正在加载视频...

视频加载失败

返回首页

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...

kozlovski's profile picture

Stanislav Kozlovski

18,341 subscribers

16,663 次观看 • 2 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

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.
0:24

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.

Denis Magda

28,473 次观看 • 4 个月前

Just Use Postgres feat. Denis Magda Denis literally just published a book called “Just Use Postgres”. In this conversation, that turned out to be super fun, we talked about: • what MySQL got wrong w.r.t community • the recent explosion of Postgres development in the world (Supabase, Neon, Crunchy, ClickHouse) • when Postgres instead of Kafka • the significance of the meme “just use postgres” • Postgres for data analytics Chapters (00:00) Intro (04:26) The Just Use Postgres Movement (08:36) The Small Data Trend and Its Implications (15:49) The Shift from Distributed Systems to Simplicity (17:15) Updating your priors re: PG capabilities (20:28) MySQL's OSS community split & our experiences with such things (39:48) Postgres for Analytics (55:49) PG in the lake house (59:27) AI Marketing Slop (01:02:19) How you'd build a brand new app on Postgres (01:05:24) Speedrun through PG extensions (01:11:56) PG as a Message Queue
1:30:33

Just Use Postgres feat. Denis Magda Denis literally just published a book called “Just Use Postgres”. In this conversation, that turned out to be super fun, we talked about: • what MySQL got wrong w.r.t community • the recent explosion of Postgres development in the world (Supabase, Neon, Crunchy, ClickHouse) • when Postgres instead of Kafka • the significance of the meme “just use postgres” • Postgres for data analytics Chapters (00:00) Intro (04:26) The Just Use Postgres Movement (08:36) The Small Data Trend and Its Implications (15:49) The Shift from Distributed Systems to Simplicity (17:15) Updating your priors re: PG capabilities (20:28) MySQL's OSS community split & our experiences with such things (39:48) Postgres for Analytics (55:49) PG in the lake house (59:27) AI Marketing Slop (01:02:19) How you'd build a brand new app on Postgres (01:05:24) Speedrun through PG extensions (01:11:56) PG as a Message Queue

Stanislav Kozlovski

13,036 次观看 • 5 个月前

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.
13:46

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

19,991 次观看 • 1 个月前

How Postgres 17 improves IN queries In prior releases, postgres would loop and do multiple "primitive" index scans as many as your IN elements. In Postgres 17 it takes an array and eliminates the need for multiple scans. This only applies for constant values
3:39

How Postgres 17 improves IN queries In prior releases, postgres would loop and do multiple "primitive" index scans as many as your IN elements. In Postgres 17 it takes an array and eliminates the need for multiple scans. This only applies for constant values

Hussein Nasser

143,151 次观看 • 1 年前

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.
0:26

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 次观看 • 7 个月前

Finish the week with a Postgres video 🌟 All about using JSON in Postgres: - when you should use it - how to use it efficiently - how JSONs actually get stored in postgres.
21:29

Finish the week with a Postgres video 🌟 All about using JSON in Postgres: - when you should use it - how to use it efficiently - how JSONs actually get stored in postgres.

Gwen (Chen) Shapira

27,599 次观看 • 1 年前

Meet the new look of Neon Postgres. A total of ~1,200 hours of teamwork and five months from the initial kickoff to bring this design beauty to prod. Rebranding, design, dev, and motion. Ship faster with Postgres!
0:47

Meet the new look of Neon Postgres. A total of ~1,200 hours of teamwork and five months from the initial kickoff to bring this design beauty to prod. Rebranding, design, dev, and motion. Ship faster with Postgres!

Alex Barashkov

105,285 次观看 • 5 个月前

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥
0:38

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

Charly Wargnier

20,181 次观看 • 4 个月前

sqlit is a lazygit style SQL TUI for the terminal. It supports 10+ databases (Postgres, SQLite, Supabase, ClickHouse, DuckDB, etc), has query history, autocomplete and more. Peter Adams (Maxteabag on GitHub) made sqlit using textualize.io and is Terminal Tool of the Week! ⭐️
1:36

sqlit is a lazygit style SQL TUI for the terminal. It supports 10+ databases (Postgres, SQLite, Supabase, ClickHouse, DuckDB, etc), has query history, autocomplete and more. Peter Adams (Maxteabag on GitHub) made sqlit using textualize.io and is Terminal Tool of the Week! ⭐️

Terminal Trove

137,289 次观看 • 6 个月前

Use Neon and ClickHouse to get the best of both worlds: the flexibility of Postgres for transactions + the performance of ClickHouse for analytics With PeerDB, data streaming is simple 👇
1:43

Use Neon and ClickHouse to get the best of both worlds: the flexibility of Postgres for transactions + the performance of ClickHouse for analytics With PeerDB, data streaming is simple 👇

Neon Postgres

11,422 次观看 • 1 年前

We just shipped Managed DBs in Mastra platform. Over the last couple months, a number of folks getting started asked us for off-the-shelf storage So we added support for Postgres (backed by Neon Postgres) and LibSQL/Turso.
0:29

We just shipped Managed DBs in Mastra platform. Over the last couple months, a number of folks getting started asked us for off-the-shelf storage So we added support for Postgres (backed by Neon Postgres) and LibSQL/Turso.

