Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

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

35,291 subscribers

19,892 Aufrufe • vor 23 Tagen •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Are processes or threads better for handling connections? Postgres and MySQL make the perfect case-study for this tradeoff.

Are processes or threads better for handling connections? Postgres and MySQL make the perfect case-study for this tradeoff.

Ben Dicken

14,234 Aufrufe • vor 16 Tagen

Postgres: process-per-connection MySQL: thread-per-connection Learn more about processes, threads, connection pooling, and how they impact database performance in our latest interactive article.

Postgres: process-per-connection MySQL: thread-per-connection Learn more about processes, threads, connection pooling, and how they impact database performance in our latest interactive article.

PlanetScale

349,333 Aufrufe • vor 8 Monaten

All types of databases, Redis, Postgres, Clickhouse, Mysql etc. now display important stats and metrics aswell as most run queries in the Maple service map

All types of databases, Redis, Postgres, Clickhouse, Mysql etc. now display important stats and metrics aswell as most run queries in the Maple service map

Makisuo

241,960 Aufrufe • vor 9 Tagen

At scale, you can only put off sharding for so long. YouTube scaled MySQL with Vitess, and now we're building the same for Postgres.

At scale, you can only put off sharding for so long. YouTube scaled MySQL with Vitess, and now we're building the same for Postgres.

PlanetScale

54,747 Aufrufe • vor 18 Tagen

How do Postgres and MySQL achieve data durability? Our very own Chris Sinjakli spoke at SREcon about how this works and the many engineering challenges it presents.

How do Postgres and MySQL achieve data durability? Our very own Chris Sinjakli spoke at SREcon about how this works and the many engineering challenges it presents.

PlanetScale

11,568 Aufrufe • vor 6 Monaten

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 Aufrufe • vor 4 Monaten

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.

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 Aufrufe • vor 4 Monaten

DBngin is such a good little app for managing database local DB servers. Highly recommended if your app needs a MySQL, Postgres or Redis DB

DBngin is such a good little app for managing database local DB servers. Highly recommended if your app needs a MySQL, Postgres or Redis DB

Wes Bos

85,386 Aufrufe • vor 1 Jahr

I’m building an iOS app called Schema, a way to access databases when you’re away from your desk. Schema works with Postgres and MySQL. Here’s a teaser, and I’ll share some of the more clever UI over the coming weeks. I’m growing the size of the TestFlight. Reach out if you’d like access.

I’m building an iOS app called Schema, a way to access databases when you’re away from your desk. Schema works with Postgres and MySQL. Here’s a teaser, and I’ll share some of the more clever UI over the coming weeks. I’m growing the size of the TestFlight. Reach out if you’d like access.

Allan

60,143 Aufrufe • vor 1 Jahr

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

211,738 Aufrufe • vor 2 Monaten

B-tree database lookups are way slower without a memory cache in place + a good replacement policy for OLTP. MySQL uses a segmented LRU whereas Postgres uses a clock-sweep algorithm. Both good choices for different reasons.

B-tree database lookups are way slower without a memory cache in place + a good replacement policy for OLTP. MySQL uses a segmented LRU whereas Postgres uses a clock-sweep algorithm. Both good choices for different reasons.

Ben Dicken

51,627 Aufrufe • vor 4 Monaten

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

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 Aufrufe • vor 1 Jahr

🎉 Celebrate 30 years of #MySQL at the MySQL & #HeatWave Summit 2025, April 22–23 in Redwood Shores, CA! Join MySQL experts like Daniël Van Eeden, Daniel Nichter, René Cannaò, plus speakers from Uber, Yelp, PlanetScale & more. 🎟️ Free, sign up today!

🎉 Celebrate 30 years of #MySQL at the MySQL & #HeatWave Summit 2025, April 22–23 in Redwood Shores, CA! Join MySQL experts like Daniël Van Eeden, Daniel Nichter, René Cannaò, plus speakers from Uber, Yelp, PlanetScale & more. 🎟️ Free, sign up today!

MySQL

13,380 Aufrufe • vor 1 Jahr

Meet Schema — A new Postgres & MySQL database client for iOS. It’s great whether you’re a developer working at scale, a founder watching signups, or vibe coding your next big thing. Schema is available today.

Meet Schema — A new Postgres & MySQL database client for iOS. It’s great whether you’re a developer working at scale, a founder watching signups, or vibe coding your next big thing. Schema is available today.

Allan

129,093 Aufrufe • vor 1 Jahr

Schema can now push a notification when a row is inserted. Super useful getting a push when there's activity on some of my other projects. Working towards a release soon. Message me if you're using Postgres or MySQL and would like to be added to the TestFlight.

Schema can now push a notification when a row is inserted. Super useful getting a push when there's activity on some of my other projects. Working towards a release soon. Message me if you're using Postgres or MySQL and would like to be added to the TestFlight.

Allan

39,127 Aufrufe • vor 1 Jahr

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

28,461 Aufrufe • vor 4 Monaten

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

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

16,648 Aufrufe • vor 2 Monaten

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.

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

27,568 Aufrufe • vor 1 Jahr

Postgres databases now come with LLM-powered index recommendations. Indexes are crucial for database performance, and these recommendations help ensure your queries are executing optimally.

Postgres databases now come with LLM-powered index recommendations. Indexes are crucial for database performance, and these recommendations help ensure your queries are executing optimally.

PlanetScale

38,328 Aufrufe • vor 6 Monaten

postgres is the best database

postgres is the best database

Sam Meech Ward

382,868 Aufrufe • vor 5 Monaten