docker run -d \ --name tdb-v2 \ -p 8086:8086 \ -v /data/tdb2:/var/lib/tdb2 \ -e TDB_RETENTION_POLICY=30d \ tdb/v2:latest

50k vehicles × 20 metrics every 30 seconds → 1.2B points/day. Keep raw 7 days, hourly averages 1 year.

Note: If you are referring to a specific proprietary system (e.g., a company’s internal TDB, a renamed tool like TDengine, or a module in Apache Jena), this guide covers general principles of modern TDB V2 architecture. Adjust based on your actual stack.

: During the SARS-CoV-2 pandemic, TDB V2 was the primary vehicle for distributing infection control protocols and Personal Protective Equipment (PPE) standards to ensure clinic safety.

SELECT max(voltage) FROM power FILL(linear) GROUP BY time(10m)

| Feature | Description | |---------|-------------| | | Each data point has a version (for backfill/update). | | Automatic downsampling | Pre-aggregated views: 1m → 5m → 1h using configurable policies. | | Retention policies | Drop older partitions automatically (e.g., keep 30d raw, 1y downsampled). | | Time-weighted functions | TWA (time-weighted average) built-in. | | Out-of-order handling | Accepts late arrivals (configurable max lateness). | | Vectorized execution | Query engine operates on column batches, not rows. |

Based on the architecture and typical evolution of the system—specifically regarding the transition from v1 to v2—the "complete feature" set focuses on moving from a simple key-value abstraction to a fully managed Multi-Version Concurrency Control (MVCC) system with Reactive Materialized Views .

version: 2 storage: partition_interval: 1h # how often new partition created block_max_size_mb: 256 compression: "lz4" # lz4, zstd, snappy bloom_filter_fp: 0.01