Microsoft Dynamics AX Support

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Microsoft Dynamics AX general performance analysis scripts page 5

This is page 5 of 8 of the general performance analysis scripts online for the Performance Analyser 1.20 tool. See page 1 for the introduction. Use the links in the table below to navigate between pages.

– General analysis
Analyse SQL Configuration Page 1
Analyse SQL Indexes Page 2
Analyse SQL Queries Page 3
Analyse Blocking Page 4
Baseline – benchmark queries Page 5
– AX Specific
Analyse AX Configuration Page 6
Analyse AX Indexes Page 7
Analyse AX Queries Page 8

Baseline – benchmark queries

INDEX_CHANGES_SINCE_BASELINE
QUERIES_SLOWER_THAN_BASELINE
QUERIES_FASTER_THAN_BASELINE
NEW_QUERIES_NOT_IN_BASELINE
QUERIES_IN_BASELINE_BUT_NOT_IN_CURRENT
TRANSACTION_VOLUME_BY_HOUR
TRANSACTION_VOLUME_BY_HOUR_DETAIL
DISK_IO_BY_HOUR
BAD_SQL_WAIT_STATS
DB_GROWTH
TABLE_ACTIVITY
ACTIVITY_COMPARISON_BETWEEN_RUNS

 

USE [DynamicsPerf]
GO
SELECT *
FROM   STATS_COLLECTION_SUMMARY
ORDER  BY STATS_TIME DESC

GO

—————————————————————-

—  INDEX_CHANGES_SINCE_BASELINE

—  show index changes from BASELINE
—————————————————————-

EXEC SP_INDEX_CHANGES
  @BASELINE = ‘BASE_to_compare_to’,
  @COMPARISON_RUN_NAME = ‘Feb_26_2020_804AM’

—————————————————————-

—  QUERIES_SLOWER_THAN_BASELINE

—  queries that got worse  from BASELINE
—————————————————————-

SELECT A.QUERY_HASH,
       A.EXECUTION_COUNT,
       A.BEFORE_AVG_TIME,
       A.CURRENT_AVG_TIME,
       A.[TIME_DIFF(ms)],
       A.[%DECREASE],
       A.SQL_TEXT,
       B.QUERY_PLAN AS BEFORE_PLAN,
       C.QUERY_PLAN AS AFTER_PLAN
FROM   (SELECT DISTINCT V1.QUERY_HASH,
                        V1.EXECUTION_COUNT,
                        V1.AVG_ELAPSED_TIME                                                               AS BEFORE_AVG_TIME,
                        V2.AVG_ELAPSED_TIME                                                               AS CURRENT_AVG_TIME,
                        V2.AVG_ELAPSED_TIME – V1.AVG_ELAPSED_TIME                                         AS ‘TIME_DIFF(ms)’,
                        Cast(( V2.AVG_ELAPSED_TIME – V1.AVG_ELAPSED_TIME ) / CASE V1.AVG_ELAPSED_TIME
                                                                               WHEN 0 THEN 1
                                                                               ELSE V1.AVG_ELAPSED_TIME
                                                                             END * 100 AS DECIMAL(14, 3)) AS ‘%DECREASE’,
                        V1.SQL_TEXT,
                        V1.QUERY_PLAN_HASH                                                                AS BEFORE_PLAN_HASH,
                        V2.QUERY_PLAN_HASH                                                                AS AFTER_PLAN_HASH
        FROM   QUERY_STATS_HASH_VW V1
               INNER JOIN QUERY_STATS_HASH_VW V2
                       ON V1.QUERY_HASH = V2.QUERY_HASH
        WHERE  V1.RUN_NAME = ‘BASE_to_compare_to’
               AND V2.RUN_NAME = ‘Feb_26_2020_804AM’
               AND V1.AVG_ELAPSED_TIME < V2.AVG_ELAPSED_TIME
               AND V1.QUERY_HASH <> 0x0000000000000000) AS A
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W1
                    WHERE  W1.QUERY_PLAN_HASH = A.BEFORE_PLAN_HASH) AS B
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W2
                    WHERE  W2.QUERY_PLAN_HASH = A.AFTER_PLAN_HASH) AS C
ORDER  BY 6 DESC

