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Inverted indexes are the perfect structure for text search. "Inverted" here doesn't mean upside-down. Rather that it's an inversion of the original data. We take every distinct word from a set of documents and build a "posting list" of references back to every location where that word is used.... show more

Ben Dicken

48,165 subscribers

32,297 次观看 • 18 天前 •via X (Twitter)

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10 条评论

Eddy 的头像

Eddy18 天前

FAST balls indeed

Rishabh Chauhan 的头像

Rishabh Chauhan18 天前

will you open source it?

Ratul Dawar 的头像

Ratul Dawar17 天前

But this doesn’t include like queries right?

Ben Dicken 的头像

Ben Dicken17 天前

The index is the structure for making searches fast. TIN has a complete syntax for specifying search queries, TINQL

Samantha 的头像

Samantha18 天前

Hi Ben, just following up on the DM I just sent regarding a potential Bloomberg interview. Would love to hear from you when you get a chance.

shane borden 🛩️ 的头像

shane borden 🛩️18 天前

How did you resolve the hot block 0 issue with regular GiN indexes. No more pending list? Fast update?

Darius Rey 的头像

Darius Rey18 天前

I Use it too I how I can Filter/orga my data in the cash (Looked by superbuffer)

Wanten 的头像

Wanten17 天前

@grok 这个索引方式支持多字符串词组吗？

liprais 的头像

liprais18 天前

good idea,now try with chinese texts

Pankaj Kumar 的头像

Pankaj Kumar18 天前

Half the teams I see bolt on Elasticsearch before they have 10k rows. Postgres was fine, Ben.

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.

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

28,597 次观看 • 8 个月前

Built a full-text search over all of Hacker News (~49M stories, comments, jobs & polls, syncing live per hour) to be 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠𝐥𝐲 𝐟𝐚𝐬𝐭 with no Next.js level cache and all magic at the Neon Postgres layer: 🎉 • Every keystroke is a live query against Neon Postgres • BM25 (powered by Lakebase Search) ranking runs inside the Postgres • No connection pool, just SQL-over-HTTP round trips • pg_prewarm loads the table + every index into cache • partial BM25 index per type, predicate matching the WHERE clause • Deployed on Vercel Just Postgres doing Postgres things, close to the compute 🐘

Built a full-text search over all of Hacker News (~49M stories, comments, jobs & polls, syncing live per hour) to be 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠𝐥𝐲 𝐟𝐚𝐬𝐭 with no Next.js level cache and all magic at the Neon Postgres layer: 🎉 • Every keystroke is a live query against Neon Postgres • BM25 (powered by Lakebase Search) ranking runs inside the Postgres • No connection pool, just SQL-over-HTTP round trips • pg_prewarm loads the table + every index into cache • partial BM25 index per type, predicate matching the WHERE clause • Deployed on Vercel Just Postgres doing Postgres things, close to the compute 🐘

Rishi Raj Jain

50,085 次观看 • 20 天前

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.

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 • 10 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

Stanislav Kozlovski

13,036 次观看 • 8 个月前

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

Databricks

10,503 次观看 • 3 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 🔥

🚨 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 ♨️

20,190 次观看 • 7 个月前

🙏A word of hope from Neil Oliver. I also believe that the global plan will collapse. The global parasites will double down - and it will take resilience and resistance from all of us - to build a better world for the next generation. 💚💜

🙏A word of hope from Neil Oliver. I also believe that the global plan will collapse. The global parasites will double down - and it will take resilience and resistance from all of us - to build a better world for the next generation. 💚💜

J.K. Lunden

13,330 次观看 • 15 天前

"It's tough. This is my third year and every year at the end, it's 'what's it going to look like for next year?' I don't love that. I'm kind of tired of it." Paul Skenes on the Pirates missing the playoffs again: "We are in a good position for next year. But it's going to be tough to think like that all offseason...I don't think there's anything that needs to be completely overhauled."

"It's tough. This is my third year and every year at the end, it's 'what's it going to look like for next year?' I don't love that. I'm kind of tired of it." Paul Skenes on the Pirates missing the playoffs again: "We are in a good position for next year. But it's going to be tough to think like that all offseason...I don't think there's anything that needs to be completely overhauled."

Ashley Wenskoski

104,757 次观看 • 10 天前

Aliko Dangote in Tanzania: "The governments of East Africa will be part owners of this refinery. It doesn't matter where the location is. And Tanzania, we have offered Tanzania to also be a part owner of this refinery. So the technical teams who have agreed they are going to look at where it's best suited and wherever it's best suited we'll do that refinery."

