Ingestion: Pipelines & Grok

[!NOTE] This module explores the core principles of Ingestion: Pipelines & Grok, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. The Architecture Shift: Logstash \to Ingest Nodes

Old Way: App → Filebeat → Logstash (Heavy Java Process) → Elasticsearch

• Pros: Powerful.
• Cons: Another cluster to manage. Single point of failure.

New Way (Ingest Pipelines): App → Filebeat → Elasticsearch (Ingest Node)

• Pros: Serverless ETL. Runs inside ES.
• Mechanism: Before indexing, the document passes through a chain of Processors.

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2. The Power of Grok

Unstructured logs are useless. "2023-10-01 12:00:00 ERROR [Auth] Failed login" Grok turns this into JSON using Regex patterns. %{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} \[%{DATA:module}\] %{GREEDYDATA:msg}

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3. Interactive: Grok Debugger

Test your patterns against raw logs.

Raw Log Line: Grok Pattern:

Structured Output (JSON)

{}

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4. Hardware Reality: Ingest CPU

Ingest pipelines run on the write path.

• Heavy Regex (Grok) = High CPU usage.
• Impact: Slows down indexing throughput.
• Solution: Use Dedicated Ingest Nodes (node.roles: [ingest]). Keep them separate from Data/Master nodes so your search performance doesn’t degrade during log spikes.