application performance troubleshooting 1x1 - part 2 - noch mehr schweine und schlangen

Noch mehr Schweine und Schlangen

Neue Tipps zum Performance Troubleshooting

Rainer SchuppeAppDynamics GmbH

Rainer

+Rainer Schuppe

Customer SupportSystem Support / OpsConsultant / DevSolution ArchitectSales Engineer

Where to start? What to do? Who to blame?

Tooling

Usecases - Symptoms & Diagnostics

Reprise: Why care about performance

failure everyday

BIG DATA

Hadoop Cassandra MongoDB

Coherence

Memcached

CLOUD

Amazon EC2 Windows Azure

VMWare

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Weblogic Oracle

.NET

MQ

ATG, Vignette, Sharepoint

SQL Server

JBoss

Tomcat

Tomcat

Mule, Tibco, AG

ESB

.NET

Tomcat

SOA

WEB 2.0

Browser Logic AJAX Web Frameworks

Release 3.4 Release 3.5 Release 3.6 Release 4.0

AGILE






Complexity increases

Generic Troubleshooting Process

DiagnosisTriageAlert / Detection

Fix Solution Finding

Rootcause Detection

Move on with life

Data / Information

• Determine who needs to fix it

• Starts with overview and comparison to „normal“ performance

• First level task (Operators)

• First indication of problem type

• Works best with transactional data

Triage

BIG DATA


Coherence

Memcached

CLOUD


VMWare

Login Search Flight


Weblogic Oracle

.NET

MQ


SQL Server

JBoss

Tomcat

Tomcat

Mule, Tibco, AG

ESB

.NET

Tomcat

SOA

WEB 2.0



AGILE






100 ms

50 ms

45,3 ms50 ms

10 ms60 ms

150 ms160 ms

145 ms

145 ms145 ms

145 ms145 ms

10 ms

250 ms

300 ms300 ms

310 ms

15 ms

1 ms 250 ms

BIG DATA


Coherence

Memcached

CLOUD


VMWare

Login Search Flight


Weblogic Oracle

.NET

MQ


SQL Server

JBoss

Tomcat

Tomcat

Mule, Tibco, AG

ESB

.NET

Tomcat

SOA

WEB 2.0



AGILE






�Problem

Key:

= bad

= not bad

• Determine the root of the problem

• Uses first level information to narrow scope

• Needs specialists

• Lots of data / information needed in real time and historical

• Usually needs iterations

• More than 1 tool used in the process

Diagnose

• Confirm the rootcause after you diagnosed it

• Document it

• Recreate it in test if possible

• Needs the same data as diagnostics

Rootcause detection

• Find a solution for the problem

• Architect a workaround or a fix

• Again needs the diagnostic data

• Run some test runs with different options - check them in realtime

• Confirm the idea for the fix

• May be a different team then the trouble shooters

Solution finding

• Intuition

• Experience

• Tools

• Logfiles

• Communication

How to get the data?

© val-j - sxc.hu

Tooling

Concurrency Data Volume Resource

3 Key Things Impact Performance & Availability

Development

Data Volume ResourceConcurrency

QA/Test


Production


Why do things crash and slow down?

LoggingARMBytecode Instrumentation / AspectsSamplingJMX (Java Management Extensions)PMI (IBM WebSphere specific)

Technologies DevTestProd

Pros:

• Anything can be logged

• Easy to implement (if you have the sourcecode)Cons:

• Only what the developer thinks is needed

• I/O heavy

• No chance for change if you don‘t own the source code

• Lots of files - no TX context usually

• How to correlate in distributed environment?

Logfiles DevTestProd

Logging can be the source of problems itselfe.g. Log4Net

• Synchronous local file system access

• The more you log the longer it takes

• Can only be diagnosed with another tool

Logfiles - 2 DevTestProd

Bringing down production with Logging

Microsoft.Win32.Win32Native:SetFilePointerWin32

Pros:

• No config needed

• Lots of data - lots of detailCons:

• Lots of data - not suitable for production

• Needs experience

• No transactional concept / context

Profiler DevTest

Profiler

Pros:

• Built into most application servers

• JConsole is part of the JDK

• Easy to implement MBeansCons:

• No transaction context

• Not available for 3rd party

• No historical data

• Usually one JVM only

JMX (and similar) DevTestProd

JMX (and similar)

Pros:

• They are free

• Transaction context (most of them)

• Quick setup (the commercial ones)Cons:

• Usually functionally constrained (commercial)

• Hard to configure (open source)

• Usually no history

APM tools (free) DevTestProd

Pros:

• Transactions, Historical data

• Distributed monitoring

• Deep dive diagnostics

• Production fitCons:

• Costly

• Choose the right one

APM tools (commercial) DevTestProd

There are just 2 sorts of issues

Diagnosis

• Constantly slow (Turtle)• Slowly, but constantly slower• Exponentially slower• Suddenly slower • Sporadically slow• Spontaneous crash

50 shades of slow (appx.)

