Ingest processors (grok, date, rename) in Elasticsearch

Introduction

Ingest processors help you change or organize data before saving it. They make data easier to search and understand.

You want to extract parts of a text message into separate fields.

You need to convert a text date into a real date format.

You want to change a field name to a clearer or standard name.

Syntax

Elasticsearch

{
  "processors": [
    {
      "grok": {
        "field": "message",
        "patterns": ["%{COMMONAPACHELOG}"]
      }
    },
    {
      "date": {
        "field": "timestamp",
        "formats": ["dd/MMM/yyyy:HH:mm:ss Z"]
      }
    },
    {
      "rename": {
        "field": "old_field_name",
        "target_field": "new_field_name"
      }
    }
  ]
}

The grok processor extracts data using patterns from text.

The date processor converts text to date format for better sorting and searching.

The rename processor changes field names to keep data clear.

Examples

This grok example extracts client IP, method, and request path from a log message.

Elasticsearch

{
  "grok": {
    "field": "log",
    "patterns": ["%{IP:client} %{WORD:method} %{URIPATHPARAM:request}"]
  }
}

This date processor converts an ISO8601 string into a date object.

Elasticsearch

{
  "date": {
    "field": "event_time",
    "formats": ["ISO8601"]
  }
}

This rename processor changes the field name from 'temp' to 'temperature'.

Elasticsearch

{
  "rename": {
    "field": "temp",
    "target_field": "temperature"
  }
}

Sample Program

This pipeline extracts fields from a common Apache log message, converts the timestamp to a date, and renames the client IP field to 'ip_address' for clarity.

Elasticsearch

{
  "description": "Example pipeline using grok, date, and rename processors",
  "processors": [
    {
      "grok": {
        "field": "message",
        "patterns": ["%{COMMONAPACHELOG}"]
      }
    },
    {
      "date": {
        "field": "timestamp",
        "formats": ["dd/MMM/yyyy:HH:mm:ss Z"]
      }
    },
    {
      "rename": {
        "field": "clientip",
        "target_field": "ip_address"
      }
    }
  ]
}

Important Notes

Grok patterns are reusable templates to find parts of text easily.

Date formats must match the text exactly or the processor will fail.

Renaming helps keep your data consistent and easy to understand.

Summary

Ingest processors change data before saving it to make searching easier.

Grok extracts parts of text, date converts text to date, rename changes field names.

Use these processors together to clean and organize your data automatically.

Practice

1. What is the main purpose of the grok processor in Elasticsearch ingest pipelines?

easy

A. To extract structured fields from unstructured text

B. To convert string fields into date objects

C. To rename existing fields in the document

D. To delete unwanted fields from the document

Solution

1. Step 1: Understand the role of grok processor

   The grok processor is designed to parse unstructured text and extract meaningful fields using patterns.

2. Step 2: Compare with other processors

   Date processor converts strings to dates, rename changes field names, and delete removes fields, so these are not the main purpose of grok.

3. Final Answer:

   To extract structured fields from unstructured text -> Option A

4. Quick Check:

   grok = extract fields [OK]

Hint: Grok = extract parts from text, not rename or convert [OK]

Common Mistakes:

• Confusing grok with date processor
• Thinking grok renames fields
• Assuming grok deletes fields

2. Which of the following is the correct syntax to rename a field old_name to new_name using the rename processor in an ingest pipeline?

easy

A. "rename": { "source_field": "old_name", "field": "new_name" }

B. "rename": { "field": "old_name", "target_field": "new_name" }

C. "rename": { "field": "new_name", "target_field": "old_name" }

D. "rename": { "source": "new_name", "target": "old_name" }

Solution

1. Step 1: Recall rename processor syntax

   The rename processor uses "field" for the current field name and "target_field" for the new name.

2. Step 2: Match options with correct syntax

   "rename": { "field": "old_name", "target_field": "new_name" } correctly uses "field": "old_name" and "target_field": "new_name". Others swap or use wrong keys.

