DB2 Workload Manager Histograms

DB2 9.5 Monitoring

Performance Tuning and Problem Determination

Agenda• What I’m going to talk about

– Monitoring features new to DB2 9.5– Measuring against SLAs so that the system can

be tuned to achieve them– Using new monitoring features for problem

determination• What I’m not going to talk about

– Monitoring features that existed prior to DB2 9.5– Tuning individual queries, bufferpool hit ratios,

etc.

New DB2 9.5 Monitoring Features• Types of Information

– Statistics• Counters• High watermarks•Histograms

– Activities• Types of Presentation

– Table functions– Event monitors

The Bell Curve

The Bell Curve in Height

The Power Law Curve

If Height Were A Power Law “If the average

height of two hundred men was five foot ten; the most frequent (or modal) height would be held by dozens of men who were each only a foot tall …”

If Height Were A Power Law “…the median height

would be two feet tall (a hundred men shorter than two feet, and a hundred taller)…”

If Height Were A Power Law “Most important, in

such a distribution, the five tallest men would be 40, 50, 66, 100, and 200 feet tall respectively.”

– Clay Shirky, Here Comes Everybody

“When we encounter a system like a database where there is no such thing as a representative query, the habits of mind that come from thinking about averages are not merely useless, they’re harmful.”

Drawing A Histogram – Step 1

Drawing A Histogram – Step 2

Query Response Time1 0.53

2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20

Bin Range Frequency[ 0 - 1 ]

( 1 - 2 ]

( 2 - 3 ]

( 3 - 4 ]

( 4 - 5 ]

( 5 - 6 ]

( 6 - 7 ]


2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20


2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20

Bin Range Frequency[ 0 - 1 ] 1

( 1 - 2 ]

( 2 - 3 ]

( 3 - 4 ]

( 4 - 5 ]

( 5 - 6 ]

( 6 - 7 ]


( 1 - 2 ] 4

( 2 - 3 ]

( 3 - 4 ]

( 4 - 5 ]

( 5 - 6 ]

( 6 - 7 ]


2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20


( 1 - 2 ] 4

( 2 - 3 ] 3

( 3 - 4 ]

( 4 - 5 ]

( 5 - 6 ]

( 6 - 7 ]


2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20


( 1 - 2 ] 4

( 2 - 3 ] 3

( 3 - 4 ] 2

( 4 - 5 ] 5

( 5 - 6 ] 4

( 6 - 7 ] 1


2 1.21

3 1.24

4 1.36

5 1.63

6 2.41

7 2.48

8 2.77

9 3.28

10 3.67

11 4.34

12 4.53

13 4.70

14 4.73

15 4.79

16 5.38

17 5.72

18 5.75

19 5.90

20 6.20

Bin Range Frequency

[ 0 - 1 ] 1

( 1 - 2 ] 4

( 2 - 3 ] 3

( 3 - 4 ] 2

( 4 - 5 ] 5

( 5 - 6 ] 4

( 6 - 7 ] 1

Why Are Histograms Useful?• Reveal hidden distribution details• Detect outliers• Can be aggregated

Reveal Hidden Distribution Details

• Month 1 - all users are happy– Average response time: 19 seconds– Standard deviation: 6 seconds


• Month 2 - some users are complaining– Average response time: 19 seconds– Standard deviation: 6 seconds


• Month 1 - all users are happy– Average response time: 19 seconds– Standard deviation: 6 seconds


• Month 2 - some users are complaining– Average response time: 19 seconds– Standard deviation: 6 seconds

Outliers

Detecting Outliers• Sometimes, outliers have a small, even

negligible effect on the average– Adding the heaviest land animal on earth to a group

of 1000 people changes the average weight by less than 10%, that is, little more than 10 pounds.

Detecting Outliers• Sometimes, outliers have a significant effect

– Adding the richest man on earth to a group of 1000 people changes the average net worth by 40,000%, that is, tens of millions of dollars.

Net Worth Histogram

Aggregating Histograms• Aggregate short periods of time into

longer periods– Daily response times for day-to-day

decision-making– Monthly reports

Aggregating Averages In Innumeracy, John

Allen Paulos gives an example of a sports anomaly involving batting averages.

