Microsoft Dynamics AX Support

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Analysis scripts for Performance Analyzer v2.0 Page 3

This is page 3 of 10 of the general performance analysis scripts online for the Performance Analyzer 2.0 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
SSRS Report Analysis Page 6
Query Alerts Page 7
– AX Specific
Analyse AX Configuration Page 8
Analyse AX Indexes Page 9
Analyse AX Queries Page 10
/*********************************************************************
Copy one of the following links and press Ctrl-F and click FIND NEXT
in order to go to that section of the scripts
EXPENSIVE_QUERIES_BY_TIME_PERIOD
EXPENSIVE_QUERIES_HISTORICAL
EXPENSIVE_QUERIES_BY_LAST_COLLECTION   (CURR_VW)
MISSING_INDEX_QUERIES
QUERIES_WITH_MULTIPLE_EXECUTION_PLANS
********************************************************************/
USE DynamicsPerf
GO
—————————————————————-

—  EXPENSIVE_QUERIES_BY_TIME_PERIOD

— List top 100 most expensive queries

— NOTE: REQUIRES SQL 2012 to run this query
—————————————————————-
–REH Per 5 min collection time
SELECT TOP 100 *
FROM   (SELECT SERVER_NAME,
DATABASE_NAME,
–STATS_TIME,
QUERY_HASH,
QUERY_PLAN_HASH,
(SELECT SQL_TEXT
FROM   QUERY_TEXT QT
WHERE  CTE.QUERY_HASH = QT.QUERY_HASH
AND CTE.SERVER_NAME = QT.SERVER_NAME
AND CTE.DATABASE_NAME = QT.DATABASE_NAME)                                      AS SQL_TEXT,
(SELECT QUERY_PLAN
FROM   QUERY_PLANS QP
WHERE  CTE.QUERY_PLAN_HASH = QP.QUERY_PLAN_HASH
AND CTE.SERVER_NAME = QP.SERVER_NAME
AND CTE.DATABASE_NAME = QP.DATABASE_NAME)                                      AS QUERY_PLAN,
SUM(CAST(TIME_THIS_PERIOD / 1000.000 AS DECIMAL(20, 3)))                               AS TOTAL_TIME_MS,
SUM(EXECUTIONS_THIS_PERIOD)                                                            AS TOTAL_EXECUTIONS,
CASE SUM(EXECUTIONS_THIS_PERIOD)
WHEN 0 THEN 0
ELSE ( SUM(CAST(TIME_THIS_PERIOD / 1000.000 AS DECIMAL(20, 3))) / SUM(EXECUTIONS_THIS_PERIOD) )
END                                                                                    AS AVG_TIME_MS,
SUM(CAST(WORKER_TIME_THIS_PERIOD / 1000.000 AS DECIMAL(20, 3)))                        AS WORK_TIME,
SUM(CAST(( TIME_THIS_PERIOD – WORKER_TIME_THIS_PERIOD ) / 1000.000 AS DECIMAL(14, 3))) AS WAIT_TIME
FROM   QUERY_STATS_CTE_VW CTE
WHERE  STATS_TIME BETWEEN ‘2016-10-13 16:15:00.307’ AND ‘2016-10-13 16:25:00.307’
–AND QUERY_HASH = 0x24A42A762C8879C3
GROUP  BY SERVER_NAME,
DATABASE_NAME,
—         STATS_TIME,
QUERY_HASH,
QUERY_PLAN_HASH) AS A
— WHERE SQL_TEXT LIKE ‘%XXXXX%’
— WHERE QUERY_HASH = 0x00000000000
ORDER  BY 7 DESC
—————————————————————-

—  EXPENSIVE_QUERIES_HISTORICAL

— List top 100 most expensive queries
—————————————————————-
SELECT TOP 100 *
FROM   QUERY_HISTORY_VW QS — Queries from last data collection only
WHERE
1=1
AND FLAG = ‘M’ AND DATE = ‘7/1/2016’  — 1ST DAY OF MONTH REPRESENTS THAT MONTH
–AND FLAG = ‘D’ AND DATE = ‘7/20/2016’
— AND  QUERY_HASH = 0x35DBB41368AFED7C — find a specific query
— AND SQL_TEXT LIKE ‘%SQL_TEXT_HERE%’  — find all SQL statements that contain a specific text i.e. table name

