Ingest processors (grok, date, rename) in Elasticsearch - Step-by-Step Execution

Concept Flow - Ingest processors (grok, date, rename)

Start: Receive document

↓

Apply grok processor

↓

Apply date processor

↓

Apply rename processor

↓

Output: Transformed document

The document flows through grok to parse text, then date to convert strings to dates, and finally rename to change field names.

Execution Sample

Elasticsearch

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

This pipeline parses an Apache log message, converts the timestamp to a date, and renames the client IP field.

Execution Table

Step	Processor	Input Document	Action	Output Document
1	grok	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326"}	Parse message into fields like clientip, timestamp, verb, etc.	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "10/Oct/2000:13:55:36 -0700", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}
2	date	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "10/Oct/2000:13:55:36 -0700", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}	Convert timestamp string to ISO8601 date format	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "2000-10-10T13:55:36-07:00", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}
3	rename	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "2000-10-10T13:55:36-07:00", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}	Rename field 'clientip' to 'ip_address'	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "ip_address": "127.0.0.1", "timestamp": "2000-10-10T13:55:36-07:00", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}

💡 All processors applied, document transformed with parsed fields, date converted, and field renamed.

Variable Tracker

Variable	Start	After grok	After date	After rename
document	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326"}	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "10/Oct/2000:13:55:36 -0700", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "clientip": "127.0.0.1", "timestamp": "2000-10-10T13:55:36-07:00", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}	{"message": "127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] \"GET /apache.gif HTTP/1.0\" 200 2326", "ip_address": "127.0.0.1", "timestamp": "2000-10-10T13:55:36-07:00", "verb": "GET", "request": "/apache.gif", "response": "200", "bytes": "2326"}

Key Moments - 3 Insights

Why does the 'timestamp' field change format after the date processor?

The date processor converts the timestamp string from a custom format to a standard ISO8601 date format, as shown in execution_table step 2.

What happens if the rename processor tries to rename a field that does not exist?

The rename processor will fail or skip renaming if the source field is missing; in our example, it successfully renames 'clientip' to 'ip_address' as shown in step 3.

How does the grok processor extract multiple fields from a single message string?

Grok uses patterns to parse the message and create new fields like 'clientip' and 'timestamp', demonstrated in execution_table step 1.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of 'timestamp' after the date processor runs?

A "2000-10-10T13:55:36-07:00"

B "10/Oct/2000:13:55:36 -0700"

C "13:55:36"

D "Oct 10 2000"

💡 Hint

Check execution_table row 2, Output Document column.

At which step is the field 'clientip' renamed to 'ip_address'?

A Step 1

B Step 2

C Step 3

D No renaming happens

💡 Hint

Look at execution_table step 3, Action and Output Document.

If the grok processor fails to parse the message, what happens to the document in the pipeline?

A The document proceeds unchanged to the date processor

B The pipeline stops with an error

C The rename processor runs anyway

D The timestamp field is automatically created

💡 Hint

Grok parsing failure usually stops the pipeline; see key_moments about grok processor behavior.

Concept Snapshot

Ingest processors transform documents step-by-step.
Grok parses text into fields using patterns.
Date converts string timestamps to date objects.
Rename changes field names.
Processors run in order, modifying the document.
Errors in processors can stop the pipeline.

Full Transcript

This visual execution shows how an Elasticsearch ingest pipeline processes a document using grok, date, and rename processors. First, the grok processor parses a log message string into multiple fields like clientip and timestamp. Then, the date processor converts the timestamp string into a standard date format. Finally, the rename processor changes the field name clientip to ip_address. The execution table traces each step with input and output documents, showing how the document changes. The variable tracker highlights the document state after each processor. Key moments clarify common confusions such as why the timestamp format changes and what happens if rename targets a missing field. The quiz tests understanding by asking about field values and processor effects. This step-by-step flow helps beginners see exactly how ingest processors transform data in Elasticsearch.

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