Batch Ingestion

SUMMARY

Batch ingestion is a data loading pattern where records are collected into groups and processed together in discrete intervals, rather than handled individually in real-time. This approach is particularly important for time-series databases, offering efficient ways to load historical data, perform bulk updates, and optimize resource usage.

How batch ingestion works

Batch ingestion aggregates data into chunks before processing, following a collect-then-process pattern. This differs from real-time ingestion where records are processed immediately as they arrive. The batch process typically involves:

Data collection and staging
Validation and transformation
Bulk loading into the target database
Post-load verification and cleanup

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Advantages of batch processing

Performance optimization

Batch ingestion enables several performance optimizations:

Reduced I/O operations through consolidated writes
Efficient index updates
Better resource utilization
Opportunity for parallel processing

Data quality control

Batching provides opportunities for comprehensive data validation:

Pre-load data cleansing
Schema validation
Deduplication checks
Referential integrity verification

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Common batch ingestion patterns

Time-based batching

Time-series data is often batched by temporal windows:

INSERT INTO weather 
SELECT * FROM weather_staging
WHERE timestamp BETWEEN '2023-01-01' AND '2023-01-02';

Size-based batching

Data can be batched based on record count or volume:

INSERT INTO trades
SELECT * FROM trade_buffer
WHERE batch_size <= 1000000;

Challenges and considerations

Late arriving data

Batch processes must handle late arriving data appropriately, especially in time-series contexts. This might involve:

Maintaining separate queues for late data
Implementing merge strategies
Updating historical aggregates

Resource management

Batch operations require careful resource planning:

Memory allocation for large batches
CPU utilization during processing
Storage I/O capacity
Network bandwidth for distributed systems

Data consistency

Ensuring data consistency during batch operations involves:

Transaction management
Rollback capabilities
Error handling and recovery
Progress tracking and checkpointing

Best practices for batch ingestion

Size optimization: Choose batch sizes that balance throughput with resource constraints
Monitoring: Implement comprehensive monitoring for:
- Batch completion status
- Processing duration
- Error rates
- Resource utilization
Error handling: Develop robust error management:
- Failed record isolation
- Retry mechanisms
- Error logging and alerting
Performance tuning: Regular optimization of:
- Batch size
- Processing windows
- Resource allocation
- Index maintenance

Integration with time-series systems

When working with time-series data, batch ingestion often requires special consideration for:

Timestamp handling
Partition alignment
Historical data backfilling
Aggregation maintenance

The following example shows a typical batch ingestion pattern for time-series data:

WITH batch_window AS (
    SELECT * FROM staging_data
    WHERE timestamp BETWEEN '2023-01-01' AND '2023-01-02'
)
INSERT INTO production_table
SELECT * FROM batch_window;