opensearch-docs-cn/_data-prepper/common-use-cases/log-enrichment.md

---
layout: default
title: Log enrichment with Data Prepper
parent: Common use cases
nav_order: 50
---

# Log enrichment with Data Prepper

You can perform different types of log enrichment with Data Prepper, including:

- Filtering.
- Extracting key-value pairs from strings.
- Mutating events.
- Mutating strings.
- Converting lists to maps.
- Processing incoming timestamps.

## Filtering

Use the [`drop_events`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/drop-events/) processor to filter out specific log events before sending them to a sink. For example, if you're collecting web request logs and only want to store unsuccessful requests, you can create the following pipeline, which drops any requests for which the response is less than 400 so that only log events with HTTP status codes of 400 and higher remain.

```json
log-pipeline:
  source:
  ...
  processor:
    - grok:
        match:
          log: [ "%{COMMONAPACHELOG_DATATYPED}" ]
    - drop_events:
        drop_when: "/response < 400"
  sink:
    - opensearch:
        ...
        index: failure_logs
```
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The `drop_when` option specifies which events to drop from the pipeline.

## Extracting key-value pairs from strings

Log data often includes strings of key-value pairs. For example, if a user queries a URL that can be paginated, the HTTP logs might contain the following HTTP query string:

```json
page=3&q=my-search-term
```
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To perform analysis using the search terms, you can extract the value of `q` from a query string. The [`key_value`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/key-value/) processor provides robust support for extracting keys and values from strings.

The following example combines the `split_string` and `key_value` processors to extract query parameters from an Apache log line:

```json
pipeline
 ...
  processor:
    - grok:
      match:
        message: [ "%{COMMONAPACHELOG_DATATYPED}" ]
    - split_string:
      entries:
        - source: request
          delimiter: "?"
    - key_value:
      source: "/request/1"
      field_split_characters: "&"
      value_split_characters: "="
      destination: query_params
```
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## Mutating events

The different [mutate event]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/mutate-event/) processors let you rename, copy, add, and delete event entries.

In this example, the first processor sets the value of the `debug` key to `true` if the key already exists in the event. The second processor only sets the `debug` key to `true` if the key doesn't exist in the event because `overwrite_if_key_exists` is set to `true`.

```json
...
processor:
  - add_entries:
    entries:
    - key: "debug"
      value: true 
...
processor:
  - add_entries:
    entries:
    - key: "debug"
      value: true 
      overwrite_if_key_exists: true
...
```
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You can also use a format string to construct new entries from existing events. For example, `${date}-${time}` will create a new entry based on the values of the existing entries `date` and `time`.

For example, the following pipeline adds new event entries dynamically from existing events:

```json
processor:
  - add_entries:
    entries:
    - key: "key_three"
      format: "${key_one}-${key_two}
```
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Consider the following incoming event:

```json
{
   "key_one": "value_one",
   "key_two": "value_two"
}
```
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The processor transforms it into an event with a new key named `key_three`, which combines values of other keys in the original event, as shown in the following example:

```json
{
   "key_one": "value_one",
   "key_two": "value_two",
   "key_three": "value_one-value_two"
}
```
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## Mutating strings

The various [mutate string]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/mutate-string/) processors offer tools that you can use to manipulate strings in incoming data. For example, if you need to split a string into an array, you can use the `split_string` processor:

```json
...
processor:
  - split_string:
    entries:
    - source: "message"
      delimiter: "&"
...
```
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The processor will transform a string such as `a&b&c` into `["a", "b", "c"]`.

## Converting lists to maps

The [`list_to_map`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/list-to-map/) processor, which is one of the mutate event processors, converts a list of objects in an event to a map.