• Sudden outage• Always erroneous• Sporadically Errormessages• Silent death / Bleed to death• Increasing errorrates• Wrong / meaningless error messages

The wonderful world of errors

• Look at symptoms• Eliminate definite non-causes• Prioritize the suspicions• Confirm suspicion / Eliminate suspicion

• Compare with „normal“• Gather more information• Define root cause and confirm it• Redo from Start

Diagnosis – Rough Flow

• Bad Coding• Too much load• Backend not reachable / slow• Conflicting resources• Memory Leak• Resource Leak• Network / Hardware Problem

Possible Causes(in no particular order)

Example

• Resource Contention

• Exceptions

• Load Issues

Symptoms

• User complaints - Slow performance

• Exceptions appeared in logfiles

• Alerts for ops triggered

0

2.750

5.500

8.250

11.000

time 10:01 10:03 10:05 10:07 10:09 10:11 10:13 10:15 10:17 10:19 10:21 10:23 10:25 10:27 10:29

Average Response Time (ms)

Connection Timeout

0

3,75

7,50

11,25

15,00

10:00 10:02 10:04 10:06 10:08 10:10 10:12 10:14 10:16 10:18 10:20 10:22 10:24 10:26 10:28 10:30

Connection Pool vs. Errors

org.hibernate.util.JDBCExceptionReporter : Cannot get a connection, pool error Timeout waiting for idle object

1st Diagnosis

• OK - We do have a problem

• Database connection pool depleted

• Waiting times stacking

• 10 minutes until errors appear in logs

• But WHY?

Open Questions

• Which database?

• Which DB Pool?

• Transaction specific?

• Problem on DB?

• What is the load?

How to find data

• Check log for DB connection info

• Ask architect which TX are using this pool

• Use JMX to check pool metrics

• Check load info (if available)

Who else is using the DB?

Ask your architect!

Who else is using the DB?Ask your tool!

What did we find out?

• There were other TX‘s using the DB

• It was just a single transaction with the DB problem

• This TX had a specific DB connection pool

OK - Now let‘s check the load

Trx/min

Avg RTPool LimitPool Usage

Trx Stalls

Load and DB connections

Why the sudden load increase?

Root Cause

• Loadbalancer was not working correctly

• DB connection pool size was not appropriate for this load

• Many different pools made this config necessary

BIG DATA


Coherence

Memcached

CLOUD


VMWare

Login Search Flight


Weblogic Oracle

.NET

MQ


SQL Server

JBoss

Tomcat

Tomcat

Mule, Tibco, AG

ESB

.NET

Tomcat

SOA

WEB 2.0



AGILE






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Browser(s)

NetworkNativeMobile

App

MOBILE

The missing link

Enduser Monitoring helps

Complete the picture

A quick look at memory

and resources

Linear Memory Leak• Symptoms:

• OOM (Out of memory error)• Slow over time with spikes• Sawtooth with upward trend

• Causes• Objects added to linear structures without being removed

(e.g., linked lists)• Other API misuse (addListener() without corresponding

removeListener(), etc.)

• Aggregate detection: • linear growth in heap utilization• GC time growth

• Specific detection:• Figure out object types being leaked• Verbose GC• Find related APIs and search code for misuse

Linear Memory Leak

• Challenges• References - many small objects are referenced in one

collection• Death by 1000 cuts (Papierschnitte)

• Specific detection:• Figure out object types being leaked• Verbose GC• Find related APIs and search code for misuse

Linear Memory Leak

• Heap Dump Comparison• Needs at least 2 dumps• Stops the JVM• Can take several minutes each• Creates tons of data• Finds the object, not the code responsible for the leak

• Profiler• High overhead - not for production• Lots of data

• APM Solution• Collection based algorithm – finds only collection leaks• Instance counting• Trade off between low overhead and usefulness of data

Specific detection

• Causes:• Objects added to most data structures

without being removed (e.g., vectors, hashtables)

• Other API misuse (as Linear Leak)• Aggregate detection:

• exponential growth in heap• Specific detection:

• Same as Linear Leak

Exponential Memory Leak

• Causes:• API misuse of Java objects with resource-

style lifecycle (create->use->destroy)• Aggregate detection:

• Slow over time• Growth in heap (if you’re lucky)

• Specific detection:• Audit code for API misuses• Object instance tracking

Resource Leak

Resource conflict (block / wait)

• Causes:• Overcautious data integrity strategy• Synchronising is always good

• Aggregate detection: • Stalled threads• High thread usage - low CPU usage

• Specific detection:• Thread dumps as needed• Stack traces / graphs• CPU block / wait timing measurement

Resource conflict / blocking

Fragen ?

