Shadowing Practice: SELECT COUNT (*) can impact your Backend Application Performance, here is why - Learn English Speaking with Video
Creating lesson...
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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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Vocabulary and speaking notes for this lesson
This C1 speaking lesson is built on the video “SELECT COUNT (*) can impact your Backend Application Performance, here is why”. The speaker keeps coming back to these words: index, count, row, table, scan. This video has 156 sentences and 1723 words to shadow. The speech runs for 10:37. The speaker talks at a natural 162 words per minute, close to everyday conversation. 86% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| scan verb | /skæn/ | To examine sequentially, carefully, or critically; to scrutinize; to behold closely. |
| execute verb | /ˈɛksɪˌkjuːt/ | To kill, especially as punishment for a capital crime. |
| null noun | /nʌl/ | A non-existent or empty value or set of values. |
| analyze verb | /ˈæn.əˌlaɪz/ | To subject to analysis. |
| query noun | /ˈkwɪɹ.i/ | A question, an inquiry (US), an enquiry (UK). |
| cache noun | /kæʃ/ | Such a store of physical supplies, placed by humans or other animals for practical reasons. |
| visibility noun | /ˌvɪz.əˈbɪl.ɪ.ti/ | The condition of being visible. |
| proportional adjective | At a constant ratio (to). Two magnitudes (numbers) are said to be proportional if the second varies in a direct relation arithmetically to the first. Symbol: ∝. | |
| vacuum noun | /ˈvæ.kjuːm/ | A region of space that contains no matter. |
| concurrency noun | /kəŋˈkʌɹənsi/ | The property or an instance of being concurrent; something that happens at the same time as something else. |
| millisecond noun | /ˈmɪlɪˌsɛkənd/ | An SI unit of time equal to 10⁻³ seconds. Symbol: ms |
| misconception noun | /ˌmɪs.kənˈsɛp.ʃən/ | A mistaken belief, a wrong idea. |
| planner noun | /ˈplænɚ/ | One who plans. |
| scanner noun | /ˈskænə/ | One who scans. |
| fetch verb | /fɛt͡ʃ/ | To retrieve; to bear towards; to go and get. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| go back to verb | To stem from; to originate in. |
| come back verb | To return to a place. |
| go back verb | To return to a place or state after having been there at a previous time. |
Sentences worth repeating
Short, complete lines from the video that you can reuse in everyday conversation:
- 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?
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Present perfect have/has + past participle — a past action that still matters now | have returned · has been reduced · have updated |
| Passive voice be + past participle — the focus is on what happens, not who does it | has been reduced · have been updated · is actually deleted |
| Relative clauses who / which + clause — extra information about a person or thing | time, which is · index which is |
Pronunciation to watch
The speaker uses 40 contractions and reduced forms, such as I'm, don't, you're. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: cache /kæʃ/, misconception /ˌmɪs.kənˈsɛp.ʃən/
- Long words — get the stress right: visibility /ˌvɪz.əˈbɪl.ɪ.ti/, concurrency /kəŋˈkʌɹənsi/, millisecond /ˈmɪlɪˌsɛkənd/, misconception /ˌmɪs.kənˈsɛp.ʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like scan, execute, null.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.