Ingestion Deduplication

How ingestion deduplication works

Ingestion deduplication uses unique identifiers or composite keys to detect and handle duplicate records during the real-time data ingestion process. The system typically maintains a temporal window of recently seen records to compare incoming data against potential duplicates.

Deduplication strategies

Natural key deduplication

Uses business-meaningful fields as unique identifiers, such as transaction IDs or event timestamps combined with source identifiers.

Synthetic key deduplication

Generates hash values from multiple fields to create a unique fingerprint for each record.

Time-series specific considerations

In time-series contexts, deduplication often involves:

1. Time-based windows for duplicate detection
2. Handling out-of-order data arrival
3. Balancing deduplication accuracy with ingestion performance

For example, in financial market data, the same trade might be reported multiple times:

# Pseudo-code for time-series deduplication
def deduplicate_trade(trade):
    key = f"{trade.timestamp}:{trade.symbol}:{trade.price}:{trade.volume}"
    if key in recent_trades_window:
        return "duplicate"
    recent_trades_window.add(key)
    return "unique"

Performance implications

Deduplication processes must balance several factors:

• Memory usage for maintaining duplicate detection windows
• CPU overhead for comparison operations
• Ingestion latency impact
• Storage efficiency gains from eliminating duplicates

Common challenges

Late-arriving data

When data arrives out of order, the deduplication window must be large enough to catch duplicates while small enough to maintain performance.

High-cardinality data

With high cardinality datasets, maintaining efficient lookup structures for deduplication becomes challenging.

System coordination

In distributed systems, coordination between nodes is needed to maintain consistent deduplication across the entire system.

Industrial applications

In industrial settings with high-frequency sensor data, deduplication is crucial for:

• Ensuring accurate equipment monitoring
• Preventing false alerts from duplicate readings
• Optimizing storage utilization
• Maintaining data quality for analytics

The system must handle these requirements while maintaining high-throughput ingestion capabilities:

Deduplication

QuestDB provides native deduplication during ingestion.

Isn't that much more simple?!