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The Art of PostgreSQL: The Transcript, part II

This article is a transcript of the conference I gave at Postgres Open 2019, titled the same as the book: The Art of PostgreSQL. It’s availble as a video online at Youtube if you want to watch the slides and listen to it, and it even has a subtext!

Some people still prefer to read the text, so here it is. This text is the second part of the transcript of the video. The first part is available at The Art of PostgreSQL: The Transcript, part I .


As a developer writing SQL is the main thing you do with PostgreSQL. Because you know it’s ACID, you don’t have to care about that. So what you need to care about is implementing your business in the application. So you do SQL.

Here’s a very simple use case that I found. The data was open data at the time, but it’s not easy to download anymore. I will need to change the example later but I didn’t do that yet.

So when you download the data you have a CSV file that looks like this, with the year, the date, the number of shares exchanged on the stock exchange that day, the number of trades that exchanged those shares and the amount of dollars that were present. What you can see here is that I’m picking text as the data type for those columns where obviously it’s numeric.

But there’s a comma to separate the thousands, there’s a dollar sign. So if you just say to PostgreSQL “hey, it’s numbers”, it will not be happy with that. Then we use COPY on the client side. So you know the \copy (backslash-copy), it runs in psql and so the source file is going to be required on the client side of the connection. So you can actually use that over a connection to a server. You don’t need to upload the file to the PostgreSQL server before you do that. That’s why it’s \copy (backslash copy) rather than COPY.

And COPY is actually not just a command in Postgres it’s also a protocol. It’s a streaming protocol. So if you have a huge set of data, it’s going to be streamed. So that’s pretty fast, it’s pretty efficient. And also if you write your application code in Python or Java or something… as soon as you have more than a couple of lines to INSERT into the database in the same table, you should use COPY. It’s way faster. The threshold I usually have in mind is like 10 lines. If you’re uploading 10 lines of data into the same table, use COPY. It’s a protocol that is implemented in your driver already. So just use COPY from the driver.

But then we don’t want to use text, we want to use integer and numeric. Because it’s money here, you use numeric, right? You knew that right? You don’t use float for money, otherwise you lose money. So don’t do it.

And the thing that I wanted to show here is: who knew that you can do in a single SQL command, that is going to do a single table rewrite, you can actually rewrite three different columns in the same statement and use text processing functions to transform the data and make it suitable for the new data type. Who knew you could do that?

So, another way to do that would have been for me to use copy from program and you could run like a sed or an awk or Python filter to filter the data out and make it look like what PostgreSQL is going to expect. But if I had done that, then I couldn’t have shown you that you can do it in a single statement of three columns at a time and an ALTER TABLE.

Something that many developers told me, when I was a consultant, and even now when I do meetups and things like that… many developers they don’t like SQL because it’s a black box. And they’re like “yeah, we don’t even know the algorithm that is going to be used. We write a query and what is going to happen on the server? nobody knows… so we prefer to write the code our own way.”

So here’s an example on top of the data we say already. It’s the list of the 10 days in the dataset where you have the most dollars exchanged that day, in the New York Stock Exchange. So it’s a top-10, it’s easy, right. You run through the data and at the end of it you sort the data and at the end of it you just output the top 10 lines.

So the example is in Python here. When you want to write that in Python, maybem if you have like a couple of millions of lines in the database, maybe you don’t want to have all that in memory at the same time. Because all you want at the end is the top-10. So the amount of lines you want in memory at any time is 10. Not 2 millions. Not 2 billions. So, to do that in Python you use heapq and there’s an API, like you heapify the list of 10 and then you heappushpop and if it’s greater than what you have already it replaces an entry and if not it’s not included, that’s it. And the whole data structure in memory is going to have 10 lines, for the whole running of your script. So it’s easy to control and it’s nice. Okay.

How do you do that in Postgres?

Well, ORDER BY LIMIT 10, that’s it. Maybe that’s simpler that way. I don’t know what you think, but if I had to write and maintain this (Python version) or this (SQL version), I think I would do the SQL version.

But then the question I have is how do you know that this query is going to use the same thinking we did before? Only 10 lines in memory at all time rather than using all the memory for all the rows. And if you have one billion rows it’s a problem. So how do you figure that out in Postgres?

Well you use EXPLAIN.

My favorite variant of EXPLAIN is using ANALYZE, VERBOSE, and BUFFERS options, because that’s where you have the most information. You need to be careful though because ANALYZE will actually run the query. So if you have for example an UPDATE that is slow because of a foreign key that does not have an index or things like that, as it happens, and you want to EXPLAIN the update… you can EXPLAIN an UPDATE. Maybe you want to BEGIN, ANALYZE the UPDATE, and then ROLLBACK. So that the UPDATE didn’t happen when you work around it. So EXPLAIN ANALYZE is running the query.

