Search Tips   |   Advanced Search


Performance testing
IBM WebSphere Portal v8

  1. Overview
  2. Tasks
    1. Pre-test
    2. Tests
    3. Primary monitoring -
    4. Baseline Tuning
    5. Other Tuning
    6. Secondary monitoring
    7. Monitor IHS
    8. Monitor: Plug-in

  3. Analysis Tools
  4. Large pages
  5. Heap size tuning
  6. Session timeout
  7. Data Source Tuning
  8. Nursery Size
  9. Shared Class Cache Size
  10. MaxDirectMemorySize
  11. iprofileMemoryConsumptionLimit
  12. -XtlhPrefetch (Software Prefetch)
  13. iprofileMemoryConsumptionLimit
  14. Web Container Thread Pool Size
  15. ORB thread pool size
  16. Prepared statement cache size
  17. Avoid refetching static content after login
  18. VMM Context Pool
  19. VMM Caches
  20. Disable tagging and rating
  21. Mashup multipart tuning
  22. Enable Caching of ra:collecton URLs
  23. Disable Portlet Capability Filter
  24. Disable JCR Text Search
  25. Site Toolbar
  26. Get Rid of Cache Invalidations
  27. Disable Portlet Capability Filter
  28. Navigator Service
  29. Registry Service
  30. DRS replication modes
  31. Cache Manager
  32. Reduce the Size of unused caches
  33. Cache sizes
  34. Disable Search
  35. Disable personalization visibility rules
  36. Disable PZN Preprocessor
  37. Federated LDAP
  38. DB2 server
  39. IBM Tivoli Directory Server tuning
  40. IHS Tuning
  41. Network Tuning
  42. WCM Object Cache
  43. WCM Cache Manager Service
  44. Access Control Data Management Service
  45. Enable servlet and portlet caching
  46. Web Content Viewer Portlet Caching
  47. My Connections
  48. Web Services Clients
  49. Enable Subscriber only
  50. Monitor the WCM Content Viewer Portlet Cache
  51. Analyze garbage collection
  52. Execute concurrency test
  53. Execute throughput test
  54. Soak test
  55. Execute stress test

  56. Appendix
    1. nmon logging
    2. Memory and paging
    3. Think time
    4. Saturation
    5. Monitor JVM logs
    6. Get AIX statistics using nmon
    7. iostat
    8. svmon
    10. Monitor LDAP
    11. Tune dynamic cache with the cache monitor
    12. Web Server Plug-In Analyzer
    13. Monitor plug-in maximum number of connections
    14. Memory leaks
    15. Look for database contention
    16. Enable server-status
    17. Set cloneIds


Objectives include...

Estimated concurrent users is 30,000/hr...

System is running AIX v7.1 64-bit operating, using TDS LDAP and DB2 database.

Tuning is all about iterations and repeatability. After implementing various tuning changes below, we will execute use cases to determine if new setting has improved or degraded performance.

We will be using Rational Performance to generate load and reports.

For authenticated access, generate sufficient unique test user IDs exist in the LDAP. The portal artifacts are generally cached on a per-user basis. If the load simulation uses the same user ID for all tests, performance appears artificially high because the artifacts do not need to be loaded from the LDAP directory and the database.

Testing tasks


Task Resource Status Notes
Define objectives Team Complete
Create RPT tests Tester Complete
Enable PMI in WAS console Middleware Complete
Verify trace is disabled Middleware Complete
Install IBM Support Assistant Middleware Complete
Download nmon analyzer Middleware Complete
Enable Web Server Status Middleware Complete
Enable extended PMI Metrics Middleware Complete
Configure TPV log settings Middleware Complete
Install Cache Monitor Middleware Pending We will get baseline measurements first, then enable caching and install monitor.


Task Resource Status Notes
Before each test start logging Middleware Active
Concurrency Tester Active
Throughput Tester Pending
Soak Tester Pending 2-3 days. Look for memory leaks
Stress Tester Pending

Primary monitoring -

Task Resource Status Notes
Req/sec Middleware Active
Resp time Middleware Active
Advice Middleware Active
Data Source Middleware Active Pool Size, Percent Used
Prepared statement cache size Middleware Active PrepStmtCacheDiscardCount
Web Container Thread Pool size Middleware Active PercentMaxed, ActiveCount
ORB thread pool size Middleware Pending PercentMaxed

Baseline Tuning

We applied these tuning parameters after the first baseline metrics are collected.

Task Resource Status Notes
Enable servlet and portlet caching Middleware Complete
Large pages Middleware Complete
Heap size Middleware Complete Set initial and maximum heap size to 3584
Session timeout Middleware Complete
Disable tagging and rating Middleware Complete
Mashup multipart tuning Middleware Complete
Enable Caching of ra:collecton URLs Middleware Complete
Disable Portlet Capability Filter Middleware Complete
Disable JCR Text Search Middleware Complete
Enable Subscriber Only Middleware Complete
Navigator Service Middleware Complete
Registry Service Middleware Complete
Cache Manager Middleware Complete
WCM Object Cache Middleware Complete
Web Content Viewer Portlet Caching Middleware Complete
My Connections Middleware Complete
Web Services Clients Middleware Complete

Other Tuning

These tuning parameters will be set after analysis of test results

Task Resource Status Notes
Nursery size Middleware Pending
Shared class cache size Middleware Pending
MaxDirectMemorySize Middleware Pending
iprofileMemoryConsumptionLimit Middleware Pending
-XtlhPrefetch (Software Prefetch) Middleware Pending
com.ibm.websphere.alarmthreadmonitor.threshold.millis Middleware Pending
Avoid fefetching static content after login Middleware Pending
VMM context pool Middleware Pending
VMM caches Middleware Pending
Subscriber only Middleware Pending
Site toolbar Middleware Pending
Get rid of cache invalidations Middleware Pending
DRS replication modes Middleware Pending
Reduce the size of unused caches Middleware Pending
Cache sizes Middleware Pending
Disable search Middleware Pending
Disable personalization visibility rules Middleware Pending
Disable PZN Preprocessor Middleware Pending
Federated LDAP Middleware Pending
DB2 server OS Middleware Pending
DB2 Catalog statistics Middleware Pending
DB2 reorg Middleware Pending
DB2 buffer pools and self tuning memory manager Middleware Pending
DB2 self tuning memory Middleware Pending
DB2 DFT_QUERYOPT Middleware Pending
DB2 MAX_JOINS_IN_UNION Middleware Pending
DB2 transaction log Middleware Pending
IHS Tuning Middleware Pending
Enable in-memory caching in IHS Middleware Pending
Add cache headers in IHS Middleware Pending
Content compression on the HTTP server Middleware Pending
WebSphere HTTP plugin tuning Middleware Pending
Network Tuning Middleware Pending
WCM Cache Manager Service Middleware Pending
Access Control Data Management Service Middleware Pending

Secondary monitoring

nmon Middleware Pending NA_UserGuide v34.doc, nmon_analyzer.xls
WCM Content Viewer portlet cache Middleware Pending
Analyze garbage collection Middleware Pending
Monitor cache Middleware Pending  
Monitor JVM logs Middleware Pending No testing if fatal errors exist.
Monitor disk I/O Middleware Pending nohup iostat 15 240 > /tmp/iostat.txt &
Monitor AIX physical memory usage Middleware Pending vmstat 10 360 > /tmp/vmstat.txt 2>1 &
Monitor AIX virtual memory Middleware Pending
Monitor TCP_TIMEWAIT Middleware Pending
Monitor LDAP Admin Pending  
Monitor for core dumps Middleware Pending $WP_PROFILE/javacore.*
Thread and Monitor Dump Analyzer Middleware Optional
HeapAnalyzer Middleware Optional
Database Connection Pool Analyzer Middleware Optional
Look for database contention Middleware Optional
Dynamic Cache Statistics Collector Middleware Optional
Set cloneIds Middleware Optional Send traffic to a particular server for debugging.
Portlet load monitoring Middleware Optional

