Designing a time-series database for market data involves optimizing storage for timestamped financial data with high write throughput, compression ratios of 10-20×, and sub-millisecond query performance for trading algorithms and risk calculations.
Why It Matters
Market data systems process 50-100 million ticks per day for major exchanges, requiring databases that handle 500,000+ writes per second during peak trading. Proper design reduces storage costs by 85% through compression and enables microsecond-latency queries for algorithmic trading. Poor architecture can cost firms $10-50M annually in missed trading opportunities and regulatory reporting delays.
How It Works in Practice
- 1Partition data by instrument and time intervals (hourly/daily) to optimize query performance for specific securities and date ranges
- 2Implement columnar storage with delta encoding to achieve 15-20× compression ratios on repetitive price and volume data
- 3Configure write-optimized ingestion pipelines that batch incoming ticks into 100-1000 record chunks before persistence
- 4Design retention policies with automated tiering from hot storage (1-30 days) to cold storage (historical data) based on access patterns
- 5Index timestamps and instrument identifiers using B-tree or LSM-tree structures for sub-millisecond point-in-time lookups
Common Pitfalls
Failing to implement proper data lineage tracking can violate MiFID II transaction reporting requirements for audit trails
Over-indexing secondary dimensions like bid-ask spreads creates write bottlenecks during high-volume trading periods
Using generic RDBMS instead of purpose-built time-series databases results in 100× slower query performance for analytical workloads
Key Metrics
| Metric | Target | Formula |
|---|---|---|
| Write Throughput | >500K/sec | Total records ingested per second during peak market hours |
| Query Latency P99 | <10ms | 99th percentile response time for single-instrument historical queries |
| Storage Efficiency | >15x compression | Raw data size divided by compressed storage footprint |