IBM Business Analytics Proven Practices

Entity Analytics Performance Guide for DB2

Product(s): IBM SPSS Modeler Premium; Area of Interest: Performance


Content series:

This content is part # of # in the series: IBM Business Analytics Proven Practices

Stay tuned for additional content in this series.

This content is part of the series:IBM Business Analytics Proven Practices

Stay tuned for additional content in this series.

Purpose of Document

This document describes advanced settings for IBM SPSS Modeler Premium Entity Analytics (EA) which will assist in tuning Entity Analytics based on specific situations.


This document is applicable to the IBM SPSS Modeler Premium Entity Analytics 16.0.0 release and its use of IBM DB2.

Exclusions and Exceptions

The information contained in this document may evolve over time. It is important to note that not all data or hardware is equal and your results may not be directly comparable to the results shown or described in this document.


Entity Analytics is a premium component of IBM SPSS Modeler. Users of this document should have a base understanding of SPSS Modeler, Entity Analytics, and the data they will be accessing.


Entity Analytics in IBM SPSS Modeler 16 utilizes an IBM DB2 database as its repository. While the default settings that are shipped with the product work well in most situations, as users start to push the limits of Entity Analytics it will be needed to change the settings to get optimal performance. This document will describe the settings in the environment that can have an effect on performance related tasks.

EA feature components and configuration settings

Entity Analytics installs a number of components that are useful to understand in order to get maximum performance from your system.

SPSS Modeler nodes

EA Export node

The EA Export node allows the user to map data fields within a dataset computed by an SPSS Modeler stream to the features in a selected EA Entity Type (for example PERSON) and export that dataset to a specified EA data source within the EA repository. EA performs a compute intensive resolution process as each record is loaded into the DB2 repository.

Tuning parameters in the EA system can improve the performance of this node in terms of the rate at which records can be loaded into the repository.

EA Source node

The EA Source Node provides a way to obtain from the repository a summary of which records exported to each EA data source were resolved to which Entity ID. The summary can be filtered by data source.

Executing streams which read data from this node should not have any performance issues that require tuning.

EA Streaming node

The EA Streaming node provides a mechanism for searching the EA repository for potential matches with records from an incoming stream. The user maps fields from the input records to features in the EA repository and selects the kind of matching to perform. The output will include each input record and has the option of including further records describing information known on each entity in the repository which is a potential match for the input record.

Tuning EA parameters can reduce the amount of time taken to process each record when this node is executed.

Entity Analytics processing location

Entity Analytics can be installed in a local installation mode on a client-only machine and can also be installed in distributed mode to a server. In both situations EA is installed on the client. When making the recommended configuration changes always make the changes where EA is being processed and the database resides.


The EA feature contains an IBM DB2 database backend which uses a default configuration providing complete transaction recoverability and adequate performance for smaller datasets. The DB2 database offers the following benefits,

  • Good configuration for getting started with EA.
  • Guarantees that your work is recoverable.
  • Configured for minimal system resource usage.

Configuration settings

Configuration files are provided with system defaults. However, when dealing with larger datasets, configuration changes are required to achieve better performance. Some of these configuration changes are,

  • Increase the number of records processed in a single batch.
  • Increase EA concurrency with the concurrency setting in the g2.ini file.
  • Reduce input/output (I/O) operations with configuration changes.
    • Page cleaners
    • Transaction logging
    • Optimizer hints
  • Use a fast dedicated disk for database.

EA workload characteristics

Effects of EA on the system

Entity Analytics processes export records and inserts or updates features and elements into the backing database. Each export record can result in many inserts and updates to the database consuming memory and causing I/O.

I/O latency is the single highest contributing factor to overall performance for datasets larger than available memory. EA is self-optimizing and over time will reduce the strain on the backend database but eventually the volume of data can overwhelm the I/O capacity and performance will suffer.

Effects of data on EA

The data itself can also affect performance of the EA system. A highly related dataset can result in lower performance due to more candidates being used in matching and scoring. Sorted datasets can also reduce performance when using multiple threads due to locking on the same entity. In addition, the number of features in the dataset will affect overall performance.

EA feature performance best practices

Dedicated database disk

If possible, place the DB2 database on a dedicated disk. I/O operations to the system disk and pagefile usage can cause bursts of activity to the disk which will affect performance. When installing SPSS Modeler choose a dedicated disk other than the operating system disk, the DB2 databases will also be created on that disk.

If SPSS Modeler is already installed, see the Solid State Drives best practice for moving the database location.

Solid State Drives

Solid state drives (SSDs) are becoming more commonplace in every type of system. They provide much higher I/O rates compared to standard hard drives.

