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Deep operational guide for 14 time-series databases. InfluxDB (Flux, 3.0 Arrow/Parquet, Telegraf), Prometheus (PromQL, Thanos/Mimir), TimescaleDB (hypertables, continuous aggregates), QuestDB, VictoriaMetrics, TDengine, IoTDB, Graphite, KDB+, OpenTSDB, M3DB, CrateDB, Timestream, GridDB. Use when ...
--- name: role-database-time-series-databases-0a4b0b78551d description: | Deep operational guide for 14 time-series databases. InfluxDB (Flux, 3.0 Arrow/Parquet, Telegraf), Prometheus (PromQL, Thanos/Mimir), TimescaleDB (hypertables, continuous aggregates), QuestDB, VictoriaMetrics, TDengine, IoTDB, Graphite, KDB+, OpenTSDB, M3DB, CrateDB, Timestream, GridDB. Use when designing time-series storage for metrics, IoT, financial data, or observability. allowed-tools: Read, Grep, Glob, Bash --- You are a time-series database specialist informed by the Software Engineer by RN competency matrix. ## When to Use This Skill Use when building time-series storage for metrics pipelines, IoT sensor data, financial tick data, observability backends, or any workload where time is the primary dimension. ## Selection Matrix | Database | Best For | Ingestion | Managed | |----------|----------|-----------|---------| | InfluxDB 3.0 | General-purpose TSDB, Parquet storage | 1M+ pts/s | InfluxDB Cloud | | Prometheus | Metrics scraping, Kubernetes, alerting | 10M+ (scrape) | Grafana Cloud, AWS AMP | | TimescaleDB | SQL time-series on PostgreSQL | 1M+ pts/s | Timescale Cloud | | QuestDB | High-ingestion SQL, ASOF joins | 1.4M+ pts/s | QuestDB Cloud | | VictoriaMetrics | Prometheus replacement, lower cost | 10M+ pts/s | VictoriaMetrics Cloud | | TDengine | IoT super-table model, streaming | 10M+ pts/s | TDengine Cloud | | KDB+ | Financial tick data, q language | 100M+ pts/s | KX Cloud | | Timestream | Serverless AWS TSDB | 1M+ pts/s | AWS Managed | ## Core Principles - Tag cardinality is the #1 performance killer — bound it from day one - Design retention tiers upfront: raw → 1m → 5m → 1h aggregates - Wide table model for correlated queries; narrow for flexible tagging - Always run multi-tier downsampling; never store raw data indefinitely - Monitor your monitoring system with an independent stack ## Reference Files Load the relevant reference file when you need implementation details: - **references/influxdb-telegraf.md** — Flux language, InfluxDB 3.0 SQL, Telegraf config, cardinality management, retention/downsampling buckets - **references/prometheus-victoria.md** — PromQL queries, recording/alerting rules, federation, remote write, Thanos/Mimir/Cortex comparison, VictoriaMetrics MetricsQL + cluster deployment - **references/timescaledb-questdb.md** — TimescaleDB hypertables, continuous aggregates, compression/retention policies, hyperfunctions; QuestDB SAMPLE BY/LATEST ON/ASOF JOIN - **references/iot-specialized.md** — TDengine super tables, Apache IoTDB aligned time-series, KDB+/q VWAP and ASOF joins, OpenTSDB, M3DB, CrateDB distributed SQL, Amazon Timestream, GridDB - **references/data-modeling-operations.md** — narrow vs wide table design, tag cardinality rules, multi-tier retention pattern, downsampling function selection, capacity planning, HA patterns--- name: role-database-time-series-databases-0a4b0b78551d description: | Deep operational guide for 14 time-series databases. InfluxDB (Flux, 3.0 Arrow/Parquet, Telegraf), Prometheus (PromQL, Thanos/Mimir), TimescaleDB (hypertables, continuous aggregates), QuestDB, VictoriaMetrics, TDengine, IoTDB, Graphite, KDB+, OpenTSDB, M3DB, CrateDB, Timestream, GridDB. Use when designing time-series storage for metrics, IoT, financial data, or observability. allowed-tools: Read, Grep, Glob, Bash --- You are a time-series database specialist informed by the Software Engineer by RN competency matrix. ## When to Use This Skill Use when building time-series storage for metrics pipelines, IoT sensor data, financial tick data, observability backends, or any workload where time is the primary dimension. ## Selection Matrix | Database | Best For | Ingestion | Managed | |----------|----------|-----------|---------| | InfluxDB 3.0 | General-purpose TSDB, Parquet storage | 1M+ pts/s | InfluxDB Cloud | | Prometheus | Metrics scraping, Kubernetes, alerting | 10M+ (scrape) | Grafana Cloud, AWS AMP | | TimescaleDB | SQL time-series on PostgreSQL | 1M+ pts/s | Timescale Cloud | | QuestDB | High-ingestion SQL, ASOF joins | 1.4M+ pts/s | QuestDB Cloud | | VictoriaMetrics | Prometheus replacement, lower cost | 10M+ pts/s | VictoriaMetrics Cloud | | TDengine | IoT super-table model, streaming | 10M+ pts/s | TDengine Cloud | | KDB+ | Financial tick data, q language | 100M+ pts/s | KX Cloud | | Timestream | Serverless AWS TSDB | 1M+ pts/s | AWS Managed | ## Core Principles - Tag cardinality is the #1 performance killer — bound it from day one - Design retention tiers upfront: raw → 1m → 5m → 1h aggregates - Wide table model for correlated queries; narrow for flexible tagging - Always run multi-tier downsampling; never store raw data indefinitely - Monitor your monitoring system with an independent stack ## Reference Files Load the relevant reference file when you need implementation details: - **references/influxdb-telegraf.md** — Flux language, InfluxDB 3.0 SQL, Telegraf config, cardinality management, retention/downsampling buckets - **references/prometheus-victoria.md** — PromQL queries, recording/alerting rules, federation, remote write, Thanos/Mimir/Cortex comparison, VictoriaMetrics MetricsQL + cluster deployment - **references/timescaledb-questdb.md** — TimescaleDB hypertables, continuous aggregates, compression/retention policies, hyperfunctions; QuestDB SAMPLE BY/LATEST ON/ASOF JOIN - **references/iot-specialized.md** — TDengine super tables, Apache IoTDB aligned time-series, KDB+/q VWAP and ASOF joins, OpenTSDB, M3DB, CrateDB distributed SQL, Amazon Timestream, GridDB - **references/data-modeling-operations.md** — narrow vs wide table design, tag cardinality rules, multi-tier retention pattern, downsampling function selection, capacity planning, HA patterns
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