Advanced ASE Performance Tuning Tips

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Advanced ASE Performance Tuning
Tips
Janis Griffin
Senior DBA / Performance Evangelist

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© 2015 SOLARWINDS
Who Am I?
» Senior DBA / Performance Evangelist for Solarwinds
§ Janis.Griffin@solarwinds.com
§ Twitter - @DoBoutAnything
§ Current – 25+ Years in Oracle, Sybase, SQL Server
§ DBA and Developer
» Specialize in Performance Tuning
» Review Database Performance for Customers and
Prospects
» Common Thread – Paralyzed by Tuning

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© 2015 SOLARWINDS
Before we start, do you know…

» The most important problem in your database instance?
»
» If that new program is hurting database performance?
»
» Did that recent fix really solved the database issue?
»
» Which bottlenecks in your database directly impacted end-
user service?
»

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© 2015 SOLARWINDS
Agenda - 4 Tips
» Focus on Tuning Queries

» Utilize Response Time Analysis (RTA)

» Examine Query Execution Plans

» Use SQL or Query Diagramming
§ Who registered yesterday for Tuning Class
§ Check order status
§ Current paychecks for specific employees
§

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© 2015 SOLARWINDS
Why Focus on Queries
» Most Applications
§ Read and Write data to/from database
§ Simple manipulation of data
§ Deal with smaller amounts of data
» Most Databases
§ Examine larger amounts of data, return a little
§ Inefficiencies quickly become bottleneck
» Why do SQL tuning?
§ “Gives the biggest bang for your buck”
§ Changes to SQL are Safer
§ Most of performance issues are SQL related

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© 2015 SOLARWINDS
Challenges Of Tuning
» SQL Tuning is Hard
§ Who should tune – DBA or Developer
§ Which SQL to tune
§
» Requires Expertise in Many Areas
§ Technical – Plan, Data Access, SQL Design
§ Business – What is the Purpose of SQL?
§
» Tuning Takes Time
§ Large Number of SQL Statements
§ Each Statement is Different
§
» Low Priority in Some Companies
§ Vendor Applications
§ Focus on Hardware or System Issues
§
» Never Ending

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© 2015 SOLARWINDS
Which SQL to Tune
Methods for Identifying

» User / Batch Job Complaints
§ Known Poorly Performing SQL
§ Trace Session/Process
§
» Queries Performing Most I/O (LIO / PIO)
§ Table or Index Scans

» Queries Consuming CPU

» Highest Response Times - DPA (formally Ignite)



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© 2015 SOLARWINDS
Response Time Analysis (RTA)
§Understand the total time a Query spends in
Database
§Measure time while Query executes
§SAP ASE helps by providing Wait Events
Focus on Response Time

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Wait Time Tables (ASE 12.5.0.3+)
http://froebe.net/blog/wp-content/uploads/2013/09/ASE-15.7-Monitoring-Tables-Diagram-Sybase-Inc_.pdf

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© 2015 SOLARWINDS
Response Time Query
» MDA Query Example

SELECT mP.Login, mP.DBName, mPL.ClientHost,
 RTRIM(mPL.Application), mPP.ObjectName,
 mP.WaitEventID, mPS.SQLText, mPS.BatchID,
 mPS.LineNumber, mP.LineNumber CurrentLineNumber
FROM monProcessSQLText mPS,
 monProcessLookup mPL,
 (monProcess mPLEFT OUTER JOIN
 monProcessProcedures mPP
 ON mP.SPID=mPP.SPID AND mP.KPID=mPP.KPID)
WHERE mP.SPID=mPL.SPID AND mP.KPID=mPL.KPID
AND mP.SPID=mPS.SPID AND mP.KPID=mPS.KPID
AND coalesce(mP.FamilyID,0) = 0 AND mP.BatchID > 0


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© 2015 SOLARWINDS
Top Wait Events At Server Level
select WaitEventID,
 convert(numeric(16,0),Waits) as "Waits",
 convert(numeric(16,0),WaitTime) as "WaitTime"
 into #waits1
 from monSysWaits
go
select Description,
 convert(int,sum(w.Waits)) as "Count",
 convert(int,sum(w.WaitTime)/1000) as "Seconds"
 from #waits1 w,
 monWaitEventInfo ei
 where w.WaitEventID = ei.WaitEventID
 group by Description
 order by 3 desc

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© 2015 SOLARWINDS
RTA – Proactive
Refreshed on :08/12/14 02:35:18 PM

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© 2015 SOLARWINDS
Users Complain: 10am – 12pm Slow
Top 15 SQL Statements | GIBSON:5000 | August 8, 2014 to August 12, 2014
Daily Time Range: 10:00 AM-12:00 PM

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© 2015 SOLARWINDS
RTA – Firefighting
Day: Tuesday – August 10,2014 Refreshed on: 08/12/2014 05:09:57 PM