• Causes:• Infinite loop in code

• Aggregate detection: • Stalled threads• Permanently high usage of CPU / threads

• Specific detection:• Thread dumps as needed• Stack traces / graphs

Bad Coding: Infinite Loop

• Causes:• Idiot with a “Learn Java in 24 Hours” book

• Aggregate Detection: • Response time measurement• Aggregate CPU utilization

• Specific Detection: • Detailed CPU utilization

• Typical Cure:• Cache of data or of performed calculations

Bad Coding: CPU-Bound Component

• Causes:• Poorly implemented data bridge layer, or simply

too many of them• DB -> XML -> XSLT -> More XML -> “Custom

Data Management Layer” -> Consumer

• Aggregate Detection: • Response time measurements

• Specific Detection: • Call graphs - Call trace (stack trace not

enough)• Ask for a design or architecture document

Layer-itis

• Causes:• Hibernate fixes everything• Massive SQL statements (length and amount)• Wrong data strategy

• Aggregate Detection: • Response time measurements• DB time measurements

• Specific Detection: • Call stacks / snapshots

O/R Mapper misuse

Caching issues

• Causes: • Continual attempts to call backend +

unavailable backend• Aggregate Detection / Specific Detection:

• Response time measurement• Backend detection - measurement (time

& # of calls)• Stalled TX count• Exceptions • Busy thread count

The Unending Retry

don’t forget about thrown exceptions

• Causes: • Fundamental error in threading / lock

acquisition strategy• Aggregate Detection:

• Stalled threads / permanently high concurrent usage

• Specific Detection: • Deadlock detection in JVM• Thread dumps• Busy thread count

Threading: Deadlock / Livelock

Found one Java-level deadlock:============================="Thread-2": waiting to lock monitor 102054308 (object 7f3113800, a java.lang.Object), which is held by "Thread-1""Thread-1": waiting to lock monitor 1020348b8 (object 7f3113810, a java.lang.Object), which is held by "Thread-2" Java stack information for the threads listed above:==================================================="Thread-2": at DeadlockTest$2.run(DeadlockTest.java:42) - waiting to lock <7f3113800> (a java.lang.Object) - locked <7f3113810> (a java.lang.Object) at java.lang.Thread.run(Thread.java:680)"Thread-1": at DeadlockTest$1.run(DeadlockTest.java:26) - waiting to lock <7f3113810> (a java.lang.Object) - locked <7f3113800> (a java.lang.Object) at java.lang.Thread.run(Thread.java:680)

Threading: Deadlock

• Causes: • Many threads bottlenecked waiting for

one lock• Aggregate Detection:

• Stalled threads / high concurrent usage• Exponential slowness• Low CPU usage

• Specific Detection: • Request response time monitoring• CPU block / wait timing

Threading: Chokepoint

Threading: Chokepoint

• Causes: • Overusage of internal resource (threads,

database connections, etc.)• Underallocation of same

• Aggregate Detection:• Stalled threads / high concurrent usage• Call rate and average response time of internal

resource• Specific Detection:

• Also compare with methods from Resource Leak, External Bottleneck, and Overusage of External System

Internal Resource Bottleneck

• Causes: • External system (database, authentication server) is

slow• Compare with Overusage of external system

• Aggregate Detection:• Response time on backend calls• Exceptions

• Specific Detection: • Callgraphs• Specific monitoring on those backends

External Bottleneck

Commit happy

Trx/min

Avg RTPool LimitPool Usage

Trx Stalls

Production Ground to a halt for 2 hours And again the next day

• Causes: • Poor design or tuning of interaction with backend system

(e.g., join between two million-row tables for each user logon)

• O/R mapper misconfiguration• Aggregate Detection:

• Response time measurement• Specific Detection:

• Timing on backend systems• Also need tools for those backend systems

Overusage of External System

excessive database access

query too much data

Copyright © 2013 AppDynamics. All rights reserved.

Applications will become more Complex and Change Faster

86

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Browser(s)

Network

Oracle.NET Service

NativeMobile

App

MOBILE

CDN

WEB 2.0

AGILERelease 1.1Release 1.2Release 1.23Release 1.5

MonolithicDistributed

ApacheESB/MQ

JBoss Service

WebLogic Service

PHP Service

3rd Party Web Service

SOA

Tomcat Service

VMWarePrivate

JBoss Service

Amazon EC2Public

Hadoop

CLOUD

Sybase

MySQL

SQL Server

PostgreSQL

BIG DATA

Cassandra

Memcached

NOSQL

DB2

• One interesting problem occurs when the size of transactions with backend systems needs to be tuned

• Can be intertwined with / exacerbated by Layer-itis and Overusage of External System

Many small requests

System constantly wastes resources

dispatching / unmarshalling many xactions and results

“Death by a thousand cuts”

One HUGE request

System periodically slows to a crawl as many resources get

thrown at large chunk of work

“Pig in a Python”

“Just Right”

application performance troubleshooting 1x1 - part 2 - noch mehr schweine und schlangen

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