Db2 for z/OS is Serious About Analytics: Leveraging SQL · PDF fileDb2 for z/OS is Serious...

Db2 for z/OS is Serious About Analytics:

Leveraging SQL to Analyze Your Data

Where it Resides

Maryela Weihrauch, IBM Distinguished Engineer,

[email protected]

© 2016 IBM Corporation9/25/2017World of Watson 2016 2

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Agenda

• Db2 and Analytics – The bigger picture

• Db2 12 Optimizer enhancements

• OLAP BIFs in DB2 and IDAA

– OLAP Specification

– Rolling Aggregations

– Grouping

– Math functions


System z: re-inventing business around an integrated data lifecycle

Reduced data movement, reduced complexity, reduced configuration resources

More accurate, more secure, more available

Better business response

VSAM

Db2

VSAM

IMS

DB2AA

HTAP -Db2 with Analytics Accelerator

OLAP

Db

2 fo

r z/O

S

Pro

ce

ss

ing

IBM

Db

2 A

na

lytic

s A

cce

lera

tor

Db2 for z/OS CPU

savings target

• Operational (in

transaction) analytics

• (complex) OLTP

Accelerator focus

• Ad-hoc queries

• Complex queries

scanning large

amount of data

• ETL

acceleration/virtual

transformation

Complex queries (more history)

OLTP Transactions

High concurrency

Hybrid Transactional &

Analytical Processing

Standard reports


Optimizer

Acce

lera

tor D

RD

AR

equ

esto

r

Application

Applicatio

n

Interface

Queries executed with Accelerator

Queries executed without Accelerator

Heartbeat (availability and performance indicators)

Query execution run-time for queries

that cannot be or should not be off-

loaded to Accelerator

SPU

MemorySPU

MemorySPU

MemorySPU

Memory

SM

PH

ost

Heartbeat

Db2 for z/OS

CPU FPGA

CPU FPGA

CPU FPGA

CPU FPGA

CPU FPGA

CPU FPGA

CPU FPGA

CPU FPGA

Accelerator

Query Execution Flow


Db2 12 high-level query performance focus

• Query focus based upon newer workloads (modern applications)– Complex views or table UDFs

– UNION ALL

– Outer joins

– Join predicates with (stage 2) expressions

– CASE expressions, CAST functions, scalar functions

• Addressing general bottlenecks

– More sort avoidance, work file and sparse index usage optimization

– Reducing prepare cost and frequency

– List prefetch

– I/O performance

– Reduce unnecessary prefetch scheduling

• NOTE: 100% zIIP eligible for parallel child tasks


Significant CPU reduction in Db2 query workloads

• 30% - 90% reduction in elapsed time and CPU for SQL containing – Complex outer joins, UNION ALL, UDFs and table UDFs

– Combinations of table expressions, views and outer joins

– VARGRAPHIC data type

– Disorganized data, poorly clustered indexes

– Nearly 100% new access paths vs. Db2 11

34%

66,00%

80%

85%

90%

0% 5% 10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%

SAP CDS FIN

Cust1-Uncl

WAS Portal

SAP CDS Fiori

Cryst. Rpt.-Long

CPU time %

Complex real-time analytics - outer joins, UNION ALL, UDF/Table UDFs

Real-time analytics - large GROUP BY sorts/WF usage, sparse index on VARGRAPHIC

Reporting - poorly clustered index – list prefetch and hybrid join benefit

Complex OLTP -UNION ALL

Complex real-time analytics – outer joins, table expr, CASE, DGTTs

Db2 performanceworkloads

Db2 class 2 CPU reduction


Significant CPU reduction in Db2 query workloads..

15,70%

17,00%

18,00%

19,00%

23,77%

24,00%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90%

Cryst.Rpt-…

Cust1-Clus

TPCH-…

TPCH-Seq

Cust2

BIDAY-ShortLarge sorts for GROUP BY

Quick complex SQL

Sort intensive and unclustered (DEG=1)

Sort intensive and unclustered (DEG=ANY)

Sub-second complex analytics queries

Traditional query workload


Db2 class 2 CPU

reduction

15% - 25% reduction in elapsed time and CPU for more traditional query workloads

Sort intensive and work file processing

Lots of aggregating

Many new access paths vs. Db2 11


Significant CPU reduction in Db2 query workloads..

