{"slug": "timescaledb", "name": "TimescaleDB", "category": "databases", "category_name": "Databases", "description": "", "recommenders": 3, "commenters": 3, "warners": 0, "score": 2.813, "rising": 1.67, "first": "2026-01-11", "latest": "2026-10-27", "why": "if you\u2019re evaluating postgres, check out timescaledb.", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 3, "neutral": 1, "compare": 0, "warn": 0}, "spark": [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1], "listed": true, "loved": [], "caveat": "", "short_quote": "if you\u2019re evaluating postgres, check out timescaledb.", "icon": true, "domain": null, "github_repo": null, "official_url": "https://www.timescale.com", "pricing": "", "aliases": ["TimescaleDB"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 804079762, "item_id": 50254985, "url": "https://news.ycombinator.com/item?id=50254985", "by": "jauco", "date": "2026-10-27", "stance": "recommend", "quote": "if you\u2019re evaluating postgres, check out timescaledb.", "excerpt": "We\u2019re on azure and a surprisingly good choice has been to use their eventhub to ingest the telemtry and then to consume it in batches of a few seconds using a consumer. Time series is hard because it is a lot of data that comes in all the time, this allows you to relax those requirements a bit. After eventhub we\u2019re using ducklake. But I\u2019m not at the point where I\u2019d flat out advise it to other people (just code immatu", "context": "", "use_case": "time series metrics", "switched_from": "", "story": "Ask HN: How do you handle time-series metrics at scale without a large bill?"}, {"id": 786973985, "item_id": 49185874, "url": "https://news.ycombinator.com/item?id=49185874", "by": "Gayithri2026", "date": "2026-08-05", "stance": "recommend", "quote": "TimescaleDB + pgvectorscale instead of a hosted vector DB (~75% cheaper)", "excerpt": "Location: San Jose, CA (SF Bay Area) Remote: Yes Willing to relocate: No Technologies: Python, FastAPI, PostgreSQL/pgvector, RAG, LangChain, PydanticAI, Celery, Redis, Docker, AWS, Langfuse, Java, Spring Boot R\u00e9sum\u00e9/CV: https://github.com/Gayithri606/resume/blob/main/GayithriPonnapalli-Resume.pdf Email: gayithri@pojoai.com AI engineer building RAG chatbots and AI-driven workflow automation. Five years of production J", "context": "AI engineer building RAG chatbots", "use_case": "vector storage for RAG", "switched_from": "", "story": "Ask HN: Who wants to be hired? (August 2026)"}, {"id": 759961904, "item_id": 47497619, "url": "https://news.ycombinator.com/item?id=47497619", "by": "dataviz1000", "date": "2026-03-23", "stance": "recommend", "quote": "I use TimescaleDB which is fast with the compression.", "excerpt": "https://massive.com/docs/flat-files/quickstart I use TimescaleDB which is fast with the compression. People say there are better but I don\u2019t think I can fit another year of data on my disk drive either or", "context": "", "use_case": "storing stock and options data", "switched_from": "", "story": "Printable Claude Code Cheat Sheet (auto-updated daily)"}]}], "caveats": [], "who": [{"by": "Gayithri2026", "context": "AI engineer building RAG chatbots", "url": "https://news.ycombinator.com/item?id=49185874", "date": "2026-08-05"}], "tags": [], "use_cases": ["time series metrics", "vector storage for rag", "storing stock and options data", "time-series data storage"], "compared": [], "makers": [], "threads": [{"url": "https://news.ycombinator.com/item?id=50253890", "title": "Ask HN: How do you handle time-series metrics at scale without a large bill?", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49156682", "title": "Ask HN: Who wants to be hired? (August 2026)", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47495527", "title": "Printable Claude Code Cheat Sheet (auto-updated daily)", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=46577242", "title": "Ask HN: What Are You Working On? (January 2026)", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {}, "2026-01": {"neutral": 1}, "2026-02": {}, "2026-03": {"recommend": 1}, "2026-04": {}, "2026-05": {}, "2026-06": {}, "2026-07": {}, "2026-08": {"recommend": 1}, "2026-09": {}, "2026-10": {"recommend": 1}}, "quotes": [{"id": 804079762, "item_id": 50254985, "url": "https://news.ycombinator.com/item?id=50254985", "by": "jauco", "date": "2026-10-27", "stance": "recommend", "quote": "if you\u2019re evaluating postgres, check out timescaledb.", "excerpt": "We\u2019re on azure and a surprisingly good choice has been to use their eventhub to ingest the telemtry and then to consume it in batches of a few seconds using a consumer. 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But I\u2019m not at the point where I\u2019d flat out advise it to other people (just code immatu", "context": "", "use_case": "time series metrics", "switched_from": "", "story": "Ask HN: How do you handle time-series metrics at scale without a large bill?"}, {"id": 786973985, "item_id": 49185874, "url": "https://news.ycombinator.com/item?id=49185874", "by": "Gayithri2026", "date": "2026-08-05", "stance": "recommend", "quote": "TimescaleDB + pgvectorscale instead of a hosted vector DB (~75% cheaper)", "excerpt": "Location: San Jose, CA (SF Bay Area) Remote: Yes Willing to relocate: No Technologies: Python, FastAPI, PostgreSQL/pgvector, RAG, LangChain, PydanticAI, Celery, Redis, Docker, AWS, Langfuse, Java, Spring Boot R\u00e9sum\u00e9/CV: https://github.com/Gayithri606/resume/blob/main/GayithriPonnapalli-Resume.pdf Email: gayithri@pojoai.com AI engineer building RAG chatbots and AI-driven workflow automation. Five years of production J", "context": "AI engineer building RAG chatbots", "use_case": "vector storage for RAG", "switched_from": "", "story": "Ask HN: Who wants to be hired? (August 2026)"}, {"id": 759961904, "item_id": 47497619, "url": "https://news.ycombinator.com/item?id=47497619", "by": "dataviz1000", "date": "2026-03-23", "stance": "recommend", "quote": "I use TimescaleDB which is fast with the compression.", "excerpt": "https://massive.com/docs/flat-files/quickstart I use TimescaleDB which is fast with the compression. People say there are better but I don\u2019t think I can fit another year of data on my disk drive either or", "context": "", "use_case": "storing stock and options data", "switched_from": "", "story": "Printable Claude Code Cheat Sheet (auto-updated daily)"}, {"id": 745253217, "item_id": 46578326, "url": "https://news.ycombinator.com/item?id=46578326", "by": "dataviz1000", "date": "2026-01-11", "stance": "neutral", "quote": "I have GBs of time-series data in a TimescaleDB database.", "excerpt": "Using Claude Opus 4.5 to query time-series data. I have GBs of time-series data in a TimescaleDB database. It\u2019s more complicated than this, but the gist is: I use natural language to ask questions about relationships in the data, Claude Opus 4.5 generates queries, and it finds patterns. For example, I classify tens of thousands of news articles using different classification models. Then I ask Claude to write a query", "context": "", "use_case": "time-series data storage", "switched_from": "", "story": "Ask HN: What Are You Working On? (January 2026)"}]}