Batting Averages “Babe Ruth hits for

a higher batting average for the first half of the season and hits for a higher batting average in the second half of the season as well, …”

Batting Averages• “… but Lou Gehrig

ends up with a higher batting average for the season as a whole.”

Batting AveragesPlayer Half Average At Bats

Babe Ruth First .300 200

Second .400 100

Lou Gehrig First .290 100

Second .390 200

Batting AveragesPlayer Half Average At Bats Average × At

Bats

Babe Ruth First .300 200 60.0

Second .400 100


Second .390 200


Bats


Second .400 100 40.0


Second .390 200


Bats


Second .400 100 40.0

Lou Gehrig First .290 100 29.0

Second .390 200 78.0


Bats


Second .400 100 40.0

300 100.0


Second .390 200 78.0


Bats


Second .400 100 40.0

300 100.0


Second .390 200 78.0

300 107.00

Range(second

s)

January

[ 0 - 1 ] 438

( 1 - 2 ] 1350

( 2 - 3 ] 3238

( 3 - 4 ] 6049

( 4 - 5 ] 8802

( 5 - 6 ] 9974

( 6 - 7 ] 8802

( 7 - 8 ] 6049

( 8 - 9 ] 3238

( 9 - 10 ] 1350

( 10 - 11 ] 438

( 11 - 12 ] 0

( 12 - 13 ] 0

( 13 - 14 ] 0

( 14 - 15 ] 0

( 15 - 16 ] 0

( 16 - 17 ] 0

( 17 - 18 ] 0

( 18 - 19 ] 0

( 19 - 20 ] 0

( 20 - 21 ] 0

Range(second

s)

January February

Jan + Feb

[ 0 - 1 ] 438 0 438

( 1 - 2 ] 1350 0 1350

( 2 - 3 ] 3238 0 3238

( 3 - 4 ] 6049 0 6049

( 4 - 5 ] 8802 0 8802

( 5 - 6 ] 9974 0 9974

( 6 - 7 ] 8802 0 8802

( 7 - 8 ] 6049 0 6049

( 8 - 9 ] 3238 0 3238

( 9 - 10 ] 1350 0 1350

( 10 - 11 ] 438 613 1051

( 11 - 12 ] 0 1890 1890

( 12 - 13 ] 0 4533 4533

( 13 - 14 ] 0 8469 8469

( 14 - 15 ] 0 12322 12322

( 15 - 16 ] 0 13963 13963

( 16 - 17 ] 0 12322 12322

( 17 - 18 ] 0 8469 8469

( 18 - 19 ] 0 4533 4533

( 19 - 20 ] 0 1890 1890

( 20 - 21 ] 0 613 613

Range(second

s)

January February

March First Quarter

[ 0 - 1 ] 438 0 0 438

( 1 - 2 ] 1350 0 0 1350

( 2 - 3 ] 3238 0 0 3238

( 3 - 4 ] 6049 0 0 6049

( 4 - 5 ] 8802 0 0 8802

( 5 - 6 ] 9974 0 526 10500

( 6 - 7 ] 8802 0 1620 10422

( 7 - 8 ] 6049 0 3886 9935

( 8 - 9 ] 3238 0 7259 10497

( 9 - 10 ] 1350 0 10562 11912

( 10 - 11 ] 438 613 11968 13019

( 11 - 12 ] 0 1890 10562 12452

( 12 - 13 ] 0 4533 7259 11792

( 13 - 14 ] 0 8469 3886 12355

( 14 - 15 ] 0 12322 1620 13942

( 15 - 16 ] 0 13963 526 14489

( 16 - 17 ] 0 12322 0 12322

( 17 - 18 ] 0 8469 0 8469

( 18 - 19 ] 0 4533 0 4533

( 19 - 20 ] 0 1890 0 1890

( 20 - 21 ] 0 613 0 613

DB2 WLM Histograms• Activity

– Execution time– Queue time– Lifetime– Inter-arrival time– Estimated cost

• Request– Execution time

Lifecycle Of An Activity

Inter-arrival Times

How Much Data Is Enough?

