       ROWS 2 FOLLOWING)                                    AS "c_2"
     , AVG(years) OVER(ORDER BY id
                       ROWS 1 PRECEDING)                                    AS "1_c"
     , AVG(years) OVER(ORDER BY id
                       ROWS BETWEEN 1 FOLLOWING AND 2 FOLLOWING)            AS "1_2"
FROM staff
WHERE dept IN (15,20)
AND id > 20
AND years > 1
ORDER BY id;
ID YEARS p_f p_f p_c p_c p_c c_f c_2 1_c 1_2

50

10

6

6

10

10

10

6

7

10

6

70

7

6

6

8

8

8

6

5

8

4

110

5

6

6

7

7

7

5

5

6

6

170

4

6

6

6

6

6

6

6

4

8

190

8

6

6

6

6

6

8

8

6

-

7.1.30. RANGE Usage

The RANGE phrase limits the aggregation result to a range of numeric values - defined relative to the value of the current row being processed (see Window Definition). The range is obtained by taking the value in the current row (defined by the ORDER BY expression) and adding to and/or subtracting from it, then seeing what other matching rows are in the range.

When using a RANGE, only one expression can be specified in the ORDER BY, and that expression must be numeric.

In the following example, the RANGE function adds to and/or subtracts from the DEPT field. For example, in the function that is used to populate the RG10 field, the current DEPT value is checked against the preceding DEPT values. If their value is within 10 digits of the current value, the related YEARS field is added to the SUM:

RANGE usage
SELECT dept
     , name
     , years
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                ROWS BETWEEN 1 PRECEDING AND CURRENT ROW))    AS row1
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                ROWS BETWEEN 2 PRECEDING AND CURRENT ROW))    AS row2
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                RANGE BETWEEN 1 PRECEDING AND CURRENT ROW))   AS rg01
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                RANGE BETWEEN 10 PRECEDING AND CURRENT ROW))  AS rg10
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                RANGE BETWEEN 20 PRECEDING AND CURRENT ROW))  AS rg20
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                RANGE BETWEEN 10 PRECEDING AND 20 FOLLOWING)) AS rg11
     , SMALLINT(SUM(years) OVER(ORDER BY dept
                                RANGE BETWEEN CURRENT ROW AND 20 FOLLOWING))  AS rg99
FROM staff
WHERE id < 100
AND years IS NOT NULL
ORDER BY dept
       , name;

ANSWER

DEPT NAME YEARS ROW1 ROW2 RG01 RG10 RG20 RG11 RG99

15

Hanes

10

10

10

17

17

17

32

32

15

Rothman

7

17

17

17

17

17

32

32

20

Pernal

8

15

25

15

32

32

43

26

20

Sanders

7

15

22

15

32

32

43

26

38

Marengh

5

12

20

11

11

26

17

17

38

O’Brien

6

11

18

11

11

26

17

17

42

Koonitz

6

12

17

6

17

17

17

6

Note the difference between the ROWS as RANGE expressions:

  • The ROWS expression refers to the "n" rows before and/or after (within the partition), as defined by the ORDER BY.

  • The RANGE expression refers to those before and/or after rows (within the partition) that are within an arithmetic range of the current row.

7.1.31. BETWEEN vs. ORDER BY

The BETWEEN predicate in an ordinary SQL statement is used to get those rows that have a value between the specified low-value (given first) and the high value (given last). Thus the predicate "BETWEEN 5 AND 10" may find rows, but the predicate "BETWEEN 10 AND 5" will never find any. The BETWEEN phrase in an aggregation function has a similar usage in that it defines the set of rows to be aggregated. But it differs in that the answer depends upon the related ORDER BY sequence, and a non-match returns a null value, not no-rows. Below is some sample SQL. Observe that the first two aggregations are ascending, while the last two are descending:

