如何便捷将含Grok的Logstash过滤器转换为Ingest Pipeline？

Great question—since Logstash's official ingest-convert.sh only goes one way (Ingest → Logstash), reversing the process takes a bit of work, but there are efficient ways to avoid manual JSON writing entirely. Here's how I'd approach it with your Tomcat filters and custom grok patterns:

1. Leverage Grok Syntax Compatibility #

First, know that Elasticsearch's Grok Processor shares nearly identical syntax with Logstash's Grok Filter. Core patterns (like COMBINEDAPACHELOG) work the same, and custom patterns from your grok_patterns file can be directly reused—you just need to package them correctly for Ingest.

2. Migrate Custom Grok Patterns #

You have two options for adding your custom patterns to Ingest Pipelines:

• Inline in the pipeline: Define patterns directly in the pattern_definitions field of the grok processor. This is ideal for self-contained pipelines that don't rely on shared patterns.
• Cluster-wide pattern library: Upload your grok_patterns file to Elasticsearch's centralized pattern store via the _ingest/pipeline/grok_patterns API. This lets all pipelines reuse the patterns without duplicating code.

For example, if your grok_patterns has a TOMCAT_ACCESS_LOG pattern, inline it like this:

{
  "grok": {
    "field": "message",
    "patterns": ["%{TOMCAT_ACCESS_LOG}"],
    "pattern_definitions": {
      "TOMCAT_ACCESS_LOG": "%{IPORHOST:clientip} %{USER:ident} %{USER:auth} \\[%{HTTPDATE:timestamp}\\] \"%{WORD:verb} %{URIPATHPARAM:request} HTTP/%{NUMBER:httpversion}\" %{NUMBER:response} (?:%{NUMBER:bytes}|-) \"%{DATA:referrer}\" \"%{DATA:agent}\""
    }
  }
}

3. Convert Logstash Filter Blocks to Ingest Processors #

Most Logstash filter plugins have direct equivalents in Ingest Processors. Let's break down a typical Tomcat filter example:

Logstash Filter (from 23-Tomcat-filters) #

filter {
  grok {
    match => { "message" => "%{TOMCAT_ACCESS_LOG}" }
    patterns_dir => "./grok_patterns"
    add_field => { "service" => "tomcat" }
    tag_on_failure => ["_grokparsefailure"]
  }
  date {
    match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
    target => "@timestamp"
  }
  mutate {
    remove_field => ["message", "timestamp"]
  }
}

Equivalent Ingest Pipeline JSON #

{
  "description": "Tomcat access log processing pipeline",
  "processors": [
    {
      "grok": {
        "field": "message",
        "patterns": ["%{TOMCAT_ACCESS_LOG}"],
        "pattern_definitions": {
          // Paste all your custom grok patterns here
          "TOMCAT_ACCESS_LOG": "your-custom-pattern-here"
        },
        "add_fields": {
          "service": "tomcat"
        },
        "tag_on_failure": ["_grokparsefailure"]
      }
    },
    {
      "date": {
        "field": "timestamp",
        "formats": ["dd/MMM/yyyy:HH:mm:ss Z"],
        "target": "@timestamp"
      }
    },
    {
      "remove": {
        "field": ["message", "timestamp"]
      }
    }
  ]
}

Key mapping notes to watch for:

• Logstash's match → Ingest's patterns (array of patterns)
• add_field → add_fields (plural)
• mutate { remove_field } → remove processor
• Most filter parameters translate directly—check Elasticsearch docs for edge cases like conditional logic.

4. Automate Bulk Conversion #

If you have dozens of filters, writing JSON manually is tedious. Here's a quick way to automate:

• Use a simple Ruby/Python script to parse your Logstash .conf files. Logstash uses a Ruby-based config syntax, so you can use Ruby's LogStash::Config::Parser library to load the config and extract filter details.
• Map each filter plugin to its Ingest processor equivalent, then generate the pipeline JSON.

For example, a minimal Ruby snippet to extract grok filters:

require 'logstash/config/parser'

config = LogStash::Config::Parser.parse(File.read('23-Tomcat-filters.conf'))
grok_filters = config.filters.select { |f| f.is_a?(LogStash::Filters::Grok) }

grok_filters.each do |filter|
  puts "Pattern: #{filter.match['message']}"
  puts "Add fields: #{filter.add_field}"
  # Generate corresponding JSON here using string interpolation or a JSON library
end

5. Validate the Pipeline #

Always test your pipeline with Elasticsearch's Simulate API before deploying to catch parsing issues:

# Use Kibana Dev Tools or curl
POST _ingest/pipeline/_simulate
{
  "pipeline": { /* your pipeline JSON */ },
  "docs": [
    {
      "_source": {
        "message": "your-tomcat-log-line-here"
      }
    }
  ]
}

This will show you if fields are parsed correctly, if tags are applied on failure, and if any unexpected errors occur.

内容的提问来源于stack exchange，提问作者Dennis