TimescaleOps Directory: 8 Deterministic Hypertable, MQTT IoT & Retention Dossiers
Eight deterministic engineering dossiers published by TimescaleOps covering the 25% DDR5 RAM chunk_time_interval rule, EMQX MQTT batching, Gorilla/ZSTD columnar compression, and pgBackRest ZFS archives.
Every pipeline dossier below includes deterministic PostgreSQL/TimescaleDB sizing tables, copy-ready SQL or ZFS configuration blocks, and direct preset links into the TimescaleOps #tso-calc retention calculator.
| Dossier ID | TimescaleOps Technical Dossier | Architectural Scope & Deterministic Summary |
|---|---|---|
TS-01 // CHUNK-25RAM |
TimescaleDB Hypertable chunk_time_interval Sizing (The 25% RAM Rule) | Per official TimescaleDB partitioning documentation, hypertable insert throughput stalls into random NVMe page evictions once active chunk B-tree indexes spill beyond PostgreSQL shared_buffers. On TimescaleOps, we apply the 25% DDR5 RAM budget equation to pick 7d, 1d, 6h, or 1h chunk intervals deterministically. |
TS-02 // OHLCV-CAGG |
TimescaleDB Tick-to-OHLCV Continuous Aggregates Sizing Dossier | Scanning hundreds of millions of raw hypertable tuples every time a dashboard requests a 30-day rollup wastes CPU cores and competes with live writers. TimescaleDB real-time Continuous Aggregates materialize closed time buckets incrementally and read only the open trailing bucket from the hot hypertable. |
TS-03 // QUESTDB-ILP |
QuestDB InfluxDB Line Protocol (ILP) High-Rate Ingestion Sizing Guide | When append-only sensor or market streams exceed 150,000 events per second on a single server and require time-aligned ASOF JOIN lookups without relational foreign keys, QuestDB memory-mapped columnar partitions paired with ZFS transparent compression offer a specialized alternative to relational hypertables. |
TS-04 // EMQX-IOT |
Industrial IoT Pipeline: EMQX MQTT Broker + TimescaleDB Hypertable Sizing | Opening individual PostgreSQL connections per MQTT packet from 50,000 factory PLCs or solar inverters exhausts backend process slots immediately. Pairing EMQX sink batching with a medium-wide TimescaleDB hypertable consolidates thousands of sensor frames into sequential WAL writes on a single NVMe VPS. |
TS-05 // VM-METRICS |
VictoriaMetrics vs Prometheus Disk & Cardinality RAM Sizing Dossier | Per Prometheus and VictoriaMetrics storage documentation, vanilla Prometheus TSDB averages ~1.3 to 2.2 bytes per sample and expands memory footprint during WAL replay under high label churn, whereas VictoriaMetrics LSM-tree block merges condense monotonic counters to ~0.4 to 0.8 bytes per sample. |
TS-06 // GORILLA-ZSTD |
TimescaleDB Columnar Compression Guide (segmentby & orderby Retention Math) | Choosing a high-uniqueness column for compress_segmentby fragments TimescaleDB 1,000-row columnar batches into tiny arrays and destroys storage density. We detail how Gorilla XOR, Delta-of-Delta, and ZSTD codecs pack telemetry columns and how to verify exact per-chunk savings via SQL. |
TS-07 // PGBACKREST-ZFS |
pgBackRest + ZFS Multi-Terabyte TimescaleDB Backup & PITR Architecture | Logical pg_dump exports become impractical once a TimescaleDB sensor archive crosses 500 GB. Pairing pgBackRest block-level incremental backups with immutable compressed hypertable chunks and OpenZFS snapshots protects multi-terabyte databases with minimal daily backup transfer. |
TS-08 // GRAFANA-LIVE |
Grafana Real-Time Telemetry Dashboards on TimescaleDB Without CPU Spikes | Twenty operators viewing a 90-day Grafana dashboard with a 5-second auto-refresh fire 240 SQL queries per minute against your database. Combining span-aware Continuous Aggregate SQL routing with PgBouncer transaction pooling protects TimescaleDB CPU headroom. |