How To Collect DB2 Histograms

• Activity execution time• Activity queue time• Activity lifetime

alter service class MANAGERS under MARKETING collect aggregate activity data BASE

alter work action set MAPLOADS alter work action MAPLOADS collect aggregate activity data BASE


• Activity execution time• Activity queue time• Activity lifetime• Activity inter-arrival time• DML activity estimated cost

alter service class MANAGERS under MARKETING collect aggregate activity data EXTENDED

alter work action set MAPLOADS alter work action MAPLOADS collect aggregate activity data EXTENDED


• Request execution time

alter service class MANAGERS under MARKETING collect aggregate request data BASE


• An event monitor must be active to receive the data and write it to a table, file or pipe

create event monitor DB2STATISTICSfor statistics write to table

set event monitor DB2STATISTICS state 1

Triggering A Collection• WLM_COLLECT_INT database configuration parameter

– To collect once every 24 hours (1440 minutes)

update db cfg using WLM_COLLECT_INT 1440

• WLM_COLLECT_STATS stored procedure– To collect immediately

call wlm_collect_stats()

The HISTOGRAMBIN TableDESCRIBE TABLE HISTOGRAMBIN_DB2STATISTICS

Data type ColumnColumn name schema Data type name Length Scale Nulls------------------------------- --------- ------------------- ---------- ----- -----BIN_ID SYSIBM INTEGER 4 0 NoBOTTOM SYSIBM BIGINT 8 0 NoHISTOGRAM_TYPE SYSIBM VARCHAR 64 0 NoNUMBER_IN_BIN SYSIBM BIGINT 8 0 NoSERVICE_CLASS_ID SYSIBM INTEGER 4 0 NoSTATISTICS_TIMESTAMP SYSIBM TIMESTAMP 10 0 NoTOP SYSIBM BIGINT 8 0 NoWORK_ACTION_SET_ID SYSIBM INTEGER 4 0 NoWORK_CLASS_ID SYSIBM INTEGER 4 0 No

9 record(s) selected.

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’ ORDER BY TOP

Visualizing A Histogram SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’ ORDER BY TOP

TOP NUMBER_IN_BIN----------- ------------- -1 0 1 1 2 6 3 21 5 179 8 298 12 141 19 47 29 5 44 2 68 0

Visualizing A HistogramWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’) SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMS ORDER BY TOP

Visualizing A HistogramWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’) SELECT TOP, NUMBER_IN_BIN / (SELECT MAX(NUMBER_IN_BIN) FROM HISTOGRAMS) FROM HISTOGRAMS ORDER BY TOP

Visualizing A HistogramWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’) SELECT TOP, CAST(60 * NUMBER_IN_BIN / (SELECT MAX(NUMBER_IN_BIN) FROM HISTOGRAMS) AS INTEGER) FROM HISTOGRAMS ORDER BY TOP

Visualizing A HistogramWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’) SELECT TOP, SUBSTR(REPEAT(‘#’, CAST(60 * NUMBER_IN_BIN / (SELECT MAX(NUMBER_IN_BIN) FROM HISTOGRAMS) AS INTEGER)), 1, 60) AS GRAPH FROM HISTOGRAMS ORDER BY TOP

Visualizing A HistogramWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’) SELECT TOP, SUBSTR(REPEAT(‘#’, CAST(60 * NUMBER_IN_BIN / (SELECT MAX(NUMBER_IN_BIN) FROM HISTOGRAMS) AS INTEGER)), 1, 60) AS GRAPH FROM HISTOGRAMS ORDER BY TOP

TOP GRAPH----------- ------------------------------------------------------------ -1 1 2 # 3 #### 5 ####################################

Visualizing A HistogramTOP GRAPH----------- ------------------------------------------------------------ -1 1 2 # 3 #### 5 #################################### 8 ############################################################ 12 ############################ 19 ######### 29 # 44 68 103 158 241 369 562 858 1309 1997

Controlling The Range Of A Histogram

create histogram template LIFETIME_TEMPLATE high bin value 44

alter service class SYSDEFAULTSUBCLASSunder SYSDEFAULTUSERCLASSactivity lifetime histogram template LIFETIME_TEMPLATE

call wlm_collect_stats

• Note: You must call wlm_collect_stats after changing a histogram template for the change to take effect.


• Duplicates now occur because bin sizes grow exponentially and are rounded to an integer.