3. Final Answer:

   "rename": { "field": "old_name", "target_field": "new_name" } -> Option B

4. Quick Check:

   Rename syntax = field + target_field [OK]

Hint: "field" is old name, "target_field" is new name in rename [OK]

Common Mistakes:

• Swapping source and target field names
• Using incorrect keys like "source_field"
• Confusing rename syntax with grok syntax

3. Given this ingest pipeline snippet:

{
  "processors": [
    { "grok": { "field": "message", "patterns": ["%{WORD:action} %{NUMBER:count}"] } },
    { "date": { "field": "timestamp", "formats": ["yyyy-MM-dd HH:mm:ss"] } },
    { "rename": { "field": "count", "target_field": "total" } }
  ]
}

What will be the value of the field total after processing a document with "message": "login 5" and "timestamp": "2023-06-01 12:30:00"?

medium

A. count

B. login

C. 2023-06-01T12:30:00.000Z

D. 5

Solution

1. Step 1: Extract fields using grok

   The grok pattern extracts "action" as "login" and "count" as "5" from the message "login 5".

2. Step 2: Rename "count" to "total"

   The rename processor changes the field "count" with value 5 to "total" with value 5.

3. Final Answer:

   5 -> Option D

4. Quick Check:

   Grok extracts count=5, rename moves it to total [OK]

Hint: Grok extracts fields, rename moves count to total [OK]

Common Mistakes:

• Confusing 'total' with 'action' value
• Thinking date processor changes 'total' field
• Assuming 'total' keeps old field name

4. You have this pipeline processor configuration:

{
  "processors": [
    { "grok": { "field": "log", "patterns": ["%{WORD:status} %{INT:code}"] } },
    { "rename": { "field": "status_code", "target_field": "code" } }
  ]
}

What is the main issue with this configuration?

medium

A. The grok pattern syntax is incorrect

B. The field names in rename are reversed

C. The rename processor tries to rename a field that does not exist

D. The processors are in the wrong order

Solution

1. Step 1: Check grok output fields

   The grok extracts "status" and "code" fields, but not "status_code".

2. Step 2: Analyze rename processor fields

   Rename tries to rename "status_code" to "code", but "status_code" does not exist, causing an error.

3. Final Answer:

   The rename processor tries to rename a field that does not exist -> Option C

4. Quick Check:

   Rename field must exist before renaming [OK]

Hint: Rename field must exist before renaming it [OK]

Common Mistakes:

• Assuming grok creates 'status_code' field
• Swapping rename field and target_field
• Thinking processor order causes error here

5. You want to create an ingest pipeline that:
1. Extracts user and timestamp from a log message using grok.
2. Converts the extracted timestamp string to a date.
3. Renames the user field to username.

Which of the following processor sequences correctly achieves this?

hard

A. [ { "grok": { "field": "message", "patterns": ["%{WORD:user} %{TIMESTAMP_ISO8601:timestamp}"] } }, { "date": { "field": "timestamp", "formats": ["ISO8601"] } }, { "rename": { "field": "user", "target_field": "username" } } ]

B. [ { "date": { "field": "timestamp", "formats": ["ISO8601"] } }, { "grok": { "field": "message", "patterns": ["%{WORD:user} %{TIMESTAMP_ISO8601:timestamp}"] } }, { "rename": { "field": "user", "target_field": "username" } } ]

C. [ { "rename": { "field": "user", "target_field": "username" } }, { "grok": { "field": "message", "patterns": ["%{WORD:user} %{TIMESTAMP_ISO8601:timestamp}"] } }, { "date": { "field": "timestamp", "formats": ["ISO8601"] } } ]

D. [ { "grok": { "field": "message", "patterns": ["%{WORD:username} %{TIMESTAMP_ISO8601:timestamp}"] } }, { "date": { "field": "timestamp", "formats": ["ISO8601"] } } ]

Solution

1. Step 1: Extract fields with grok first

   The grok processor must run first to extract "user" and "timestamp" from the message.

2. Step 2: Convert timestamp string to date

   The date processor converts the extracted "timestamp" string to a date object.

3. Step 3: Rename "user" to "username"

   After extraction, rename changes the field name from "user" to "username".

4. Final Answer:

   Processors in order: grok, date, rename as in option A -> Option A

5. Quick Check:

   Extract, convert date, then rename fields [OK]

Hint: Order: grok extracts, date converts, rename changes name [OK]

Common Mistakes:

• Running date before grok extraction
• Renaming before extracting fields
• Extracting username directly in grok without rename