Article Content
Article Number | 000038369 |
Applies To | RSA Product Set: NetWitness Platform RSA Product/Service Type: ESA host/ESA Correlation service RSA Version/Condition: 11.4.x |
Issue | Metric collection in Esper 8.2.0 is different than in the previous 7.1.0 version.11.4.0.xFor an Event Stream Analysis (ESA) Correlation server with rules that consume a lot of memory, the gathering of metrics can consume significant CPU resources leading to a drop in Events Per Second (EPS) being processed when the metrics are being collected. To avoid the drop in EPS, the default interval to collect metrics in RSA NetWitness Platform 11.4.0.x is set to a large value (999999 days). This prevents the Esper metrics from being collected. 11.4.1 and higherIn a typical deployment, rule metrics calculations finish quickly, usually within seconds. If a rule uses a significant amount of memory, it may take a long time to calculate the metrics. During this time, the Event Stream Analysis (ESA) Correlation server does not analyze events and will result in an overall Events Per Second (EPS) processing drop. The ESA Correlation server will attempt to calculate metrics for a maximum of 15 seconds (default) and if any rules have metrics that cannot be calculated in this time, an error is shown in the logs. The ESA Correlation server will then cancel the calculation to avoid further EPS drop. This results in a maximum of 15 seconds of analysis lost every 5 minutes (background-metrics-frequency). |
Cause | Esper upgrade from 7.1.0 to 8.2.0 with RSA NetWitness Platform 11.4 and higher on the Event Stream Analysis server. |
Workaround | 11.4.0.xIf you need metrics that are collected at a more frequent interval, you can update the background-metrics-frequency parameter on the ESA Correlation service. Do not set the metrics collection interval lower than five minutes.
11.4.1 and higherIf you need metrics collected at a more frequent interval, you can update the background-metrics-frequency and metrics-timeout parameters on the ESA Correlation service. For example, if you have a rule that is using a lot of memory and it cannot be optimized, you can reduce the overall EPS drop by increasing the frequency and/or lowering the timeout.
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