Does The Join Order of My Tables Matter?

Published on: 2017-11-21

Photo by pan xiaozhen on Unsplash

I had a great question submitted to me (thank you Brandman!) that I thought would make for a good blog post:

…I’ve been wondering if it really matters from a performance standpoint where I start my queries. For example, if I join from A-B-C, would I be better off starting at table B and then going to A & C?

The short answer: Yes.  And no.

More of a watcher than a reader?  Watch this week’s episode on YouTube!

Table join order matters for performance!

Disclaimer: For this post, I’m only going to be talking about INNER joins.  OUTER (LEFT, RIGHT, FULL, etc…) joins are a whole ‘nother animal that I’ll save for time.

Let’s use the following query from WideWorldImporters for our examples:

/* 
-- Run if if you want to follow along - add  a computed column and index for CountryOfManufacture
ALTER TABLE Warehouse.StockItems SET (SYSTEM_VERSIONING = OFF);   
ALTER TABLE Warehouse.StockItems
ADD CountryOfManufacture AS CAST(JSON_VALUE(CustomFields,'$.CountryOfManufacture') AS NVARCHAR(10)) 
ALTER TABLE Warehouse.StockItems SET (SYSTEM_VERSIONING = ON); 
CREATE INDEX IX_CountryOfManufacture ON Warehouse.StockItems (CountryOfManufacture)
*/

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  Sales.Orders o						-- 73595 rows
  INNER JOIN Sales.OrderLines l			-- 231412 rows
    ON o.OrderID = l.OrderID			-- 231412 rows after join
  INNER JOIN Warehouse.StockItems s		-- 227 rows
    ON l.StockItemID = s.StockItemID	-- 1036 rows after join 
	AND s.CountryOfManufacture = 'USA'	-- 8 rows for USA	

Note: with an INNER join, I normally would prefer putting my ‘USA’ filter in the WHERE clause, but for the rest of these examples it’ll be easier to have it part of the ON.

The key thing to notice is that we are joining  three tables – Orders, OrderLines, and StockItems – and that OrderLines is what we use to join between the other two tables.

We basically have two options for table join orders then – we can join Orders with OrderLines first and then join in StockItems, or we can join OrderLines and StockItems first and then join in Orders.

In terms of performance, it’s almost certain that the latter scenario (joining OrderLines with StockItems first) will be faster because StockItems will help us be more selective.

Selective?  Well you might notice that our StockItems table is small with only 227 rows.  It’s made even smaller by filtering on ‘USA’ which reduces it to only 8 rows.

Since the StockItems table has no duplicate rows (it’s a simple lookup table for product information) it is a great table to join with as early as possible since it will reduce the total number of rows getting passed around for the remainder of the query.

If we tried doing the Orders to OrderLines join first, we actually wouldn’t filter out any rows in our first step, cause our subsequent join to StockItems to be more slower (because more rows would have to be processed).

Basically, join order DOES matter because if we can join two tables that will reduce the number of rows needed to be processed by subsequent steps, then our performance will improve.

So if the order that our tables are joined in makes a big difference for performance reasons, SQL Server follows the join order we define right?

SQL Server doesn’t let you choose the join order

SQL is a declarative language: you write code that specifies *what* data to get, not *how* to get it.

Basically, the SQL Server query optimizer takes your SQL query and decides on its own how it thinks it should get the data.

It does this by using precalculated statistics on your table sizes and data contents in order to be able to pick a “good enough” plan quickly.

So even if we rearrange the order of the tables in our FROM statement like this:

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  Sales.OrderLines l
  INNER JOIN Warehouse.StockItems s
	ON l.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'
  INNER JOIN Sales.Orders o
    ON o.OrderID = l.OrderID

Or if we add parentheses:

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  (Sales.OrderLines l
  INNER JOIN Sales.Orders o
    ON l.OrderID = o.OrderID)
  INNER JOIN Warehouse.StockItems s
	ON l.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'

Or even if we rewrite the tables into subqueries:

SELECT
  l.OrderID,
  s.CountryOfManufacture
FROM
  (
  SELECT 
    o.OrderID,
    l.StockItemId
  FROM
    Sales.OrderLines l
	INNER JOIN Sales.Orders o
      ON l.OrderID = o.OrderID
  ) l
  INNER JOIN Warehouse.StockItems s
	ON l.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'

SQL Server will interpret and optimize our three separate queries (plus the original one from the top of the page) into the same exact execution plan:

Basically, no matter how we try to redefine the order of our tables in the FROM statement, SQL Server will still do what it thinks it’s best.

