Загрузка видео...

Не удалось загрузить видео

На главную

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...

BenjDicken's profile picture

Ben Dicken

46,835 subscribers

212,679 просмотров • 6 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?
1:25

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

34,758 просмотров • 5 месяцев назад

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?
1:25

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

79,773 просмотров • 1 год назад

Rows 2.0 is live 🎉 Rows 2.0 is the celebration of more than 1 year of hundreds of product updates. Follow the launch on Product Hunt, and tell us what your think of the new Rows: In the meantime, turn up the volume and watch 🔊:
1:20

Rows 2.0 is live 🎉 Rows 2.0 is the celebration of more than 1 year of hundreds of product updates. Follow the launch on Product Hunt, and tell us what your think of the new Rows: In the meantime, turn up the volume and watch 🔊:

Rows

15,323 просмотров • 3 лет назад

Today's wins 🚀 • Reduced a SQL query from 2527ms → 372ms (~85% faster). • Claude kept recommending adding indexes. But this is where engineering fundamentals come into play. • Adding indexes to a 400M+ row table isn't something you do without understanding the tradeoffs. • Dug into the execution plan instead and found the real bottleneck: expensive filtering and CTE scans. • Reworked the query to eliminate unnecessary work instead of adding more indexes. Ended the day with a 50kg weighted plank. A good day of optimizing both databases and myself. 💪
0:28

Today's wins 🚀 • Reduced a SQL query from 2527ms → 372ms (~85% faster). • Claude kept recommending adding indexes. But this is where engineering fundamentals come into play. • Adding indexes to a 400M+ row table isn't something you do without understanding the tradeoffs. • Dug into the execution plan instead and found the real bottleneck: expensive filtering and CTE scans. • Reworked the query to eliminate unnecessary work instead of adding more indexes. Ended the day with a 50kg weighted plank. A good day of optimizing both databases and myself. 💪

Devaansh Bhandari

22,363 просмотров • 3 месяцев назад

Played around with using a Vulkan Strap on seated cable rows this morning….have to say, I can truly feel the back using the strap as it locks in the elbows.
0:44

Played around with using a Vulkan Strap on seated cable rows this morning….have to say, I can truly feel the back using the strap as it locks in the elbows.

💯 WS 💯 🇺🇸

21,304 просмотров • 6 месяцев назад

So bizarre to see the people in the first few rows dead center at a Phish NYE concert at start of Gamehendge... Have more room than anyone else in the entire arena. WTF even is this? I saw the same thing in Mexico. People up close with more space than those 30 rows back.
0:44

So bizarre to see the people in the first few rows dead center at a Phish NYE concert at start of Gamehendge... Have more room than anyone else in the entire arena. WTF even is this? I saw the same thing in Mexico. People up close with more space than those 30 rows back.

Andrew Duddy

81,937 просмотров • 2 лет назад

Galaxy Z Fold8 has a very useful feature: app-level display customization. For example, YouTube normally shows only two rows of video recommendations on the screen. After reducing the display size, it can show three rows of videos at the same time, allowing me to browse more content without constantly scrolling. This is one of the advantages of Samsung’s foldable experience. Samsung always gives users more control and more customization options, allowing each app to be displayed in the way that best fits their usage habits. A larger screen is not just about making everything bigger. It is about giving users more freedom to decide how they want to use that space.
0:43

Galaxy Z Fold8 has a very useful feature: app-level display customization. For example, YouTube normally shows only two rows of video recommendations on the screen. After reducing the display size, it can show three rows of videos at the same time, allowing me to browse more content without constantly scrolling. This is one of the advantages of Samsung’s foldable experience. Samsung always gives users more control and more customization options, allowing each app to be displayed in the way that best fits their usage habits. A larger screen is not just about making everything bigger. It is about giving users more freedom to decide how they want to use that space.

Ice Universe

90,983 просмотров • 3 месяцев назад

LAFD Bureau Of Supply and Maintenance: Went to the LAFD repair yard to document how many trucks are waiting to be repaired. Rows and rows of equipment. We could use this stuff right now.
1:31

LAFD Bureau Of Supply and Maintenance: Went to the LAFD repair yard to document how many trucks are waiting to be repaired. Rows and rows of equipment. We could use this stuff right now.

Film The Police LA

258,325 просмотров • 1 год назад

The home gym alternative to Chest Supported T Bar Rows is Chest Supported Dumbbell Rows And here is an EXCELLENT example of how to setup for and execute them PROPERLY from Jeff Alberts
0:40

The home gym alternative to Chest Supported T Bar Rows is Chest Supported Dumbbell Rows And here is an EXCELLENT example of how to setup for and execute them PROPERLY from Jeff Alberts

Dean Turner

156,688 просмотров • 5 месяцев назад

Another insane Jev use case! Traditional database filters need precise, predefined conditions. But many questions are semantic: - Is this article mainly about software engineering? - Which topic best describes it? - How technically deep does it appear? These usually require moving rows into application code, invoking a model, parsing its output, and writing the result back. pg-jev is an open-source Postgres extension that exposes Jev through SQL functions. - jev() works inside WHERE - jev_prob() returns a probability - jev_choice() selects a label - jev_score() ranks rows across ordered levels. It needs no vector column or embedding index. Under the hood, pg-jev batches rows, sends them to Jev, and returns typed answers that SQL can filter, sort, group, and combine with exact predicates. The recording below runs it against real Hacker News stories stored in Postgres. It first shows the rows, then asks Jev to find software-engineering stories, classify them by topic, and rank them by technical depth. The final query reports requests, tokens, cost, and cache hits from the database session. GitHub repo: (don't forget to star it ⭐) Postgres keeps the data and controls the query, while Jev handles the part SQL cannot express as a deterministic condition. If you want to understand what Jev is doing under the hood, I also wrote a hands-on guide to building a Jev-style model with open models, entirely locally. Read it below.
0:28