BETWEEN and ORDER BY usage
SELECT id
     , name
     , SMALLINT(SUM(id) OVER(ORDER BY id ASC
                             ROWS BETWEEN 1 PRECEDING AND CURRENT ROW)) AS apc
     , SMALLINT(SUM(id) OVER(ORDER BY id ASC
                             ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING)) AS acf
     , SMALLINT(SUM(id) OVER(ORDER BY id DESC
                             ROWS BETWEEN 1 PRECEDING AND CURRENT ROW)) AS dpc
     , SMALLINT(SUM(id) OVER(ORDER BY id DESC
                             ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING)) AS dcf
FROM staff
WHERE id     < 50
AND   years IS NOT NULL
ORDER BY id;

ANSWER

ID NAME APC ACF DPC DCF

10

Sanders

10

30

30

10

20

Pernal

30

50

50

30

30

Marenghi

50

70

70

50

40

O’Brien

70

40

40

70

The following table illustrates the processing sequence in the above query. Each BETWEEN is applied from left to right, while the rows are read either from left to right (ORDER BY ID ASC) or right to left (ORDER BY ID DESC):

ASC id (10,20,30,40)

READ ROWS, LEFT to RIGHT 1ST-ROW 2ND-ROW 3RD-ROW 4TH-ROW

1 PRECEDING to CURRENT ROW

10=10

10+20=30

20+30=40

30+40=70

CURRENT ROW to 1 FOLLOWING

10+20=30

20+30=50

30+40=70

40 =40

DESC id (40,30,20,10)

READ ROWS, RIGHT to LEFT 1ST-ROW 2ND-ROW 3RD-ROW 4TH-ROW

1 PRECEDING to CURRENT ROW

20+10=30

30+20=50

40+30=70

40 =40

CURRENT ROW to 1 FOLLOWING

10 =10

20+10=30

30+20=50

40+30=70

Preceding row is always on LEFT of current row. Following row_ is always on RIGHT of current row.
The BETWEEN predicate, when used in an ordinary SQL statement, is not affected by the sequence of the input rows. But the BETWEEN phrase, when used in an aggregation function, is affected by the input sequence.

8. Scalar Functions

=== Introduction

Scalar functions act on a single row at a time. In this section we shall list all of the ones that come with Db2 and look in detail at some of the more interesting ones. Refer to the SQL Reference for information on those functions not fully described here.

8.1. Sample Data

The following self-defined view will be used throughout this section to illustrate how some of the following functions work. Observe that the view has a VALUES expression that defines the contents- three rows and nine columns.

Sample View DDL - Scalar functions
CREATE VIEW scalar
(d1, f1, s1, c1, v1, ts1, dt1, tm1, tc1) AS
WITH temp1 (n1, c1, t1) AS
( VALUES ( -2.4
         , 'ABCDEF'
         , '1996-04-22-23.58.58.123456')
      , ( +0.0
        , 'ABCD '
        , '1996-08-15-15.15.15.151515')
     , ( +1.8
       , 'AB'
       , '0001-01-01-00.00.00.000000')
)
SELECT DECIMAL(n1,3,1)
     , DOUBLE(n1)
     , SMALLINT(n1)
     , CHAR(c1,6)
     , VARCHAR(RTRIM(c1),6)
     , TIMESTAMP(t1)
     , DATE(t1)
     , TIME(t1)
     , CHAR(t1)
FROM    temp1;

Below are the view contents:

Table 7. SCALAR view, contents (3 rows)
D1 F1 S1 C1 V1 TS1 DT1 TM1 TC1

-2.4

-2.4e+000

-2

ABCDEF

ABCDEF

1996-04-22-23.58.58.123456

1996-04-22

23:58:58

1996-04-22-23.58.58.123456

0.0

0.0e+000

0

ABCD

ABCD

1996-08-15-15.15.15.151515

1996-08-15

15:15:15

1996-08-15-15.15.15.151515

1.8

1.8e+000

1

AB

AB

0001-01-01-00.00.00.000000

0001-01-01

00:00:00

0001-01-01-00.00.00.000000

8.2. Scalar Functions, Definitions

8.2.1. ABS or ABSVAL

Returns the absolute value of a number (e.g. -0.4 returns + 0.4). The output field type will equal the input field type (i.e. double input returns double output).

