Profiling Queries with SQL Explain Analyze

Instructor: [0:00] You can't have a course on advanced SQL without talking about query performance and optimizations. Let's jump right in and write EXPLAIN select star from users. What we're looking at here is the execution plan that the Postgres planner generates for our select statement.

[0:17] This plan is brought to us by the use of this EXPLAIN keyword here. This execution plan shows how the table reference by statement will be scanned. In our case we're going to use the sequential scan for our select statement.

[0:32] The biggest takeaway from our generated plan is the cost here. There are actually two different values. They're separated by these double dots. This side, the first, is the startup cost and the second is the total cost.

[0:46] The startup time is the time it takes before the first row can be returned and this total time is what it will take to return all of the rows from our query. We have a zero startup cost and 11 for total cost. The measurement for this is units of disk page fetches. This other data is the number of rows within our user's table and this width is referencing the size of our plan.

[1:12] We can see the same output of our EXPLAIN but in a different format by stating format JSON. Other formats include XML, text, and YAML. We can give as much info as possible by stating verbose with our EXPLAIN. Some additional info we get here is which schema this table exists on and the table columns.

[1:36] I haven't even mentioned the most important thing about EXPLAIN. When using it, we don't actually run our query. For example, if we were to write "update users set all emails equal to null," we're not actually going to update the user's table.

[1:50] The values that you see here from the planner is its best guess on the cost. If we actually wanted to get a accurate cost, we have to run the query by adding "analyze with EXPLAIN" like so.

[2:04] Now we can see our EXPLAIN plan compared and contrasted to the actual implementation of that plan. The analyze option gives us rolled-up times for the planning and the execution of our query. Again, when adding the analyze option, it will actually run your query, so be careful when using it in production on commands like update and delete.

[2:26] With all this info in mind, let's go back to our original EXPLAIN query. With this select, we are performing a sequential scan. This means that the select statement will loop over every single row one after another until the end.

[2:39] There's another scan, which is typically faster, which is called the index scan. We can see that because our users table has a primary key, this automatically creates an index on user handle, and then it'll use an index scan using for that user.

[2:54] I said that index scan typically is faster as we see here in our total time to return the row. However, it has a startup cost and can actually be slower than a sequential scan in some cases. As we run our queries through the planner, we're going to see that for each aggregation, for each WHERE clause that we use, for every join that we end up using, there's going to be a cost.

[3:18] Our planner will break down and guess as much as it can when using just EXPLAIN, and as we can see, even when joining tables, we're going to be told which algorithms will be used.

[3:30] One of the biggest debates when SQL query profiling is when to use which option. Should you use a group by? Should you use a distinct? Would a sub-query work better here or possibly a timetable?

[3:41] While some principles like adding indexes are usually true, there is no silver bullet. Each table, number of rows of data, constraints, foreign keys, etc. are different, and all options need to be run through a profiler to find the best option.

[3:57] Now, both of these queries return the same result. One joins on two tables, and the second one joins on a sub-query that wastefully pulls out all the data from a table. However, looking at our times, the sub-query route is microscopically faster.

[4:13] I could rerun these multiple times and they would probably flip flop as far as who is quicker. The point is, there is no silver bullet. Make sure you try all of your options when you're trying to profile your queries.

[4:24] To close, sometimes it can be difficult to read through the planner results, especially with large queries. My go-to tool for getting a pretty visual is this website. I'll put the link in the notes of this video. Once we copy and paste our query plan into this section here, it's going to break down the plan into an easy-to-read breakdown, adding colors to indicate which parts were slow and which were fast.