Monitor IHS

Task Resource Status Notes
Monitor CPU, I/O, memory and paging Middleware Pending
Monitor TCP/IP Middleware Pending netstat
Web server access_log Middleware Pending grep DD/Mon access_log | grep -v 200 | grep -v 302
Monitor virtual memory
Pending svmon

Monitor: Plug-in

Task Resource Status Notes
Monitor plug-in maximum number of connections Middleware Pending
Monitor for marked down servers Middleware Pending
Web Server Plug-In Analyzer Middleware Pending

Analysis Tools

yslow Analyze web pages
Extended Cache Monitor A WAS utility that provides statistics (number of entries used, cache hit ratio, etc.) on the DynaCache instances.
IBM Support Assistant (ISA) Workbench Various Java-based tools to analyze thread dumps and verbose garbage collection (GC) output, specifically, "IBM Thread and Monitor Dump Analyzer for Java" and "IBM Monitoring and Diagnostic Tools for Java - Garbage Collection and Memory Visualizer".
Cache Viewer Portlet A WebSphere Portal Server portlet (provided by IBM Support) that provides additional information about the internal caches used by Portal.
Fiddler A free tool that breaks down the response timeline from the end-user's perspective.
Page & Portlet Render Timers Displays server-side page and portlet render times for a given page, making it easy to identify the "long poles" in terms of the slowest portlets on the page.
Portlet Load Monitoring Filter Limit the number of concurrent requests and the average response time allowed for JSR 168 or JSR 286 portlets. If a portlet exceeds either the defined maximum number of concurrent requests, or the average response time, then Portlet Load Monitoring no longer allows further requests by the portlet. Instead, the portal renders the portlet as unavailable, and the portlet code is no longer called for further requests. This way, the Portal installation is protected from non-responsive portlets consuming an increasing number of threads.
Network Traces Use tcpdump or snoop to capture packets to and from the various servers.
Wireshark Analyze the network trace output files obtained from tcpdump and snoop.
J2EE Monitoring Tool Optional. ITCAM, CA's Introscope, and HP Diagnostics. Provides insight into where the time is spent from a JVM perspective.

Large pages

IBM has measured a 10% throughput improvement after enabling large pages. To set...

  1. Go to...

      Servers | Server Types | WebSphere appservers | WebSphere_Portal | Server Infrastructure: Java and Process Management | Process Definition | Java Virtual Machine

    ...and add -Xlp to the Generic JVM Arguments field

  2. Allocate 4GB of RAM as large pages of 16MB each.
    vmo -r \
        -o lgpg_regions=288 \
        -o lgpg_size=16777216 bosboot \
        -ad /dev/ipldevice reboot \

  3. Reboot the system

  4. After reboot, enable large page support.

      vmo -p -o v_pinshm=1

    This setting persists across reboots

    For portals running under a non-root user id, grant large page usage to that user...

      chuser capabilities=CAP_BYPASS_RAC_VMM,CAP_PROPAGATE portal_user

  5. Restart the Portal Server.

  6. Verify large pages are being used, run...

      vmstat -l 1 5

    ...and verify alp column (active large pages) is a non-zero value.

    wasadmin@prodline2wps1 / ->vmstat -l 1 5
     System configuration: lcpu=16 mem=16384MB ent=2.00
     kthr    memory              page              faults              cpu           large-page
    ----- ----------- ------------------------ ------------ ----------------------- -----------
     r  b   avm   fre  re  pi  po  fr   sr  cy  in   sy  cs us sy id wa    pc    ec   alp   flp
     1  0 1352547 2419795   0   0   0   0    0   0 109 6101 1702  5  1 94  0  0.19   9.5     0     0
     2  0 1352547 2419795   0   0   0   0    0   0 127 3655 1921  4  1 96  0  0.15   7.3     0     0
     1  0 1352547 2419795   0   0   0   0    0   0  79 6171 2028 10  1 89  0  0.28  13.8     0     0
     1  0 1352547 2419795   0   0   0   0    0   0  37 2985 1619  5  1 94  0  0.17   8.5     0     0
     5  0 1352547 2419795   0   0   0   0    0   0  64 2192 1820  1  1 99  0  0.05   2.5     0     0

  7. In portal, change to 64K page size for text (TEXTPSIZE) and stack (P1ACKPSIZE), and 16MB page size for data area (DATASIZE). Go to...

      Servers | Server Types | WebSphere appservers | WebSphere_Portal | Server Infrastructure: Java and Process Management | Process Definition | Environment Entries | New

    ...and set...

    Name LDR_CNTRL

Consider setting the memory management option "lru_file_repage" to 0 to ensure that computational memory is prioritized over file I/O buffers. This setting ensures that in situations where physical memory becomes limited, AIX will not swap out the Java processes in favor of file I/O buffers.

Heap size tuning

For IBM WebSphere Portal v8, running on AIX 64-bit, IBM recommends generational concurrent (gencon) garbage collection (enabled by default), with both Initial Heap Size and Maximum Heap Size set to 3584...

To force verbosegc log rolling...

To monitor, download logs...

...and run them through the IBM Pattern Modeling and Analysis Tool for Java Garbage Collector (part of IBM Support Assistant). Verify system is spending no more than 10% of time in garbage collection. If verbosegc.log indicates a large number of mark stack overflows (MSOs), to override the default, and provide additional mark stack space, set -Xgcthreads

To cause any Java object allocations greater than 1M to be recorded in native_stderr.log, go to...

...and set...

For Out of Memory errors that coincide with large object allocations when verbosegc.log shows plenty of available heap, fragmentation is the likely culprit. To fix, set -Xloratio0.1.

Session timeout

Set Timeout: 15 minutes

Data Source Tuning

Initial setting, go to...

...and set...

To monitor, in TPV, under the JDBC Connection Pools module, watch...

If Percent Used counter is consistently low, decreasing the number of connections in the pool. If high, increase. Lower settings for the connection pool size (10-30 connections) typically perform better than higher settings (more than 100). When threads are waiting on JDBC pool resources in WAS, you see the threads in a condition wait (CW) state in the WAS connection pool (J2C) classes. In this case, we might need to increase the pool size for this data source.

Nursery Size

IBM recommends increasing nursery size from the default. To set...

...and add -Xmn1024m to the Generic JVM Arguments

Shared Class Cache Size

By default, WAS enables a 50 MB class data sharing cache. For portal performance, IBM recommends setting to 120MB. To set...

  1. From the WAS console, go to...

      Servers | Server Types | WebSphere appservers | WebSphere_Portal | Server Infrastructure: Java and Process Management | Process Definition | Java Virtual Machine

    ...and add -Xscmx150m to the Generic JVM Arguments field

  2. Stop Portal server

  3. From command-line...

      cd AppServer root/java/bin
      ./java -Xshareclasses:name=webspherev80_%g,groupAccess,destroy

    To determine the name of the shareclasses we need to use for your platform and release. Search we need to use, search native_stderr.log and look for...


  4. Look for the message...

      JVMSHRC010I Shared cache "webspherev80xxx" is destroyed. Could not create the Java virtual machine.

  5. Start Portal Server

  6. Check cache size in use

      ./java -Xshareclasses:name=webspherev80_%g,groupAccess,printStats


MaxDirectMemorySize sets an upper bound on the amount of native memory allocated for DirectByteBuffers. This allows the system to avoid GC cleanup before growing the amount of physical memory allocated for these buffers.

To start, IBM recommends setting.

After increasing, monitor for paging and memory leaks.


The JIT compiler, by default, has a memory limit of 18MB. After reaching this limit, further JIT profiling stops and JIT compilation will not be optimal. IBM recommends setting...

To monitor...

  1. In the WAS console, go to...

      Server Types | WebSphere appservers | WebSphere_Portal | Server Infrastructure: Java and Process Management | Process Definition | Environment Entries

    ...and set...