ORDER  BY TOTAL_ELAPSED_TIME DESC  — Queries consuming most TOTAL time on SQL

— ORDER  BY AVG_LOGICAL_READS DESC  — Queries potentially causing large disk i/o
— ORDER  BY EXECUTION_COUNT DESC  — High execution count could be loops in application code
— ORDER  BY TOTAL_LOGICAL_READS DESC  — Queries to review to potentially reduce disk i/o
/** NOTE MUST HAVE INSTALLED FULLTEXT INDEXES FOR THIS QUERY **/
–;WITH FT_CTE (QUERY_HASH)
–AS
–(SELECT QUERY_HASH
— FROM   QUERY_TEXT
— WHERE  CONTAINS (SQL_TEXT, ‘SELECT’) — find all SQL statements that contain a specific text i.e. table name
–)
;WITH FT_CTE2 (QUERY_PLAN_HASH, QUERY_PLAN)
AS
(SELECT QUERY_PLAN_HASH, QUERY_PLAN FROM   QUERY_PLANS
WHERE  CONTAINS (C_QUERY_PLAN, ‘”INVENTDIM” AND “INDEX SCAN”‘)
–WHERE  CONTAINS (C_QUERY_PLAN, ‘”I_6143RECID”‘)
)
SELECT TOP 100 *
FROM   QUERY_HISTORY_VW QS — Queries from last data collection only
–INNER JOIN FT_CTE FT ON QS.QUERY_HASH = FT.QUERY_HASH
INNER JOIN FT_CTE2 FT2 ON QS.QUERY_PLAN_HASH = FT2.QUERY_PLAN_HASH
WHERE
1=1
–AND FLAG = ‘M’ –AND DATE = ‘7/1/2016’  — 1ST DAY OF MONTH REPRESENTS THAT MONTH
AND FLAG = ‘D’ AND DATE = ‘7/20/2016’
ORDER BY TOTAL_ELAPSED_TIME DESC
— Show all queries who’s avg is GREATER then then monthly avg for that query/plan
SELECT TOP 100 QM.AVG_ELAPSED_TIME AS MONTHLY_AVG_TIME,
QS.AVG_ELAPSED_TIME  – QM.AVG_ELAPSED_TIME AS DELTA_TIME,
QS.*
FROM   QUERY_HISTORY_VW QS — Queries from last data collection only
INNER JOIN QUERY_HISTORY_VW QM
ON QS.SERVER_NAME = QM.SERVER_NAME
AND QS.DATABASE_NAME = QM.DATABASE_NAME
AND QS.QUERY_HASH = QM.QUERY_HASH
AND QS.QUERY_PLAN_HASH = QM.QUERY_PLAN_HASH
AND QM.DATE = ‘7/1/2016’  — Prev. Data
AND QM.FLAG = ‘M’
AND QS.AVG_ELAPSED_TIME > QM.AVG_ELAPSED_TIME
WHERE  QS.FLAG = ‘D’ AND QS.DATE = ‘7/20/2016’  –Cur. Data
ORDER  BY QS.TOTAL_ELAPSED_TIME DESC
— Show all queries who’s avg is greater then previous month
SELECT TOP 100 QM.AVG_ELAPSED_TIME AS PREV_MONTHLY_AVG_TIME,
QS.AVG_ELAPSED_TIME  – QM.AVG_ELAPSED_TIME AS DELTA_TIME,
QS.*
FROM   QUERY_HISTORY_VW QS — Queries from last data collection only
INNER JOIN QUERY_HISTORY_VW QM
ON QS.SERVER_NAME = QM.SERVER_NAME
AND QS.DATABASE_NAME = QM.DATABASE_NAME
AND QS.QUERY_HASH = QM.QUERY_HASH
AND QS.QUERY_PLAN_HASH = QM.QUERY_PLAN_HASH
AND QM.DATE = ‘5/1/2016’  — Prev. Month
AND QM.FLAG = ‘M’
AND QS.AVG_ELAPSED_TIME > QM.AVG_ELAPSED_TIME
WHERE  QS.FLAG = ‘M’ AND QS.DATE = ‘7/1/2016’ –Cur. Month
ORDER  BY QS.TOTAL_ELAPSED_TIME DESC
— Show queries that have new plans this month
SELECT TOP 100 QS.*
FROM   QUERY_HISTORY_VW QS — Queries from last data collection only
LEFT JOIN QUERY_HISTORY_VW QM
ON QS.SERVER_NAME = QM.SERVER_NAME
AND QS.DATABASE_NAME = QM.DATABASE_NAME
AND QS.QUERY_HASH = QM.QUERY_HASH
AND QS.QUERY_PLAN_HASH = QM.QUERY_PLAN_HASH
AND QM.DATE = ‘5/1/2016’  — Prev. Month
AND QM.FLAG = ‘M’
AND QS.AVG_ELAPSED_TIME > QM.AVG_ELAPSED_TIME
WHERE  QS.FLAG = ‘M’ AND QM.QUERY_HASH IS NULL  AND QS.DATE = ‘7/1/2016’–Cur. Month
ORDER  BY QS.TOTAL_ELAPSED_TIME DESC
—————————————————————-

—  EXPENSIVE_QUERIES_BY_LAST_COLLECTION

— List top 100 most expensive queries

—————————————————————-
SELECT TOP 100 *
FROM   QUERY_STATS_CURR_VW QS — Review queries for all data collections
WHERE
1=1
— AND  QUERY_HASH = 0x35DBB41368AFED7C — find a specific query
— AND SQL_TEXT LIKE ‘%SQL_TEXT_HERE%’  — find all SQL statements that contain a specific text i.e. table name
— AND LAST_EXECUTION_TIME > ‘XXXXXXX’   — find all queries that have executed after a specific time
— AND DATABASE_NAME = ‘XXXXXXXXX’       — find all queries for a specific database
— AND MAX_ELAPSED_TIME /10 > AVG_ELAPSED_TIME  — Find all queries potentially getting blocked or paramater sniffing issues
ORDER  BY TOTAL_ELAPSED_TIME DESC  — Queries consuming most TOTAL time on SQL
— ORDER  BY AVG_LOGICAL_READS DESC  — Queries potentially causing large disk i/o
— ORDER  BY EXECUTION_COUNT DESC  — High execution count could be loops in application code
— ORDER  BY TOTAL_LOGICAL_READS DESC  — Queries to review to potentially reduce disk i/o
—————————————————————————————-