ABS function examples
SELECT d1      AS d1
     , ABS(D1) AS d2
     , f1      AS f1
     , ABS(f1) AS F2
FROM scalar;

ANSWER (float output shortened)

D1 D2 F1 F2

-2.4

2.4

-2.400e+0

2.400e+00

0.0

0.0

0.000e+0

0.000e+00

1.8

1.8

1.800e+0

1.800e+00

8.2.2. ACOS

Returns the arccosine of the argument as an angle expressed in radians. The output format is double.

8.2.3. ADD_DAYS

Returns a datetime value that represents the first argument plus a specified number of days.

8.2.4. ADD_HOURS

Returns a timestamp value that represents the first argument plus a specified number of hours.

8.2.5. ADD_MINUTES

Returns a timestamp value that represents the first argument plus a specified number of minutes.

8.2.6. ADD_MONTHS

Returns a datetime value that represents expression plus a specified number of months.

8.2.7. ADD_SECONDS

Returns a timestamp value that represents the first argument plus a specified number of seconds and fractional seconds.

8.2.8. ADD_YEARS

Returns a datetime value that represents the first argument plus a specified number of years.

8.2.9. AGE

Returns a numeric value that represents the number of full years, full months, and full days between the current timestamp and the argument.

8.2.10. ARRAY_DELETE

Deletes elements from an array.

8.2.11. ARRAY_FIRST

Returns the minimum array index value of the array.

8.2.12. ARRAY_LAST

Returns the maximum array index value of the array.

8.2.13. ARRAY_NEXT

Returns the next larger array index value for an array relative to the specified array index argument.

8.2.14. ARRAY_PRIOR

Returns the next smaller array index value for an array relative to the specified array index argument.

8.2.15. ASCII

Returns the ASCII code value of the leftmost input character. Valid input types are any valid character type up to 1 MEG. The output type is integer.

ASCII function examples
SELECT c1
     , ASCII(c1)           AS ac1
     , ASCII(SUBSTR(c1,2)) AS ac2
FROM scalar
WHERE c1 = 'ABCDEF';

ANSWER

C1 AC1 AC2

ABCDEF

65

66

The CHR function is the inverse of the ASCII function.

8.2.16. ASIN

Returns the arcsine of the argument as an angle expressed in radians. The output format is double.

8.2.17. ATAN

Returns the arctangent of the argument as an angle expressed in radians. The output format is double.

8.2.18. ATAN2

Returns the arctangent of x and y coordinates, specified by the first and second arguments, as an angle, expressed in radians. The output format is double.

8.2.19. ATANH

Returns the hyperbolic acrctangent of the argument, where the argument is and an angle expressed in radians. The output format is double.

8.2.20. BIGINT

Converts the input value to bigint (big integer) format. The input can be either numeric or character. If character, it must be a valid representation of a number.

BIGINT function example
WITH temp (big) AS
(VALUES BIGINT(1)
  UNION ALL
SELECT big * 256
FROM temp
WHERE big < 1E16)
SELECT big
FROM temp;

ANSWER

BIG

1

256

65536

16777216

4294967296

1099511627776

281474976710656

72057594037927936

Converting certain float values to both BIGINT and decimal will result in different values being returned (see below). Both results are arguably correct, it is simply that the two functions use different rounding methods:

Convert FLOAT to DECIMAL and BIGINT, SQL
WITH temp (f1) AS
(VALUES FLOAT(1.23456789)
  UNION ALL
SELECT f1 * 100
FROM temp
WHERE f1 < 1E18
)
SELECT f1         AS float1
     , DEC(f1,19) AS decimal1
     , BIGINT(f1) AS bigint1
FROM temp;
FLOAT1 DECIMAL1 BIGINT1

+1.23456789000000E+000

1.

1

+1.23456789000000E+002

123.

123

+1.23456789000000E+004

12345.

12345

+1.23456789000000E+006

1234567.

1234567

+1.23456789000000E+008

123456789.

123456788

+1.23456789000000E+010

12345678900.

12345678899

+1.23456789000000E+012

1234567890000.

1234567889999

+1.23456789000000E+014

123456789000000.

123456788999999

+1.23456789000000E+016

12345678900000000.

12345678899999996

+1.23456789000000E+018

1234567890000000000.

