IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

« Apache anomaly detection configurations Auditbeat anomaly detection configurations »

› › ›

APM anomaly detection configurations

edit

APM anomaly detection configurations

edit

These anomaly detection job wizards appear in Kibana if you have data from APM Agents or an APM Server stored in Elasticsearch. For more details, see the datafeed and job definitions in the apm_* folders in GitHub.

NodeJS

edit

Detect abnormal traces, anomalous spans, and identify periods of decreased throughput.

abnormal_span_durations_nodejs

For data from Elastic APM Node.js agents (where agent.name is nodejs).
Models the duration of spans.
Detects spans that are taking longer than usual to process.

abnormal_trace_durations_nodejs

For data from Elastic APM Node.js agents (where agent.name is nodejs).
Models the duration of trace transactions.
Detects trace transactions that are processing slower than usual.

decreased_throughput_nodejs

For data from Elastic APM Node.js agents (where agent.name is nodejs).
Models the transaction rate of the application.
Detects periods during which the application is processing fewer requests than normal.

RUM Javascript

edit

Detect problematic spans and identify user agents that are potentially causing issues.

abnormal_span_durations_jsbase

For data from Elastic APM RUM JavaScript agents (where agent.name is js-base).
Models the duration of spans.
Detects spans that are taking longer than usual to process.

anomalous_error_rate_for_user_agents_jsbase

This job can help detect browser compatibility issues.

For data from Elastic APM RUM JavaScript agents (where agent.name is js-base).
Models the error rate of user agents.
Detects user agents that are encountering errors at an above normal rate.

decreased_throughput_jsbase

For data from Elastic APM RUM JavaScript agents or Elastic APM Node.js agents (where agent.name is js-base).
Models the transaction rate of the application.
Detects periods during which the application is processing fewer requests than normal.

high_count_by_user_agent_jsbase

This job is useful in identifying bots.

For data from Elastic APM RUM JavaScript agents (where agent.name is js-base).
Models the request rate of user agents.
Detects user agents that are making requests at a suspiciously high rate.

Transactions

edit

Detect anomalies in transactions from your APM services.

high_mean_transaction_duration

For transaction data where processor.event is transaction.
Models duration of transactions by transaction type for APM services.
Detects anomalies in high mean of transaction duration.

« Apache anomaly detection configurations Auditbeat anomaly detection configurations »