CSV Ingestion

How CSV ingestion works

CSV ingestion typically follows a structured pipeline:

1. File parsing and validation
2. Schema mapping and type inference
3. Timestamp identification and parsing
4. Data transformation and loading

Schema mapping considerations

When ingesting CSV data into time-series databases, several key aspects require attention:

• Timestamp column identification
• Data type inference and conversion
• Column name mapping
• Handling missing values
• Character encoding validation

Optimizing CSV ingestion performance

Batch processing

For large CSV files, batch processing improves ingestion efficiency:

# Pseudocode for optimized CSV batch processing
def process_csv_batches(file_path, batch_size=10000):
    current_batch = []
    
    for row in csv_reader:
        current_batch.append(row)
        
        if len(current_batch) >= batch_size:
            ingest_batch(current_batch)
            current_batch = []

Memory management

Efficient memory usage during ingestion involves:

• Streaming large files instead of loading entirely into memory
• Buffer size optimization
• Proper resource cleanup
• Memory-mapped file access when appropriate

Common challenges and solutions

Data quality issues

CSV ingestion must handle various data quality challenges:

• Malformed records
• Inconsistent delimiters
• Quoted fields with embedded commas
• Line ending variations
• Invalid timestamps

Performance optimization

Several techniques can enhance ingestion performance:

• Parallel processing for large files
• Pre-allocation of resources
• Efficient string parsing
• Type conversion optimization
• Index management during bulk loads

Integration with time-series workflows

CSV ingestion often connects with broader data processing pipelines:

• ETL processes
• Data validation workflows
• Real-time analytics
• Historical data backfilling
• Data migration projects

For example, a typical ingestion workflow might look like:

WITH 'trades.csv' AS input 
SELECT * FROM input 
WHERE timestamp > '2024-01-01'
LIMIT 10;

Best practices

1. Always validate timestamp formats and timezone handling
2. Implement proper error handling and logging
3. Consider performance impact on existing data
4. Monitor system resources during ingestion
5. Maintain data quality checks throughout the process

By following these practices, organizations can ensure reliable and efficient CSV ingestion into their time-series databases while maintaining data integrity and system performance.