1234567889999999488

See Floating Point Numbers for a discussion on floating-point number manipulation.

8.2.21. BINARY

Returns a fixed-length binary string representation of a string of any data type.

8.2.22. BIT Functions

There are five BIT functions:

  • BITAND 1 if both arguments are 1.

  • BITANDNOT Zero if bit in second argument is 1, otherwise bit in first argument.

  • BITOR 1 if either argument is 1.

  • BITXOR 1 if both arguments differ.

  • BITNOT Returns opposite of the single argument.

The arguments can be SMALLINT (16 bits), INTEGER (32 bits), BIGINT (64 bits), or DECFLOAT (113 bits). The result is the same as the argument with the largest data type.

Negative numbers can be used in bit manipulation. For example the SMALLINT value -1 will have all 16 bits set to "1" (see example on Updating BIT Values). As their name implies, the BIT functions can be used to do bit-by-bit comparisons between two numbers:

BIT functions examples
WITH temp1 (b1, b2) AS
(VALUES ( 1, 0) ,( 0, 1)
      , ( 0, 0) ,( 1, 1)
      , ( 2, 1) ,(15,-7)
      , (15, 7) ,(-1, 1)
      , (15,63) ,(63,31)
      , (99,64) ,( 0,-2)
)
, temp2 (b1, b2) AS
 (SELECT SMALLINT(b1)
       , SMALLINT(b2)
  FROM temp1)
SELECT b1
     , b2
     , HEX(b1)          AS "hex1"
     , HEX(b2)          AS "hex2"
     , BITAND(b1,b2)    AS "and"
     , BITANDNOT(b1,b2) AS "ano"
     , BITOR(b1,b2)     AS "or"
     , BITXOR(b1,b2)    AS "xor"
FROM temp2;

ANSWER

B1 B2 hex1 hex2 and ano or xor

1

0

0100

0000

0

1

1

1

0

1

0000

0100

0

0

1

1

0

0

0000

0000

0

0

0

0

1

1

0100

0100

1

0

1

0

2

1

0200

0100

0

2

3

3

15

-7

0F00

F9FF

9

6

-1

-10

15

7

0F00

0700

7

8

15

8

-1

1

FFFF

0100

1

-2

-1

-2

15

63

0F00

3F00

15

0

63

48

63

31

3F00

1F00

31

32

63

32

99

64

6300

4000

64

35

99

35

0

-2

0000

FEFF

0

0

-2

-2

Displaying BIT Values

It can sometimes be hard to comprehend what a given BASE 10 value is in BIT format. To help, the following user-defined-function converts SMALLINT numbers to BIT values:

Function to display SMALLINT bits
CREATE FUNCTION bitdisplay(inparm SMALLINT)
RETURNS CHAR(16)
BEGIN ATOMIC
    DECLARE outstr VARCHAR(16);
    DECLARE inval INT;
    IF inparm >= 0 THEN
        SET inval = inparm;
    ELSE
        SET inval = INT(65536) + inparm;
    END IF;
    SET outstr = '';
    WHILE inval > 0 DO
        SET outstr = STRIP(CHAR(MOD(inval,2))) || outstr;
        SET inval = inval / 2;
    END WHILE;
    RETURN RIGHT(REPEAT('0',16) || outstr,16);
END!

Below is an example of the above function in use:

BIT_DISPLAY function example
WITH temp1 (b1) AS
(VALUES (32767)
     , (16383)
     , ( 4096)
     , ( 118)
     , ( 63)
     , ( 16)
     , ( 2)
     , ( 1)
     , ( 0)
     , ( -1)
     , ( -2)
     , ( -3)
     , ( -64)
     , (-32768)
)
, temp2 (b1) AS
(SELECT SMALLINT(b1)
FROM temp1
)
SELECT b1
     , HEX(b1)        AS "hex1"
     , BITDISPLAY(b1) AS "bit_display"
FROM temp2;