But what if SQL Server doesn’t know best?

The majority of the time I see SQL Server doing something inefficient with an execution plan it’s usually due to something wrong with statistics for that table/index.

Statistics are also a whole ‘nother topic for a whole ‘nother day (or month) of blog posts, so to not get too side tracked with this post, I’ll point you to Kimberly Tripp’s introductory blog post on the subject: https://www.sqlskills.com/blogs/kimberly/the-accidental-dba-day-15-of-30-statistics-maintenance/

The key thing to take away is that if SQL Server is generating an execution plan where the order of table joins doesn’t make sense check your statistics first because they are the root cause of many performance problems!

Forcing a join order

So you already checked to see if your statistics are the problem and exhausted all possibilities on that front.  SQL Server isn’t optimizing for the optimal table join order, so what can you do?

Row goals

If SQL Server isn’t behaving and I need to force a table join order, my preferred way is to do it via a TOP() command.

I learned this technique from watching Adam Machanic’s fantastic presentation on the subject and I highly recommend you watch it.

Since in our example query SQL Server is already joining the tables in the most efficient order, let’s force an inefficient join by joining Orders with OrderLines first.

Basically, we write a subquery around the tables we want to join together first and make sure to include a TOP clause. 

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  (
  SELECT TOP(2147483647) -- A number of rows we know is larger than our table.  Watch Adam's presentation above for more info.
	o.OrderID,
	l.StockItemID
  FROM
    Sales.Orders o
    INNER JOIN Sales.OrderLines l
      ON o.OrderID = l.OrderID
  ) o
  INNER JOIN Warehouse.StockItems s
    ON o.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'

Including TOP forces SQL to perform the join between Orders and OrderLines first – inefficient in this example, but a great success in being able to control what SQL Server does.

This is my favorite way of forcing a join order because we get to inject control over the join order of two specific tables in this case (Orders and OrderLines) but SQL Server will still use its own judgement in how any remaining tables should be joined.

While forcing a join order is generally a bad idea (what happens if the underlying data changes in the future and your forced join no longer is the best option), in certain scenarios where its required the TOP technique will cause the least amount of performance problems (since SQL still gets to decide what happens with the rest of the tables).

The same can’t be said if using hints…

Query and join hints

Query and join hints will successfully force the order of the table joins in your query, however they have significant draw backs.

Let’s look at the FORCE ORDER query hint.  Adding it to your query will successfully force the table joins to occur in the order that they are listed:

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  Sales.Orders o
  INNER JOIN Sales.OrderLines l
    ON o.OrderID = l.OrderID
  INNER JOIN Warehouse.StockItems s
    ON l.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'
OPTION (FORCE ORDER)

Looking at the execution plan we can see that Orders and OrderLines were joined together first as expected:

The biggest drawback with the FORCE ORDER hint is that all tables in your query are going to have their join order forced (not evident in this example…but imagine we were joining 4 or 5 tables in total).

This makes your query incredibly fragile; if the underlying data changes in the future, you could be forcing multiple inefficient join orders.  Your query that you tuned with FORCE ORDER could go from running in seconds to minutes or hours.

The same problem exists with using a join hints:

SELECT
  o.OrderID,
  s.CountryOfManufacture
FROM
  Sales.Orders o 
  INNER LOOP JOIN Sales.OrderLines l
    ON o.OrderID = l.OrderID
  INNER JOIN Warehouse.StockItems s
    ON l.StockItemID = s.StockItemID
	AND s.CountryOfManufacture = 'USA'

Using the LOOP hint successfully forces our join order again, but once again the join order of all of our tables becomes fixed:

A join hint is probably the most fragile hint that forces table join order because not only is it forcing the join order, but it’s also forcing the algorithm used to perform the join.

In general, I only use query hints to force table join order as a temporary fix.

Maybe production has a problem and I need to get things running again; a query or join hint may be the quickest way to fix the immediate issue.  However, long term using the hint is probably a bad idea, so after the immediate fires are put out I will go back and try to determine the root cause of the performance problem.

Summary

  • Table join order matters for reducing the number of rows that the rest of the query needs to process.
  • By default SQL Server gives you no control over the join order – it uses statistics and the query optimizer to pick what it thinks is a good join order.
  • Most of the time, the query optimizer does a great job at picking efficient join orders.  When it doesn’t, the first thing I do is check to see the health of my statistics and figure out if it’s picking a sub-optimal plan because of that.
  • If I am in a special scenario and I truly do need to force a join order, I’ll use the TOP clause to force a join order since it only forces the order of a single join.
  • In an emergency “production-servers-are-on-fire” scenario, I might use a query or join hint to immediately fix a performance issue and go back to implement a better solution once things calm down.