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© 2015 SOLARWINDS
RTA - Blocking Issue
Day: Tuesday – August 10,2014 Refreshed on: 08/12/2014 05:02:02
SQL Statements Executed by Blocking Session 125 while Holding the Lock | GIBSON:5000
August 10, 2014 – 12:00PM to 4:00PM

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RTA – Long Term Trends
Top 15 SQL Statements | GIBSON:5000 | July 31,2014 to August 24, 2014

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RTA – Current Problem
Refreshed on: 08/12/2014 05:34:42 PM

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RTA – Current Problem
Refreshed on: 08/12/2014 05:39:06 PM

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Identify End-to-END Time
Accurate End-to-End Response Time Analysis

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© 2015 SOLARWINDS
Get Table & COLUMN Info
» Understand objects in execution plans
§ Table Definitions & Segment sizes
• Is it a View – get underlying definition
• Number of Rows / Partitioning

§ Examine Columns in Where Clause
• Cardinality of columns
• Data Skew / Histograms
§
§ Statistic Gathering
• Tip: Out-of-date statistics can impact performance

» Understand expensive data access targets
§ TableScans versus IndexScans
§
» Know the indexes – if multi-column, order is important

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© 2015 SOLARWINDS
Get The Execution Plan
» SET SHOWPLAN ON
§ set statistics io, time on
 simulate, subquerycache, plancost
§ set noexec on
 http://help.sap.com/Download/Multimedia/ASE_16.0/ptstats.pdf
» Interactive SQL (Sybase iAnyWhere or SAP dbisql)
§ Plan Viewer– must execute query
§ Details, XML & Advanced (abstract & missing)
» Abstract Plans-sysqueryplans /sysquerymetrics
§ sp_configure'enable metrics capture', 1
» Application Tracing (v15.0.2+)
§ set tracefile "<file-path>" for <spid>
§ Works when you know a problem will occur


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© 2015 SOLARWINDS
Get The Execution Plan - tracing
set statistics plancost on
set tracefile ’/db1/trc/trc32.tx’ for 32

© 2015 SOLARWINDS
Examine the Execution Plan
» Find Expensive Operators
§ Examine cost of each step
§ Look for full table or index scans

» Review the ‘restrict’ & ‘sort’ operators
§ Are the ‘restrict’ steps early in plan?
§ Large sorts that don’t fit into memory
§ Look for Implicit conversions when using parmeters
» Review JOIN operators
§ Nested loop
• effective when there is a useful index available
§ Merge Join
• good when most of the rows must be processed and they are
already sorted by join keys
§ Hash Join
• good when most of the rows from source sets are processed
§ Nary Nested Loop
• More efficient when you have more than 2 nested loops
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© 2015 SOLARWINDS
Engineer out the Stupid

» Look for Performance Inhibitors

§ Cursor or row by row processing

§ Parallel processing - OLAP vs OLTP
§
§ Hard-coded Hints
§
§ Nested views that use link servers

§ Abuse of Wild Cards (*) or No Where Clause
§
§ Code-based SQL Generators (e.g. Hibernate)
§
§ Non-SARG-able / Scalar Functions
• Select… where upper(first_name) = ‘JANIS’

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Execution Plan Example

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© 2015 SOLARWINDS
» Who registered yesterday for SQL Tuning

SELECT s.FNAME, s.LNAME, r.SIGNUP_DATE
FROM STUDENT s
INNER JOIN REGISTRATION r ON s.STUDENT_ID =
r.STUDENT_ID
INNER JOIN CLASS c ON r.CLASS_ID = c.CLASS_ID
WHERE c.NAME = 'SQL TUNING'
AND r.SIGNUP_DATE BETWEEN @BegDate AND
@EndDate
AND r.CANCELLED = 'N‘

Case Study 1

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Relationship Diagram for Tables in Query

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Execution Plan

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SQL Diagramming
» Great Book “SQL Tuning” by Dan Tow
§ Great book that teaches SQL Diagramming
§ http://www.singingsql.com
REGISTRATION
STUDENT CLASS
5
1
3
0
1
.
05
.
002
select count(1) from registration where cancelled = 'N'
and signup_date between '2015-01-29 00:00' and '2015-01-30 00:00'
4,141 / 79,999 = 0.05
select count(1) from class where name = 'SQL TUNING'
2 / 1,000 = .002

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© 2015 SOLARWINDS
create index cl_name ON CLASS(NAME)
New Execution Plan
Why would an TableScan still occur on REGISTRATION?

