How should I store a tick before analysis? - briefly
Store each tick in an immutable, time‑ordered format such as a columnar database or indexed flat file (e.g., Parquet, CSV) that records timestamps and all relevant metadata. Choose a storage solution that enables rapid sequential reads and random access for both batch and real‑time analysis.
How should I store a tick before analysis? - in detail
Store each market tick as a discrete record that preserves the original timestamp, price, volume, and any auxiliary fields required for downstream calculations. Choose a storage medium that matches the expected query patterns and volume.
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File‑based storage
CSV – simple, human‑readable, suitable for low‑frequency data; ensure ISO‑8601 timestamps with nanosecond precision.
Parquet – columnar, compressed, efficient for batch analytics; store timestamps as INT64 Unix epoch nanoseconds; enable predicate push‑down on symbol and time range.
Binary formats (Protocol Buffers, Avro) – compact, schema‑driven; embed a version identifier to handle schema evolution. -
Database solutions
Time‑series databases (InfluxDB, TimescaleDB, kdb+) – automatic partitioning by time, built‑in downsampling, retention policies. Index on symbol and timestamp for rapid point‑in‑time retrieval.
Relational databases – use a composite primary key (symbol, timestamp) and a clustered index; store numeric fields in appropriate types (BIGINT for volume, DOUBLE for price). -
Message queues for real‑time ingestion
Kafka topics with a compacted key (symbol) allow replayable streams; configure log retention to match analysis window. Use schema registry to enforce consistency. -
Data integrity measures
Compute a checksum (e.g., CRC32) for each record and store it alongside the payload. Validate checksums on read to detect corruption. -
Compression and retention
Apply lossless compression (Snappy, LZ4) to reduce storage footprint without affecting numeric precision. Define tiered retention: hot storage for the most recent days, warm storage (compressed files) for historical data, cold storage (object storage, Glacier) for long‑term archive. -
Metadata and provenance
Record source identifier, ingestion timestamp, and any transformation flags in a separate metadata table or sidecar file. This enables reproducibility of analytical results. -
Security and access control
Encrypt data at rest (AES‑256) and in transit (TLS). Assign role‑based permissions to restrict read/write access to authorized services.
Implement a pipeline that writes each incoming tick to the chosen medium within a single atomic transaction. Verify that the write succeeds before acknowledging receipt. This approach guarantees that raw market data remains intact, searchable, and ready for any subsequent statistical or machine‑learning analysis.