    Name TR_PrintCompStats
    Value 1

  2. Go to...

      Server Types | WebSphere appservers | WebSphere_Portal | Server Infrastructure: Java and Process Management | Process Definition | Java Virtual Machine

    ...and add...


  3. Start Portal

  4. Run a performance measurement.

  5. Stop the Portal server; do not kill it

  6. Find the following in native_sderr.log.

    The text in red shows that 32MB is needed, so the limit needs to be set higher than that value for optimal performance.

      JIT memory usage
      _heapSegMemory=37827 KB
      _stackSegMemory=5184 KB
      _freeSegMemory=12385 KB
      _maxHeapSegMemory=173963 KB
      _maxStackSegMemory=960 KB
      _persistentSegMemory=82062 KB (IProfiler=33526 KB CHTable=4145 KB Assumptions=0 KB)
      _livePersistentSegMemory=82062 KB
      _livePersistentUsedMemory=82048 KB

-XtlhPrefetch (Software Prefetch)

This Generic JVM Argument prefetches bytes in the thread local heap ahead of the current allocation pointer during object allocation. It helps reduce the performance cost of subsequent allocations. Use it for AIX 64-bit JDK.


The portal log may contain the following warnings after a long period of heavy usage:

These messages are spurious and can be disabled by setting the following Generic JVM argument...

...where xxxxx is greater than the number of milliseconds mentioned in the error message, for example 40,000.

Web Container Thread Pool Size

In the TPV Thread Pools module watch...

If value PercentMaxed counter is consistently in the double digits, the web container can be a bottleneck, and we need to increase the number of threads. If the number of active threads are significantly lower than the number of threads in the pool, consider lowering the thread pool size.

To set...

Current settings:

ORB thread pool size

To monitor, in TPV, under the Thread Pools module, watch the PercentMaxed counter. If the value of this counter is consistently in the double digits, the ORB might be a bottleneck, and we need to increase the number of threads in the pool.

To configure the ORB thread pool size...

Prepared statement cache size

Watch the PrepStmtCacheDiscardCount counter of the JDBC Connection Pools module. Goal is to get lowest value possible.

To set cache size...

Current settings for JCR data source...

Avoid refetching static content after login

With the Portal 8.0 theme many resources do not change before and after logging in. These resources include the ra: collection URLs that are part of the theme. The same URL can safely be used for authenticated and unauthenticated users.

To set, check...

VMM Context Pool

Tune the VMM Context Pool to improve the performance of concurrent access to an LDAP server.

To set manually, edit...

...and change the contextPool settings to..

<config:contextPool enabled="true" initPoolSize="10" maxPoolSize="30" poolTimeOut="0" poolWaitTime="3000" prefPoolSize="30"/>

To monitor the number of active LDAP connections...

VMM Caches

To tune VMM search...

To set manually, edit...

If there are more than 6000 concurrent users, set...

<config:searchResultsCache cacheSize="12000" 
             cacheTimeOut="1200" enabled="true" searchResultSizeLimit="1000"/>

...and change the attributesCache settings to...

<config:attributesCache attributeSizeLimit="2000" cacheSize="12000" cacheTimeOut="1200" enabled="true"/>

Disable tagging and rating

Disabling tagging and rating can improve capacity by 3%.

To disable, go to...

...and set...

com.ibm.wps.cp.tagging.isTaggingEnabled Value false
com.ibm.wps.cp.rating.isRatingEnabled Value false

Mashup multipart tuning

The Portal 8.0 theme multipart downloading can be disabled to improve performance. Disabling may cause performance issues on client side aggregation themes from earlier Portal releases.

To disable, go to...

...and set...

cc.multipart.enabled false

Enable Caching of ra:collecton URLs

Go to...

...and set...

Disable Portlet Capability Filter

The portlet capability filter allows developers to get friendly error messages on pages if the theme profile in place for a page does not contain all the capabilities the portlets on the page require. In production this filter should be disabled.

To disable, go to...

...and set...

resourceaggregation.enableRuntimePortletCapabilitiesFilter false

Disable JCR Text Search

Go to...

...and delete all JCR collections.


...and set...

Site Toolbar

For rendering only sites where page management is not taking place locally, the Site Toolbar can be disabled.

To disable, go to...

...and set...

global.toolbar.enabled false
default.toolbar.enabled false

Get Rid of Cache Invalidations

To reduce unnecessary cache invalidations, go to...

...and set...

cache.dynamic.content.spot false

Run benchmarks to determine if the net effect is positive.

Disable Portlet Capability Filter

The portlet capability filter allows developers to get friendly error messages on pages if the theme profile in place for a page does not contain all the capabilities the portlets on the page require. In production this filter should be disabled.

To disable, go to...

...and set...

resourceaggregation.enableRuntimePortletCapabilitiesFilter false

Navigator Service

The navigator service content model is periodically reloaded by WebSphere Portal. In a production environment where new pages for unauthenticated users are rarely created, setting this reload time to an hour or more will give better performance.

This service also controls the HTTP cache-control headers which will be sent on unauthenticated pages. Increasing these cache lifetimes in a production environment can reduce load.

To increase, go to...

...and set...

public.expires 3600 Cache expiration time in seconds, for unauthenticated pages in browser caches and proxy caches.
public.reload 3600 (seconds) Reload interval, in seconds, for internal cache of the list of pages visible to unauthenticated users.
remote.cache.expiration 28800 (seconds) Cache expiration, in seconds, for caches outside of portal server for authenticated and unauthenticated pages

Registry Service

Portal uses the registry service to replicate information about resource types into memory. This replicated information is periodically reloaded. In a production environment, we expect this type of information changes very infrequently, so we set a very long reload time.

To increase replication interval, go to...

...and set...

default.interval 28800 Reload frequency for any object types not explicitly specified in the file.
bucket.transformation application.interval 28800 Reload frequency for transformation application definitions
bucket.transformation. interval 28800 Reload frequency for transformation definitions

DRS replication modes

WebSphere Portal requires the WAS Dynamic Cache Service (dynacache) and cache replication be enabled. The default mode of replication, PUSH, can cause performance problems in the portal environment. WebSphere Portal V6.0.1.5 and V6.1 change the default for all Portal and WCM dynacaches to be NOT SHARED instead of PUSH.

The use of NOT SHARED is strongly recommended for the vast majority of WebSphere Portal configurations. Three actions are needed to ensure that each Portal cluster member is fully optimized for WebSphere Portal V6.0.1.4 and earlier. The first is to set the replication mode to NOT SHARED using the WAS console for each cluster member. The second is to install Portal PK64925. The third is to install WMM PK62457 and add the parameter cachesSharingPolicy with a value of NOT_SHARED to the LDAP section of the wmm.xml files on each node. We can check out further details here.

WCM dynacaches also should be set to NOT SHARED with the exception of the "menu" cache. To complete this task, in the dmgr console, navigate to...

...and change each of the individual cache instances to a mode of NOT SHARED. As of the time of this writing, there are 11 instances for WebSphere Content Manager.

Finally, there are WAS changes that can further, although marginally, reduce the amount of network traffic between cluster members due to replication events. For each cluster member (either WCM or portal), navigate to...

...and define the following properties:

Cache Manager

To set, go to...

