How do you store a tick? - briefly
Place the specimen in a labeled, airtight vial with a desiccant and keep it at a low, stable temperature (e.g., −20 °C). Log the collection date, location, and species identifiers in a digital database for later analysis.
How do you store a tick? - in detail
Storing individual market ticks requires a system that preserves temporal precision, handles high‑frequency volume, and supports rapid retrieval. The process consists of several essential steps.
First, choose an appropriate data model. A flat record containing timestamp, price, volume, and trade identifier is the minimal viable structure. Adding fields such as bid/ask quotes, exchange code, and trade conditions improves analytical capability without significantly increasing storage overhead.
Second, select a storage medium. Options include:
- Binary flat files (e.g., Protocol Buffers, Apache Arrow) – provide compact representation and fast sequential reads.
- Column‑oriented databases (e.g., ClickHouse, Apache Parquet) – enable efficient compression and column‑level filtering.
- Time‑series databases (e.g., InfluxDB, TimescaleDB) – offer built‑in retention policies and time‑range queries.
Third, apply compression techniques. Delta encoding on timestamps, run‑length encoding for repeated values, and dictionary compression for textual fields reduce disk usage by 60‑80 % while preserving exact data.
Fourth, implement indexing. Create primary indexes on timestamps for range scans and secondary indexes on symbols or exchange identifiers for selective retrieval. Partitioning data by day or hour further improves query performance and simplifies maintenance.
Fifth, ensure data integrity. Use checksums (e.g., CRC32) on each record batch, employ write‑ahead logs, and replicate data across nodes to protect against loss.
Finally, design a data ingestion pipeline. Capture ticks from a feed handler, serialize them into the chosen format, batch them into fixed‑size blocks (e.g., 1 MB), and write asynchronously to storage. Monitoring latency, throughput, and error rates guarantees that the system meets the demands of high‑frequency environments.
By combining a lean record schema, efficient binary storage, aggressive compression, strategic indexing, and robust ingestion, a reliable repository for tick‑level information can be constructed and maintained.