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MySQL vs Postgres: storage engine addition. Clustered indexes (MySQL) and heap tables (Postgres) each have unique advantages. Covered the differences and practical implications in the latest stream.

Ben Dicken

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19,991 görüntüleme • 2 ay önce •via X (Twitter)

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Your Postgres is 100x slower than traditional OLAP engines. A deceptively simple OSS extension fixes this. Here's an interview where we dive into the deep engineering around how this is achieved. Joining me (and leading the conversation) is Marco Slot: an engineer with an EXTENSIVE and impressive career history around PostgreSQL: 👉 Created pg_cron in 2017 (3.7k stars) - a tool to run cron-jobs in Postgres 👉 Built pg_incremental - fast, reliable, incremental batch processing inside PostgreSQL itself 👉 co-created pg_lake (after working on Crunchy Data's Warehouse, and getting acquired into Snowflake) 👉 Helped get pg_documentdb (MongoDB-on-Postgres) off the ground Marco Slot is a world-class expert in Postgres extensions. He seriously impressed me with his knowledge over the course of a private LinkedIn conversation, and now that I type out his resume - I understand where it came from. He should be on everyone's radar. So I brought him on the pod. In our full 2-hour deep-dive, we went over: • 🔥 how pg_lake makes analytics 100x faster (literally) • 🔥 perf internals like vectorized execution & CPU branching • 🤔 practical differences between OLTP and OLAP database development (and the age-old mission in uniting both) • 🤔 how (and why) pg_lake intercepts query plans and delegates parts of the query tree to DuckDB • 💡 why Postgres is architecturally terrible at analytical queries (and how vectorized execution fixes this) • 💡 Marco's hard-won experience through a decade+ career in Postgres • 🏆 Iceberg's role as the TCP/IP for tables • 🏆 what the real moat of PostgreSQL is Developments like pg_lake are a real reason why "Just Use Postgres" is much more than a meme, and it'll continue to dominate discourse. I promise you will learn a lot from this episode. Timestamps: (0:02) What is pg_lake? (2:23) Postgres' 100x slower problem and columnar storage experiments they had to make Postgres fast for analytics (6:00) practical examples and internals (16:20) perf internals - vectorized execution & CPU optimization (23:00) pg_lake architecture (why DuckDB isn't embedded) and the connection-per-process issue (29:16) how pg_lake intercepts the query plan tree and delegates parts to DuckDB (41:09) Iceberg catalogs (48:24) postgres to iceberg ingestion patterns (and pg_incremental) (53:40) Marco's (long) career: early AWS, Citus, Microsoft, Crunchy Data & Snowflake (1:04:20) Marco's observations around the merging between OLTP and OLAP (and the subtle dev differences there) (1:15:30) reverse ETL (1:33:08) Iceberg as the TCP/IP for tables (1:35:00) Marco's thoughts on the "Just Use Postgres" fever

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Stanislav Kozlovski

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Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.

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Introducing db-mover A GUI tool to copy or download database content directly from your browser. Try it here: Check code: I work with a lot of databases: Mongo, Postgres, MySQL, Redis. Every time I need to move data, I am digging through old bash scripts and praying I do not mess up. Recently I got stuck. I was moving a staging database to a new server and I was not at my PC. I only had my phone. I could not run my scripts and had to wait until I got home to fix it. That's why I built db-mover. It is simple, paste your source URL, paste your destination URL, and hit start. You get real-time progress bars and live logs. 100% open source. Give it a try if you want to move data without the terminal. You can also suggest new feature , send in improvements you want added. #OpenSource #DevTools #Database #mongodb #postgres #mysql

Introducing db-mover A GUI tool to copy or download database content directly from your browser. Try it here: Check code: I work with a lot of databases: Mongo, Postgres, MySQL, Redis. Every time I need to move data, I am digging through old bash scripts and praying I do not mess up. Recently I got stuck. I was moving a staging database to a new server and I was not at my PC. I only had my phone. I could not run my scripts and had to wait until I got home to fix it. That's why I built db-mover. It is simple, paste your source URL, paste your destination URL, and hit start. You get real-time progress bars and live logs. 100% open source. Give it a try if you want to move data without the terminal. You can also suggest new feature , send in improvements you want added. #OpenSource #DevTools #Database #mongodb #postgres #mysql

JC Coder

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As part of our effort to move the platform toward declarative code, new tables in the public schema will no longer be automatically exposed to the Data API in new Supabase projects starting May 30th. Instead, tables will need to be explicitly granted each permission to each role, either through the dashboard or via SQL. Explicit Postgres grants are reviewable, diffable, and greppable. They also give you per-role control: anon and authenticated need different privileges in most schemas, and an explicit grant makes that difference visible in your migrations. This was always possible; now it's the default.

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Steve Wiltfong

18,567 görüntüleme • 1 yıl önce

Asynchronous IO is the best feature In Postgres 18 yet. PG 18 has been released with many exciting features such as UUIDv7, Over explain module, composite index skip scans, and the most anticipated asynchronous IO with worker and io_uring mode which I uncover in this show. Hope you enjoy it 0:00 Intro 1:30 Synchronous vs Asynchronous calls 3:00 Synchronous IO 6:30 Asynchronous IO 10:00 Postgres 17 synchronous io 17:20 The challenge of Async IO in Postgres 18 20:00 io_method worker 23:00 io_method io_uring 29:30 io_method sync 31:08 Async IO isn’t done! 31:30 Support for backend writers 32:36 Improve worker io_method 33:00 direct io support 37:00 Summary

Asynchronous IO is the best feature In Postgres 18 yet. PG 18 has been released with many exciting features such as UUIDv7, Over explain module, composite index skip scans, and the most anticipated asynchronous IO with worker and io_uring mode which I uncover in this show. Hope you enjoy it 0:00 Intro 1:30 Synchronous vs Asynchronous calls 3:00 Synchronous IO 6:30 Asynchronous IO 10:00 Postgres 17 synchronous io 17:20 The challenge of Async IO in Postgres 18 20:00 io_method worker 23:00 io_method io_uring 29:30 io_method sync 31:08 Async IO isn’t done! 31:30 Support for backend writers 32:36 Improve worker io_method 33:00 direct io support 37:00 Summary

Hussein Nasser

44,037 görüntüleme • 9 ay önce

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.

Santiago

86,565 görüntüleme • 2 ay önce

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 görüntüleme • 8 ay önce