Aliko Dangote in Tanzania: "The governments of East Africa will be part owners of this refinery. It doesn't matter where the location is. And Tanzania, we have offered Tanzania to also be a part owner of this refinery. So the technical teams who have agreed they are going to look at where it's best suited and wherever it's best suited we'll do that refinery."

Kenyan Wall Street

66,582 次观看 • 4 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!

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

187,280 次观看 • 9 个月前

Buttigieg: Nowhere in the Constitution does it say that there have to be nine Supreme Court justices. That one doesn't even take a constitutional amendment. It just takes a readiness to set up a court that fits this country. We could have 13 seats matching the district structure of the federal judiciary, but also a process that makes it less partisan. We cannot have partisan warfare every time there's an opening on the court

Buttigieg: Nowhere in the Constitution does it say that there have to be nine Supreme Court justices. That one doesn't even take a constitutional amendment. It just takes a readiness to set up a court that fits this country. We could have 13 seats matching the district structure of the federal judiciary, but also a process that makes it less partisan. We cannot have partisan warfare every time there's an opening on the court

Acyn

1,683,147 次观看 • 3 个月前

Merino on the FA Cup: “Every game matters. For us, it doesn't matter if it's Carabao [Cup], FA [Cup], friendly. It's not only about the competition we're playing for. It's about we're playing for the [Arsenal] badge. We're playing for our reputation. “Every game that we wear this shirt we have to do our best and we have to win and we have to put on a good performance so the main thing is show that we are Arsenal that we are ready to compete every single time and the rest will take care of itself.”

Merino on the FA Cup: “Every game matters. For us, it doesn't matter if it's Carabao [Cup], FA [Cup], friendly. It's not only about the competition we're playing for. It's about we're playing for the [Arsenal] badge. We're playing for our reputation. “Every game that we wear this shirt we have to do our best and we have to win and we have to put on a good performance so the main thing is show that we are Arsenal that we are ready to compete every single time and the rest will take care of itself.”

Connor Humm

245,661 次观看 • 8 个月前

Database table size impacts performance in more ways than one: a) B-tree depth. Using 8k pages and a 16b uuid: 1 level = ~370 rows 2 levels = ~138k rows 3 levels = ~50m rows 4 levels = ~20b rows The lookup cost on a table with 100k rows is not the same as one with 1b rows. This can apply both to the table itself (MySQL cluster index) as well as the indexes. Sometimes a single query requires many of them. b) Small table → fits in RAM → fast reads. The larger the table, the more likely to read from disk plus churn the cache. c) # of indexes. Each adds maintenance overhead for insertions, and for Postgres vacuum overhead as well. Keep an eye on this! It's useful to take regular stock of your tables + indexes. Clean bloat. Remove unused indexes. Partition if needed.

Database table size impacts performance in more ways than one: a) B-tree depth. Using 8k pages and a 16b uuid: 1 level = ~370 rows 2 levels = ~138k rows 3 levels = ~50m rows 4 levels = ~20b rows The lookup cost on a table with 100k rows is not the same as one with 1b rows. This can apply both to the table itself (MySQL cluster index) as well as the indexes. Sometimes a single query requires many of them. b) Small table → fits in RAM → fast reads. The larger the table, the more likely to read from disk plus churn the cache. c) # of indexes. Each adds maintenance overhead for insertions, and for Postgres vacuum overhead as well. Keep an eye on this! It's useful to take regular stock of your tables + indexes. Clean bloat. Remove unused indexes. Partition if needed.

Ben Dicken

212,679 次观看 • 5 个月前

A furious Thomas Tuchel asked if he accepts the referee's apology: "Very difficult. This is a semifinal, it's not the moment for apologies. Really not. It's not the moment for two huge violations of rules and then an apology. We all have to go to our limits, all have to suffer, all have to play without mistakes. The referees at this level also have to do that. When you don't deliver, it doesn't help when you apologize, even though of course as sportsmen we accept it" 🎥 FC Bayern München

A furious Thomas Tuchel asked if he accepts the referee's apology: "Very difficult. This is a semifinal, it's not the moment for apologies. Really not. It's not the moment for two huge violations of rules and then an apology. We all have to go to our limits, all have to suffer, all have to play without mistakes. The referees at this level also have to do that. When you don't deliver, it doesn't help when you apologize, even though of course as sportsmen we accept it" 🎥 FC Bayern München

Bayern & Germany

1,360,489 次观看 • 2 年前

$"put me on bro" for the next 6 months you're going to spend more time at your desk than with your friends and family you are going to miss events, turn down plans and work when you don't feel like it you are going to wake up stressed, go to sleep thinking about the goal, and question whether it's even worth it after all that, you are going to take every dollar you've saved and put it right back into business knowing there is a chance it doesn't work you are going to watch other people enjoy their money while you are risking yours and after all of that sacrifice, all of that work, all of that uncertainty, you are still only a fraction of the way of becoming successful$

