IN vs UNION ALL

Published on: 2019-04-30

Watch this week’s episode on YouTube.

When you need to filter query results on multiple values, you probably use an IN() statement or multiple predicates separated by ORs:

WHERE Col1 IN ('A','B','C')

or

WHERE Col1 = 'A' OR Col1 = 'B' OR Col1 = 'C'

While SQL Server will generate the same query plan for either syntax, there is another technique you can try that can sometimes can improve performance under certain conditions: UNION ALL.

This post is a continuation of my series to document ways of refactoring queries for improved performance. I’ll be using the StackOverflow 2014 data dump for these examples if you want to play along at home.

Lookups and Scans

Let’s say we have the following index on our dbo.Badges table:

CREATE NONCLUSTERED INDEX [IX_Badges] ON [dbo].[Badges] ([Name]) INCLUDE ([UserId]);

Next let’s run these two separate queries:

/* Query 1 */
SELECT 
    Name, UserId, Date 
FROM 
    dbo.Badges 
WHERE 
    Name = 'Benefactor'
OPTION(MAXDOP 1)

/* Query 2 */
SELECT 
    Name, UserId, Date 
FROM 
    dbo.Badges 
WHERE 
    Name = 'Research Assistant'
OPTION(MAXDOP 1)

Note I’m enforcing MAXDOP 1 here to remove any performance differences due to parallelism in these demos.

The nonclustered index doesn’t cover these queries – while SQL Server can seek the index for the Name predicate in the WHERE clause, it can’t retrieve all the columns in the SELECT from the index alone. This leaves SQL Server with a tough choice to make:

  1. Does it scan the whole clustered index to return all the required columns for the rows requested?
  2. Does it seek to the matching records in the nonclustered index and then perform a key lookup to retrieve the remaining data?

So, what does SQL Server decide to do?

Execution plans

For Query 1, SQL Server thinks that reading the entire clustered index and returning only the rows where Name = 'Benefactor' is the best option.

SQL Server takes a different approach for Query 2 however, using the non-covering nonclustered indexes to find the records with Name = 'Research Assistant' and then going to look up the Date values in the clustered index via a Key Lookup

The reason SQL server chooses these two different plans is because it thinks it will be faster to return smaller number of records with a Seek + Key Lookup approach (“Research Assistant”, 127 rows), but faster to return a larger number of records with a Scan (“Benefactor”, 17935 rows).

Kimberly Tripp has an excellent post that defines where this “tipping point” from a key lookup to a clustered index scan typically occurs, but the important thing to keep in mind for this post is that we can sometimes use SQL Server’s ability to switch between these two approaches to our advantage.

Combining Queries with IN

So, what plan does SQL Server generate when we combine our two queries into one?

SELECT 
    Name, UserId, Date 
FROM 
    dbo.Badges 
WHERE 
    Name IN ('Benefactor','Research Assistant')
OPTION(MAXDOP 1)
Key Lookup

Interestingly enough SQL Server decides to retrieve the requested rows from the nonclustered index and then go lookup the remaining Date column in the clustered index.

If we look at the page reads (SET STATISTICS IO ON;) we’ll see SQL Server had to read 85500 pages to return the data requested:

(18062 rows affected)
Table 'Badges'. Scan count 2, logical reads 85500, physical reads 20, read-ahead reads 33103, ...

Without correcting our index to include the Date column, is there some way we can achieve the same results with better performance?

UNION ALL

In this case it’s possible to rewrite our query logic to use UNION ALL instead of IN/ORs:

SELECT 
    Name,UserId,Date 
FROM 
    dbo.Badges 
WHERE 
    Name = 'Benefactor' 
UNION ALL
SELECT 
    Name,UserId,Date 
FROM 
    dbo.Badges 
WHERE 
    Name = 'Research Assistant'
OPTION(MAXDOP 1)
UNION ALL execution plan

We get the same exact results through a hybrid execution plan.

In this case, our plan mirrors what SQL Server did when running our original two queries separately:

  • The rows where Name = 'Benefactor' are returned by scanning the clustered index.
  • The nonclustered index is seeked with clustered index lookups for the Name = 'Research Assistant' records.

Looking at the IO statistics for this UNION ALL query:

(18062 rows affected)
Table 'Badges'. Scan count 2, logical reads 50120, physical reads 6, read-ahead reads 49649, ...

Even though this query reads the whole clustered index to get the Benefactor rows, the total number of logical reads is still smaller than the seek/key lookup pattern seen in the combined query with IN(). This UNION ALL version gives SQL Server the ability to build a hybrid execution plan, combining two different techniques to generate a plan with fewer overall reads.

IN or UNION ALL?

There’s no way to know for sure without trying each variation.

