Streaming Vector Indexing

Overview

Streaming vector indexing processes vectors incrementally as they arrive, making them immediately searchable without waiting for batch jobs. Essential for real-time applications.

Batch vs Streaming

Batch Indexing

• Collect vectors
• Build index periodically (hourly/daily)
• High latency (hours)
• Better index quality

Streaming Indexing

• Index each vector on arrival
• Immediate searchability (seconds)
• Slightly degraded index quality
• Continuous updates

Implementation Patterns

Dual-Index Strategy

# Recent vectors: in-memory, immediately searchable
recent_index.add(new_vector)

# Older vectors: optimized disk index
if time_to_merge:
    optimized_index.merge(recent_index)
    recent_index.clear()

# Search both
results = search([recent_index, optimized_index], query)

Delta Index

• Main index: batch-built, optimized
• Delta index: streaming, fast insertion
• Periodic merge to main

Challenges

• Index quality degradation
• Resource overhead
• Merge complexity
• Consistency during merges

Use Cases

• Social media feeds
• News recommendation
• Real-time monitoring
• Fraud detection
• Live event search

Database Support

• Milvus: Streaming updates with growing segments
• Pinecone: Real-time upserts
• Weaviate: Immediate indexing
• Qdrant: Online indexing

Best Practices

1. Use dual-index for best quality + freshness
2. Monitor index quality metrics
3. Schedule periodic rebuilds
4. Test merge strategies
5. Benchmark search latency

Pricing

Higher resource usage than batch; varies by database.