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Database Diagram

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create index reg_alt on REGISTRATION(CLASS_ID)
New Execution Plan

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© 2015 SOLARWINDS
create index reg_alt ON REGISTRATION
 (CLASS_ID, SIGNUP_DATE,CANCELLED)
Better Execution Plan

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© 2015 SOLARWINDS
» Lookup Shipment Status for caller

SELECT o.OrderID, c.FirstName, c.LastName,
 p.ProductID, p.Description,
 s.ActualShipDate, s.ShipStatus, s.ExpectedShipDate
FROM SalesOrder o
INNER JOIN OrderItem i ON i.OrderID = o.OrderID
INNER JOIN Customer c ON c.CustomerID = o.CustomerID
INNER JOIN Shipment s ON s.ShipmentID = i.ShipmentID
LEFT OUTER JOIN Product p ON p.ProductID = i.ProductID
LEFT OUTER JOIN Address a ON a.AddressID = s.AddressID
WHERE c.LastName LIKE ISNULL('Griffin','') + '%'
AND o.OrderDate >= DATEADD(day, -30,getdate())
AND s.ShipStatus <> 'C'
order by LastName

Case Study 2

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Database Diagram

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Execution Plan

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SQL Diagramming
o .02
.005
SELECT COUNT(1)*1.0/(SELECT COUNT(1) FROM Customer) FROM Customer
WHERE LastName LIKE 'Griffin%'
0.005
SELECT COUNT(1)*1.0/(SELECT COUNT(1) FROM [SalesOrder]) FROM
[SalesOrder] WHERE OrderDate >= DATEADD(day, -30, GETDATE())
.02
SELECT COUNT(1)*1.0/(SELECT COUNT(1) FROM [Shipment]) FROM [Shipment]
WHERE ShipStatus <> 'C'
.04
i c
ps
a
.
04

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© 2015 SOLARWINDS
New Execution Plan
» CREATE INDEX CUST_LASTNAME ON Customer(LastName)
»

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© 2015 SOLARWINDS
Data Skew Problems
» Only 4% of rows are <> ‘C’
» How about changing the query?
§ AND s.ShipStatus = 'I'
» Add Histogram on ShipStatus

SELECT ShipStatus, COUNT(1)Cnt
FROM [Shipment]
GROUP BY ShipStatus

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© 2015 SOLARWINDS
Case Study 3
» Current paychecks for specific employees

 SELECT e.empno, e.first_name, e.last_name,
 d.dname,l.street_address,l.city,
 l.state_province, l.postal_code
 FROM emp e
 INNER JOIN dept d ON e.deptno = d.deptno
 INNER JOIN loc l ON d.location_id = l.location_id
 WHERE (e.first_name = @first or e.last_name = @last)
 AND EXISTS (
 SELECT 1
 FROM wage_pmt w
 WHERE w.empno = e.empno
 AND w.pay_date>= DATEADD(day,-31,GETDATE())
 )

» Execution Stats – 6681 Logical I/O
» Average Execution - 15.012 seconds
» Resource - 99% CPU


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© 2015 SOLARWINDS
SQL Diagramming
select count(1) from wage_pmt
where pay_date >= DATEADD(day,-31,GETDATE())
109568 / 1027192 = .106
select max(cnt), min(cnt)
from (select last_name, count(1) cnt from emp group by last_name)
128 / 6848 = .018 – max
64 /= 6848 = .009 – min
emp
dept
wage_pmt
1000
1
150
1
.02
.10
loc
1
9
.009

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© 2015 SOLARWINDS
New Execution Plan
» create index IX_EMP_LN on emp(last_name)
» Create index IX_EMP_FN on emp(first_name

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© 2015 SOLARWINDS
Better Execution Plan
» create index IX_WAGE_EMPNO on wage_pmt(empno)
»
»
»
»
»
»
»
»
»
»
»



Execution time = 0.546 seconds

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© 2015 SOLARWINDS
Best execution Plan
Covered Index:
create index IX_EMP on emp(empno, last_name, first_name, deptno)
Time: 0.066 secs

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© 2015 SOLARWINDS
Monitor
» Monitor the improvement
§ Be able to prove that tuning made a difference
§ Take new metric measurements
§ Compare them to initial readings
§ Brag about the improvements – no one else will
» Monitor for next tuning opportunity
§ Tuning is iterative
§ There is always room for improvement
§ Make sure you tune things that make a difference
» Shameless Product Pitch - DPA

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© 2015 SOLARWINDS
Improved Performance
Average Wait Time per Execution for SQL Statement Current Paychecks by Name | GIBSON:5000
March 15, 2015
Daily Time Range: 5:00 PM-12:00 AM
Logical Reads = 1198 Execution Time = .066 seconds

© 2015 SOLARWINDS
Takeaway Points
» Tuning Queries gives more “bang for the buck”
» Make sure you are tuning the correct query
» Use Wait Events and Response Time Analysis
§ Locking problems may not be a Query Tuning issue
§ Wait Events tell you where to start
» Use “Execution or Query Plans”
» SQL Diagramming - “Get it right the First Time”
§ Query Tuner Tools can mislead you
» Monitor for next tuning opportunity

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