But if you have a slow performing query that is filtering on multiple values within a column, it might be worth trying to get SQL Server to use a different plan by rewriting the query.

Thanks for reading. You might also enjoy following me on Twitter.

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Correlated Subqueries vs Derived Tables

Published on: 2019-04-23

Watch this week’s episode on YouTube.

Correlated subqueries provide an intuitive syntax for writing queries that return related data. However, they often perform poorly due to needing to execute once for every value they join on.

The good news is that many correlated subqueries can be rewritten to use a derived table for improved performance.

This post is a continuation of my series to document ways of refactoring queries for improved performance. I’ll be using the StackOverflow 2014 data dump for these examples if you want to play along at home.

When was each user’s first badge awarded?

StackOverflow awards users badges for things like asking good questions, hitting certain vote thresholds, and more.

I want to write a query that figures out on what date did each user receive their first badge.

Using a correlated subquery, I might write my query as follows:

SET STATISTICS IO, TIME ON;

SELECT DISTINCT
    UserId,
    FirstBadgeDate = (SELECT MIN(Date) FROM dbo.Badges i WHERE o.UserId = i.UserId)
FROM
    dbo.Badges o

The syntax of the correlated subquery here makes it clear that for each UserId we want to return the MIN(Date) associated with that UserId from the badges table.

Looking at the execution plan and time and IO statistics (abbreviated for clarity) we see:

Execution plan for correlated subquery
(1318413 rows affected)
Table 'Worktable'. Scan count 0, logical reads 0, ...
Table 'Workfile'. Scan count 0, logical reads 0, ...
Table 'Badges'. Scan count 2, logical reads 43862, ...

(1 row affected)

 SQL Server Execution Times:
   CPU time = 3625 ms,  elapsed time = 8347 ms.

So, what’s going on here? We read ~8 million rows of data from our index on the dbo.Badges table and then calculate the MIN(Date) for each UserId. This is the “correlated” part of our query, which then gets joined back to the dbo.Badges table using a Hash Match join operator.

Our join doesn’t eliminate any rows so the ~8 million rows continue flowing through until near the very end where we have another Hash Match operator, this time being used to dedupe the rows for the DISTINCT part of query, reducing the final result to ~1 million rows.

Eliminating the Correlated Subquery

What would things look like if we rewrote this correlated subquery as a derived table in the FROM clause?

SELECT DISTINCT
    o.UserId,
    FirstBadgeDate
FROM
    dbo.Badges o
    INNER JOIN 
        (SELECT 
            UserId, 
            MIN(Date) as FirstBadgeDate 
        FROM 
            dbo.Badges GROUP BY UserId
        ) i
    ON o.UserId = i.UserId
Execution plan with derived table
(1318413 rows affected)
Table 'Workfile'. Scan count 0, logical reads 0, ...
Table 'Worktable'. Scan count 0, logical reads 0, ...
Table 'Badges'. Scan count 2, logical reads 43862, ...

(1 row affected)

 SQL Server Execution Times:
   CPU time = 2516 ms,  elapsed time = 5350 ms.

If we look at the IO statistics, it’s interesting to note that there is no difference in reads between these two queries.

Looking at the CPU time statistics however, this derived table query consistently comes in about 33% faster than the correlated subquery example. Why is that?

Looking at the execution plan reveals some details: in this plan, you can see we read in from the dbo.Badges index and go straight into a Hash Match operator. The top stream is deduping our data on UserId, taking it from ~8 million rows to ~1 million rows. The bottom stream does the same deduping while also calculating the MIN(DATE) for each UserId grouping.

When both of those streams join together, the final hash match operator is only joining ~1 million rows with ~1 million rows (as opposed to the first query that was joining ~8 million rows with ~1 million rows).

This last join is the reason for the performance improvement: because this execution plan can reduce the number of rows sooner the final join ends up having to do less work. Additionally, the records were already distinct going into the join, saving us from an extra deduping step.

Further Reducing Redundancy

You may have noticed that both of these queries are a little redundant: they both call on the dbo.Badges table unnecessarily. The best option to improve query performance would be to rewrite it as:

SELECT 
    UserId, 
    MIN(Date) as FirstBadgeDate 
FROM 
    dbo.Badges 
GROUP BY 
    UserId
the best execution plan

While this is the most efficient query of the three, most real-world queries and scenarios aren’t this easy to simplify.

When your queries have more joins, WHERE clauses, and more, knowing how to refactor from a correlated subquery to a derived table query is critical to potentially improving performance.

Thanks for reading. You might also enjoy following me on Twitter.

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Window Functions vs GROUP BYs

Published on: 2019-04-16

Watch this week’s episode on YouTube.