And what we see here is a sort method top-N heapsort using this amount of memory. I think we covered it. PostgreSQL implements exactly the same algorithm as what we did manually in Python before.

So rather than writing all the code that is doing exactly that, but client side, which means you need to fetch 2 millions lines before you do anything, and then you sort them out. Maybe you can sort them out and send only 10 lines to the client and it’s way faster and way more efficient. And it’s only 4 lines of SQL.

Question from the audience: some other systems like SQL server are going to give you hints about maybe where you should look at because the query is not as fast as you expect it to be.

Answer: PostgreSQL itself will not do that but we have several Open Source tooling around it like the website explain.depesz.com or PEV which is a visual explain tool. Those will do that for you. So one of those lines (from the EXPLAIN output) will be in red, splashy, and that’s where you should focus. You actually have all the information you need (in the explain plan) if you know how to read it. But it takes some practice. So using something visual to get there is a good use of your time.

Usually when you have this kind of data and you’re working on an application in a team, one of the first things people are going to ask you, either the marketing department or maybe the finance department, or maybe accountants, I don’t know. Some of your colleagues are going to be interested into some kind of monthly reports. So how do you write a monthly report in SQL?

It’s pretty easy.

Here I’m showing off several things like… I’m using psql in that example and the \set command is setting a value to a variable in psql and then I can use the variable value here (:'start'). So whatever the month is I can change that and run the same query again and again. And I can also pass the value of that at the command line of the script. At the psql command line rather than in the file itself. Just so you know.

And then we’re using to_char to do some pretty formatting. And then, oh, that’s something you should pay attention to. We don’t use BETWEEN for the dates. Do you know why? I could have said BETWEEN start AND end but I didn’t do that, because BETWEEN is going to be inclusive for both the start and the end. Here I say the last day is the first day plus one month, and Postgres will figure that out. Remember, I told you, it’s object oriented. So the + operator with two integers, the result is an integer, or maybe a bigint. If it’s a date and an interval, then it’s something completely different, but it’s still a + operator that you’re using. It’s like operator overloading in C++ except that it’s very simple to use and it just works. And so interval '1 month' because it’s attached to a date then PostgreSQL will look-up in the calendar and decide how many days that month was. Of course the example I picked is February so it will have to decide how many days you had that year in February. But PostgreSQL can do that for you.

So if you use BETWEEN and this kind of expression (date ... + interval '1 month') then you might count March the first twice. Once in your February report and once in your March report. So that’s a bug, and then you have the accountants running to you and trying to explain the data with you because they will never have the same result as you have. And you’re like “I don’t understand your problem”… and yeah that’s a bug here. So you use greater or equal and less than strict, and you don’t have that bug. So that’s a small trick. Maybe you knew about it already but I’ve run through many developers enough that I would, you know, take some time on it.

So here’s the result without surprise. Okay, it’s some data. We don’t know the numbers so we don’t focus on them but there’s a bug. Can you spot the bug?

Missing days, exactly, congrats. So we have 28 days that year in February, but only 19 rows output. Maybe marketing department, or accounting won’t be very happy about that. Maybe they want to have the days without activity.

So let’s fix it, it’s easy. You run the same query right and then you do a lookup in the calendar in your application. Here it’s in Python but it could be anything else. And if the day of the calendar has some data associated with it, then you output that data, but otherwise you output zero. And here’s the result, fixed.

Or is it really? So where is that code going to be used actually? So maybe it’s going to be used in only one place and then it’s fixed. Good enough.

But maybe you have an application with different kind of people using it, like the frontend people, backoffice people. Maybe you have a little started in the finance department that was, you know, a quick PHP script where your backend used to be in Java and now it’s being rewritten in Django. You know, real life happens. So maybe it’s not that easy to say to everybody “hey, just use the calendar lookup”… because mostly what happens is that the calendar default implementation in Python is going to be surprisingly different from the one in Java. Maybe because of the way they handle the default timezone, and maybe you have some other bugs. I don’t know, it can get pretty complex.

But the thing that is common for all of those is that they are talking to our beloved PostgreSQL database. So maybe you wan to solve the days with no activity in SQL.

So who think that you know how to solve that in SQL? Like, you know, just a gut feeling, let’s see.

So there’s one thing that is very useful in PostgreSQL and it’s called generate_series. And as I said before we like boring names in PostgreSQL, so can you guess what generate_series is going to do? It’s going to generate a series. And it’s going to begin with the first date and then it’s going to go up to the next date but it’s inclusive, like BETWEEN is. So we say we go up to + 1 month - 1 day, so that we don’t step over to the next month, because that would be a problem. And we do that one day at a time, we step 1 day. It could be 1 hour or it could be 13 hours if that’s your business requirement. Here it’s one day at a time, that’s easy.

