Практика Shadowing: SELECT COUNT (*) can impact your Backend Application Performance, here is why - Изучайте разговорный английский по видео
Создание урока...
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aggregating large entries in the database to perform a count for
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example is a lot of work the database has to sort
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through large number of records whether it's in an index
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or in the raw heap table itself doing this too often can impact the performance of both your database
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and your application let us discuss why count can be slow and an alternative if you really don't want the actual count, but yes, you want an estimate.
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Let's just jump into it.
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All right, guys.
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So there are many ways you can execute a count on your data.
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So I have here a grades table, our famous student grades table.
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So there is a G, which the grade itself.
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That's the ID of the students, right?
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And I think it has like around 60 million rows that I created.
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So what I'm going to do here is do a select count G from grades,
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where id between 1000 and 4000 give me the grades of those people
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but i just want to count if you execute that it's
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so fast right now don't pay attention to the speed
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because i have caching going on i executed this query many times
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but i want to pay attention to the number that come back
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2900 and the reason this number is a little bit low from comparing 2000
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and 4000 which around should be around 3000
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because count g will return entries that are not null
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and just just think about it i have an index on the id field
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that means the database have used
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that index to pull the rows in order to count them
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so we're on the index but we asked the database to do a count on g
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that means we had to go to the table to check those uh the value of g whether it's null
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or not So let's do an explain.
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Let's just add an explain analyze before these puppies.
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Let's see what happened here.
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Why do that?
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Let's pay attention to what happened here.
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We're doing an index scan on Postgres.
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We have returned 3001 rows because guess what?
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We're in the index.
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The actual index entries, I don't have any deleted rows by the way here.
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So 3001 is about right, but the final count has been reduced
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because we went back to the table to get the value G because that's what we asked it.
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We ask, hey, count the value G.
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And when you do count G here or count any field, the database will filter through the fields and only count the not null values.
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And I have few nulls there.
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Okay.
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So now it uses an index scan.
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And that tells me that, hey, I scan the index
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but i had to go to the table it's not an index only scan right let's spice things up
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and see do the same thing here
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but i'm going to do a select star this time
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and a lot of people have a misconception
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that count star actually goes to the table and fetch all the fields
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and count the fields no almost no database do this anymore
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right count star essentially means just count whatever entries you have right
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this will include null values if you're scanning the index give me the values right
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and you can see that we got a higher number
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so let's take a look at the plan that did
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that postgres used to do that stuff what did you use postgres tell me
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if you will look at the plan now look at this it's It's an index only scan
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and always index only scan always trumps
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and always it's better than the actual index scan because I don't really need to go back to the table.
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Again, don't pay attention to these numbers because I have caches all over the place.
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I just want you to, the most important thing is to understand the plan.
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Running these numbers don't mean anything right now because first of all, I'm in a container.
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I have a large amount of memory, so the database will start caching these pages if I execute them over and over again.
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But just understand that plan is the most important thing.
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And as a result, the larger the number of rows come back, the more work the database is doing.
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So 118 milliseconds.
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Index only scan.
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All right, Jose, what are you trying to do here?
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Here's what I'm going to do.
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I'm going to do an update now.
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I'm going to do an update grades.
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Set g equal 20, where id is between 1,000 and 4,000.
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So those rows between 1,000 and 4,000, I'm going to slam all of them and update them.
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Change this.
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Let's see how the Postgres will freak out now.
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What will happen?
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I'm going to execute this count star, and then let's see what will happen.
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all of a sudden guys look this number jumped again not by much
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but it is significant the more rows the more actual real data you have this will go back
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but but look at that it still says index only scan
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but look at this heap features i want you to pay
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attention to this the moment you start seeing heap features that means index only scan yeah we did only scan the index,
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but we had to go back to the table 6,002 times, right?
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For this amount of values, right?
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I'm not sure these are the blocks or the actual rows.
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I have to go back and check.
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But we had to go back this amount of time, which is expensive, right?
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Why?
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because we have updated the values the visibility map told the index scanner
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that hey by the way yeah you're scanning the index
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and i have i'm going to give you only values in the index which is
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usually fast again if you're not scanning the whole index
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but these rows that i'm scanning in the index might have been updated might have been deleted
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so i have to go back to the heap
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where the actual visibility of the row exists to check if the row is actually deleted.
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Because when you delete something in Postgres, the index is not immediately marked as deleted.
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It just adds a new record and keeps the old records for MVCC reasons,
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so multiple concurrency control, so other transactions can't see those old tuples.
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All right.
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So how do we fall?
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How do you solve this problem?
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Very simple.
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You just vacuum the table.
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Vacuuming the table will update the visibility map saying that, by the way, those old rows that you just updated, nobody's reading them.
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Nope.
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There are no running transactions that are reading them.
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In a production system, there might be, but not now.
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So if I do now the same query again, so if we do it again, you can see that we got heap features zero.
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All right.
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And guys, every time I increase that number 3004, you can see that this is going to get slower and slower and slower.
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Just to show you that, for example, now it took one and a half second to execute.
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And then if I increase that number a little bit, you can see, oh, you can feel it.
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So count is not cheap, right?
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It was cheap for the 3,000 rows that I'm going to return.
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But every time you do that, the plan now changes.
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It says, okay, we're still going to do an index, but I'm going to use threading.