0%-15% reduction in elapsed time and CPU for

Simple query workloads

SQL where vast majority of CPU and elapsed time are due to data scanning (IDAA candidates)

As % drops - fewer new access paths vs. Db2 11

3,02%

4,52%

8,66%

11,00%

13,18%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90%

BIDAY-Long

SAP BW

TPCD

TPCH-SQLPL

Cust3 Traditional simple query application

Sort intensive, clustered version of TPCH

Lightweight query app.

Star Join, mostly scanning overhead (IDAA candidate)

Brute force data scanning (IDAA candidate)


Db2 class 2 CPU reduction


• Rebind without APREUSE to see full potential

• Queries with new access paths consistently were the biggest winners

• Typical performance gains for new access path

– 10%-40% reduction in CPU and elapsed time (can be much higher)

• Typical performance gains with no access path change

– 0%-20% reduction in CPU and elapsed time

• Set DB2 zparm ACCELMODEL to enable accelerator modeling – Supports static and dynamic SQL– Explain will externalize reason if query is not eligible for acceleration– DB2 writes new fields (class 2 elapsed time and CPU time spent in

DB2 to execute SQL eligible for acceleration) in IFCID 3 and IFCID 148, similar to the SECP CPU field – needs to be formatted by a monitor

Give Db2 12 optimizer a chance


BIF-Analytics through SQL

V9 V10 V11 V12

RANK √

DENSE_RANK √

ROW_NUMBER √

Moving AVERAGE √

Moving SUM √

Grouping √

ROLLUP √

CUBE √

MEDIAN √

PERCENTILE √

LISTAGG* √


OLAP Specifications

• OLAP specifications can be used to determine ranking, row numbering, and aggregation as a scalar value

• The OLAP specification is defined using a Window

– A window may specify three (3) components (each is optional)

1. Partitioning – PARTITION BY CLAUSE

2. Ordering – ORDER BY clause

3. Aggregation Group – ROWS/RANGE


Differences between the function types

Function Type

Action Examples

Scalar compute a single value for the current row

SUBSTR, CONCAT, LTRIM

Aggregate collapse a group of rows into a single row and compute a single value for this group

MAX, MIN, SUM, COUNT

Moving Aggregate

compute a single value for the current row based on some or all rows in a defined group

Aggregate function, like above, for a window


RANK, DENSE_RANK, ROW_NUMBER

• RANK/DENSE_RANK

– Returns the ordinal rank of a row in the window

• RANK: may have a gap in the ranking

• DENSE_RANK: no gaps in the ranking

• ROW_NUMBER

– Returns the sequential row number


RANK Example

SELECT EMPNO, LASTNAME, SALARY+BONUS AS TOTAL_SALARY,

RANK() OVER(ORDER BY SALARY+BONUS DESC) AS RANK_SALARY

FROM DSN8A10.EMP WHERE SALARY+BONUS > 30000 ORDER BY RANK_SALARY;

+-----------------------------------------------------------+

| EMPNO | LASTNAME | TOTAL_SALARY | RANK_SALARY |

+-----------------------------------------------------------+

1_| 000010 | HAAS | 53750.00 | 1 |

2_| 200010 | HEMMINGER | 47500.00 | 2 |

3_| 000110 | LUCCHESI | 47400.00 | 3 |

4_| 000020 | THOMPSON | 42050.00 | 4 |

5_| 000050 | GEYER | 40975.00 | 5 |

6_| 000030 | KWAN | 39050.00 | 6 |

7_| 000070 | PULASKI | 36870.00 | 7 |

8_| 000060 | STERN | 32850.00 | 8 |

9_| 000220 | LUTZ | 30440.00 | 9 |

10_| 200220 | JOHN | 30440.00 | 9 |

11_| 000090 | HENDERSON | 30350.00 | 11 |

+-----------------------------------------------------------+

LUTZ & JOHN have same total salary therefore they get the same ranking

HENDERSON gets the rank based off of the number of rows that previously qualified the prior rank


