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The most important database feature you hope to never need: Backups. What Postgres does under the hood is far more interesting than a simple data copy. We're diving deep into the process in our latest article.

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PlanetScale

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53,741 görüntüleme • 1 ay önce •via X (Twitter)

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more important than what you do is the conviction in what you’re doing $TRUST the process🙏
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more important than what you do is the conviction in what you’re doing $TRUST the process🙏

Trust The Process. 🙏

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What Postgres can’t do alone, it can tackle with DuckDB. The pg_duckdb extension shows how two databases can work hand in hand by querying analytical data with DuckDB and joining it with transactional data in Postgres. The extension embeds DuckDB directly into Postgres. DuckDB literally runs inside a Postgres process. How it works (see diagram): 1. An app sends a query to Postgres joining portfolio data (Postgres) with historical market data (lakehouse) 2. pg_duckdb detects lakehouse access and delegates execution to DuckDB 3. DuckDB pulls required market data from the lakehouse, queries Postgres tables, and performs the join 4. Postgres returns the final result to the app
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What Postgres can’t do alone, it can tackle with DuckDB. The pg_duckdb extension shows how two databases can work hand in hand by querying analytical data with DuckDB and joining it with transactional data in Postgres. The extension embeds DuckDB directly into Postgres. DuckDB literally runs inside a Postgres process. How it works (see diagram): 1. An app sends a query to Postgres joining portfolio data (Postgres) with historical market data (lakehouse) 2. pg_duckdb detects lakehouse access and delegates execution to DuckDB 3. DuckDB pulls required market data from the lakehouse, queries Postgres tables, and performs the join 4. Postgres returns the final result to the app

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Tonight we played with energy, passion and soul. This early in the process, that is the most important thing I can ask. Travelling Potters, we appreciate you more than you know! 🔴⚪️

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Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.
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Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

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How do you back up 100TB in the same time it takes to back up 1TB? Massively parallel backups, powered by sharding.
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How do you back up 100TB in the same time it takes to back up 1TB? Massively parallel backups, powered by sharding.

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❔ A Question for Our Community 👇 What’s more important in crypto today: speed, security, or simplicity? 🤔 As crypto tools become more advanced, users face a quiet trade-off. Lightning-fast transactions can sacrifice safety. Ultra-secure platforms might overwhelm with complexity. And simple, sleek interfaces? Sometimes they hide the real power under the hood. Each direction shapes how we interact with Web3—how we trade, store, and trust. But if you had to lean into just one today… what truly matters most? Drop your thoughts in the comments! 💬

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