Загрузка видео...

Не удалось загрузить видео

На главную

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,...

BdKozlovski's profile picture

Stanislav Kozlovski

17,978 subscribers

13,036 просмотров • 5 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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 просмотров • 1 год назад

`bunx pg-here` global one-liner to run a standalone postgres instance with all data within the current folder throw-away postgres anytime anywhere. cli args for version/user/pass/db configs. docs: stop using docker just to launch a postgres!!
0:33

`bunx pg-here` global one-liner to run a standalone postgres instance with all data within the current folder throw-away postgres anytime anywhere. cli args for version/user/pass/db configs. docs: stop using docker just to launch a postgres!!

murat 🍥

50,537 просмотров • 4 месяцев назад

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 просмотров • 1 год назад

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 просмотров • 1 год назад

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 просмотров • 4 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 1 год назад

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
1:39:30

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

Stanislav Kozlovski

16,663 просмотров • 2 месяцев назад

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 просмотров • 5 месяцев назад

All Create apps now ship with a built-in Postgres database (powered by Neon Postgres) • zero setup. just chat about your app • migrations and queries, added automagically • edit data like a spreadsheet in our viewer • 1 GB free storage Build full stack apps that scale big
0:23

All Create apps now ship with a built-in Postgres database (powered by Neon Postgres) • zero setup. just chat about your app • migrations and queries, added automagically • edit data like a spreadsheet in our viewer • 1 GB free storage Build full stack apps that scale big

Anything

70,027 просмотров • 1 год назад

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.
3:38

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

PlanetScale

114,704 просмотров • 5 месяцев назад

we've upgraded the table filters in supabase just ask what you want to find and it will intelligently apply the right postgres filters:
0:21

we've upgraded the table filters in supabase just ask what you want to find and it will intelligently apply the right postgres filters:

Paul Copplestone - e/postgres

12,195 просмотров • 4 месяцев назад

📣 My next course, Introduction to Postgres, is going to be 100% free! The kind folks at Supabase are sponsoring it so that everyone can learn how to use Postgres confidently. But that's not all... I'm changing things up a bit. I'm making a bet. Watch the video to see more!
2:08

📣 My next course, Introduction to Postgres, is going to be 100% free! The kind folks at Supabase are sponsoring it so that everyone can learn how to use Postgres confidently. But that's not all... I'm changing things up a bit. I'm making a bet. Watch the video to see more!

Aaron Francis

332,521 просмотров • 8 месяцев назад

Introducing dba: The Autonomous AI Agent for Postgres
1:00

Introducing dba: The Autonomous AI Agent for Postgres

Tembo

26,537 просмотров • 1 год назад

.Paul Copplestone - e/postgres dropping the special announcement on monday
0:27

.Paul Copplestone - e/postgres dropping the special announcement on monday

Supabase

78,666 просмотров • 2 лет назад

I benchmarked 96 combinations of Postgres 17 and 18. There's a few surprising results, but overall Postgres 18 has some nice improvements. Read all about it at the link below.
0:32

I benchmarked 96 combinations of Postgres 17 and 18. There's a few surprising results, but overall Postgres 18 has some nice improvements. Read all about it at the link below.

Ben Dicken

11,395 просмотров • 8 месяцев назад

postgres is the best database
0:43

postgres is the best database

Sam Meech Ward

382,918 просмотров • 5 месяцев назад

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 просмотров • 1 год назад

Excited to introduce the in-browser Postgres sandbox with AI assistance 🚀
0:30

Excited to introduce the in-browser Postgres sandbox with AI assistance 🚀

Greg Richardson

192,310 просмотров • 1 год назад

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 просмотров • 1 год назад