When working with EA databases, much of the resulting database size is dependent on how much actual data is being exported. A guideline of 10,000 bytes per export record can be used as an approximation of database size per record. This would result in a database size of approximately 100 GB for 10,000,000 exported records and require large amounts of I/O capability to sustain performance over the whole export.

While SSDs are typically utilized as the primary system disk to improve boot times and overall system performance, they should also be used to improve I/O times for processes that would otherwise be waiting for disk activity to complete.

To move a DB2 database to other disks you will perform a database backup followed by a restore to the new drive. With SPSS Modeler and all Entity Analytic jobs stopped, issue the following commands for a Windows platform.

  • md c:\eabackup
  • db2 backup database <repositoryName> to c:\eabackup
  • db2 drop database <repositoryName>
  • db2 restore database <repositoryName> from c:\eabackup on <SSD drive letter>

Increase batch size

When exporting larger datasets to EA you should increase the number of records in a batch. The cumulative overhead of starting the EA export can be reduced by increasing the batch size. The default batch size is 1000 and is adequate for small datasets. There should never be a need to reduce the batch size below 1000.

Please note that progress report updates are displayed by SPSS Modeler after each batch is completed and interrupting stream execution will wait for the current batch to complete loading.

Add the export_batch_size parameter to the end of the ea.cfg file located under <ModelerServerInstallLocation>\ext\bin\pasw.entityanalytics. When updating the ea.cfg file, Modeler Server or Modeler Client will need to be restarted depending on where the EA processing is executed.

Example syntax: export_batch_size, 1000

Increase EA concurrency

Increasing concurrency increases the number of worker threads in the EA Export node to process records from the batch. If your system has more than 1 CPU you can increase the concurrency up to a maximum of 4.

Do not increase concurrency to more than the number of logical CPUs in your system. The EA feature can consume all CPU cycles available as long as the I/O latency remains low. If you intend on doing other CPU intensive tasks on the system you may want to decrease the threads to the number of CPUs - 1.

Add or change the concurrency parameter under the [PIPELINE] section in the g2.ini. The location of the g2.ini file depends on the operating system being used.

Windows Vista, Windows 7, Windows Server 2008:


Example syntax: CONCURRENCY=1

When updating the g2.ini file, Entity Analytics will need to be stopped and restarted depending on where the EA processing is executed.

Please note that progress report updates are displayed by SPSS Modeler after each batch is completed and interrupting stream execution will wait for the current batch to complete loading.

Use alternate page cleaners

DB2 has two different page cleaning algorithms to flush changed pages to disk. Flushing pages can incur some performance overhead if the amount of pages to flush is very large. This can happen with insert intensive workloads such as EA. Using DB2’s alternate page cleaners causes changed pages to be written in a more consistent manner rather than flushing large amounts of pages at one time.

To modify the page cleaning algorithm, use the db2set command setting the DB2_USE_ALTERNATE_PAGE_CLEANING parameter to YES. See the DB2 Command references link in the Related topics section of this article for instructions on using the db2set command.

Use DB2 relaxed logging

Relational databases such as DB2 maintain database recoverability in the case of a failure by using a transaction log. All transactions are recorded in the log and the logs are flushed to disk before the next transaction can be started. At high transaction rates the flush time can be substantial and resulting in a performance impact. You can choose to relax the flush of the transaction log with a DB2 configuration change.

Setting the DB2_SKIP_LOG_WAIT environment variable to YES can greatly speed up performance. The downside is that during processing, if the system fails, it is possible that you may have lost some transactions. In the event this occurs, restart the export from the beginning.

To modify the transaction log setting, use the db2set command setting the DB2_SKIP_LOG_WAIT parameter to YES. See the DB2 Command references link in the Related topics section of this article for instructions on using the db2set command.

DB2 optimizer hint

Entity Analytics’s use of DB2 includes statements of the form select c1, c2, c3 from t1 where c1 in (?,?,?,?,?,), a form which is called an inlist. DB2 will normally use a nested loop join operation when accessing data which is optimal for EA, but if the number of parameters in the inlist is larger than 32, DB2’s optimizer will use a merge join which is not optimal for EA.

To force the DB2 optimizer to use nested loop join for inlists, use the db2set command setting the DB2_INLIST_TO_NLJN parameter to YES. See the DB2 Command references link in the Related topics section of this article for instructions on using the db2set command.

Downloadable resources

Related topics


Sign in or register to add and subscribe to comments.

Zone=Big data and analytics
ArticleTitle=IBM Business Analytics Proven Practices: Entity Analytics Performance Guide for DB2