ElasticDoctor - Elasticsearch Health Diagnostics

Data Processing Pipeline Health

Ingest pipelines transform raw data before indexing, performing tasks like parsing, enrichment, and normalization. Well-configured pipelines improve data quality and search relevance, while poorly designed ones can become performance bottlenecks and cause indexing failures.

The ingest pipelines check evaluates your data processing workflows for efficiency, reliability, and best practices. It identifies performance bottlenecks, configuration issues, and optimization opportunities that can improve both indexing speed and data quality.

Ingest Pipeline APIs

Pipeline Management APIsES 5.0+

GET /_ingest/pipeline - List all pipelines

GET /_ingest/pipeline/pipeline_name - Get specific pipeline

GET /_nodes/stats/ingest - Pipeline performance stats

POST /_ingest/pipeline/pipeline_name/_simulate - Test pipeline

✅ What This Check Monitors

• Pipeline configuration and syntax
• Processing performance and latency
• Error rates and failure patterns
• Resource usage and efficiency
• Processor optimization opportunities
• Data transformation accuracy

🔧 Common Processors

• Grok: Pattern matching and extraction
• Date: Timestamp parsing and formatting
• GeoIP: IP address geolocation
• User Agent: Browser/device detection
• Script: Custom data transformation
• Split: Array field processing

Pipeline Performance Analysis

1. Processing Performance Metrics

{
  "nodes": {
    "node_id": {
      "ingest": {
        "total": {
          "count": 50000,
          "time_in_millis": 125000,
          "current": 5,
          "failed": 23
        },
        "pipelines": {
          "log_pipeline": {
            "count": 30000,
            "time_in_millis": 75000,
            "failed": 12,
            "processors": [
              {
                "grok": {
                  "count": 30000,
                  "time_in_millis": 45000,
                  "failed": 8
                }
              }
            ]
          }
        }
      }
    }
  }
}

Performance Thresholds

• Good: <50ms avg processing time
• Warning: 50-200ms processing time
• Critical: >200ms processing time
• Error Rate: >1% failure rate

ElasticDoctor Analysis

• Identifies slow processors
• Calculates processing efficiency
• Detects error patterns
• Recommends optimizations

2. Processor Optimization

# Example: Optimizing Grok patterns
# Inefficient - multiple complex patterns
{
  "grok": {
    "field": "message",
    "patterns": [
      "%{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} %{DATA:message}",
      "%{HTTPDATE:timestamp} %{WORD:level} %{GREEDYDATA:message}"
    ]
  }
}

# Optimized - specific, simpler patterns
{
  "grok": {
    "field": "message",
    "pattern": "%{TIMESTAMP_ISO8601:timestamp} %{WORD:level} %{GREEDYDATA:message}",
    "pattern_definitions": {
      "TIMESTAMP_ISO8601": "%{YEAR}-%{MONTHNUM}-%{MONTHDAY}[T ]%{HOUR}:%{MINUTE}:%{SECOND}"
    }
  }
}

Fast Processors

• Set (field assignment)
• Remove (field deletion)
• Rename (field renaming)
• Convert (type conversion)

Moderate Processors

• Date (timestamp parsing)
• GeoIP (location lookup)
• User Agent (parsing)
• Dissect (structured parsing)

Slow Processors

• Grok (regex matching)
• Script (custom logic)
• Enrich (external lookups)
• Attachment (document parsing)

3. Error Handling and Resilience

# Robust pipeline with error handling
{
  "description": "Parse web logs with error handling",
  "processors": [
    {
      "grok": {
        "field": "message",
        "pattern": "%{COMBINEDAPACHELOG}",
        "on_failure": [
          {
            "set": {
              "field": "grok_error",
              "value": "Failed to parse log format"
            }
          }
        ]
      }
    },
    {
      "date": {
        "field": "timestamp",
        "formats": ["dd/MMM/yyyy:HH:mm:ss Z"],
        "on_failure": [
          {
            "set": {
              "field": "date_error", 
              "value": "Failed to parse timestamp"
            }
          }
        ]
      }
    }
  ],
  "on_failure": [
    {
      "set": {
        "field": "pipeline_error",
        "value": "Pipeline processing failed"
      }
    }
  ]
}

Error Patterns

• Grok pattern mismatches
• Date format parsing errors
• Missing required fields
• Type conversion failures

Best Practices

• Add on_failure handlers
• Use conditional processors
• Validate input data formats
• Monitor error rates

Common Pipeline Issues

🚨 Critical: High Processing Latency

Pipeline processing time significantly impacts indexing performance and cluster throughput.