ANSWER

B1 hex1 bit_display

32767

FF7F

0111111111111111

16383

FF3F

0011111111111111

4096

0010

0001000000000000

118

7600

0000000001110110

63

3F00

0000000000111111

16

1000

0000000000010000

2

0200

0000000000000010

1

0100

0000000000000001

0

0000

0000000000000000

-1

FFFF

1111111111111111

-2

FEFF

1111111111111110

-3

FDFF

1111111111111101

-64

C0FF

1111111111000000

-32768

0080

1000000000000000

Updating BIT Values

Use the BITXOR function to toggle targeted bits in a value. Use the BITANDNOT function to clear the same targeted bits. To illustrate, the next query uses these two functions to toggle and clear the last four bits, because the second parameter is 15, which is b"1111":

Update bits #1
WITH temp1 (b1) AS
( VALUES (32767)
       , (21845)
       , (4096)
       , (0)
       , (-1)
       , (-64)
)
, temp2 (b1, s15) AS
(SELECT SMALLINT(b1)
      , SMALLINT(15)
 FROM temp1
)
SELECT b1
     , BITDISPLAY(b1)                AS "b1_display"
     , BITXOR(b1,s15)                AS "xor"
     , BITDISPLAY(BITXOR(b1,s15))    AS "xor_display"
     , BITANDNOT(b1,s15)             AS "andnot"
     , BITDISPLAY(BITANDNOT(b1,s15)) AS "andnot_display"
FROM temp2;

Below is the answer:

B1 b1_display xor xor_display andnot andnot_display

32767

0111111111111111

32752

0111111111110000

32752

0111111111110000

21845

0101010101010101

21850

0101010101011010

21840

0101010101010000

4096

0001000000000000

4111

0001000000001111

4096

0001000000000000

0

0000000000000000

15

0000000000001111

0

0000000000000000

-1

1111111111111111

-16

1111111111110000

-16

1111111111110000

-64

1111111111000000

-49

1111111111001111

-64

1111111111000000

The next query illustrate the use of the BITAND function to return those bits that match both parameters, and the BITNOT function to toggle all bits:

Update bits #2, query
WITH temp1 (b1) AS
(VALUES (32767)
      , (21845)
      , (4096)
      , (0)
      , (-1)
      , (-64)
)
, temp2 (b1, s15) AS
(SELECT SMALLINT(b1)
      , SMALLINT(15)
FROM temp1
)
SELECT b1
     , BITDISPLAY(b1)             AS "b1_display"
     , BITAND(b1,s15)             AS "and"
     , BITDISPLAY(BITAND(b1,s15)) AS "and_display"
     , BITNOT(b1)                 AS "not"
     , BITDISPLAY(BITNOT(b1))     AS "not_display"
FROM temp2;

Below is the answer:

B1 b1_display and and_display not not_display

32767

0111111111111111

15

0000000000001111

-32768

1000000000000000

21845

0101010101010101

5

0000000000000101

-21846

1010101010101010

4096

0001000000000000

0

0000000000000000

-4097

1110111111111111

0

0000000000000000

0

0000000000000000

-1

1111111111111111

-1

1111111111111111

15

0000000000001111

0

0000000000000000

-64

1111111111000000

0

0000000000000000

63

0000000000111111

8.2.23. BLOB

Converts the input (1st argument) to a blob. The output length (2nd argument) is optional.

8.2.24. BOOLEAN

Returns the actual Boolean value that corresponds to a non-Boolean representation of a Boolean value.

8.2.25. BTRIM

Removes the characters that are specified in a trim string from the beginning and end of a source string.

8.2.26. CARDINALITY

Returns a value of type BIGINT that is the number of elements in an array.

8.2.27. CEIL or CEILING

Returns the next smallest integer value that is greater than or equal to the input (e.g. 5.045 returns 6.000). The output field type will equal the input field type.

CEIL function examples
SELECT d1
     , CEIL(d1) AS d2
     , f1
     , CEIL(f1) AS f2
FROM scalar;

ANSWER (float output shortened)

D1 D2 F1 F2

-2.4

-2.

-2.400E+0

-2.000E+0

0.0

0.

+0.000E+0

+0.000E+0

1.8

2.