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The database decided to do multiple threads, multiple workers, not necessarily threads, multiple workers to scan that index.
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So I can give you the results, right?
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Still, we're good we didn't do any heap features
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but it took six seconds to return this many rows right so every time you increase that number goes larger
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the operation is going to go slower.
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It's just proportional.
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It's just so proportional.
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So all right.
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So what if I, I'm saying I don't care about the actual row count, right?
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I don't really care about giving the actual exact value, right?
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But here's what I want to do.
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I want you just to give me an estimate.
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If you do just an explain, right?
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And let's just format this so it's JSON-y, right?
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If you do that, without an analyze again, without an analyze, analyze actually execute the query.
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Explain will not execute the query, but it will estimate it.
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It will estimate that, hey, I'm going to use the index.
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I am planning that I might get this much rows back, 2,868, right?
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So compared to the select count, so it's not an accurate number.
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It's 200 values up.
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And a lot of people use this.
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And I actually got this trick from a blog that I'm going to reference below.
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Let's remember that the table itself has some statistics to update itself.
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So Postgres, without actually looking at the table, it knows roughly how many rows are on the table.
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roughly how much rows will come back are this are these actual correct numbers absolutely not
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but if you're building instagram or you're building something
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that like count the likes or something like
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that this is way better right so i can quickly estimate this stuff
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and as you update your table obviously a good idea to do an analyze on your grades table
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or your table that will update the statistics to the correct numbers
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and obviously this operation is going to take a long time all right guys that's it for me today
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that was count and how count is essentially a lot of work for the database
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and if you really don't need an actual count correct number especially
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if it's in the millions why would you show the the user 60 million
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and 320 exact number right almost no one does that right
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and let's see yeah so it's on estimate always matter
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if you're working with a fewer rows you can do an actual account but
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if you expect the table to grow avoid you using an
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excellent account use do an estimate with a planner like that
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if that works for you hope
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that that's way more than enough all right again guys i'm
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gonna see you in the next one you guys stay awesome goodbye
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Лексика и советы по произношению к этому уроку
Этот урок разговорной практики уровня C1 построен на видео «SELECT COUNT (*) can impact your Backend Application Performance, here is why». Чаще всего повторяются слова: index, count, row, table, scan. В этом видео 156 предложений и 1723 слов для шедоуинга. Речь длится 10:37. Говорящий говорит в естественном темпе, около 162 слов в минуту, близком к повседневной речи. 86% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.
Ключевая лексика этого видео
Самые сложные слова из видео (15), с произношением и значением:
| Слово | Произношение | Значение |
|---|---|---|
| scan глагол | /skæn/ | разгля́дывать |
| execute глагол | /ˈɛksɪˌkjuːt/ | казни́ть |
| null существительное | /nʌl/ | нуль, пусто́е по́ле |
| analyze глагол | /ˈæn.əˌlaɪz/ | анализи́ровать, проанализи́ровать |
| query существительное | /ˈkwɪɹ.i/ | запро́с, вопро́с |
| cache существительное | /kæʃ/ | запа́с, тайни́к |
| visibility существительное | /ˌvɪz.əˈbɪl.ɪ.ti/ | ви́димость |
| proportional прилагательное | пропорциона́льный | |
| vacuum существительное | /ˈvæ.kjuːm/ | ва́куум |
| millisecond существительное | /ˈmɪlɪˌsɛkənd/ | миллисеку́нда |
| misconception существительное | /ˌmɪs.kənˈsɛp.ʃən/ | заблужде́ние |
| scanner существительное | /ˈskænə/ | ска́нер |
| fetch глагол | /fɛt͡ʃ/ | приноси́ть, принести́ |
| aggregate существительное | /ˈæɡ.ɹɪ.ɡət/ | масса, совокупность |
| spouse существительное | /spaʊ̯s/ | супру́г, супру́га |
Фразовые глаголы, которые вы услышите
| Слово | Значение |
|---|---|
| come back глагол | возвраща́ться, возврати́ться |
| go back глагол | возвраща́ться, возврати́ться |
Фразы, которые стоит повторять
Короткие законченные фразы из видео, которые пригодятся в повседневном разговоре:
- Let's just jump into it.
- Let's just add an explain analyze before these puppies.
- Let's see what happened here.
- Let's pay attention to what happened here.
- How do you solve this problem?
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | have returned · has been reduced · have updated |
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | has been reduced · have been updated · is actually deleted |
| Придаточные определительные who / which + предложение — уточнение о человеке или предмете | time, which is · index which is |
Произношение, на которое стоит обратить внимание
Говорящий использует 40 сокращённых и редуцированных форм, например I'm, don't, you're. Произносите их коротко, так, как слышите.
- Звуки «sh» и «zh»: cache /kæʃ/, misconception /ˌmɪs.kənˈsɛp.ʃən/
- Длинные слова — следите за ударением: visibility /ˌvɪz.əˈbɪl.ɪ.ti/, concurrency /kəŋˈkʌɹənsi/, millisecond /ˈmɪlɪˌsɛkənd/, misconception /ˌmɪs.kənˈsɛp.ʃən/
Как заниматься с этим видео
- Прослушайте всё видео один раз молча и выпишите незнакомые слова.
- Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
- Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как scan, execute, null.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.