DENSE_RANK –Rank based on uniqueness


DENSE_RANK() OVER(ORDER BY SALARY+BONUS DESC) AS RANK_SALARY


+-----------------------------------------------------------+


+-----------------------------------------------------------+

1_| 000010 | HAAS | 53750.00 | 1 |

2_| 200010 | HEMMINGER | 47500.00 | 2 |

3_| 000110 | LUCCHESI | 47400.00 | 3 |

4_| 000020 | THOMPSON | 42050.00 | 4 |

5_| 000050 | GEYER | 40975.00 | 5 |

6_| 000030 | KWAN | 39050.00 | 6 |

7_| 000070 | PULASKI | 36870.00 | 7 |

8_| 000060 | STERN | 32850.00 | 8 |

9_| 000220 | LUTZ | 30440.00 | 9 |

10_| 200220 | JOHN | 30440.00 | 9 |

11_| 000090 | HENDERSON | 30350.00 | 10 |

+-----------------------------------------------------------+

HENDERSON gets a ranking based on the # of unique total salaries that precede it


ROW_NUMBER - Sequential row number


ROW_NUMBER() OVER(ORDER BY SALARY+BONUS DESC) AS RANK_SALARY


+-----------------------------------------------------------+


+-----------------------------------------------------------+

1_| 000010 | HAAS | 53750.00 | 1 |

2_| 200010 | HEMMINGER | 47500.00 | 2 |

3_| 000110 | LUCCHESI | 47400.00 | 3 |

4_| 000020 | THOMPSON | 42050.00 | 4 |

5_| 000050 | GEYER | 40975.00 | 5 |

6_| 000030 | KWAN | 39050.00 | 6 |

7_| 000070 | PULASKI | 36870.00 | 7 |

8_| 000060 | STERN | 32850.00 | 8 |

9_| 000220 | LUTZ | 30440.00 | 9 |

10_| 200220 | JOHN | 30440.00 | 10 |

11_| 000090 | HENDERSON | 30350.00 | 11 |

+-----------------------------------------------------------+


Moving AVG Example

SELECT Territory, Month, Sales,

AVG(Sales) OVER (PARTITION BY Territory

ORDER BY Month

ROWS 2 PRECEDING) AS Moving_Avg

FROM Sales_History;

Territory Month Sales

East 199810 10

East 199811 4

East 199812 10

East 199901 7

East 199902 10

West 199810 8

West 199811 12

West 199812 11

West 199901 7

West 199902 6

Aggregation group

Compute the average sales over the current month and the preceding two months, for each territory, and month


Territory Month Sales Moving_Average

East 199810 10 10

East 199811 4 7

East 199812 10 8

East 199901 7 7

East 199902 10 9

West 199810 8 8

West 199811 12 10

West 199812 7 9

West 199901 11 10

West 199902 6 8

Moving averagein action

The results of the query illustrate the “moving average”.In the first row 10 is the average of only that row.The second row is the average of the first 2 rows 10 + 4.The third row and all of the following rows through to the end of the table are the average of the current row and the two preceding 10 + 4 + 10 divided by 3.

Note that the moving average resets on a change in territory starting at row 6 (change EAST to WEST).


Moving SUM Example

SELECT Territory, Month, Sales,

SUM(Sales) OVER (PARTITION BY Territory

ORDER BY Month ASC

ROWS UNBOUNDED PRECEDING) AS Cumulative_Sum

FROM Sales_History;

Aggregation group


Territory Month Sales Cumulative_Sum

East 199810 10 10

East 199811 4 14

East 199812 10 24

East 199901 7 31

East 199902 10 41

West 199810 8 8

West 199811 12 20

West 199812 7 27

West 199901 11 38

West 199902 6 44

Cumulative Sum in action


Grouping Sets –GROUPING, ROLLUP, CUBE

• Grouping sets allow multiple groups to be specified

• Querying and reporting are made easier and faster by producing a single result from essentially performing a UNION ALL of two or more groups of rows

• The group-by-clause has been enhanced:– grouping-sets

• Allows multiple grouping clauses to be specified in a single statement

• Allows the groups to be computed with a single pass over the data

• Can be used to determine subtotals and grand totals

– super-groups

• Pre-defined grouping-sets : ROLLUP, CUBE


ROLLUP• Creates subtotals that ‘roll up’ from the most detailed level to a

grand total

• N elements translate to N+1 grouping sets:

The order specified is significant to the result:

GROUP BY ROLLUP (C1,C2,C3)

GROUP BY GROUPING SETS ((C1,C2,C3),

(C1,C2),

(C1),

());

equivalent

GROUP BY ROLLUP (C1,C2)GROUP BY GROUPING SETS ((C1,C2),

(C1),

());

equivalent

GROUP BY ROLLUP (C2,C1)

GROUP BY GROUPING SETS ((C2,C1),

(C2),

());

equivalent

versus


CUBE • Creates subtotals for all permutations

• N elements translate to 2n grouping sets:

• Unlike ROLLUP, the order specified is NOT significant to the result therefore use ORDER BY to guarantee the order