+1.800E+0

+2.000E+0

Usually, when Db2 converts a number from one format to another, any extra digits on the right are truncated, not rounded. For example, the output of INTEGER(123.9) is 123. Use the CEIL or ROUND functions to avoid truncation.

8.2.28. CHAR

The CHAR function has a multiplicity of uses. The result is always a fixed-length character value, but what happens to the input along the way depends upon the input type:

  • For character input, the CHAR function acts a bit like the SUBSTR function, except that it can only truncate starting from the left-most character. The optional length parameter, if provided, must be a constant or keyword.

  • Date-time input is converted into an equivalent character string. Optionally, the external format can be explicitly specified (i.e. ISO, USA, EUR, JIS, or LOCAL).

  • Integer and double input is converted into a left-justified character string.

  • Decimal input is converted into a right-justified character string with leading zeros. The format of the decimal point can optionally be provided. The default decimal point is a dot. The '+' and '-' symbols are not allowed as they are used as sign indicators.

Below are some examples of the CHAR function in action:

CHAR function examples - characters and numbers
SELECT name
     , CHAR(name, 3)
     , comm
     , CHAR(comm)
     , CHAR(comm,'@')
FROM staff
WHERE id BETWEEN 80 AND 100
ORDER BY id;

ANSWER

NAME 2 COMM 4 5

James

Jam

128.20

00128.20

00128@20

Koonitz

Koo

1386.70

01386.70

01386@70

Plotz

Plo

-

-

-

The CHAR function treats decimal numbers quite differently from integer and real numbers. In particular, it right-justifies the former (with leading zeros), while it left-justifies the latter (with trailing blanks). The next example illustrates this point:

CHAR function examples - positive numbers_
WITH temp1 (n) AS
(VALUES (3)
  UNION ALL
 SELECT n * n
 FROM temp1
 WHERE n < 9000
)
SELECT n AS int
     , CHAR(INT(n)) AS char_int
     , CHAR(FLOAT(n)) AS char_flt
     , CHAR(DEC(n)) AS char_dec
FROM temp1;

ANSWER

INT CHAR_INT CHAR_FLT CHAR_DEC

3

3

3.0E0

00000000003.

9

9

9.0E0

00000000009.

81

81

8.1E1

00000000081.

6561

6561

6.561E3

00000006561.

43046721

43046721

4.3046721E7

00043046721.

Negative numeric input is given a leading minus sign. This messes up the alignment of digits in the column (relative to any positive values). In the following query, a leading blank is put in front of all positive numbers in order to realign everything:

Align CHAR function output - numbers_
WITH temp1 (n1, n2) AS
(VALUES (SMALLINT(+3)
       , SMALLINT(-7))
  UNION ALL
SELECT n1 * n2
     , n2
FROM temp1
WHERE n1 < 300
)
SELECT n1
     , CHAR(n1) AS i1
     , CASE
         WHEN n1 < 0 THEN CHAR(n1)
         ELSE '+' CONCAT CHAR(n1)
       END AS i2
     , CHAR(DEC(n1)) AS d1
     , CASE
         WHEN n1 < 0 THEN CHAR(DEC(n1))
         ELSE '+' CONCAT CHAR(DEC(n1))
       END AS d2
FROM temp1;

ANSWER

N1 I1 I2 D1 D2

3

3

+3

00003.

+00003.

-21

-21

-21

-00021.

-00021.

147

147

+147

00147.

+00147.

-1029

-1029

-1029

-01029.

-01029.

7203

7203

+7203

07203.

+07203.

Both the I2 and D2 fields above will have a trailing blank on all negative values - that was added during the concatenation operation. The RTRIM function can be used to remove it.

DATE-TIME Conversion

The CHAR function can be used to convert a date-time value to character. If the input is not a timestamp, the output layout can be controlled using the format option:

  • ISO: International Standards Organization.

  • USA: American.

  • EUR: European, which is usually the same as ISO.

  • JIS: Japanese Industrial Standard, which is usually the same as ISO.

  • LOCAL: Whatever your computer is set to.