There are many options available for improving the performance of a query: indexes, statistics, configuration settings, etc…

However, not all environments allow you to use those features (eg. vendor databases), leaving query rewriting as the only option.

This is the first post in a series to document common ways to refactor queries without otherwise altering the database. The goal of these posts will be to provide examples of performance pitfalls in queries and how to rewrite those queries to generate different query plans that (hopefully) improve performance.

I’ll be using the StackOverflow 2014 data dump for these examples if you want to play along at home.

Who was first to earn each badge?

StackOverflow awards users badges for things like asking good questions, hitting certain vote thresholds, and more.

I want to write a query that figures out who is the first person awarded each badge. In cases where there is a tie for the first person to receive that badge, I want to return the user with the lowest UserId.

Window functions make this type of question easy to write a query for:

SELECT DISTINCT
    Name,
    FIRST_VALUE(UserId) OVER (PARTITION BY Name ORDER BY Date,UserId) AS UserId
FROM
    dbo.Badges b
ORDER BY
    Name,UserId

If you’ve used FIRST_VALUE before, this query should be easy to interpret: for each badge Name, return the first UserId sorted by Date (earliest date to receive the badge) and UserId (pick the lowest UserId when there are ties on Date).

This query was easy to write and is simple to understand. However, the performance is not great: it takes 46 seconds to finish returning results on my machine.

Window function execution plan

Note: I assumed this table started off with the following index:

CREATE NONCLUSTERED INDEX IX_Badges__Name_Date_UserId ON [dbo].[Badges] (Name,Date,UserId);

Why so slow?

If we SET STATISTICS IO ON we’ll notice that SQL Server reads 46767 pages from a nonclustered index. Since we aren’t filtering our data, there’s not much we can do to make that faster.

Reading right to left, next up we see two Segment operators. These don’t add much overhead since our data is sorted on our segments/groups, so making SQL Server identify when our sorted rows change values is trivial.

Next up is the Window Spool operator which “Expands each row into the set of rows that represent the window associated with it.” While it looks innocent by having a low relative cost, this operator is writing 8 million rows/reading 16 million rows (because of how Window Spool works) from tempdb. Ouch.

After that the Stream Aggregate operator and Compute Scalar operators check to see if the first value in each window being returned from the Window Spool is null and then return the first non-null value. These operations are also relatively painless since the data flowing through is already sorted.

The Hash Match operator then dedupes the data for our DISTINCT and then we sort the remaining ~2k rows for our output.

So while our query looks simple, the fact that our whole table of data is getting written to and read from tempdb before being deduped and sorted is a real performance killer.

Removing tempdb usage the old-fashioned way

When I say “the old fashioned way”, I mean rewriting our window function to use more traditional aggregate functions and a GROUP BY:

SELECT
    b.Name,
    MIN(b.UserId) AS UserId
FROM
    dbo.Badges b
    INNER JOIN
    (
    SELECT
        Name,
        MIN(Date) AS Date
    FROM
        dbo.Badges
    GROUP BY
        Name
    ) m
        ON b.Name = m.Name
        AND b.Date = m.Date
GROUP BY
    b.Name
ORDER BY
    Name,UserId

I think by most people’s standards, this query is not as easy to read. While not overly complex, it does take up a lot more screen space and is complicated by multiple GROUP BYs and a derived table.

And while the query may look ugly on the outside, it’s what lies below the surface that really matters:

GROUP BY execution plan

What a beautifully simple execution plan. And it finishes executing almost instantly.

Let’s break down what’s going on. First, we start with similar Index Scan and Segment operators as the previous query so no real difference there.

At this point you may have noticed that while the written query uses two GROUP BYs and two MIN functions that are then joined together, there are not two Index Scans, two sets of aggregations, and no join happening in the execution plan.

SQL Server can use an optimization with the Top operator that allows it to take the sorted data and return only the Name and UserId rows for the top Name and Date values within a group (essentially matching the MIN logic). This is a great example of how the optimizer can take a declarative SQL query and decide how to efficiently return the data needs.

At this point, the Top operator filters our 8 million rows down to around 30k rows. 30k rows get deduped a lot faster with our Stream Aggregate operator, and since the data is already sorted we don’t need an extra Sort operator.

Overall, this second query runs so much better than the original because SQL Server doesn’t have to go to tempdb for any operations – all the data is pre-sorted in the index and can flow through.

So I shouldn’t use Window Functions?

Not necessarily – it comes down to a trade offs.

I almost always start with a window function because of how easy they are to write and read. Plus I think they are fun to write as well.

However, if the window function is having to read/write a lot of data to tempdb and it’s affecting the overall performance of your query, a rewrite may be necessary.

In that case, I much rather take more verbose syntax to get a 2000x performance boost.

Thanks for reading. You might also enjoy following me on Twitter.

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