So we generate a series of dates, that’s our calendar. And then we do a LEFT JOIN. For each day of the calendar we’re going to look if we have data or not in the main table. That’s easy. That’s exactly the lookup that was implemented in Python before. It’s exactly the same thing but it’s done with PostgreSQL on the database side.

So we generate some values and we do a LEFT JOIN and here’s the result, fixed, in SQL. Easy, right?

Question: How come you have zeroes here, where does that come from?

Answer: That’s a good question, because those zeroes didn’t exist in the data set. We invented zeroes, we didn’t have any data. LEFT JOIN is going to provide you with NULL values. And coalesce is going to return the first argument of the function here that is not NULL.

I encourage you to look at PostgreSQL documentation to have more details about it. In some communities when you tell people “yeah, go look at the documentation”, maybe it’s a little rude to do that… you know the RTFM situation. In our case, we take pride in the PostgreSQL documentation that it is being very well maintained. So if you do a patch for PostgreSQL that is technically very good, like something awesome, but not documented, it’s going to be rejected. Any patch that goes into PostgreSQL needs to maintain the documentation that goes with it, in the same patch. So we are very proud of our documentation system. So we when say “please read the docs” it’s because we’ve been spending time writing it the good way, the think, and we would be happy that it’s useful. So in the PostgreSQL community it’s polite. So… don’t take it the wrong way.

Usually the next step for Marketing is going to be “okay, I want week on week evolution as a percentage in the same result set.” So, the slide is a copy/paste of the result because it’s easier to explain it that way.

And I’ve tried to change some colors, there’s a color code. So basically you want from this Wednesday, you want to know that it was 10% less dollars exchanged than the previous Wednesday. Okay, you understand the result set? It’s just a percentage of difference with the previous week, same day the week before.

So who know how to implement that in SQL? Okay, the other, I think you don’t know SQL. I’m sorry. We’ll get back to that, it’s a strong statement I know.

So to implement it we use a CTE here on the left and maybe you recognize it, it’s exactly the same query as before. The generate_series with a LEFT JOIN. Ah, there’s a little difference, let’s go back to that later. So we compute that and we use computed_data here as a from clause for the next part of the query. And we compute the difference of dollars between the dollards from last week and the dollars from this week.

But how do we get those dollars from last week? It’s because we use window functions. The window functions allow us in one query… The projection operator, the select clause is the projection operator, it works one row at a time — and here we want to see the values from another row. So we are going to say it’s a peer row, a friend. So we have many friends now. And with window functions the other values from our friends’ rows.

And who are going to be our friend here? Well it depends on the clause you put in the OVER clause. So PARTITION BY and ORDER BY. PARTITION BY, you can think about it a little like GROUP BY. So it’s going to say our friends are going to be the other rows who have the same value as us for the PARTITION BY clause. So we PARTITION BY extract(isodow from date).

ISODOW you know what that means? DOW is Day Of Week. And of course in some countries Sunday is one, the first, and some other countries it’s Monday. And then in come countries we begin at zero, not one, so Sunday is zero, Monday is one. Well it’s crazy. So of course there’s an ISO standard about it. So we use the ISO definition for the Day Of Week. And so anything that is a Wednesday is going to be 3 and anything that is 3 is going to be rows that we are allowed to see from the current one.

So we are going to be able to see any row that has the same Day Of Week as the current row. And we are going to see them in an order that we speficy, by date. And we’re going to lag(…, 1) so we’re going to be able to see the one that is a week before us.

Question: Is there a reason that you are using to_char in the main query but extract(isodow ...) in the window function?

Answer: so yeah the to_char here is going to be “Wednesday” or maybe going to use the locale that you have on your computer, and this (extract(isodow ...)) is going to be a number, so maybe it’s easier reason about numbers from that clause. And also those queries are for slides and showing off things.

And so that’s a window function. So, who’s been using window functions before? Nice (there was quite some people in the audience that did!).

It was invented in ‘92 like around, you know, UTF-8 and maybe IPv6, things that everybody uses everyday, nowadays… no? maybe not… So that’s SQL from 92. So if you are not comfortable with it and it’s 2019 maybe it’s time to learn SQL again.

So then the copy/paste from the result of the query is what we saw before.

And the SQL standard, the current version is 2016. And any new version of the standard is deprecating every produced one. So there’s only one current version of the SQL standard. So ‘92 is interesting for historic purposes. If you do historic research, that’s very interesting. If you do production database or application development, you don’t care about that. The only one that you care about is 2016.


That’s it for the first part of this presentation. We covered from about 15 mins in to 30 mins of this talk. I will publish the transcript for part III and then part IV later this week, so stay tuned to this blog if you like this content!

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