...and set...

cacheinstance.com.ibm.wps.ac.AccessControlUserContextCache.size 6000
cacheinstance.com.ibm.wps.ac.ChildResourcesCache.lifetime 28800
cacheinstance.com.ibm.wps.ac.CommonRolesCache.size 33000
cacheinstance.com.ibm.wps.ac.ExternalOIDCache.lifetime -1
cacheinstance.com.ibm.wps.ac.OwnedResourcesCache.enabled FALSE
cacheinstance.com.ibm.wps.ac.PermissionCollectionCache.lifetime -1
cacheinstance.com.ibm.wps.ac.ProtectedResourceCache.lifetime 14400
cacheinstance.com.ibm.wps.datastore.services.Identification.SerializedOidString.cache.size 5000
cacheinstance.com.ibm.wps.model.factory.UserSpecificModelCache.size 6000 This cache uses more memory per object than other caches. The value should be set based on the number of concurrent, logged in users. Larger values may require a larger JVM heap size. A value of 6,000 was used to support 6,000 virtual users in the benchmark measurements. Increasing to 7,000 in other measurements increased throughput but required a heap size of 4,608 MB.
cacheinstance.com.ibm.wps.pe.portletentity.lifetime 28800
cacheinstance.com.ibm.wps.pe.portletentity.size 45000 Monitor this cache and reduce size if not heavily used
cacheinstance.com.ibm.wps.policy.services.PolicyCacheManager.lifetime 43200
cacheinstance.com.ibm.wps.puma.CommonPrincipalCache.size 30000
cacheinstance.com.ibm.wps.puma.DN_OID_Cache.size 30000 Higher values can get a better hit ratio but not increase throughput
cacheinstance.com.ibm.wps.puma.OID_DN_Cache.size 5000
cacheinstance.com.ibm.wps.resolver.data.cache.DataSourceCache.size 8000
cacheinstance.com.ibm.wps.resolver.data.cache.FirstLevelDataSourceCache.size 2003
cacheinstance.com.ibm.wps.services.vpmapping.HostnameToVirtualPortalIDCache.lifetime -1
cacheinstance.com.ibm.wps.spa.parser.locale.FirstLevelLocalizationParserCache.size 1009
cacheinstance.com.ibm.wps.spa.parser.skin.FirstLevelSkinParserCache.size 1009
cacheinstance.com.ibm.wps.spa.parser.theme.FirstLevelThemeParserCache.size 2003
cacheinstance.com.lotus.cs.services.UserEnvironment.size 4500 Tune this if live names support is used
cacheinstance.DigestCache.cache.size 45000

Reduce the Size of unused caches

To cut down on Portal memory usage, for unused or lightly used caches which are over 5000 entries, IBM recommends cutting their size in half. To determine if caches are unused, monitor the caches when portal is running a representative workload mix under a high load. The caches that are unused under those circumstances can be safely reduced in size.

Cache sizes

When changing the size of a cache, using a prime number as the size may give slightly better results. This has been verified for first level caches.

Disable Search

If search is not being used, it can be disabled to improve performance...

In the WebSphere Portal Administration Page...

These include the JCR collection and the “Default Search Collection”. These search collections do not include the WCM Authoring search indexer.

Disable personalization visibility rules

A large performance gain can be achieved by disabling the processing of personalization rules for pages and portlets.

To reenable...

Disable PZN Preprocessor

We are not using personalization rules, so we turn off the PZN preprocessor.


...and set...

Federated LDAP

Throughput can be improved by disabling nested group caches.

DB2 server

Set the filesystem which will hold the Portal databases to be an Enhanced Journal File System (JFS2) because a large file system is limited to 64GB.

Turn on concurrent I/O (CIO) for Enhanced Journal File System as this improves performance.

Increase AIX maximum number of processes per user to 4096. The default 500 processes per user is too low for database server.

DB2 catalog statistics

Catalog statistics should be recomputed periodically...

The first command creates file runstats.db2, which contains all of the runstats commands for all of the tables. The second command uses the file to run these commands. Run on each Portal database. Run on the JCR and release database data population after significant content population or changes.

To determine which tables might benefit from reorganization

To reorganize a table based upon its primary key:

Set database parameter MaxAppls to be greater than the maximum JDBC pools size for all portal data sources.

Use System Managed Storage (SMS) for temporary table spaces. This saves time in buffer writes and improves disk utilization.

DB2 reorg

For each database, to improve performance...

Look in the reorg column for entries marked with a star or asterisk * in the file xyz.out.

For each line with *, note the tablename and run the following command for each tablename:

After you have run the reorg command for each tablenames:

The file db2rbind.out is created when there is an error.

DB2 buffer pools

DB2 9.7 supports automatic tuning of bufferpool sizes. IBM recommends enabling this setting on the JCR database.

For initial bufferpool size of JCR database...

Other Portal database domains can use the default settings for DB2.

Fix out of memory errors in DB2

DB2 self tuning memory

IBM recommends enabling the DB2 9.7 self tuning memory feature of DB2 9.7. Recommended settings...


This parameter controls how much optimization to use when compiling queries. The default value in DB 9.7 is 5. However, the Portal Info Center recommends setting this value to 2 for the JCR database. Performance benchmarks see the best results with this value as well.

To set...


The DB2 registry variable MAX_JOINS_IN_UNION limits the number of joins the Query Rewrite component of DB2 is allowed to push down into a UNION operator. Some JCR queries result in a large number of unions with the default settings. Changing this setting limits the number of unions DB2 we use in a query, potentially reducing processing time. This setting should be changed when query preparation times are high on queries with multiple joins.

In general, the more complex the SQL statement and the more complex the database, the more possible ways there are for Query Rewrite to rewrite the query and the more possible query plans there are for DB2 to evaluate. Setting MAX_JOINS_IN_UNION in this case limits the number of possible query plans that DB2 considers, and hence the amount of processing that is done to prepare the query while still generating a good query plan.

Note that setting this value has no effect at query optimization level 1 (see previous section for query optimization). Performance benchmarks show the best performance with the default setting. No change was observed using a setting of 30. However, data population with deep content hierarchies may benefit from changing this parameter to a higher value.

To set...

To return to the default setting...

Transaction log

For large content population sizes, IBM recommends increasing the transaction log size as well manually size the sortheap. Using an automatic setting caused a problem where the DB2 STMM would decrease the size of the sortheap too drastically and cause poor performance on certain JCR queries. The following settings were appropriate for up to 1 million content items in their testing. IBM recommends monitoring sortheap during a peak workload and tuning appropriately.

IBM Tivoli Directory Server tuning

For a portal data population of 100,000 users and 15,000 groups, IBM recommends the following settings...

ibm-slapdACLCacheSize 25000
ibm-slapdEntryCacheSize 100000
ibm-slapdFilterCacheSize 1000
ibm-slapdFilterCacheBypassLimit 100
ibm-slapdDbConnections 15
ibm-slapdGroupMembersCacheSize 15000
ibm-slapdGroupMembersCacheBypassLimit 100

IHS Tuning

For ST initial tuning, in...

...we are setting...

ThreadLimit 1000
ServerLimit 3 This value should be set to MaxClients / ThreadsPerChild.
StartServers 3
MaxClients 3000
MinSpareThreads 30
MaxSpareThreads 3000 Should be set to the same values as MaxClients.
ThreadsPerChild 1000
MaxRequestsPerChild 0
KeepAliveTimeout 5 This value is less than the think time we define in our load measurement scripts to ensure that testing is conservative - forcing virtual users to open new TCP connections for each new page view. In a live environment, it can be helpful to increase the KeepAliveTimeout. However, a higher timeout value can increase contention for HTTP server processes, so if we are running out of HTTP processes, decrease this value.
MaxKeepAliveRequests 0 Selecting 0 lets an unlimited number of requests calls to a single TCP connection.

To increase MaxClients, either the ServerLimit or ThreadLimit or both must also be increased. Unix based HTTP servers cache information in each process. So, when increasing settings, raise the number of threads first before raising the number of processes. This reduces the overall memory needed by the HTTP server.

The values used in the performance benchmarks are set to ensure there are sufficient resources to handle hundreds of hits per second from thousands of users. For smaller environments, lower values which use less server resources can be used. For example, to serve several hundred hits per second, ServerLimit could be set to 5, ThreadLimit could be set to 160 and MaxClients could be set to 800.

To monitor the number of running, and available Web server processes, enable the server-status module.

The web server must allow for sufficient concurrent requests to make full use of the application server infrastructure. The web server also acts as a filter and keeps users waiting in the network to avoid flooding the servers if more requests than the system can handle are incoming.

To set MaxClients...

...where for TEST env...

MaxClients is currently set to 600.

