opensearch-docs-cn/_data-prepper/common-use-cases/event-aggregation.md

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default	Event aggregation	Common use cases	40

Event aggregation

You can use Data Prepper to aggregate data from different events over a period of time. Aggregating events can help to reduce unnecessary log volume and manage use cases like multiline logs that are received as separate events. The aggregate processor is a stateful processor that groups events based on the values for a set of specified identification keys and performs a configurable action on each group.

The aggregate processor state is stored in memory. For example, in order to combine four events into one, the processor needs to retain pieces of the first three events. The state of an aggregate group of events is kept for a configurable amount of time. Depending on your logs, the aggregate action being used, and the number of memory options in the processor configuration, the aggregation could take place over a long period of time.

Basic usage

The following example pipeline extracts the fields sourceIp, destinationIp, and port using the grok processor and then aggregates on those fields over a period of 30 seconds using the aggregate processor and the put_all action. At the end of the 30-second period, the aggregated log is sent to the OpenSearch sink.

aggregate_pipeline:  
   source:
     http:
      path: "/${pipelineName}/logs"
   processor:
     - grok:
         match: 
           log: ["%{IPORHOST:sourceIp} %{IPORHOST:destinationIp} %{NUMBER:port:int}"]
     - aggregate:
         group_duration: "30s"
         identification_keys: ["sourceIp", "destinationIp", "port"]
         action:
           put_all:
   sink:
     - opensearch:
         ...
         index: aggregated_logs

{% include copy-curl.html %}

For example, consider the following batch of logs:

{ "log": "127.0.0.1 192.168.0.1 80", "status": 200 }
{ "log": "127.0.0.1 192.168.0.1 80", "bytes": 1000 }
{ "log": "127.0.0.1 192.168.0.1 80" "http_verb": "GET" }

{% include copy-curl.html %}

The grok processor will extract keys such that the log events will look like the following example. These events now have the data that the aggregate processor will need for the identification_keys.

{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "port": 80, "status": 200 }
{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "port": 80, "bytes": 1000 }
{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "port": 80, "http_verb": "GET" }

{% include copy-curl.html %}

After 30 seconds, the aggregate processor writes the following aggregated log to the sink:

{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "port": 80, "status": 200, "bytes": 1000, "http_verb": "GET" }

{% include copy-curl.html %}

Removing duplicates

You can remove duplicate entries by deriving keys from incoming events and specifying the remove_duplicates option for the aggregate processor. This action immediately processes the first event for a group and drops all following events in that group.

In the following example, the first event is processed with the identification keys sourceIp and destinationIp:

{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "status": 200 }

{% include copy-curl.html %}

The pipeline will then drop the following event because it has the same keys:

{ "sourceIp": "127.0.0.1", "destinationIp": "192.168.0.1", "bytes": 1000 }

{% include copy-curl.html %}

The pipeline processes this event and creates a new group because the sourceIp is different:

{ "sourceIp": "127.0.0.2", "destinationIp": "192.168.0.1", "bytes": 1000 }

{% include copy-curl.html %}

Log aggregation and conditional routing

You can use multiple plugins to combine log aggregation with conditional routing. In this example, the pipeline log-aggregate-pipeline receives logs by using an HTTP client, like FluentBit, and extracts important values from the logs by matching the value in the log key against the Apache Common Log Format.

Two of the values that the pipeline extracts from the logs with a Grok pattern include response and clientip. The aggregate processor then uses the clientip value, along with the remove_duplicates option, to drop any logs that contain a clientip that has already been processed within the given group_duration.

Three routes, or conditional statements, exist in the pipeline. These routes separate the value of the response into 2xx, 3xx, 4xx, and 5xx responses. Logs with a 2xx or 3xx status are sent to the aggregated_2xx_3xx index, logs with a 4xx status are sent to the aggregated_4xx index, and logs with a 5xx status are sent to the aggregated_5xx index.

log-aggregate-pipeline:
  source:
    http:
      # Provide the path for ingestion. ${pipelineName} will be replaced with pipeline name configured for this pipeline.
      # In this case it would be "/log-aggregate-pipeline/logs". This will be the FluentBit output URI value.
      path: "/${pipelineName}/logs"
  processor:
    - grok:
        match:
          log: [ "%{COMMONAPACHELOG_DATATYPED}" ]
    - aggregate:
        identification_keys: ["clientip"]
        action:
          remove_duplicates:
        group_duration: "180s"
  route:
    - 2xx_status: "/response >= 200 and /response < 300"
    - 3xx_status: "/response >= 300 and /response < 400"
    - 4xx_status: "/response >= 400 and /response < 500"
    - 5xx_status: "/response >= 500 and /response < 600"
  sink:
    - opensearch:
        ...
        index: "aggregated_2xx_3xx"
        routes:
          - 2xx_status
          - 3xx_status
    - opensearch:
        ...
        index: "aggregated_4xx"
        routes:
          - 4xx_status
    - opensearch:
        ...
        index: "aggregated_5xx"
        routes:
          - 5xx_status