For example, consider the following processor configuration:

```json
...
processor:
    - list_to_map:
        key: "name"
        source: "A-car-as-list"
        target: "A-car-as-map"
        value_key: "value"
        flatten: true
...
```
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The following processor will convert an event that contains a list of objects to a map like this:

```json
{
  "A-car-as-list": [
    {
      "name": "make",
      "value": "tesla"
    },
    {
      "name": "model",
      "value": "model 3"
    },
    {
      "name": "color",
      "value": "white"
    }
  ]
}
```
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```json
{
  "A-car-as-map": {
    "make": "tesla",
    "model": "model 3",
    "color": "white"
  }
}
```
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As another example, consider an incoming event with the following structure:

```json
{
  "mylist" : [
    {
      "somekey" : "a",
      "somevalue" : "val-a1",
      "anothervalue" : "val-a2"
    },
    {
      "somekey" : "b",
      "somevalue" : "val-b1",
      "anothervalue" : "val-b2"
    },
    {
      "somekey" : "b",
      "somevalue" : "val-b3",
      "anothervalue" : "val-b4"
    },
    {
      "somekey" : "c",
      "somevalue" : "val-c1",
      "anothervalue" : "val-c2"
    }
  ]
}
```
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You can define the following options in the processor configuration:

```json
...
processor:            
    - list_to_map:
        key: "somekey"
        source: "mylist"
        target: "myobject"
        value_key: "value"
        flatten: true
...
```
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The processor modifies the event by removing `mylist` and adding the new `myobject` object:

```json
{
  "myobject" : {
    "a" : [
      {
        "somekey" : "a",
        "somevalue" : "val-a1",
        "anothervalue" : "val-a2"
      }  
    ],
    "b" : [
      {
        "somekey" : "b",
        "somevalue" : "val-b1",
        "anothervalue" : "val-b2"
      },
      {
        "somekey" : "b",
        "somevalue" : "val-b3",
        "anothervalue" : "val-b4"
      }
    "c" : [
      {
        "somekey" : "c",
        "somevalue" : "val-c1",
        "anothervalue" : "val-c2"
      }  
    ]
  }
}
```
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In many cases, you may want to flatten the array for each key. In these situations, you must choose only one object to retain. The processor offers a choice of either first or last. For example, consider the following:

```json
...
processor:
    - list_to_map:
        key: "somekey"
        source: "mylist"
        target: "myobject"
        flatten: true
...
```
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The incoming event structure is then flattened accordingly:

```json
{
  "myobject" : {
    "a" : {
      "somekey" : "a",
      "somevalue" : "val-a1",
      "anothervalue" : "val-a2"
    },
    "b" : {
      "somekey" : "b",
      "somevalue" : "val-b1",
      "anothervalue" : "val-b2"
    }
    "c" : {
      "somekey" : "c",
      "somevalue" : "val-c1",
      "anothervalue" : "val-c2"
    }
  }
}
```
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## Processing incoming timestamps

The [`date`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/date/) processor parses the `timestamp` key from incoming events by converting it to International Organization for Standardization (ISO) 8601 format:

```json
...
  processor:          
    - date:
        match:
          - key: timestamp
            patterns: ["dd/MMM/yyyy:HH:mm:ss"] 
        destination: "@timestamp"
        source_timezone: "America/Los_Angeles"
        destination_timezone: "America/Chicago"
        locale: "en_US"
...
```
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If the preceding pipeline processes the following event:

```json
{"timestamp": "10/Feb/2000:13:55:36"}
```
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It converts the event to the following format:

```json
{
  "timestamp":"10/Feb/2000:13:55:36",
  "@timestamp":"2000-02-10T15:55:36.000-06:00"
}
```
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### Generating timestamps

The `date` processor can generate timestamps for incoming events if you specify `@timestamp` for the `destination` option:

```json
...
    processor:
    - date:
        from_time_received: true
        destination: "@timestamp"
...
```
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### Deriving punctuation patterns

The [`substitute_string`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/substitute-string/) processor (which is one of the mutate string processors) lets you derive a punctuation pattern from incoming events. In the following example pipeline, the processor will scan incoming Apache log events and derive punctuation patterns from them:

```json
processor:  
    - substitute_string:
        entries:
          - source: "message"
            from: "[a-zA-Z0-9_]+"
            to:""
        - source: "message"
            from: "[ ]+"
            to: "_"  
```
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The following incoming Apache HTTP log will generate a punctuation pattern:

```json
[{"message":"10.10.10.11 - admin [19/Feb/2015:15:50:36 -0500] \"GET /big2.pdf HTTP/1.1\" 200 33973115 0.202 \"-\" \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.111 Safari/537.36\""}]

{"message":"..._-_[//:::_-]_\"_/._/.\"_._\"-\"_\"/._(;_)_/._(,_)_/..._/.\""}
```
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You can count these generated patterns by passing them through the [`aggregate`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/aggregate/) processor with the `count` action.