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

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Why Parameter Sniffing Isn’t Always A Bad Thing (But Usually Is)

Published on: 2017-08-08

Unexpected SQL Server Performance Killers #2

Photo by Jakob Owens on Unsplash

In this series I explore scenarios that hurt SQL Server performance and show you how to avoid them. Pulled from my collection of “things I didn’t know I was doing wrong for years.”

Prefer video? Watch this post on YouTube.

Last week we discussed how implicit conversions could be one reason why your meticulously designed indexes aren’t getting used.

Today let’s look at another reason: parameter sniffing.

Here’s the key: Parameter sniffing isn’t always a bad thing.

Most of the time it’s good: it means SQL Server is caching and reusing query plans to make your queries run faster.

Parameter sniffing only becomes a problem when the cached plan isn’t anywhere close to being the optimal plan for given input parameters.

So what’s parameter sniffing?

Let’s start with our table dbo.CoffeeInventory which you can grab from Github.

The key things to know about this table are that:

  1. We have a nonclustered index on our Name column.
  2. The data is not distributed evenly (we’ll see this in a minute)

Now, let’s write a stored procedure that will return a filtered list of coffees in our table, based on the country. Since there is no specific Country column, we’ll write it so it filters on the Name column:

DROP PROCEDURE IF EXISTS dbo.FilterCoffee
GO
CREATE PROCEDURE dbo.FilterCoffee
@ParmCountry varchar(30)
AS
BEGIN
 SELECT Name, Price, Description 
 FROM Sandbox.dbo.CoffeeInventory
 WHERE Name LIKE @ParmCountry + '%'
END
GO

Let’s take a look at parameter sniffing in action, then we’ll take a look at why it happens and how to solve it.

EXEC dbo.FilterCoffee @ParmCountry = 'Costa Rica'
EXEC dbo.FilterCoffee @ParmCountry = 'Ethiopia'

Running the above statement gives us identical execution plans using table scans:

In this case we explicitly specified the parameter @ParmCountry. Sometimes SQL will parameterize simple queries on its own.

That’s weird. We have two query executions, they are using the same plan, and neither plan is using our nonclustered index on Name!

Let’s step back and try again. First, clear the query plan cache for this stored procedure:

DECLARE @cache_plan_handle varbinary(44)
SELECT @cache_plan_handle = c.plan_handle
FROM 
 sys.dm_exec_cached_plans c
 CROSS APPLY sys.dm_exec_sql_text(c.plan_handle) t
WHERE 
 text like 'CREATE%CoffeeInventory%' 
-- Never run DBCC FREEPROCCACHE without a parameter in production unless you want to lose all of your cached plans...
DBCC FREEPROCCACHE(@cache_plan_handle)

Next, execute the same stored procedure with the same parameter values, but this time with the ‘Ethiopia’ parameter value first. Look at the execution plan:

EXEC dbo.FilterCoffee @ParmCountry = 'Ethiopia'
EXEC dbo.FilterCoffee @ParmCountry = 'Costa Rica'

Now our nonclustered index on Name is being utilized. Both queries are still receiving the same (albeit different) plan.

We didn’t change anything with our stored procedure code, only the order that we executed the query with different parameters.

What the heck is going on here!?

This is an example of parameter sniffing. The first time a stored procedure (or query) is ran on SQL server, SQL will generate an execution plan for it and store that plan in the query plan cache:

SELECT
 c.usecounts,
 c.cacheobjtype,
 c.objtype,
 c.plan_handle,
 c.size_in_bytes,
 d.name,
 t.text,
 p.query_plan
FROM 
 sys.dm_exec_cached_plans c
 CROSS APPLY sys.dm_exec_sql_text(c.plan_handle) t
 CROSS APPLY sys.dm_exec_query_plan(c.plan_handle) p
 INNER JOIN sys.databases d
 ON t.dbid = d.database_id
WHERE 
 text like 'CREATE%CoffeeInventory%'

All subsequent executions of that same query will go to the query cache to reuse that same initial query plan — this saves SQL Server time from having to regenerate a new query plan.

Note: A query with different values passed as parameters still counts as the “same query” in the eyes of SQL Server.

