Ingestion Format

Understanding ingestion formats

Ingestion formats provide a standardized way to structure and encode data for efficient loading into time-series databases. The format defines how timestamps, metrics, and associated metadata are organized, impacting everything from parsing performance to storage requirements.

Key components of ingestion formats

• Timestamp encoding: How temporal information is represented
• Data type specifications: Definition of metric value formats
• Schema information: Structure of the data and relationships
• Metadata handling: How tags and labels are encoded
• Compression characteristics: Built-in data reduction capabilities

Common ingestion formats

JSON

JSON is a flexible, human-readable format popular for its ease of use and wide support. While not the most efficient for high-volume ingestion, it excels in development and debugging scenarios.

{
  "timestamp": "2024-01-20T10:30:00Z",
  "metric": "cpu_usage",
  "value": 85.5,
  "tags": {
    "host": "server-01",
    "datacenter": "us-east"
  }
}

Protocol Buffers

Protocol buffer ingestion offers a compact binary format with strong schema validation, making it ideal for high-performance production environments.

CSV and Line Protocols

Text-based formats offering simplicity and widespread tool support. Often used for bulk loading historical data.

timestamp,metric,value,host,datacenter
2024-01-20T10:30:00Z,cpu_usage,85.5,server-01,us-east

Performance considerations

Parsing efficiency

The format's complexity directly impacts parsing overhead. Binary formats like Protocol Buffers typically offer superior parsing performance compared to text-based formats.

Memory utilization

Format choice affects memory usage during ingestion:

• Binary formats: Lower memory overhead
• Text formats: Higher memory requirements for parsing
• Compressed formats: Trade CPU for memory efficiency

Schema flexibility

Different formats offer varying levels of schema evolution support:

• JSON: Highly flexible but with overhead
• Protocol Buffers: Strong typing with controlled evolution
• CSV: Fixed schema with limited flexibility

Monitoring and optimization

Ingestion metrics

Key metrics to monitor:

• Parse time per record
• Memory usage during ingestion
• Error rates by format type
• Write throughput by format

Format selection criteria

Consider these factors when choosing an ingestion format:

• Data volume and velocity requirements
• Schema stability vs flexibility needs
• Development and operational complexity
• Tool ecosystem compatibility

Best practices

1. Match format to use case:

   • Development: Human-readable formats
   • Production: Optimized binary formats
   • Bulk loading: Compressed text formats

2. Consider the full pipeline:

   • Source system capabilities
   • Network transfer efficiency
   • Storage implications
   • Query requirements

3. Plan for evolution:

   • Format versioning strategy
   • Schema change procedures
   • Backward compatibility needs

4. Implement proper validation:

   • Schema validation
   • Data quality checks
   • Error handling procedures