If you’re not already a regular reader of Brad Schulz’s blog, you’re missing out on some great material. In his latest entry, he is tasked with optimizing a query run against tables that have no indexes at all.
The problem is, predictably, that performance is not very good.
The catch is that we are not allowed to create any indexes (or even new statistics) as part of our optimization efforts. In this post, I’m going to look at
the problem from a slightly different angle, and present an alternative solution to
the one Brad found. Inevitably, there’s going to be some overlap between our entries, and while you don’t necessarily need to read Brad’s post before this one, I do strongly recommend that you read it at some stage; he covers some important points that I won’t cover again here.
The Example We’ll use data from
the AdventureWorks database, copied to temporary unindexed tables. A script to create these structures is shown below: CREATE TABLE #Custs
(
CustomerID INTEGER NOT NULL,
TerritoryID INTEGER NULL,
CustomerType NCHAR(1) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL,
);
GO
CREATE TABLE #Prods
(
ProductMainID INTEGER NOT NULL,
ProductSubID INTEGER NOT NULL,
ProductSubSubID INTEGER NOT NULL,
Name NVARCHAR(50) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL,
);
GO
CREATE TABLE #OrdHeader
(
SalesOrderID INTEGER NOT NULL,
OrderDate DATETIME NOT NULL,
SalesOrderNumber NVARCHAR(25) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL,
CustomerID INTEGER NOT NULL,
);
GO
CREATE TABLE #OrdDetail
(
SalesOrderID INTEGER NOT NULL,
OrderQty SMALLINT NOT NULL,
LineTotal NUMERIC(38,6) NOT NULL,
ProductMainID INTEGER NOT NULL,
ProductSubID INTEGER NOT NULL,
ProductSubSubID INTEGER NOT NULL,
);
GO
INSERT #Custs
(
CustomerID,
TerritoryID,
CustomerType
)
SELECT C.CustomerID,
C.TerritoryID,
C.CustomerType
FROM AdventureWorks.Sales.Customer C
WITH (TABLOCK);
GO
INSERT #Prods
(
ProductMainID,
ProductSubID,
ProductSubSubID,
Name
)
SELECT P.ProductID,
P.ProductID,
P.ProductID,
P.Name
FROM AdventureWorks.Production.Product P
WITH (TABLOCK);
GO
INSERT #OrdHeader
(
SalesOrderID,
OrderDate,
SalesOrderNumber,
CustomerID
)
SELECT H.SalesOrderID,
H.OrderDate,
H.SalesOrderNumber,
H.CustomerID
FROM AdventureWorks.Sales.SalesOrderHeader H
WITH (TABLOCK);
GO
INSERT #OrdDetail
(
SalesOrderID,
OrderQty,
LineTotal,
ProductMainID,
ProductSubID,
ProductSubSubID
)
SELECT D.SalesOrderID,
D.OrderQty,
D.LineTotal,
D.ProductID,
D.ProductID,
D.ProductID
FROM AdventureWorks.Sales.SalesOrderDetail D
WITH (TABLOCK);
The query itself is a simple join of
the four tables:
SELECT P.ProductMainID AS PID,
P.Name,
D.OrderQty,
H.SalesOrderNumber,
H.OrderDate,
C.TerritoryID
FROM #Prods P
JOIN #OrdDetail D
ON P.ProductMainID = D.ProductMainID
AND P.ProductSubID = D.ProductSubID
AND P.ProductSubSubID = D.ProductSubSubID
JOIN #OrdHeader H
ON D.SalesOrderID = H.SalesOrderID
JOIN #Custs C
ON H.CustomerID = C.CustomerID
ORDER BY
P.ProductMainID ASC
OPTION (RECOMPILE, MAXDOP 1);
Remember that these tables have no indexes at all, and only
the single-column sampled statistics SQL Server automatically creates (assuming default settings).
The estimated query plan produced for
the test query looks like this (click to enlarge):
The Problem
The problem here is one of cardinality estimation –
the number of rows SQL Server expects to find at each step of
the plan.
The lack of indexes and useful statistical information means that SQL Server does not have
the information it needs to make a good estimate. Every join in
the plan shown
above estimates that it will produce just a single row as output. Brad covers
the factors that lead to
the low estimates in his post.
In reality,
the join between
the #Prods and #OrdDetail tables will produce 121,317 rows. It should not surprise you that this has rather dire consequences for
the remainder of
the query plan. In particular, it makes a nonsense of
the optimizer’s decision to use Nested Loops to join to
the two remaining tables.
Instead of scanning
the #OrdHeader and #Custs tables once (as it expected), it has to perform 121,317 full scans of each.
The query takes somewhere in
the region of twenty minutes to run to completion on my development machine.
A Solution
At this point, you may be thinking
the same thing I was: if we really are stuck with no indexes,
the best we can do is to use hash joins everywhere.
We can force
the exclusive use of hash joins in several ways,
the two most common being join and query hints. A join hint means writing
the query using
the INNER HASH JOIN syntax; using a query hint involves adding OPTION (HASH JOIN) at
the bottom of
the query.