Below are some DATE examples:

CHAR function examples - date value
SELECT CHAR(CURRENT DATE,ISO) AS iso
     , CHAR(CURRENT DATE,EUR) AS eur
     , CHAR(CURRENT DATE,JIS) AS jis
     , CHAR(CURRENT DATE,USA) AS usa
FROM sysibm.sysdummy1;

ANSWER

ISO EUR JIS USA

2005-11-30

30.11.2005

2005-11-30

11/30/2005

Below are some TIME examples: .CHAR function examples - time value

SELECT CHAR(CURRENT TIME,ISO) AS iso
     , CHAR(CURRENT TIME,EUR) AS eur
     , CHAR(CURRENT TIME,JIS) AS jis
     , CHAR(CURRENT TIME,USA) AS usa
FROM sysibm.sysdummy1;

ANSWER

ISO EUR JIS USA

19.42.21

19.42.21

19:42:21

07:42 PM

A timestamp cannot be formatted to anything other than ISO output:

CHAR function example - timestamp value
SELECT CHAR(CURRENT TIMESTAMP) AS TS
FROM sysibm.sysdummy1;

ANSWER

TS

2005-11-30-19.42.21.873002

Converting a date or time value to character, and then ordering the set of matching rows can result in unexpected orders. See CASE Checks in Wrong Sequence for details.
CHAR vs. DIGITS - A Comparison

Numeric input can be converted to character using either the DIGITS or the CHAR function, though the former does not support float. Both functions work differently, and neither gives perfect output. The CHAR function doesn’t properly align up positive and negative numbers, while the DIGITS function looses both the decimal point and sign indicator:

DIGITS vs. CHAR
SELECT d2
    , CHAR(d2)   AS cd2
    , DIGITS(d2) AS dd2
FROM
    (SELECT DEC(d1, 4, 1) AS d2
     FROM scalar
    ) AS xxx
ORDER BY 1;

ANSWER

D2 CD2 DD2

-2.4

-002.4

0024

0.0

000.0

0000

1.8

001.8

0018

Neither the DIGITS nor the CHAR function do a great job of converting numbers to characters. See Convert Number to Character for some user-defined functions that can be used instead.

8.2.29. CHARACTER_LENGTH

This function is similar to the LENGTH function, except that it works with different encoding schemas. The result is an integer value that is the length of the input string.

CHARACTER_LENGTH function example
WITH temp1 (c1) AS
(VALUES (CAST('ÁÉÌ' AS VARCHAR(10)))
)
SELECT c1                          AS C1
     , LENGTH(c1)                  AS LEN
     , OCTET_LENGTH(c1)            AS OCT
     , CHAR_LENGTH(c1,OCTETS)      AS L08
     , CHAR_LENGTH(c1,CODEUNITS16) AS L16
     , CHAR_LENGTH(c1,CODEUNITS32) AS L32
FROM temp1;

ANSWER

C1 LEN OCT L08 L16 L32

ÁÉÌ

6

6

6

3

3

8.2.30. CHR

Converts integer input in the range 0 through 255 to the equivalent ASCII character value. An input value above 255 returns 255. The ASCII function (see above) is the inverse of the CHR function.

CHR function examples
SELECT 'A' AS "c"
     , ASCII('A') AS "c>n"
     , CHR(ASCII('A')) AS "c>n>c"
     , CHR(333) AS "nl"
FROM staff
WHERE id = 10;

ANSWER

C C>N C>N>C NL

A

65

A

ÿ

At present, the CHR function has a bug that results in it not returning a null value when the input value is greater than 255.

8.2.31. CLOB

Converts the input (1st argument) to a CLOB. The output length (2nd argument) is optional. If the input is truncated during conversion, a warning message is issued. For example, in the following example the second CLOB statement will induce a warning for the first two lines of input because they have non-blank data after the third byte:

CLOB function examples
SELECT c1
     , CLOB(c1)   AS cc1
     , CLOB(c1,3) AS cc2
FROM scalar;

ANSWER

C1 CC1 CC2

ABCDEF

ABCDEF

ABC

ABCD

ABCD

ABC

AB

AB

AB

The Db2BATCH command processor dies a nasty death whenever it encounters a CLOB field in the output. If possible, convert to VARCHAR first to avoid this problem.

8.2.32. COALESCE