—————————————————————-

—  QUERIES_FASTER_THAN_BASELINE

—  queries that got faster from BASELINE
—————————————————————-

SELECT A.QUERY_HASH,
       A.EXECUTION_COUNT,
       A.BEFORE_AVG_TIME,
       A.CURRENT_AVG_TIME,
       A.[TIME_DIFF(ms)],
       A.[%IMPROVEMENT],
       A.SQL_TEXT,
       B.QUERY_PLAN AS BEFORE_PLAN,
       C.QUERY_PLAN AS AFTER_PLAN
FROM   (SELECT DISTINCT V1.QUERY_HASH,
                        V1.EXECUTION_COUNT,
                        V1.AVG_ELAPSED_TIME                                                               AS BEFORE_AVG_TIME,
                        V2.AVG_ELAPSED_TIME                                                               AS CURRENT_AVG_TIME,
                        V1.AVG_ELAPSED_TIME – V2.AVG_ELAPSED_TIME                                         AS ‘TIME_DIFF(ms)’,
                        Cast(( V1.AVG_ELAPSED_TIME – V2.AVG_ELAPSED_TIME ) / CASE V2.AVG_ELAPSED_TIME
                                                                               WHEN 0 THEN 1
                                                                               ELSE V2.AVG_ELAPSED_TIME
                                                                             END * 100 AS DECIMAL(14, 3)) AS ‘%IMPROVEMENT’,
                        V1.SQL_TEXT,
                        V1.QUERY_PLAN_HASH                                                                AS BEFORE_PLAN_HASH,
                        V2.QUERY_PLAN_HASH                                                                AS AFTER_PLAN_HASH
        FROM   QUERY_STATS_HASH_VW V1
               INNER JOIN QUERY_STATS_HASH_VW V2
                       ON V1.QUERY_HASH = V2.QUERY_HASH
        WHERE  V1.RUN_NAME = ‘BASE_to_compare_to’
               AND V2.RUN_NAME = ‘Feb_26_2020_804AM’
               AND V1.AVG_ELAPSED_TIME > V2.AVG_ELAPSED_TIME
               AND V1.QUERY_HASH <> 0x0000000000000000) AS A
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W1
                    WHERE  W1.QUERY_PLAN_HASH = A.BEFORE_PLAN_HASH) AS B
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W2
                    WHERE  W2.QUERY_PLAN_HASH = A.AFTER_PLAN_HASH) AS C
ORDER  BY 6 DESC

 

—————————————————————-

—  NEW_QUERIES_NOT_IN_BASELINE

—  NEW queries that are not in the BASELINE
—————————————————————-

SELECT A.QUERY_HASH,
       A.BEFORE_AVG_TIME,
       A.SQL_TEXT,
       B.QUERY_PLAN AS BEFORE_PLAN
FROM   (SELECT DISTINCT V1.QUERY_HASH,
                        V1.AVG_ELAPSED_TIME AS BEFORE_AVG_TIME,
                        V1.SQL_TEXT,
                        V1.QUERY_PLAN_HASH  AS BEFORE_PLAN_HASH
        FROM   QUERY_STATS_HASH_VW V1
        WHERE  V1.RUN_NAME = ‘Feb_26_2020_804AM’
               AND NOT EXISTS (SELECT QUERY_HASH
                               FROM   QUERY_STATS_HASH_VW V2
                               WHERE  V1.QUERY_HASH = V2.QUERY_HASH
                                      AND V2.RUN_NAME = ‘BASE_to_compare_to’)
               AND V1.QUERY_HASH <> 0x0000000000000000) AS A
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W1
                    WHERE  W1.QUERY_PLAN_HASH = A.BEFORE_PLAN_HASH) AS B
ORDER  BY 2 DESC

