Schema on Write

How schema-on-write works

Schema-on-write validates incoming data against a predefined schema when data is first written to the database. This approach acts as a gatekeeper, rejecting any data that doesn't conform to the expected structure. The process involves:

1. Schema definition
2. Validation at write time
3. Storage in structured format
4. Optimized read performance

Benefits and tradeoffs

Advantages

• Ensures data quality and consistency
• Faster query performance
• Reduced storage overhead
• Simplified analytics queries

Disadvantages

• Higher write latency
• Less flexibility for schema changes
• Potential ingestion failures
• Higher upfront design complexity

Comparison with schema-on-read

Unlike schema-on-read, schema-on-write frontloads data validation work. This fundamental difference affects several aspects of database operations:

Applications in time-series data

In time-series databases, schema-on-write is particularly important for:

1. Industrial sensor data validation
2. Financial market data normalization
3. IoT device data conformance
4. Regulatory compliance requirements

For example, in financial market data:

# Pseudocode example of schema validation
schema = {
    "timestamp": "datetime",
    "symbol": "string",
    "price": "decimal",
    "volume": "integer"
}

def validate_trade(data):
    if not matches_schema(data, schema):
        raise ValidationError("Invalid trade data")
    return process_trade(data)

Best practices

1. Careful Schema Design

   • Plan for future fields
   • Consider data types carefully
   • Document schema decisions

2. Performance Optimization

   • Balance validation depth with performance
   • Use appropriate data types
   • Consider batch validation for bulk loads

3. Error Handling

   • Implement clear error messages
   • Create recovery procedures
   • Log validation failures

4. Schema Evolution

   • Plan for schema updates
   • Maintain backward compatibility
   • Document schema versions

Schema-on-write remains a cornerstone of data quality management, particularly in systems where data consistency and reliability are paramount.