ThreadLimit         25
ServerLimit         64
StartServers         1
MaxClients         600
MinSpareThreads     25
MaxSpareThreads     75
ThreadsPerChild     25
MaxRequestsPerChild  0

Enable disk caching (mod_disk_cache) in IHS

Recommendation: Choose mod_disk_cache over mod_mem_cache. To render a Portal page, especially in later version of Portal, requires a lot of "statics". These statics are images, CSS files, JavaScript files, etc. A lot of these statics are delivered on the same URL regardless of your security rights.

Given that CPU cycles are considered "expensive" on the Portal/WAS servers and cheaper on the IHS/Apache servers, it is very desirable to reverse proxy these statics on the IHS servers using the mod_cache facilities.

Prior to IHS version 7, the only choice available was the "mod_mem_cache" module. Mod_mem_cache provides an RFC2616 compliant reverse proxy cache. IHS version 7 added support for the "mod_disk_cache" option in addition to the mem_cache.

Choosing the best type of cache neither intuitive nor obvious. The Apache Caching guide provides some guidance.

When first considering which type caching to use, most would immediately suggest mem_cache as the better option. From a performance perspective, serving from memory is obviously better than serving from a disk. In reality though, because of how Unix/Linux buffer file I/O works, the benefits of disk_cache become apparent. Unix allocates unused portions of memory to buffer files as they are read. So, the initial read request starts reading the file into memory. Subsequent read requests for the same file are read from memory without even touching the disk. So, with the exception of the initial load, disk_cache performs as well as mem_cache and there is only one instance of the response in memory as opposed to "per process" duplication of mem_cache. Because memory utilization is more efficient, cache hit ratios can be much higher with disk_cache.

See: Some Tuning Tips For Apache Mod_Cache (mod_disk_cache)

Enable in-memory (mod_mem_cache) caching in IHS

If we use mod_cache, edit httpd.conf and set...

LoadModule cache_module modules/mod_cache.so
<IfModule mod_cache.c>
    LoadModule mem_cache_module modules/mod_mem_cache.so
        <IfModule mod_mem_cache.c>
        CacheEnable mem /
        CacheIgnoreHeaders Set-Cookie
        MCacheSize 102400
        MCacheMaxObjectCount 10000
        MCacheMinObjectSize 1
        # Needed with themes before WP
        # MCacheMaxStreamingBuffer 6291456
    MCacheMaxObjectSize 6291456

Add Cache Headers in IHS

The HTTP protocol allows the server to tell clients how long they can cache responses. When the client has the content in their cache, they do not need to request it again, saving the round-trip time to the server to retrieve the content.

This is done by adding Cache-Control headers to the content which to make cacheable. By default, WebSphere Portal 8 will include these headers for some of the static content it serves. It is possible to use mod_expires and mod_headers in IBM HTTP Server to add the same headers to images, JavaScript and other static content for which Portal does not add headers. See Web Server Tuning for WAB for an alternative way to specify these same directives that works for base portal and WAB.

Add the following to the httpd.conf file.

LoadModule expires_module modules/mod_expires.so
LoadModule headers_module modules/mod_headers.so
# avoid gzip bug in IE 6 (from http://www.contentwithstyle.co.uk/blog/147)
BrowserMatch ^Mozilla/4\.[0678] no-gzip
BrowserMatch \bMSIE\s7 !no-gzip !gzip-only-text/html
Base Portal Tuning
WebSphere Portal 8.0 Performance Tuning Guide 49
AllowEncodedSlashes On
ExpiresActive On
ExpiresByType text/plain "access plus 1 week"
ExpiresByType image/gif "access plus 1 week"
ExpiresByType image/jpg "access plus 1 week"
ExpiresByType image/jpeg "access plus 1 week"
ExpiresByType image/png "access plus 1 week"
ExpiresByType application/x-javascript "access plus 1 week"
ExpiresByType text/javascript "access plus 1 week"
ExpiresByType text/css "access plus 1 week"
ExpiresByType application/xml "access plus 1 week"
ExpiresByType application/vnd.mozilla.xul+xml "access plus 1 week"
ExpiresByType application/x-www-form-urlencoded "access plus 1 week"
ExpiresByType text/html "access plus 1 week"
ExpiresByType text/xml "access plus 1 week"
ExpiresByType application/atom+xml "access plus 1 week"

# note the following max-age=86400 is just an example.
# A lower value might be more appropriate for the site 
# Also, note that with the next setting, all html will have the Cache-Control
# header added. Your site might need to be more selective.
# Make sure the LoadModule directives above have been issued.  
# Alternatively we can have a check if module is loaded before invoking  
# the following directives.  
# That is not shown here because the intent is they should be loaded.

 <LocationMatch "\.(gif|jpeg|jpg|png|ico|jsp|css|js|swf|json|htm|html)$">
    header set Cache-Control "public,max-age=86400"
 <LocationMatch "/mccenabler/js/com/ibm/mm/livetext/.*\.cfg">
    header set Cache-Control "public,max-age=86400"
 <LocationMatch "/mccbuilder/widget-catalog/.*\.xml">
    header set Cache-Control "public,max-age=86400"
 # added so apache server status won't be cached
<LocationMatch "/server-status">
    Header set Cache-Control "no-cache,max-age=0"

Content Compression on the HTTP Server

IBM HTTP Server supports compression using the mod_deflate module. When enabled, the HTTP server checks the Accept-Encoding header sent by the browser to see if it can accept a compressed version of the content. If so, the HTTP server will compress the content before sending it to the browser.

IBM has observed an average of 65% network traffic reduction when deflate compression is enabled. However, the compression operation does not come for free as they also observed approximately a 10% processor utilization increase on the HTTP server.

In benchmarks, the best performance was seen when Portal gzipped the content once at startup and the HTTP server cached the compressed version of the content. However, this environment does not have a significant amount of other static content. In sites with a large number of static files, performance may be improved by compressing that content as well. This can be done dynamically by the HTTP server.

To enable gzip compression in IBM HTTP Server, add the following lines in httpd.conf:

# compress everything but images
LoadModule deflate_module modules/mod_deflate.so

# these next 3 are for outputting to a log and go along with a LogFormat not mentioned here.  
# See http://publib.boulder.ibm.com/httpserv/manual70/mod/mod_deflate.html
DeflateFilterNote Input instream
DeflateFilterNote Output outstream
DeflateFilterNote Ratio ratio

# Insert filter SetOutputFilter DEFLATE
# Netscape 4.x has some problems...
BrowserMatch ^Mozilla/4 gzip-only-text/html
# Netscape 4.06-4.08 have some more problems BrowserMatch ^Mozilla/4\.0[678] no-gzip
# MSIE masquerades as Netscape, but it is fine
BrowserMatch \bMSIE !no-gzip !gzip-only-text/html
 # Do not compress images
SetEnvIfNoCase Request_URI \
\.(?:gif|jpe?g|png|exe)$ no-gzip dont-vary

WebSphere HTTP Plugin Tuning

Set following plugin parameters are set:

Network Tuning

Set the following network tuning parameters on all the AIX systems...

tcp_sendspace 131072
tcp_recvspace 131072
udp_sendspace 65536
udp_recvspace 655360
somaxconn 10000
tcp_nodelayack 1
rfc1323 1

These parameters can be set using the no command or through smit. In smit, the path to the change these is

To make the changes permanent, also select “Save Current Parameters for Next Boot”.

These tuning settings - particularly the tcp_sendspace and tcp_recvspace values - will allocate a significant amount of memory for network buffers. These can cause a performance problem if the system has a limited amount of memory. In that case, it may make sense to reduce these values.

For more info, see...

  • Network interface optimization
  • AIX network performance tuning

    WCM Object Cache

    Cache Name Value Used
    abspath 8000
    abspathreverse 8000
    processing 10000
    session 6000
    strategy 8000
    summary 4000

    To set, in the WAS console...

    WCM Cache Manager Service

    From the WAS console, go to...

