If Splunk DB Connect retrieves a large amount of data from your database, it might affect your database performance, especially for the initial run. Subsequent runs of the same query might have less impact, as the database might cache results and only retrieve new data since the previous run of the query.

To use Splunk DB Connect in a distributed search environment, including search head clusters, you must determine the planned use cases. For ad hoc, interactive usage of database connections by live users, install the app on search heads. For scheduled indexing from databases and output of data to databases, install the app on heavy forwarders.

• Total number of Forwarders in the deployment, and the hardware specifications of each.
• Total expected data volume to transfer.
• Number of database inputs per Forwarder.
• Dataset size, per input, per interval.
• Execution Frequency, the interval length between a database input's separate executions.
• Fetch size (Not all JDBC drivers use this parameter for returning result sets).

Overloading the system can lead to data loss, so performance measurement and tuning can be critical. Use performance expections as the reference to plan your deployment, and monitor expected data returns for loss conditions.

This section provides measured throughput data achieved under certain operating conditions. Use the information here as a basis for estimating and optimizing the DB Connect throughput performance in your own production environment. As performance might vary based on user characteristics, application usage, server configurations, and other factors, Splunk can't guarantee specific performance results.

• Server: 8-core 2.60GHz CPU, 16GB RAM, 1Gb Ethernet NIC, 64bit Linux
• JVM config: MaxHeapSize = 4GB. (For more information about the JVM memory setting, see "Performance tuning advice".)
• Data Source: Oracle 11g

While it's impossible to provide prescriptive advice for maximizing performance in every situation, the following observations and tips can help you tune and improve performance in your unique distributed deployment:

1. Only select columns if necessary. A table can contain many types of columns. When ingesting data from a database into DB Connect, you likely don't need all of them. Therefore, instead of using a SELECT * FROM ... clause to retrieve all the columns, select only what you need by using a SELECT columnNeeded1, columnNeeded2, ... FROM ... clause. More columns means more memory claimed by the task server; omit those unnecessary columns to make smarter use of your available memory. See SQL tips and tricks for more details.
2. Avoid reaching the 5MB/10MB limit. Large column sizes can cause DB Connect to potentially run out of memory and behave erratically, so DB Connect has a column size limit of 10MB for data columns that hold two-byte data types and 5MB for one-byte data types. Splunk truncates data for columns with data that exceeds these limits. If possible, trim the amount of data stored per column so that you avoid the DB Connect hard caps.
3. Adjust the fetch size based on your scenario. The Fetch Size input parameter specifies the number of rows returned at a time from a database, which defaults to 300 rows. A higher fetch size means Splunk receives more records per database request, so you can use fewer database requests to retrieve the same total number of records. This increases resource utilization on the database and in DB Connect, but can lead to performance improvements. Lowering the fetch size parameter can help prevent the Task Server from hitting its memory limit. If you receive out of memory errors when you increase the fetch size, you might need to increase the memory heap size from its default of 1/4 of system RAM.
4. Reduce the total number of database inputs. It can increase the amount of data that each input is taking in. This helps ensure that CPU cores have to handle fewer processes within a given window of time. Small datasets can be slower than large because of environment initialization.
5. Reduce the concurrency of scheduled database tasks. Shifting start times for scheduled tasks reduces choke points during which inputs and outputs have to share resources. For more information, see "Set parameters" in Create and manage database inputs.
6. Adjust batch_upload_size field. The batch_upload_size field defines the number of events sent to splunkd through HEC per request, which defaults to 1,000 records. A higher batch upload size means Splunk sends more records per HTTP post, so you can use fewer server transactions to index the same total number of records. This increases resource utilization on the Forwarder, but can lead to performance improvements. You can increase the batch_upload_size field under $SPLUNK_HOME/etc/apps/splunk_app_db_connect/local/db_inputs.conf to have better performance.
7. Specify sufficient hardware. In general, Splunk best practice is to use the same hardware specifications for DB Connect as requiredfor Splunk Enterprise. Increased hardware might be necessary for increased indexing loads.
8. Configure Java for performance. Current Java engines automatically reserves 25% of the machine's RAM when opening. If your JVM Options setting is as -Xmx1024m (which is the default value from DB Connect version 2.0 to 2.2). You can remove it and use the default JVM setting. For more information about changing JVM options, see "JVM Options" in Configure DB Connect Settings.
9. Configure Splunk for performance. Increase Splunkd's index queue size and number of Parallel Ingestion Pipelines to avoid concurrency limits.
10. Configure DB Connect for performance Set SchedulerThreadPoolSize to match the number of processor cores.

During testing, varying the following factors had a negligible effect on performance:

• There was no discernable performance difference between running in batch mode (all events processed) and running in rising column mode (only the new events processed) with the same dataset.
• The number of defined database connections does not limit performance. The number of connections is different from the number of database inputs.

Since version 3.10.0 we use Splunk Modular Inputs, so the input execution is triggered by calling a REST API, then it is processed using Java concurrent features.

If you are still experiencing performance issues, or want to receive feedback tailored to your setup, you have the following options:

• Post a request to the community on Splunk Answers.
• Contact Splunk Support.