"put me on bro" for the next 6 months you're going to spend more time at your desk than with your friends and family you are going to miss events, turn down plans and work when you don't feel like it you are going to wake up stressed, go to sleep thinking about the goal, and question whether it's even worth it after all that, you are going to take every dollar you've saved and put it right back into business knowing there is a chance it doesn't work you are going to watch other people enjoy their money while you are risking yours and after all of that sacrifice, all of that work, all of that uncertainty, you are still only a fraction of the way of becoming successful

alexei

48,747 次观看 • 3 个月前

Rick Rubin on being addicted to the miracle of creation: “I like the moment of revelation. So we're working on something and it's just okay. It's boring. I'd rather not be there. That's how it feels. But then something happens and it's like magic. Something appears. It's not like we made it. Something comes up in the process. It's conjured in the process. It’s like a miracle and that's the thing that's addictive. That feeling when it's like it's not good, it's not good, it's not good, it's not good. We try something and it doesn't work. Try something else and it doesn't work. But then all of the sudden what we're listening to is really cool. That's the [addictive] feeling, that moment of discovery where it goes from nothing to something really good. And then the whole rest of the process after that is protecting that because it's super delicate. When it happens it's like this miracle happened and this magic thing happened and now we have to protect it through the rest of the process to not ruin it. It's scary because you don't want it to end. You don't want it to stop because we know if it stops, we can't control it. We can't do it again. It's not like that. It's this moment in time where something magical happens. So we spend a lot of time waiting and I can't say that's fun. Waiting is not fun.”

Rick Rubin on being addicted to the miracle of creation: “I like the moment of revelation. So we're working on something and it's just okay. It's boring. I'd rather not be there. That's how it feels. But then something happens and it's like magic. Something appears. It's not like we made it. Something comes up in the process. It's conjured in the process. It’s like a miracle and that's the thing that's addictive. That feeling when it's like it's not good, it's not good, it's not good, it's not good. We try something and it doesn't work. Try something else and it doesn't work. But then all of the sudden what we're listening to is really cool. That's the [addictive] feeling, that moment of discovery where it goes from nothing to something really good. And then the whole rest of the process after that is protecting that because it's super delicate. When it happens it's like this miracle happened and this magic thing happened and now we have to protect it through the rest of the process to not ruin it. It's scary because you don't want it to end. You don't want it to stop because we know if it stops, we can't control it. We can't do it again. It's not like that. It's this moment in time where something magical happens. So we spend a lot of time waiting and I can't say that's fun. Waiting is not fun.”

David Senra

12,759 次观看 • 4 个月前

"despite having some interviews, or perfomances with a missing member, what's important that at the end of the day we'll still be 6 as katseye moving forward, and through all of this. and when we have these things we have a lot of commitment to the fans to show up, and so it's really important for all 6 of us to give something back to the fans no matter what it is, just to keep showing up for them, and always acknowledging the fact that we'll have somebody missing, but doesn't change the fact that we're still 6 as katseye, it's FOREVER gonna be that way, and so every time we see somebody say like "oh they're 5 they're gonna kick this person out" or like there's like beef, like no, like what daniela said we're prioritizing our health, and if we need recovery we're gonna take that time because all 6 of us are in this for the long run"

"despite having some interviews, or perfomances with a missing member, what's important that at the end of the day we'll still be 6 as katseye moving forward, and through all of this. and when we have these things we have a lot of commitment to the fans to show up, and so it's really important for all 6 of us to give something back to the fans no matter what it is, just to keep showing up for them, and always acknowledging the fact that we'll have somebody missing, but doesn't change the fact that we're still 6 as katseye, it's FOREVER gonna be that way, and so every time we see somebody say like "oh they're 5 they're gonna kick this person out" or like there's like beef, like no, like what daniela said we're prioritizing our health, and if we need recovery we're gonna take that time because all 6 of us are in this for the long run"

daily sophia

37,049 次观看 • 7 个月前

This is what we mean, if ladies who likes us back, show this type of interest, of course we are gonna pursue. The "give me a hug" and "text me" shows initiative from the woman, to take that leap of faith. It takes two to tango fr

This is what we mean, if ladies who likes us back, show this type of interest, of course we are gonna pursue. The "give me a hug" and "text me" shows initiative from the woman, to take that leap of faith. It takes two to tango fr

👑𝗞𝗶𝗻𝗴𝗷𝗼𝗻𝗮𝗵🌴🥥

57,582 次观看 • 4 个月前