    ...and set...

    Parameter Value Used
    cacheinstance.com.ibm.wps.ac.CommonRolesCache.size 50000
    cacheinstance.com.ibm.wps.ac.ProtectedResourceCache.size 20000
    cacheinstance.com.ibm.wps.cp.models.ModelCache.CategoryModel.lifetime 28800
    cacheinstance.com.ibm.wps.cp.models.ModelCache.ResourceModel.lifetime 28800
    cacheinstance.com.ibm.wps.cp.models.ModelCache.ResourceModel.size 2000
    cacheinstance.com.ibm.wps.cp.models.ModelCache.TagModel.lifetime 28800
    cacheinstance.com.ibm.wps.cp.models.ModelCache.TagModel.size 2000
    cacheinstance.com.ibm.wps.pe.portletentitycounter.size 5000
    cacheinstance.com.ibm.wps.resolver.resource.AbstractRequestDispatcherFactory.size 100
    cacheinstance.com.ibm.wps.ac.OwnedResourcesCache.enabled true

    Access Control Data Management Service

    By using the loadRolesParentBased algorithm in the authoring environment, IBM showed an approximate 70% improvement in capacity was seen in performance benchmarks. The loadRolesParentBased setting changes the loading algorithm for the ROLE_INST and LNK_USER_ROLE tables in the database. Instead of loading all assigned roles for a specific principal, only the currently requested access control data is loaded and cached. This is beneficial to a dynamic type of workload like authoring where there are many items being created and deleted that require cache updates. Be aware that this setting may increase database load since fewer items will be cached so be sure that your database server has spare capacity before setting.

    To set, from the WAS console go to...

    ...and set...

    accessControlDataManagement.acucIgnoreResourceTypes null value should be the string “null”, not blank
    accessControlDataManagement.loadRolesParentBased true Use the loadRolesParentBased setting in WCM authoring environments only.

    Enable servlet and portlet caching

    This is especially helpful when the Web Content Viewer portlet is displaying non-personalized content.

    To set...

    1. Enable servlet caching in the WAS console...

        Servers | Server Types | WebSphere appservers | WebSphere_Portal | Web Container Settings | Web container | Enable servlet caching

    2. Enable portlet fragment caching in the WAS console...

        Servers | Server Types | WebSphere appservers | WebSphere_Portal | Portlet Container Settings | Portlet container | Enable portlet fragment caching

    3. Restart the Portal Server

    4. Login to portal as an administrator and navigate to page containing the rendering portlet to enable portlet caching or select "Edit Layout" for the page in the "Manage Pages" Administration portlet.

    5. Select "Configure" or "Edit Shared Settings" from the dropdown in the upper part of the portlet. When set under Configure mode, the settings apply to all instances of this portlet. In Edit mode the settings only apply to that one instance.

    Web Content Viewer Portlet Caching

    Log in as a Portal Administrator and navigate to the “Manage Pages” portlet.

    Edit Page properties for the following pages

    ...and select "Edit Shared Settings" from the dropdown in the upper part of the portlet.

    Under Portlet Settings | Portlet Cache Options | Cache Scope: Select "Shared cache across users"

    Under Portlet Settings | Portlet Cache Options | Cache Expiration: Select "Cache expires after this many seconds" and set 86400

    My Connections

    Solutions Console: Resources | Object pool managers | My HTTP Connection Pool Manager | Custom object pools | com.myco.portal.net.http.PoolableConnection | Custom Properties

    closeOnReturn: true
    connectionTimeout: 10000
    timeout: 20000

    Web Services Clients

    Solutions Console: Services | Policy Sets | Application policy sets | MyCo SOAP Services Policy | HTTP transport

    Read timeout: 30
    Write timeout: 30
    Connection timeout: 5

    Enable Subscriber only

    This should be applied to production servers only

    To specify, go to...

    ...and set...

    deployment.subscriberOnly true

    Monitor the WCM Content Viewer Portlet Cache

    1. Install the Cache Monitor

    2. Give an administrator account access to the application.

        Applications | Application Types | WebSphere enterprise applications | Dynamic Cache Monitor | Security role to user/group mapping | administrator | Map Users | user account | "selected"

    3. Login to the Cache Monitor application.


    4. Select the baseCache and click OK

    5. At this point any WCM Web Content Viewer JSR 286 portlet with caching enabled should add entries to this cache.

    6. To look at the contents of the cache, click on the Cache Content link on the left side menu.

    In addition to viewing the contents of the cache, we can also use the Cache Monitor application to view cache statistics, invalidate individual cache entries, and compare cache content.

    Analyze garbage collection

    We analyze JVM garbage collection patterns using an IBM Support Assistant tool such as PMAT. We leave verboseGC enabled, even during production. The amount of log data is not large; however, it is invaluable in terms of the visibility that this log brings to heap utilization problems.

    Garbage collection normally consumes 5% to 20% of total execution time. The optimum average time between GCs is at least 5-6 times the average duration of a single garbage collection

    Execute concurrency test

    Verify the site is able to handle the required number of concurrent users. The testing done to test for throughput doubles for testing the concurrency.

    Similar to peak throughput expected, the business will have peak concurrency expected. There would also be business requirements of the site to maintain performance past peak concurrency expected, which we call the required business concurrency. Corresponding to the throughput breaking point, there would be a concurrency breaking point.

    Execute throughput test

    The preferred approach for stress testing a system is to stick to the realistic think time and gradually vary the number of virtual users so the throughput exceeds the expected peak throughput (TP), then reaches the throughput level corresponding to the maximum business capacity (TP), and, eventually, reaches the breaking point (Tx) after sustaining the maximum system capacity (TC).

    This approach will give you realistic test results that are easy to interpret. The increase in throughput will be accompanied by an increase in CPU consumption, eventually maximizing the CPU when maximum throughput is achieved (TM).

    However, in case we are testing on a test environment that is not as powerful as your production environment and you find we are limited by your test hardware such we are not able to increase the number of virtual users to take the system to the breaking point (Tx), then an alternative would be to decrease the thinktime. Decreasing the think time in your test scenarios way past the expected think time is akin to increasing virtual users way past the expected value. We can even turn your think time down to zero to maximize the stress on the site for a given number of virtual users.

    This approach also benefits if we need to maximize your CPU consumption and throughput very quickly, instead of having a long trial and error process. However, the trade-off for this approach is the lack of ready-to-use test results. You would need to scale the results that you get from this test methodology so that we can interpret the corresponding behavior on your production environment for more realistic scenarios.

    Such a scaling activity would require running a few experimental test cases and interpreting the results to come up with a scaling factor. Most of the time, especially for small and medium sized customers, such a scaling may turn out to be a linear factor. Any such scaling would need to be repeated with any major change in your system hardware, software, application, data, or even scenarios.

    Once the system has reached maximum throughput (TC), the response times of further client interactions may not be relied upon. In our testing we find that WebSphere Application Server scales to increase in workload extremely well. So, although WebSphere Application Server handles all the additional work given to an application under stress, the queuing-up of workload leads to longer response times. For example, as the number of virtual users keep increasing and there are no more CPU cycles to accommodate this additional workload, then each virtual user gets a smaller and smaller time share of the CPU, driving the response times higher.

    These response times may be unacceptable for shoppers or users of the site.

    So, although the above approach provides you with an easy mechanism to stress the system and to look for bottlenecks, it still leaves the question of observed response times unanswered.

    Soak test

    Reliability testing is done around expected peak capacity (P) and higher. Usually, the testing should be conducted close to required minimum business capacity (B) but not more than the maximum system capacity (C).

    The key factors of reliability testing is the duration for which the test is run as well as the variable workload to mimic typical day-to-day activities on the site.

    Reliability runs are usually run for days and can be half a day, one day, and so on, generally going up to seven days. A three-day reliability run is very common, since this is usually long enough to start showing symptoms of many performance problems, should they exist. A three-day test also gives you an opportunity to start it on a Friday, observe the test for a little while, then leave it running for the weekend, and then come back on Monday and observe the test in execution for another little while before it completes.