In the case of the examples above, the first time the query was executed was with the parameter for “Costa Rica”. Remember when I said this dataset was heavily skewed? Let’s look at some counts:

SELECT 
  LEFT(Name,CHARINDEX(' ',Name)) AS Country, 
  COUNT(*) AS CountryCount 
FROM dbo.CoffeeInventory 
GROUP BY 
  LEFT(Name,CHARINDEX(' ',Name))

“Costa Rica” has more than 10,000 rows in this table, while all other country names are in the single digits.

This means that when we executed our stored procedure for the first time, SQL Server generated an execution plan that used a table scan because it thought this would be the most efficient way to retrieve 10,003 of the 10,052 rows.

This table scan query plan is only optimal for Costa Rica . Passing in any other country name into the stored procedure would return only a handful of records, making it more efficient for SQL Server to use our nonclustered index.

However, since the Costa Rica plan was the first one to run, and therefore is the one that got added to the query plan cache, all other executions ended up using the same table scan execution plan.

After clearing our cached execution plan using DBCC FREEPROCCACHE, we executed our stored procedure again but with ‘Ethiopia’ as our parameter. SQL Server determined that a plan with an index seek is optimal to retrieve only 6 of the 10,052 rows in the table. It then cached that Index Seek plan, which is why the second time around the ‘Costa Rica’ parameter received the execution plan with Index Seek.

Ok, so how do I prevent parameter sniffing?

This question should really be rephrased as “how do I prevent SQL Server from using a sub-optimal plan from the query plan cache?”

Let’s take a look at some of the techniques.

1. Use WITH RECOMPILE or OPTION (RECOMPILE)

We can simply add these query hints to either our EXEC statement:

EXEC dbo.FilterCoffee @ParmCountry = 'Ethiopia' WITH RECOMPILE
EXEC dbo.FilterCoffee @ParmCountry = 'Costa Rica' WITH RECOMPILE

or to our stored procedure itself:

DROP PROCEDURE IF EXISTS dbo.FilterCoffee
GO
CREATE PROCEDURE dbo.FilterCoffee
@ParmCountry varchar(30)
AS
BEGIN
 SELECT Name, Price, Description 
 FROM Sandbox.dbo.CoffeeInventory 
 WHERE Name LIKE @ParmCountry + '%'

 OPTION (RECOMPILE)
END
GO

What the RECOMPILE hint does is force SQL Server to generate a new execution plan every time these queries run.

Using RECOMPILE eliminates our parameter sniffing problem because SQL Server will regenerate the query plan every single time we execute the query.

The disadvantage here is that we lose all benefit from having SQL Server save CPU cycles by caching execution plans.

If your parameter sniffed query is getting ran frequently, RECOMPILE is probably a bad idea because you will encounter a lot of overheard to generate the query plan regularly.

If your parameter sniffed query doesn’t get ran often, or if the query doesn’t run often enough to stay in the query plan cache anyway, then RECOMPILE is a good solution.

2. Use the OPTIMIZE FOR query hint

Another option we have is to add either one of the following hints to our query. One of these would get added to the same location as OPTION (RECOMPILE) did in the above stored procedure:

OPTION (OPTIMIZE FOR (@ParmCountry UNKNOWN))

or

OPTION (OPTIMIZE FOR (@ParmCountry = 'Ethiopia'))

OPTIMIZE FOR UNKNOWN will use a query plan that’s generated from the average distribution stats for that column/index. Often times it results in an average or bad execution plan so I don’t like using it.

OPTIMIZE FOR VALUE creates a plan using whatever parameter value specified. This is great if you know your queries will be retrieving data that’s optimized for the value you specified most of the time.

In our examples above, if we know the value ‘Costa Rica’ is rarely queried, we might optimize for index seeks. Most queries will then run the optimal cached query plan and we’ll only take a hit when ‘Costa Rica’ is queried.

3. IF/ELSE

This solution allows for ultimate flexibility. Basically, you create different stored procedures that are optimized for different values. Those stored procedures have their plans cached, and then an IF/ELSE statement determines which procedure to run for a passed in parameter:

DROP PROCEDURE IF EXISTS dbo.FilterCoffee
GO
CREATE PROCEDURE dbo.FilterCoffee
@ParmCountry varchar(30)
AS
BEGIN
 IF @ParmCountry = 'Costa Rica'
 BEGIN
  EXEC dbo.ScanningStoredProcedure @ParmCountry
 END
 ELSE
 BEGIN
  EXEC dbo.SeekingStoredProcedure @ParmCountry
 END
END
GO

This option is more work (How do you determine what the IF condition should be? What happens more data is added to the table over time and the distribution of data changes?) but will give you the best performance if you want your plans to be cached and be optimal for the data getting passed in.

