Tick Data Storage Architecture
Tick data storage architecture describes how financial systems persist, organize, and serve high-frequency tick data from exchanges and venues. A good design balances write throughput, long-term retention, and low-latency queries for trading, risk, and surveillance use cases.
Why Tick Data Needs Specialized Storage
Tick feeds combine extreme volume, fine timestamp precision, and strict retention requirements. Systems must absorb millions of updates per second, keep years of history, and still allow fast reconstruction of the market at any point in time.
Architectures typically treat ticks as an immutable event stream, written in append-only fashion, then partitioned by time (day or hour) and often by symbol or venue. This aligns with common query patterns such as “all trades in instrument X between T1 and T2” or “all quotes for a venue around a flash event.”
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Logical Layout and Compression Patterns
Physically, ticks are usually stored in a columnar layout: timestamps, prices, sizes, side flags, and symbol identifiers live in separate columns. This improves scan efficiency and unlocks specialized compression per field.
Common patterns include:
- Time-based partitions with symbol or venue as a key, often backed by a time-series index.
- Dictionary-encoded symbols and venues to compress repeated identifiers.
- Delta or delta-of-delta encoding for monotonically increasing timestamps and typically smooth price series.
- Run-length encoding for boolean flags such as trade/quote markers or liquidity indicators.
For order-book-level feeds, many architectures separate order book data storage from trade ticks, but reuse similar compression building blocks described in time-series compression algorithms.
Retrieval, Query Paths, and Performance
Query engines exploit the storage layout to prune irrelevant data early. Time filters skip whole partitions; symbol filters restrict to a narrow key range; columnar access reads only the fields needed for a calculation such as VWAP or implementation shortfall.
Hot, recent partitions often live on faster tiers or in cache, while older data moves to cheaper media via storage tiering or export to open formats for bulk research. Vectorized scans and sequential I/O are preferred over heavy indexing, since most tick workloads are dominated by wide time-range scans under tight latency budgets.
Related Concepts and Further Reading
Tick storage architectures sit alongside market data time-series schema design and market data time-series database choices.
For a practical view of ingestion pipelines feeding such architectures, see Ingesting Financial Tick Data Using a Time-Series Database.