Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Other
TimescaleDB
Store and query time-series data with hypertables, compression, and continuous aggregates.
skill openclawclawhub Free
Store and query time-series data with hypertables, compression, and continuous aggregates.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
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- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Hypertables
Convert table to hypertable: SELECT create_hypertable('metrics', 'time') Must have time column (TIMESTAMPTZ recommended)—partition key for chunks Call BEFORE inserting data—converting large tables is expensive Can't undo easily—plan schema before converting
Chunk Interval
Default 7 days per chunk—tune based on data volume SELECT set_chunk_time_interval('metrics', INTERVAL '1 day') for high-volume Chunks should be 25% of memory—too small = overhead, too large = slow queries Check chunk sizes: SELECT * FROM chunks_detailed_size('metrics')
time_bucket
time_bucket('1 hour', time) groups timestamps—like date_trunc but with arbitrary intervals Use in GROUP BY for aggregation: GROUP BY time_bucket('5 minutes', time) Origin parameter for offset: time_bucket('1 day', time, '2024-01-01'::timestamptz) Beats date_trunc for non-standard intervals—15min, 4h, etc.
Continuous Aggregates
Materialized views that auto-refresh—pre-compute expensive aggregations CREATE MATERIALIZED VIEW hourly_stats WITH (timescaledb.continuous) AS SELECT ... Add refresh policy: SELECT add_continuous_aggregate_policy('hourly_stats', ...) Query aggregate view instead of raw hypertable—orders of magnitude faster
Real-Time Aggregates
Continuous aggregates include recent data automatically—no stale reads WITH (timescaledb.continuous, timescaledb.materialized_only = false) for real-time Combines materialized historical + live recent—transparent to queries Small performance cost for real-time—disable if batch-only acceptable
Compression
Compress old chunks to save 90%+ storage: ALTER TABLE metrics SET (timescaledb.compress) Add compression policy: SELECT add_compression_policy('metrics', INTERVAL '7 days') Compressed chunks are read-only—can't update/delete individual rows Decompress for modifications: SELECT decompress_chunk('chunk_name')
Retention
Auto-delete old data: SELECT add_retention_policy('metrics', INTERVAL '90 days') Drops entire chunks—efficient, no row-by-row delete Retention runs on scheduler—data persists slightly past interval Combine with compression: compress at 7d, drop at 90d
Indexing
Time column auto-indexed in hypertable—don't add redundant index Add indexes on filter columns: CREATE INDEX ON metrics (device_id, time DESC) Composite indexes with time last—enables chunk exclusion Skip indexes on rarely-filtered columns—each index slows writes
Insert Performance
Batch inserts critical—single-row inserts are slow Use COPY or multi-value INSERT: INSERT INTO metrics VALUES (...), (...), ... Parallel COPY with timescaledb-parallel-copy tool—saturates I/O Out-of-order inserts work but slower—prefer time-ordered writes
Query Patterns
Always include time range in WHERE—enables chunk exclusion WHERE time > now() - INTERVAL '1 day' skips old chunks entirely ORDER BY time DESC with LIMIT for "latest N"—index scan, fast Avoid SELECT * on wide tables—fetch only needed columns
Distributed Hypertables
Multi-node for horizontal scale—data sharded across nodes Create access node + data nodes—access node coordinates queries More operational complexity—start single-node, distribute when needed Not needed for most workloads—single node handles millions of rows/sec
Category context
Long-tail utilities that do not fit the current primary taxonomy cleanly.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md