——————————————————————————

—  QUERIES_IN_BASELINE_BUT_NOT_IN_CURRENT

—   queries that were in the BASELINE but not in the comparison capture
——————————————————————————-

SELECT A.QUERY_HASH,
       A.BEFORE_AVG_TIME,
       A.SQL_TEXT,
       B.QUERY_PLAN AS BEFORE_PLAN
FROM   (SELECT DISTINCT V1.QUERY_HASH,
                        V1.AVG_ELAPSED_TIME AS BEFORE_AVG_TIME,
                        V1.SQL_TEXT,
                        V1.QUERY_PLAN_HASH  AS BEFORE_PLAN_HASH
        FROM   QUERY_STATS_HASH_VW V1
        WHERE  V1.RUN_NAME = ‘BASE_to_compare_to’
               AND NOT EXISTS (SELECT QUERY_HASH
                               FROM   QUERY_STATS_HASH_VW V2
                               WHERE  V1.QUERY_HASH = V2.QUERY_HASH
                                      AND V2.RUN_NAME = ‘Feb_26_2020_804AM’)
               AND V1.QUERY_HASH <> 0x0000000000000000) AS A
       CROSS APPLY (SELECT TOP 1 QUERY_PLAN
                    FROM   QUERY_PLANS W1
                    WHERE  W1.QUERY_PLAN_HASH = A.BEFORE_PLAN_HASH) AS B
ORDER  BY 2 DESC

 

—————————————————————-

—  TRANSACTION_VOLUME_BY_HOUR

—  Show change in row counts by hour
—————————————————————-

USE [DynamicsPerf]

–Hourly Totals
SELECT *
FROM   PERF_HOURLY_ROWDATA_VW
WHERE  ROWRANK = 9999
       AND DATABASE_NAME <> ‘NULL’
ORDER  BY STATS_TIME DESC

—————————————————————-

—  TRANSACTION_VOLUME_BY_HOUR_DETAIL

—  Show details of change in row counts by hour
—————————————————————-

SELECT *
FROM   PERF_HOURLY_ROWDATA_VW
WHERE  STATS_TIME = ‘ENTER_STATS_TIME_HERE_FROM_PREVIOUS_QUERY’
       AND TABLE_NAME <> ‘NULL’
ORDER  BY ROWRANK

—————————————————————-

—  DISK_IO_BY_HOUR

—  Hourly Change in Disk IO Stats by File
—————————————————————-

SELECT *
FROM   PERF_HOURLY_IOSTATS_VW
WHERE DATABASE_NAME= ‘Dynamics’
ORDER BY STATS_TIME DESC, DATABASE_NAME, FILE_ID

—————————————————————-

—  BAD_SQL_WAIT_STATS

— IO bottleneck : If Top 2 values for wait stats include IO, (ASYNCH_IO_COMPLETION,IO_COMPLETION,LOGMGR,,WRITELOG,PAGEIOLATCH_x_xxx) there is an IO bottleneck.
— Blocking bottleneck: If top 2 wait_stats values include locking (LCK_M_BU, LCK_M_IS, LCK_M_IU, LCK_% …), there is a blocking bottleneck
— Parallelism: Cxpacket waits > 5%
—————————————————————-

/*********************************************************************************************

************************************************************************************************/

SELECT STATS_TIME,
       RANK,
       WAIT_TYPE,
       WAITING_TASKS_LAST_HOUR,
       WAIT_TIME_MS_LAST_HOUR
FROM   PERF_HOURLY_WAITSTATS_VW
WHERE  ( WAIT_TYPE LIKE ‘PAGEIOLATCH_%’
          OR WAIT_TYPE LIKE ‘ASYNCH_IO_COMPLETION%’
          OR WAIT_TYPE LIKE ‘IO_COMPLETION%’
          OR WAIT_TYPE LIKE ‘LOGMGR%’
          OR WAIT_TYPE LIKE ‘WRITELOG%’ )
       AND RANK < 3
       AND WAIT_TIME_MS_LAST_HOUR > 0