—    MISSING_INDEX_QUERIES

— Identify queries that the optimizer suspects can be optimized
— by new or changed indexes:

— NOTE: DO NOT add these indexes verbatim without deep analysis.
—  Large INCLUDED Column lists are NOT recommended for ERP solutions

— 1-Make sure the index isn’t creating a subset duplicate of another index
— 2-Make sure that the Application code is written correctly
— 3-Make sure the query actually matches a business process
— 4-Make sure it’s not a one off exception
— 5-Make sure the Reads you save is less than the Writes you’ll cause
—  by adding the index

—  SQL Server doesn’t know that the code isn’t correct, or forgot
—   to pass criteria to the database

—  YOU CAN’T FIX CHALLENGES IN CODE OR BUSINESS PROCESSES WITH ONLY INDEXES !!

—  If you have more then 30 indexes on a Dynamics Table you should reevaluate the indexes on that table

— *CAUTION*

–•The missing index DMVs don’t take into account the overhead that new indexes can create (extra disk space, slight impact on insert/delete perf, etc).
–•It’s probable that the DMVs may not recommend the ideal column order for multi-column indexes.
–•The missing index DMVs don’t make recommendation about whether an index should be clustered or nonclustered.
–*A bug in SQL 2008/R2 that recommends an index that already exists
–*No guarantee that the SQL Optimizer will actually use the suggested index.  You need to verify usage.
——————————————————————————————
SELECT TOP 100 *
FROM   MISSING_INDEXES_CURR_VW
WHERE  NOT EXISTS (SELECT QUERY_HASH
FROM   COMMENTS C
WHERE  C.QUERY_HASH = MISSING_INDEXES_CURR_VW.QUERY_HASH) — Remove queries that have comments
AND INDEX_IMPACT > 75
AND EXECUTION_COUNT > 100 –In Dev/Test/QA lower this value to 1
AND AVG_ELAPSED_TIME > 20
AND AVG_LOGICAL_READS > 1000
ORDER  BY TOTAL_LOGICAL_READS DESC
—————————————————————-

—   QUERIES_WITH_MULTIPLE_EXECUTION_PLANS

— List queries that have more than 1 execution plans
— Is a strong indicator of parameter sniffing issues
—————————————————————-
SELECT TOP 100 SERVER_NAME,
DATABASE_NAME,
QUERY_HASH,
(SELECT SQL_TEXT
FROM   QUERY_TEXT QT
WHERE  QT.QUERY_HASH = A.QUERY_HASH
AND QT.DATABASE_NAME = A.DATABASE_NAME
AND QT.SERVER_NAME = A.SERVER_NAME) AS SQL_TEXT,
NUM_PLANS                                   AS NO_OF_PLANS,
MIN_TIME                                    AS MIN_AVG_TIME,
MAX_TIME                                    AS MAX_AVG_TIME,
STUFF ((SELECT ‘, ‘
+ CONVERT(VARCHAR(64), QH1.QUERY_PLAN_HASH, 1)
+ ‘ time(ms)= ‘
+ CAST(QH1.AVG_ELAPSED_TIME AS VARCHAR(20))
+ CHAR(10)
FROM   QUERY_HISTORY_VW QH1
WHERE  QH1.QUERY_HASH = A.QUERY_HASH
AND QH1.DATABASE_NAME = A.DATABASE_NAME
AND QH1.SERVER_NAME = A.SERVER_NAME
AND QH1.FLAG = ‘M’
AND QH1.DATE = A.DATE
ORDER  BY QH1.AVG_ELAPSED_TIME
FOR xml path(”)), 1, 1, ””)      AS QUERY_PLAN_HASH
FROM   (SELECT DISTINCT SERVER_NAME,
DATABASE_NAME,
DATE,
QUERY_HASH,
COUNT(QUERY_PLAN_HASH) AS NUM_PLANS,
MIN(AVG_ELAPSED_TIME)  AS MIN_TIME,
MAX(AVG_ELAPSED_TIME)  AS MAX_TIME
FROM   QUERY_HISTORY_VW QV
WHERE  QV.FLAG = ‘M’
AND DATE = ‘7/1/2016’
GROUP  BY SERVER_NAME,
DATABASE_NAME,
QUERY_HASH,
DATE
HAVING COUNT(QUERY_PLAN_HASH) > 1) AS A
ORDER  BY 6 DESC
–Read the query plan from previous query
 SELECT *
FROM   QUERY_PLANS
WHERE  QUERY_PLAN_HASH = 0X0000000000000