Another insane Jev use case! Traditional database filters need precise, predefined conditions. But many questions are semantic: - Is this article mainly about software engineering? - Which topic best describes it? - How technically deep does it appear? These usually require moving rows into application code, invoking a model, parsing its output, and writing the result back. pg-jev is an open-source Postgres extension that exposes Jev through SQL functions. - jev() works inside WHERE - jev_prob() returns a probability - jev_choice() selects a label - jev_score() ranks rows across ordered levels. It needs no vector column or embedding index. Under the hood, pg-jev batches rows, sends them to Jev, and returns typed answers that SQL can filter, sort, group, and combine with exact predicates. The recording below runs it against real Hacker News stories stored in Postgres. It first shows the rows, then asks Jev to find software-engineering stories, classify them by topic, and rank them by technical depth. The final query reports requests, tokens, cost, and cache hits from the database session. GitHub repo: (don't forget to star it ⭐) Postgres keeps the data and controls the query, while Jev handles the part SQL cannot express as a deterministic condition. If you want to understand what Jev is doing under the hood, I also wrote a hands-on guide to building a Jev-style model with open models, entirely locally. Read it below.

Avi Chawla

299,936 просмотров • 23 дней назад

Rows are one of my favorite finishers. You can go heavy to get a great ego boost to really make the rest of your day as positive as it can be.
0:33

Rows are one of my favorite finishers. You can go heavy to get a great ego boost to really make the rest of your day as positive as it can be.

Austin Tejas

14,222 просмотров • 4 месяцев назад

Do you build your Figma tables from columns or rows? 🤔 I created different data cell types as column components and simply arranged them with auto layout. Quick and flexible table editing 🙌 #designsystem #ux
0:34

Do you build your Figma tables from columns or rows? 🤔 I created different data cell types as column components and simply arranged them with auto layout. Quick and flexible table editing 🙌 #designsystem #ux

Adham Dannaway

69,641 просмотров • 2 лет назад

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists
0:27

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

rahul

11,550 просмотров • 1 год назад

Trump claims he filled up the arena as the camera pans to rows of empty seats
0:32

Trump claims he filled up the arena as the camera pans to rows of empty seats

FactPost

14,999 просмотров • 19 дней назад

Inverted Rows an underappreciated tool in the pull family. Its more than a gateway drug to performing chin ups and marries up well with horizontal pushes. We try to teach and cue it as same path as a Bench Press. Here are some different versions of the Inverted Row. #strengthandconditioning #strengthandconditioningcoach #workout #backworkout #invertedrow #athlete #coach
0:41

Inverted Rows an underappreciated tool in the pull family. Its more than a gateway drug to performing chin ups and marries up well with horizontal pushes. We try to teach and cue it as same path as a Bench Press. Here are some different versions of the Inverted Row. #strengthandconditioning #strengthandconditioningcoach #workout #backworkout #invertedrow #athlete #coach

James Burk

38,436 просмотров • 8 месяцев назад

To look around the stadium during “Badlands” and to see the same scene in the last rows of the upper deck as it was in the pit was truly amazing.
0:43

To look around the stadium during “Badlands” and to see the same scene in the last rows of the upper deck as it was in the pit was truly amazing.

That One Lyric Podcast

47,156 просмотров • 5 месяцев назад

Kevin Maguire says its clear Reform are using the murder of Anne Widdecombe as a diversion from rows over 'gifts' and donations.
2:17

Kevin Maguire says its clear Reform are using the murder of Anne Widdecombe as a diversion from rows over 'gifts' and donations.

Saul Staniforth

834,604 просмотров • 3 месяцев назад

An unused test level left in the game. It contains three neatly arranged horizontal rows of low-poly versions of the playable aircraft in their ISAF, Erusean and special colors. It also includes some liveries that didn't make the final cut. It can be accessed via my AC04 table.
1:32

An unused test level left in the game. It contains three neatly arranged horizontal rows of low-poly versions of the playable aircraft in their ISAF, Erusean and special colors. It also includes some liveries that didn't make the final cut. It can be accessed via my AC04 table.

death_the_d0g

22,891 просмотров • 1 месяц назад

JUST IN: Tens of thousands of acres in the Amazon rainforest have been cut down to build a road for the COP30 climate summit in Brazil. Huge swaths of land are getting cleared out to "ease traffic to the city" of Belem for a new highway. Video shows rows and rows of deforested land.
0:30

JUST IN: Tens of thousands of acres in the Amazon rainforest have been cut down to build a road for the COP30 climate summit in Brazil. Huge swaths of land are getting cleared out to "ease traffic to the city" of Belem for a new highway. Video shows rows and rows of deforested land.

Eric Daugherty

220,629 просмотров • 1 год назад

today's experiment: silky smooth fixed-height virtual table, rendered with canvas tested with 10^50+ rows of dynamic data
0:34

today's experiment: silky smooth fixed-height virtual table, rendered with canvas tested with 10^50+ rows of dynamic data

f1shy-dev

93,573 просмотров • 1 год назад