Sam Bhagwat

27,086 次观看 • 11 天前

Your Launchpad for Postgres: Get a Postgres connection URL in seconds, use your DB, claim it later. Perfect for building products like Netlify DB, OSS projects, and agents.
2:31

Your Launchpad for Postgres: Get a Postgres connection URL in seconds, use your DB, claim it later. Perfect for building products like Netlify DB, OSS projects, and agents.

Neon Postgres

21,019 次观看 • 1 年前

With PGlite + Electric sync you can build an issue tracker handling 100k issues + 500k comments, synced from Postgres to PGlite in the browser! Super fast UI updates with all writes going though the local database, and Postgres FTS in PGlite for search 🔥
1:13

With PGlite + Electric sync you can build an issue tracker handling 100k issues + 500k comments, synced from Postgres to PGlite in the browser! Super fast UI updates with all writes going though the local database, and Postgres FTS in PGlite for search 🔥

Sam Willis

55,403 次观看 • 1 年前

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood
0:27

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood

Neon Postgres

26,526 次观看 • 1 年前

Mike Stonebraker is a Turing award winner famous for his fundamental contributions to databases (e.g. Postgres, C-Store and much more). I interviewed him recently about: • The story behind Postgres & the hardest technical challenge in building it • Where he disagreed with Google's technical decisions • Future problems in databases • Literature recommendations to learn databases • Why LLMs score 0% on his text-SQL benchmark • What if you replaced all state in an OS with a DB Timestamps: 0:00 - Intro 1:03 - How he got into databases 6:43 - Competing with Oracle 9:07 - What made Postgres special 15:55 - One size fits none 21:37 - Why he disagreed with Google 29:14 - Why he chose academia over big tech 30:58 - Replacing state in an OS with a DB 42:02 - Future problems in databases 51:36 - Technical book recommendations to learn databases 52:20 - Advice for younger self 55:52 - Outro Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:
56:50

Mike Stonebraker is a Turing award winner famous for his fundamental contributions to databases (e.g. Postgres, C-Store and much more). I interviewed him recently about: • The story behind Postgres & the hardest technical challenge in building it • Where he disagreed with Google's technical decisions • Future problems in databases • Literature recommendations to learn databases • Why LLMs score 0% on his text-SQL benchmark • What if you replaced all state in an OS with a DB Timestamps: 0:00 - Intro 1:03 - How he got into databases 6:43 - Competing with Oracle 9:07 - What made Postgres special 15:55 - One size fits none 21:37 - Why he disagreed with Google 29:14 - Why he chose academia over big tech 30:58 - Replacing state in an OS with a DB 42:02 - Future problems in databases 51:36 - Technical book recommendations to learn databases 52:20 - Advice for younger self 55:52 - Outro Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

Ryan Peterman

109,518 次观看 • 2 个月前

Flamegraphs are a great way to narrow in on performance issues + broadly to explore program execution. I built a custom viewer for Postgres queries, inspired Jan Nidzwetzki's excellent blog on the subject. Highly recommend analysis tools like this for learning query execution.
0:45

Flamegraphs are a great way to narrow in on performance issues + broadly to explore program execution. I built a custom viewer for Postgres queries, inspired Jan Nidzwetzki's excellent blog on the subject. Highly recommend analysis tools like this for learning query execution.

Ben Dicken

47,858 次观看 • 2 个月前

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 7 个月前

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!
0:25

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

186,844 次观看 • 6 个月前

Announcing the Supabase ETL! Supabase ETL is a change-data-capture pipeline that replicates your Postgres tables to analytical destinations in near real time! Available as private alpha starting today!
0:43

Announcing the Supabase ETL! Supabase ETL is a change-data-capture pipeline that replicates your Postgres tables to analytical destinations in near real time! Available as private alpha starting today!

Tyler Shukert

24,973 次观看 • 6 个月前

You're probably sick of me saying "B-tree" but these impact SO MUCH of database performance. They're used all over the place in Postgres, MySQL, and SQLite. This week I broke down B-tree lookups and how the page cache makes lookups faster.
2:39

You're probably sick of me saying "B-tree" but these impact SO MUCH of database performance. They're used all over the place in Postgres, MySQL, and SQLite. This week I broke down B-tree lookups and how the page cache makes lookups faster.

Ben Dicken

186,867 次观看 • 5 个月前