• grand-total

– the overall aggregation of previous subtotals

– Both CUBE and ROLLUP return this row as the last row

GROUP BY CUBE (C1,C2,C3)GROUP BY GROUPING SETS ((C1,C2,C3),

(C1,C2), (C1,C3), (C2,C3),

(C1), (C2), (C3),

());

equivalent

8 groups

3 elements

grand-total


Grouping Sets on DSN8B10.PROJ

DEPTNO RESPEMP PRSTAFF

B01 000020 1.00

C01 000030 2.00

C01 000030 1.00

D01 000010 12.00

D01 000010 6.50

D11 000060 9.00

D11 000220 2.00

D11 000150 3.00

D11 000160 3.00

D21 000070 6.00

D21 000230 2.00

D21 000250 1.00

D21 000270 2.00

E01 000050 6.00

E01 000050 5.00

E11 000090 5.00

E21 000100 4.00

E21 000320 1.00

E21 000330 1.00

E21 000340 1.00


B01 000020 1.00

C01 000030 3.00

D01 000010 18.50

D11 000060 9.00

D11 000150 3.00

D11 000160 3.00

D11 000220 2.00

D21 000070 6.00

D21 000230 2.00

D21 000250 1.00

D21 000270 2.00

E01 000050 11.00

E11 000090 5.00

E21 000100 4.00

E21 000320 1.00

E21 000330 1.00

E21 000340 1.00

SELECT DEPTNO, RESPEMP,

SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY DEPTNO,RESPEMP;


SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY

GROUPING SETS ((DEPTNO,RESPEMP));

17 rows after grouping20 rows originally

equivalent


ROLLUP on DSN8B10.PROJ

B01 ? 1.00

C01 ? 3.00

D01 ? 18.50

D11 ? 17.00

D21 ? 11.00

E01 ? 11.00

E11 ? 5.00

E21 ? 7.00

? ? 73.50


B01 000020 1.00

C01 000030 3.00

D01 000010 18.50

D11 000060 9.00

D11 000150 3.00

D11 000160 3.00

D11 000220 2.00

D21 000070 6.00

D21 000230 2.00

D21 000250 1.00

D21 000270 2.00

E01 000050 11.00

E11 000090 5.00

E21 000100 4.00

E21 000320 1.00

E21 000330 1.00

E21 000340 1.00


SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY

ROLLUP (DEPTNO,RESPEMP);


SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY

GROUPING SETS ((DEPTNO,RESPEMP),

(DEPTNO),

());

26 rows after ROLLUP

Grand total of ALL groups

Subtotals for each

DEPTNO group

equivalent

Subtotals for each

DEPTNO, RESPEMP group

SELECT DEPTNO, RESPEMP, SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY

GROUPING SETS ((DEPTNO,RESPEMP),

(DEPTNO),

(RESPEMP),

());

CUBE on DSN8B10.PROJ

? 000010 18.50

? 000020 1.00

? 000030 3.00

? 000050 11.00

? 000060 9.00

? 000070 6.00

? 000090 5.00

? 000100 4.00

? 000150 3.00

? 000160 3.00

? 000220 2.00

? 000230 2.00

? 000250 1.00

? 000270 2.00

? 000320 1.00

? 000330

? 000340 1.00

? ? 73.50

DEPT.. RESP.. PR...

B01 000020 1.00

C01 000030 3.00

D01 000010 18.50

D11 000060 9.00

D11 000150 3.00

D11 000160 3.00

D11 000220 2.00

D21 000070 6.00

D21 000230 2.00

D21 000250 1.00

D21 000270 2.00

E01 000050 11.00

E11 000090 5.00

E21 000100 4.00

E21 000320 1.00

E21 000330 1.00

E21 000340 1.00

SELECT DEPTNO, RESPEMP, SUM(PRSTAFF)

FROM DSN8B10.PROJ

GROUP BY

CUBE (DEPTNO,RESPEMP);

equivalent

Grand total of ALL groups

B01 ? 1.00

C01 ? 3.00

D01 ? 18.50

D11 ? 17.00

D21 ? 11.00

E01 ? 11.00

E11 ? 5.00

E21 ? 7.00

Subtotals for each

DEPTNO group

Subtotals for

each RESPEMP

group


PERCENTILE_CONT and PERCENTILE_DISC - Overview

• PERCENTILE_CONT and PERCENTILE_DISC are categorized as "inverse distribution functions" in the SQL Standard that compute a percentile value within a given group.

• MEDIAN, which we know intuitively as producing the "middle" value can be equivalently expressed as PERCENTILE_CONT(0.5), where the argument 0.5 indicates that we want the function to return the "middle" percentage value.