• Modify the query to group by top and change number_in_bin to sum(number_in_bin) to eliminate these duplicates – however, number of bins is reduced


SELECT TOP, SUM(NUMBER_IN_BIN) NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’ GROUP BY TOP ORDER BY TOP

TOP NUMBER_IN_BIN----------- ------------- -1 0 1 1 2 6 3 21 4 89 5 90 6 91 7 112 8 96 9 82

The Purpose Of The Infinite Bin• The infinite bin, or catch-all bin, is the bin

whose TOP is -1• Alerts you when the template fails to cover

the entire range of the data• To choose a better high bin value:

– For lifetime, use COORD_ACT_LIFETIME_TOP high watermark

– For estimated cost, use COST_ESTIMATE_TOP high watermark

Why Do Bins Grow Exponentially?

6 hours40 bins

= 9 minutes/bin

ServiceLevel

Agreements

Measuring For An SLA• The activity lifetime histogram provides an easy

way to measure against such an SLA. Here’s how:– Convert NUMBER_IN_BIN into a percentage– Convert the percentage into a cumulative percentage

Measuring For An SLAWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’)SELECT TOP, NUMBER_IN_BINFROM HISTOGRAMSORDER BY TOP

Measuring For An SLAWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’)SELECT TOP, CAST(100 * NUMBER_IN_BIN / (SELECT CAST(SUM(NUMBER_IN_BIN) AS DOUBLE) FROM HISTOGRAMS) AS DECIMAL(9,2)) PERCENTAGE_IN_BINFROM HISTOGRAMSORDER BY TOP

Measuring For An SLAWITH HISTOGRAMS AS(SELECT TOP, NUMBER_IN_BIN FROM HISTOGRAMBIN_DB2STATISTICS WHERE SERVICE_CLASS_ID = 13 AND HISTOGRAM_TYPE = ‘CoordActLifetime’ AND STATISTICS_TIMESTAMP = ‘2009-03-12-14.30.00.000000’)SELECT TOP, CAST(100 * NUMBER_IN_BIN / (SELECT CAST(SUM(NUMBER_IN_BIN) AS DOUBLE) FROM HISTOGRAMS) AS DECIMAL(9,2)) PERCENTAGE_IN_BIN CAST((SELECT 100 * SUM(NUMBER_IN_BIN) FROM HISTOGRAM_BIN WHERE TOP <= OUTERHIST.TOP) / (SELECT CAST(SUM(NUMBER_IN_BIN) AS DOUBLE) FROM HISTOGRAMS) AS DECIMAL(9,2)) CUMULATIVE_PERCENTAGEFROM HISTOGRAMS AS OUTERHISTWHERE TOP != -1ORDER BY TOP

Measuring For An SLATOP PERCENTAGE_IN_BIN CUMULATIVE_PERCENTAGE----------- ----------------- --------------------- 1 0.00 0.00 2 0.09 0.09 3 0.00 0.09 5 0.09 0.19 8 0.00 0.19 12 0.09 0.28 19 0.28 0.57 29 0.09 0.67 44 0.76 1.44 68 0.67 2.11 103 1.34 3.46 158 1.53 5.00 241 1.92 6.92 369 2.98 9.91 562 1.63 11.54 858 2.11 13.66 1309 0.86 14.53 1997 2.02 16.55 3046 2.02 18.57 4647 3.84 22.42 7089 5.67 28.10 10813 7.12 35.22 16493 6.35 41.57 25157 12.41 53.99 38373 15.39 69.39 58532 13.95 83.34 89280 10.49 93.84

For More Information

DB2 workload management histograms, Part 1: A gentle introduction to histograms

https://www6.software.ibm.com/developerworks/offers/kits/db2/dbakit/articles/dm-0810mcdonald/

DB2 workload management histograms, Part 2: Understanding the six histograms of DB2 workload management

https://www6.software.ibm.com/developerworks/offers/kits/db2/dbakit/articles/dm-0810mcdonald2/

DB2 workload management histograms, Part 3: Visualizing and deriving statistics from DB2 histograms using SQL


https://www6.software.ibm.com/developerworks/offers/kits/db2/dbakit/articles/dm-0810mcdonald/



DB2 Workload Manager Histograms

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