Optimization Actions:

1. Profile individual processor performance
2. Simplify complex grok patterns
3. Use dissect instead of grok where possible
4. Optimize script processors
5. Consider conditional processing

⚠️ Warning: High Error Rate

Frequent processing failures indicate configuration issues or data quality problems.

Investigation Steps:

• Analyze failed documents and error patterns
• Review grok patterns and data formats
• Add comprehensive error handling
• Test with sample data using simulate API
• Monitor data source changes

ℹ️ Info: Inefficient Processor Configuration

Pipeline design can be optimized for better performance and reliability.

Optimization Options:

• Reorder processors by efficiency
• Use conditional logic to skip unnecessary processing
• Cache expensive operations where possible
• Split complex pipelines into smaller ones
• Use appropriate processor alternatives

Pipeline Design Best Practices

✅ Performance Optimization

• Order processors by execution speed
• Use specific grok patterns, avoid GREEDYDATA
• Implement conditional processing
• Cache GeoIP and user agent databases
• Monitor processing metrics regularly
• Test with realistic data volumes

💡 Design Tips

• Keep pipelines simple and focused
• Use meaningful field names
• Document processor purpose and logic
• Version control pipeline configurations
• Test thoroughly with simulate API

❌ Common Pitfalls

• Complex, nested grok patterns
• Missing error handling
• Overly complex single pipelines
• Not monitoring performance metrics
• Hardcoded values instead of parameters
• Not testing with production data

⚠️ Performance Impact

• Slow processors block indexing
• High error rates waste resources
• Complex patterns increase CPU usage
• Poor error handling causes data loss
• Unoptimized order reduces efficiency

Pipeline Management Examples

Performance Monitoring

# Get pipeline performance statistics
GET /_nodes/stats/ingest

# Get specific pipeline details
GET /_ingest/pipeline/my_pipeline

# Test pipeline with sample data
POST /_ingest/pipeline/my_pipeline/_simulate
{
  "docs": [
    {
      "_source": {
        "message": "192.168.1.1 - - [25/Dec/2024:10:30:45 +0000] "GET /index.html HTTP/1.1" 200 1234"
      }
    }
  ]
}

# Monitor pipeline errors
GET /_ingest/pipeline/my_pipeline/_simulate
{
  "docs": [
    {
      "_source": {
        "message": "invalid log format"
      }
    }
  ]
}

ElasticDoctor Pipeline Analysis

🔍 How ElasticDoctor Analyzes Pipelines

Performance Profiling

ElasticDoctor analyzes processing times for each processor, identifying bottlenecks and recommending optimizations based on usage patterns.

Error Pattern Detection

Automatically identifies common failure patterns and suggests improvements to grok patterns, error handling, and data validation.

Configuration Optimization

Reviews pipeline configuration for best practices, processor ordering, and opportunities to improve processing efficiency.

Resource Usage Analysis

Monitors CPU and memory usage during pipeline processing to identify resource-intensive operations and scaling needs.

Optimizing Data Processing Pipelines

Key Benefits

• Improved data quality through proper transformation
• Faster indexing with optimized processing
• Reduced error rates and failed documents
• Better resource utilization and efficiency

Action Plan

• Audit existing pipeline performance
• Optimize slow processors and patterns
• Implement comprehensive error handling
• Monitor processing metrics continuously

Previous: Pending Tasks Check Next: Snapshots Check

Ingest Pipelines Check: Data Processing and Transformation