PERCENTILE_CONT vs PERCENTILE_DISC vs MEDIAN

• PERCENTILE_CONT treats the group as a "continuous distribution", meaning that if the percentage falls between two values, the result is interpolated between those two values.

– For example, the median (PERCENTILE_CONT(0.5)) of a group of 6 numbers would be average of the 3rd and 4th number, because we find the interpolated middle value between those two.

• PERCENTILE_DISC treats the group as a set of discrete values. The result is not interpolated and is always one of the values in the group. PERCENTILE_DISC(0.5) over a group of 6 numbers would return the 3rd number.

• MEDIAN is equivalent with PERCENTILE_CONT(0.5).

Salary

2450

2975

1375

Salary

2450

2975

1250

PERCENTILE_CONT

PERCENTILE_DISC

VS.

PERCENTILE_CONT and MEDIAN

• Suppose we would like to determine the salary, within each department, which is at the 50th percentile

Salary

2450

2975

1375

Result:

SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY SALARY)

FROM EMP

GROUP BY DEPT_NO;

SELECT MEDIAN(SALARY)

FROM EMP

GROUP BY DEPT_NO;

NAME SALARY DEPT_NO

Miller 1300 10

Clark 2450 10

King 5002 10

Smith 800 20

Adams 1100 20

Jones 2975 20

Scott 3000 20

Ford 3000 20

James 950 30

Martin 1250 30

Ward 1250 30

Turner 1500 30

Allen 1600 30

Blake 2850 30

MEDIAN == PERCENTILE(0.5)

PERCENTILE_DISC

Name Salary DEPT_NO

Miller 1300 10

Clark 2450 10

King 5002 10

Smith 800 20

Adams 1100 20

Jones 2975 20

Scott 3000 20

Ford 3000 20

James 950 30

Martin 1250 30

Ward 1250 30

Turner 1500 30

Allen 1600 30

Blake 2850 30

Salary

2450

2975

1250

Result:

Suppose we would like to determine the

actual salary value, within each department,

which is at the 50th percentile

SELECT PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY SALARY)

FROM EMP

GROUP BY DEPT_NO;


LISTAGG Overview

• The LISTAGG function is used to aggregate a set of string values into a single string

• The values are appended based on the order specified by the WITHIN GROUP clause

• The values may be separated by a separator

LISTAGG ALL

DID EName EID EBYear

10 Jack 0012 1983

10 Lily 0015 1990

20 Tom 0019 1983

20 Bob 0022 1976

20 Frank 0004 1983

20 Tom 0014 1985

30 Jerry 0028 1991

30 Chris 0021 1981

30 Jill 0002 1984

30 Jerry 0031 1984

30 Allan 0006 1995

DID Name_List

10 Jack; Lily

20 Bob; Frank; Tom; Tom

30 Chris; Jill; Jerry; Jerry; Allan

Result:

Output employee names, in ascending order, under

the same department according to their employee ID

and birth year from the EMP table

SELECT Dept_ID,

ListAgg(ALL E_Name, ‘; ’) WITHIN GROUP (ORDR BY Birth, Emp_ID)

AS Name_List

FROM EMP

GROUP BY Dept_ID;

*To be delivered in the service stream

LISTAGG DISTINCT

DID EName EID EBYear

10 Jack 0012 1983

10 Lily 0015 1990

20 Tom 0019 1983

20 Bob 0022 1976

20 Frank 0004 1983

20 Tom 0014 1985

30 Jerry 0028 1991

30 Chris 0021 1981

30 Jill 0002 1984

30 Jerry 0031 1984

30 Allan 0006 1995

DID Name_List

10 Jack; Lily

20 Bob; Frank; Tom

30 Chris; Jill; Jerry; Allan

Result:

Suppose we would like to output employee names, in ascending order, under the same department

according to their employee ID and birth year from the EMP table, eliminating any duplicate names

SELECT Dept_ID,

ListAgg(DISTINCT E_Name, ‘; ’) WITHIN GROUP (ORDR BY Birth, Emp_ID)

AS Name_List

FROM EMP

GROUP BY Dept_ID;


Summary • Bringing analytics to the data is generally more efficient than bringing data to analytics

– Especially true with ever growing amount of data

– Improve data governance, data quality, e2e operational reliance

• Db2 12 Optimizer enhancements focus on emerging SQL pattern

• OLAP BIFs in DB2 and IDAA

– Simple and efficient to find complex insight into data

Maryela Weihrauch IBM

[email protected]

Title

Db2 for z/OS is Serious About Analytics:

Leveraging SQL to Analyze Your Data

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