Conclusion

  1. Parameter sniffing is only bad when your data values are unevenly distributed and cached query plans are not optimal for all values.
  2. SQL Server caches the query plan that is generated from the first run of a query/stored procedure with whatever parameter values were used during that first run.
  3. Using the RECOMPILE hint is a good solution when your queries aren’t getting ran often or aren’t staying in the the query cache most of the time anyway.
  4. The OPTIMIZE FOR hint is good to use when you can specify a value that will generate a query plan that is efficient for most parameter values and are OK with taking a hit for a sub-optimal plan on infrequently queried values.
  5. Using complex logic (like IF/ELSE) will give you ultimate flexibility and performance, but will also be the worst for long term maintenance.

 

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

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Are your indexes being thwarted by mismatched datatypes?

Published on: 2017-08-01

Unexpected SQL Server Performance Killers #1

In this series I explore scenarios that hurt SQL Server performance and show you how to avoid them. Pulled from my collection of “things I didn’t know I was doing wrong for years.”


Have you ever encountered a query that runs slowly, even though you’ve created indexes for it?

There’s a few different reasons why this may happen. The one I see most frequently happens in the following scenario.

I’ll have an espresso please

Let’s say I have a table dbo.CoffeeInventory of coffee beans and prices that I pull from my favorite green coffee bean supplier each week. It looks something like this:

-- Make sure Actual Execution Plan is on
-- Let's see what our data looks like
SELECT * FROM dbo.CoffeeInventory
If you want to follow along, you can get this data set from this GitHub Gist

I want to be able to efficiently query this table and filter on price, so next I create an index like so:

CREATE CLUSTERED INDEX CL_Price ON dbo.CoffeeInventory (Price)

Now, I can write my query to find out what coffee prices are below my willingness to pay:

SELECT Name, Price FROM dbo.CoffeeInventory WHERE Price < 6.75

You would expect this query to be blazing fast and use a clustered index seek, right?

WRONG!

What the heck?

Why is SQL scanning the table when I added a clustered index on the column that I am filtering in my predicate? That’s not how it’s supposed to work!

Well dear reader, if we look a little bit closer at the table scan operation, we’ll notice a little something called CONVERT_IMPLICIT:

CONVERT_IMPLICIT: ruiner of fast queries

What is CONVERT_IMPLICIT doing? Well as it implies, it’s having to convert some data as it executes the query (as opposed to me having specified an explicit CAST() or CONVERT() function in my query).

The reason it needs to do this is because I defined my Price column as a VARCHAR(5):

Who put numeric data into a string datatype? Someone who hasn’t had their coffee yet today.

In my query however, I’m doing a comparison against a number WHERE Price < 6.75. SQL Server is saying it doesn’t know how to compare a string to a number, so it has to convert the VARCHAR string to a NUMERIC(3,2).

This is painful.

Why? Because SQL is performing that implicit conversion to the numeric datatype for every single row in my table. Hence, it can’t seek using the index because it ends up having to scan the whole table to convert every record to a number first.

And this doesn’t only happen with numbers and string conversion. Microsoft has posted an entire chart detailing what types of data type comparisons will force an implicit conversion:

https://docs.microsoft.com/en-us/sql/t-sql/data-types/data-type-conversion-database-engine

That’s a lot of orange circles/implicit conversions!

How can I query my coffee faster?

Well in this scenario, we have two options.

  1. Fix the datatype of our table to align with the data actually being stored in this (data stewards love this).
  2. Not cause SQL Server to convert every row in the column.

Number 1 above is self-explanatory, and the better option if you can do it. However, if you aren’t able to modify the column type, you are better off writing your query like this:

SELECT Name, Price FROM dbo.CoffeeInventory WHERE Price < '6.75'

Since we do a comparison of equivalent datatypes, SQL Server doesn’t need to do any conversions and our index gets used. Woo-hoo!

What about the rest of my server?

Remember that chart above? There are a lot of different data comparisons that can force a painful column side implicit conversion by SQL Server.

Fortunately, Jonathan Kehayias has written a great query that helps you find column side implicit conversions by querying the plan cache. Running his query is a great way to identify most of the implicit conversions happening in your queries so you can go back and fix them — and then rejoice in your improved query performance!

 

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

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