    Execute stress test

    For each test scenario executed:

    If any of these passing criterion are not met then it should be considered a defect. If error rate is the cause of the test case failure then the defective code needs investigation.

    If overall low performance is a concern then we need to go through the iterative process of performance tuning and re-executing the test case



    nmon logging

    Use the nmon data collection model to collect detailed status data about the operating system for further analysis.

    This example collects nmon performance results 180 times with an interval of 60 seconds, writing results to file: hostname.

    The -t parameter is used to focus our attention on the status of the top processes.

    A result file named hostname_timeSeries.nmon will be generated in the current directory.


    After collecting data, download output file and load into your nmon-enabled Xcel spreadsheet.

    Memory and paging

    Use vmstat to monitor memory and paging. If "pi" or "po" columns are above zero, paging is occuring.

     System configuration: lcpu=8 mem=6144MB ent=1.50
     kthr    memory              page              faults              cpu
    ----- ----------- ------------------------ ------------ -----------------------
     r  b   avm   fre  re  pi  po  fr   sr  cy  in   sy  cs us sy id wa    pc    ec
     2  1 1106326 221089   0   0   0  91  168   0  58 4288 2377  0  0 99  0  0.01   0.7

    Think time

    Think time is a delay in the processing of an HTTP request to reproduce the time that a human would take to read or examine the data displayed from a previous user action. Think time is calculated from the time that a request is received (that is, the display is complete on the monitor) until the time the user clicks a key or link to perform an action. We can increase, decrease, or randomize think time in tests or play it back exactly as recorded.


    Number of active virtual users at which point adding more virtual users does not result in an increase in the number of page views (PVs) .

    To effectively drive a system to saturation, add Vusers a few at a time, let the system stabilize, observe whether PVs increase, and add more Vusers as possible. ("Stabilize," in this context, means the response times are steady within a window of several minutes.) On LoadRunner, if you plot Vusers against throughput (PVs), the PVs initially rises linearly with the number of Vusers, then reaches a maximum and actually decreases slightly from that point. The saturation point is the number of Vusers at which the PVs is at maximum.

    At saturation, to determine the cause, take a Java thread dump (kill -3) against the portal Java process under test. Look for threads that are blocked or in a wait state.

    Monitor JVM logs

    Unchecked exceptions slow down JVM down and causes serial I/O to the SystemOut.log print stream, serializing the WAS transport threads.

    Results on systems with flaws are considered non-repeatable.

    WebSphere Portal should not be allowed to enter a high-load production environment with any errors in the logs.

    Get AIX statistics using nmon

    Use the nmon data collection model to collect detailed status data about the operating system for further analysis.

    This example collects nmon performance results 180 times with an interval of 60 seconds, writing results to file: hostname.

    The t parameter is used to focus our attention on the status of the top processes.

    nmon -f -t -r hostname -s 60 -c 180

    A result file named hostname_timeSeries.nmon will be generated in the current directory. This file can be imported into MS Excel



    The iostat command is used for monitoring system input/output device loading by observing the time the physical disks are active in relation to their average transfer rates. The iostat command generates reports used to change the system configuration to better balance the input/output load between physical disks and adapters.

    All statistics are reported each time the iostat command is run. The report consists of a tty and CPU header row followed by a row of tty or asynchronous I/O and CPU statistics. On multiprocessor systems, CPU statistics are calculated system-wide as averages among all processors.

    The iostat command generates four types of reports: the tty and CPU Utilization report, the Disk Utilization report, the System throughput report, and the Adapter throughput report.

    Basically, we can set the interval and count for this command.The interval parameter specifies the amount of time in seconds between each report. If the interval parameter is not specified, the iostat command generates a single report containing statistics for the time since system startup (boot). The count parameter can be specified in conjunction with the interval parameter.

    If high I/O with low CPU, disk is bottleneck


    This command is used to capture and analyze a snapshot of virtual memory.

    The svmon command displays information about the current state of memory. The displayed information does not constitute a true snapshot of memory, because the svmon command runs at the user level with interrupts enabled.

    The segment is has a set of pages and is the basic object used to report the memory consumption, so the statistics reported by svmon are expressed in terms of pages.

    A page is a block of virtual memory, while a frame is a block of real memory. Frames always have a size of 4 KB, whereas pages may have different sizes. The base page size is 4 KB. All pages inside a segment have the same size.

    The memory consumption is reported using the inuse, free, pin, virtual, and paging space counters:

    inuse counter Used frames.
    free counter Free frames from all memory pools.
    pin counter Pinned frames (that is, frames that cannot be swapped).
    virtual counter Pages allocated in the system virtual space.
    paging space counter Pages reserved or used on paging spaces.


    Time that must elapse before TCP/IP can release a closed connection and reuse its resources (TIME_WAIT state). During this time, reopening the connection to the client and server costs less than establishing a new connection.

    Reduce if a low throughput occurs due to many connections sitting in the TIME_WAIT state

    Use "no -a" to determine current value.

    To set TCP_TIMEWAIT state to 15 seconds:

    The value (1) is in 15 second increments. For example, 1 equates to 15 seconds, 2 equates to 30 seconds, and so on. IBM recommends that set the value to 1 or 2.

    Monitor LDAP

    If several threads are in the Socket.read() method of the JNDI classes, they are likely waiting on results from the LDAP directory.

    Tune dynamic cache with the cache monitor

    Use this task to interpret cache monitor statistics to improve the performance of the dynamic cache service.

    Verify that dynamic cache is enabled and the cache monitor application is installed on the appserver.

    Use the cache monitor to watch cache hits versus misses. By comparing these two values, we can determine how much dynamic cache is helping the application, and if we can take any additional steps to further improve performance and decrease the cost of processing for the appserver.

    1. Start cache monitor and click on Cache Statistics.

      Cache statistic Description
      Cache Size Maximum number of entries the cache can hold.
      Used Entries Number of cache entries used.
      Cache Hits Number of request responses served from the cache.
      Cache Misses Number of request responses that are cacheable but cannot be served from the cache.
      LRU Evictions Number of cache entries removed to make room for new cache entries.
      Explicit Removals Number of cache entries removed or invalidated from the cache based on cache policies or were deleted from the cache through the cache monitor.

    2. We can also view the following cache configuration values:

      Cache configuration value Description
      Default priority Default priority for all cache entries. Lower priority entries are moved from the cache before higher priority entries when the cache is full. We can specify the priority for individual cache entries in the cache policy.
      Servlet Caching Enabled If servlet caching is enabled, results from servlets and JSPs files are cached.
      Disk Offload Enabled Specifies if entries that are being removed from the cache are saved to disk.

    3. Wait for the appserver to add data to the cache.

      You want the number of used cache entries in the cache monitor to be as high as it can go. When the number of used entries is at its highest, the cache can serve responses to as many requests as possible.

    4. When the cache has a high number of used entries, reset the statistics. Watch the number of cache hits versus cache misses. If the number of hits is far greater than the number of misses, the cache configuration is optimal. You do not need to take any further actions. If we find a higher number of misses with a lower number of hits, the appserver is working hard to generate responses instead of serving the request using a cached value. The appserver might be making database queries, or running logic to respond to the requests.

    5. If we have a large number of cache misses, increase the number of cache hits by improving the probability that a request can be served from the cache.

      To improve the number of cache hits, we can increase the cache size or configure additional cache policies.

    If the free space after GC declines with no growing AF, then most likely cache tuning is causing the memory problems. Caching does not necessarily mean DynaCache. It includes other types of cache that are being used. One solution is reducing the in-memory cache size and letting the overflow entries use the disk cache if possible.

    IBM recommends having 40% of JVM free after caching is enabled.