 

 

–Activity between 2 data collections to look at comparisons over a longer time period
–Find all run_names

SELECT RUN_NAME
FROM   STATS_COLLECTION_SUMMARY
ORDER  BY STATS_TIME DESC

—————————————————————-

—  DB_GROWTH

–Find record count and table size differences between the runs
–Can use this to accurately predict database growth
–NOTE only TOP 1000 tables are returned
——————————————————————————–
SELECT *
FROM   fn_dbstats(‘STARTING_RUN_NAME’, ‘ENDING_RUN_NAME’)
ORDER  BY DELTA_SIZEMB DESC

 

—————————————————————-

—  TABLE_ACTIVITY

–Find record read/write and row count differences between the runs
——————————————————————-

SELECT A.TABLE_NAME,
       B.ROW_COUNT – A.ROW_COUNT                       AS DELTA_IN_ROWS,
       B.TOTALREADOPERATIONS – A.TOTALREADOPERATIONS   AS DELTA_IN_READS,
       B.TOTALWRITEOPERATIONS – A.TOTALWRITEOPERATIONS AS DELTA_IN_WRITES
FROM   INDEX_OPS_VW A
       INNER JOIN INDEX_OPS_VW B
               ON A.TABLE_NAME = B.TABLE_NAME
                  AND A.DATABASE_NAME = B.DATABASE_NAME
                  AND A.RUN_NAME = ‘STARTING_RUN_NAME’
                  AND B.RUN_NAME = ‘ENDING_RUN_NAME’
ORDER  BY 2 DESC

—————————————————————-

—  SQL_WAIT_STATS_BY_HOUR
— Hourly Change in SQL Server Wait Stats
—————————————————————-

SELECT *
FROM   PERF_HOURLY_WAITSTATS_VW
ORDER BY STATS_TIME DESC, RANK

 

—————————————————————-

—  ACTIVITY_COMPARISON_BETWEEN_RUNS


—  Comparison queries between different data captures
—————————————————————–

SELECT D1.RUN_NAME           AS RUN1,
       D2.RUN_NAME           AS RUN2,
       D1.SQL_TEXT,
       D1.QUERY_PLAN,
       D1.AVG_ELAPSED_TIME   AS RUN1_AVG_TIME,
       D2.AVG_ELAPSED_TIME   AS RUN2_AVG_TIME,
       D2.AVG_ELAPSED_TIME-D1.AVG_ELAPSED_TIME AS TIME_DIFF,
       D1.AVG_LOGICAL_READS  AS RUN1_READS,
       D2.AVG_LOGICAL_READS  AS RUN2_READS,
       D2.AVG_LOGICAL_READS-D1.AVG_LOGICAL_READS AS READS_DIFF,
       D1.AVG_LOGICAL_WRITES AS RUN1_WRITES,
       D2.AVG_LOGICAL_WRITES AS RUN2_WRITES,
       D2.AVG_LOGICAL_WRITES-D1.AVG_LOGICAL_WRITES AS WRITES_DIFF,
       D1.QUERY_HASH
FROM   QUERY_STATS_VW D1
       INNER JOIN QUERY_STATS_VW D2
         ON D1.QUERY_HASH = D2.QUERY_HASH
        AND D1.DATABASE_NAME = D2.DATABASE_NAME
WHERE  D1.QUERY_HASH <> 0x0000000000000000
       AND D1.RUN_NAME = ‘STARTING_RUN_NAME’
       AND D2.RUN_NAME = ‘ENDING_RUN_NAME’
ORDER  BY D2.AVG_ELAPSED_TIME – D1.AVG_ELAPSED_TIME