Performance reference for the Kinesis input in the Splunk Add-on for AWS
This page provides reference information about Splunk's performance testing of the Kinesis input in Splunk Add-on for AWS. The testing was performed on version 4.0.0, when the Kinesis input was first introduced. Use this information to enhance the performance of your own Kinesis data collection tasks.
Note: Many factors impact performance results, including file size, file compression, event size, deployment architecture, and hardware. These results represent reference information and do not represent performance in all environments.
While results in different environments will vary, Splunk's performance testing of the Kinesis input showed:
- Each Kinesis input can handle up to 6 MB/s of data, with a daily ingestion volume of 500 GB.
- More shards can slightly improve the performance. Three shards are recommended for large streams.
Splunk tested the performance of the Kinesis input using a single-instance Splunk Enterprise 6.4.0 on an m4.4xlarge AWS EC2 instance to ensure CPU, memory, storage, and network did not introduce any bottlenecks. Instance specs:
|Instance type||M4 Quadruple Extra Large (m4.4xlarge)|
|Storage||0 GB (EBS only)|
|EBS Optimized: Max Bandwidth||250 MB/s|
Splunk tested the following parameters to target the use case of high-volume VPC flow logs ingested through a Kinesis stream.
- Shard numbers: 3, 5, and 10 shards
- Event size: 120 bytes per event
- Number of events: 20,000,000
- Compression: gzip
- Initial stream position: TRIM_HORIZON
AWS reports that each shard is limited to 5 read transactions per second, up to a maximum read rate of 2MB per second. Thus, with 10 shards, the theoretical upper limit is 20 MB per second.
Splunk observed a data ingestion rate of 6 million events per minute at peak, which is 100,000 events per second. Because each event is 120 bytes, this indicates a maximum throughput of 10 MB/s.
Splunk observed an average throughput of 6 MB/s for a single Kinesis modular input, or a daily ingestion throughput of approximately 500 GB.
After reducing the shard number from 10 shards to 3 shards, Splunk observed a throughput downgrade of approximately 10%.
During testing, Splunk observed the following resource usage on the instance:
- normalized CPU usage of approximately 30%
- Python memory usage of approximately 700 MB
The indexer is largest consumer of CPU, and the modular input is largest consumer of memory.
Note: AWS throws a ProvisionedThroughputExceededException if a call returns 10 MB of data and subsequent calls are made within the next 5 seconds. Splunk observed this error while testing with three shards only every one to five minutes.
Performance reference for the Splunk Add-on for AWS data inputs
Use SNS alert for the Splunk Add-on for AWS
This documentation applies to the following versions of Splunk® Supported Add-ons: released