    Sometimes an excessive number of cache objects look like a memory leak since the cache grows as the application receives an increasing load. Customers and testers need to find a stabilization point where the system does not generate OutOfMemory errors due to too many cached objects. Tune the cache size according to how much heap size is being used. In Figure 24-13, the example shows an OutOfMemory exception. After further examination, you see the cached page is 60-100 KB in size and the number of cache entries is set to 5000. Therefore, half of this 1 GB heap is allocated to the cache.

    Note: A quick way to rule out caching, such a Dynamic Caching, concerns that may work for you is to try turning-off Dynamic Caching altogether, to force disk caching, or to keep all cached objects in memory. The behavior of your application under such circumstances may reveal whether caching policies or disk offload may be contributing to your caching concerns.

    Note, however, that this trial-and-error approach may change your application's error path and thus deviate from the error-causing scenarios.

    In most cases, we can adjust the DynaCache size to eliminate such problems.

    To tune the DynaCache size:

    1. Log in to the WAS administrative console.

    2. Expand...

      Servers | Application servers | server1 | Container Services | Dynamic Cache Service | Cache size

    3. Set the value of the cache size that we want.

    4. Save your changes and restart the application server.

    Web Server Plug-In Analyzer

    IBM Web Server Plug-in Analyzer for WebSphere Application Server helps discover potential problems with trace and configuration files during use of WebSphere Application Server. The tool parses both plug-in configuration and corresponding trace files and then applies pattern recognition algorithms in order to alert users of possible inconsistencies.

    The tool provides a list of HTTP return codes, URI and graphical presentations of available clusters, and server topologies from the configuration and trace files.

    Primary capabilities...

    The package includes the stand-alone code, documentation, and license files.


    How does it work?

    The tool parses WebSphere Application Server plug-in configuration files and trace files. Based on results obtained from a pattern recognition engine, IBM Web Server Plug-in Analyzer provides information about any potential problems within the configuration.

    The pattern recognition engine maintains various patterns of configurations that are not usually recommended and provides warnings if these same patterns are detected in the configuration files.

    The tool takes the following approach:

    1. Parses configuration files.

    2. Provide warnings or clues to information when configurations appear to be set inappropriately.

    3. Collects WebSphere Application Server cluster and member topology information within the configuration file.

    4. Displays a visual mapping of the cluster and member topology.

    5. Parses the plug-in trace files and creating models based on HTTP request/response header/body information, HTTP return code, URI, start/end time, cluster name, and server name.

    6. Displays the requested trace information based on query. The trace information has HTTP return code analysis and HTTP request/response header/body analysis.

    Monitor plug-in maximum number of connections

    All requests to the appservers flow through the HTTP Server plug-in. The application server selection logic in the plug-in has been enhanced so that it takes into account the number of pending connections to the application server. The maximum number of connections attribute is used to specify the maximum number of pending connections to an application server that can be flowing through a Web server process at any point in time.

    Each application server can have a maximum number of pending connections coming from Web server plug-ins.

    To change this setting, go to...

    The default setting is No Limit, which is the same as though the value is set to -1 or zero. The attribute can be set to any arbitrary value. For example, let the two appservers be fronted by two nodes running IBM HTTP Server. If the MaxConnections attribute is set to 10, then each application server could potentially get up to 20 pending connections.

    If the number of pending connections reaches the maximum limit of the application server, then it is not selected to handle the current request. If no other application server is available to serve the request, HTTP response code 503 (service unavailable) is returned to the user.

    To monitor the behavior of the plug-in when a cluster member has too many requests, use a load testing tool (such as ApacheBench or JMeter), the plug-in log, the HTTP servers' access log, and Tivoli Performance Viewer.

    For our test we have configured wasmember05 to have a maximum connection setting of 3 and wasmember06 of 4. We have one HTTP server fronting the appservers. Running a load test with 30 concurrent users against this cluster eventually results in both members having reached the maximum connections value. At this point, the user gets Error 503.

    When the plug-in detects there are no appservers available to satisfy the request, HTTP response code 503 (service unavailable) is returned. This response code appears in the Web server access log...

    Further down in the plug-in log we can see that eventually both servers respond to requests again when they have reduced their backlog.

    This feature helps you to better load balance the appservers fronted by the plug-in. If appservers are overloaded, the plug-in skips these appservers automatically and tries the next available application server.

    However, a better solution is to have an environment that can handle the load we are expecting and to have it configured correctly. This includes setting weights that correspond to the system capabilities, having the correct balance of cluster members and Web servers, and setting up the queues for requests and connections.

    Memory leaks

    In a normal Free Space after GC graph, where the application is using the Java heap properly, the red line should be approximately on a horizontal line.

    With a memory you get a declining red line. The free space available to allocate is decreasing. If we suspect a memory problem, continue running the test until an OutOfMemory exception occurs because some downward trends in free space will stabilize after a period of time. This helps you to get better support from WebSphere Application Server and the JDK if the problem is related to or the JDK.

    Look for database contention

    If the thread dumps (kill -3) indicate threads waiting in JDBC classes in Socket.read() methods, then there are likely response time issues in the database itself.

    At initial database transfer time, Portal sets up the databases with indexes that should be good initial starting points. The DBA should monitor the database and effect changes to remove bottlenecks in the system.

    Common problems...

    When thread dumps indicate excessive JDBC wait times, take snapshots for long queries. Generally, Portal and WCM queries all execute in subseconds, if not in milliseconds. Look at the execution plans for long-running queries, and see if additional indexes might be required to improve response times on problematic queries.

    When threads are waiting on JDBC pool resources in WAS, you see the threads in a condition wait (CW) state in the WAS connection pool (J2C) classes. In this case, we might need to increase the pool size for this data source. Note that in doing so, we might need to increase the number of connections the database server can handle concurrently.

    Appendix: Enable server-status

    The IBM HTTP Server server-status page is available on all supported IHS platforms. It shows performance data on a Web page in HTML format.

    To activate the server-status page, edit httpd.conf and remove the comment character (#) from the following lines:

    #LoadModule status_module modules/mod_status.so
    #<Location /server-status>
    #    SetHandler server-status
    #    Order deny,allow
    #    Deny from all #    Allow from .myco.com

    Save the changes and restart IHS.

    Open the URL...

    ...and click Refresh to update the status.

    If the browser supports refresh, we can also use the URL...

    ..to refresh every 5 seconds. We can see the number of requests currently being processed and the number of idle servers.

    Appendix: Set cloneIds

    The Workload Manager (WLM) in WebSphere does load balancing and can direct traffic to any available clone in a cluster or cell defined for the application. We can hit a clone directly through the WebSphere port defined for the WAS, but in most environments that is blocked by a firewall. To go around this block, use IHS rules that direct traffic to a clone based on URL query strings.

    The first thing to do is to identify the names for your clones. Use the name specified in the WAS administrative console. We also need to know the port from which the clone is served.

    After you have this information, edit httpd.conf files with a new stanza. For this example, the keyword is cloneID, and the CloneNames are WebSphere_Portal and WebSphere_Portal_Clone_2 with ports 9085/9086. The target WebSphere Application Server hostname is host name.ibm.com.

    <LocationMatch "cloneID$" >
    RewriteEngine on
    RewriteCond %{QUERY_P2,gRING} ^WebSphere_Portal_Clone_2
    RewriteRule /(.*)/cloneID$ http://hostname.ibm.com:9086/$1 [P]
     RewriteCond %{QUERY_P2,gRING} ^WebSphere_Portal
    RewriteRule /(.*)/cloneID$ http://hostname.ibm.com:9085/$1 [P]

    This stanza, after it is added to all your IHS servers, allows us to obtain session affinity with a specific clone. To use this stanza, for example, you access the portal application and append ?cloneID=WebSphere_Portal_Clone_2 to the URL, and your request is sent to WebSphere_Portal_Clone_2.

    This method is useful in large-scale evironments where you have many clones and we need to debug or reproduce a problem, and you don't want to search through every clone on each server to find where your request went. It is also a great tool for finding issues that are clone-specific or when to measure performance times of components based on log timestamps.