The difference is that using join hints also forces
the order of
the join, whereas
the query hint gives
the optimizer freedom to reorder
the joins at its discretion.
Adding
the OPTION (HASH JOIN) hint results in this estimated plan:
That produces
the correct output in around seven seconds, which is quite an improvement! As a purely practical matter, and given
the rigid rules of
the environment we find ourselves in, we might leave things there. (We can improve
the hashing solution a bit – I’ll come back to that later on).
Faster Nested Loops
It might surprise you to hear that we can beat
the performance of
the hash join solution shown
above using nested loops joins exclusively, and without breaking
the rules we have been set.
The key to this part is to realize that a condition like (A = B) can be expressed as (A <= B) AND (A >= B). Armed with this tremendous new insight, we can rewrite
the join predicates like so:
SELECT P.ProductMainID AS PID,
P.Name,
D.OrderQty,
H.SalesOrderNumber,
H.OrderDate,
C.TerritoryID
FROM #OrdDetail D
JOIN #OrdHeader H
ON D.SalesOrderID >= H.SalesOrderID
AND D.SalesOrderID <= H.SalesOrderID
JOIN #Custs C
ON H.CustomerID >= C.CustomerID
AND H.CustomerID <= C.CustomerID
JOIN #Prods P
ON P.ProductMainID >= D.ProductMainID
AND P.ProductMainID <= D.ProductMainID
AND P.ProductSubID = D.ProductSubID
AND P.ProductSubSubID = D.ProductSubSubID
ORDER BY
D.ProductMainID
OPTION (RECOMPILE, LOOP JOIN, MAXDOP 1, FORCE ORDER);
I’ve also added LOOP JOIN and FORCE ORDER query hints to ensure that only nested loops joins are used, and that
the tables are joined in
the order they appear.
The new estimated execution plan is:
This new query runs in under 2 seconds.
Why Is It Faster?
The main reason for
the improvement is
the appearance of
the eager Index Spools, which are also known as index-on-the-fly spools. If you read my Inside
The Optimiser series you might be interested to know that
the rule responsible is called JoinToIndexOnTheFly.
An eager index spool consumes all rows from
the table it sits
above, and builds a index suitable for
the join to seek on. Taking
the index spool
above the #Custs table as an example, it reads all
the CustomerID and TerritoryID values with a single scan of
the table, and builds an index keyed on CustomerID.
The term ‘eager’ means that
the spool consumes all of its input rows when it starts up.
The index is built in a work table in tempdb, has no associated statistics, and only exists until
the query finishes executing.
The result is that each unindexed table is only scanned once, and just for
the columns necessary to build
the temporary index. From that point on, every execution of
the inner side of
the join is answered by a seek on
the temporary index – not
the base table.
A second optimization is that
the sort on ProductMainID (required by
the ORDER BY clause) is performed early, on just
the rows coming from
the #OrdDetail table.
The optimizer has a good estimate for
the number of rows it needs to sort at that stage – it is just
the cardinality of
the table itself.
The accuracy of
the estimate there is important because it helps determine
the memory grant given to
the sort operation. Nested loops join preserves
the order of rows on its outer input, so sorting early is safe. (Hash joins do not preserve order in this way, of course).
The extra lazy spool on
the #Prods branch is a further optimization that avoids executing
the seek on
the temporary index if
the value being joined (the ‘outer reference’) hasn’t changed from
the last row received on
the outer input. It takes advantage of
the fact that rows are still sorted on ProductMainID, so if duplicates exist, they will arrive at
the join operator one after
the other.
The optimizer is quite conservative about introducing index spools into a plan, because creating and dropping a temporary index is a relatively expensive operation. It’s presence in a plan is often an indication that a useful index is missing.
I want to stress that I rewrote
the query in this way primarily as an educational exercise – I can’t imagine having to do something so horrible to a production system.
Improving
the Hash Join
I promised I would return to
the solution that uses hash joins. You might be puzzled that SQL Server can create three new indexes (and perform all those nested loops iterations) faster than it can perform three hash joins.
The answer, again, is down to
the poor information available to
the optimizer. Let’s look at
the hash join plan again:
Two of
the hash joins have single-row estimates on their build inputs. SQL Server fixes
the amount of memory available for
the hash table based on this cardinality estimate, so at run time
the hash join very quickly runs out of memory.
This results in
the join spilling hash buckets to disk, and any rows from
the probe input that hash to
the spilled buckets also get written to disk.
The join process then continues, and may again run out of memory. This is a recursive process, which may eventually result in SQL Server resorting to a bailout join algorithm, which is guaranteed to complete eventually, but may be very slow.
The data sizes in
the example tables are not large enough to force a hash bailout, but it does result in multiple levels of hash recursion. You can see this for yourself by tracing
the Hash Warning event using
the Profiler tool.
The final sort in
the plan also suffers from a similar problem: it receives very little memory and has to perform multiple sort passes, saving intermediate runs to disk (the Sort Warnings Profiler event can be used to confirm this). Notice also that because hash joins don’t preserve sort order,
the sort cannot be pushed down
the plan toward
the #OrdDetail table, as in
the nested loops plan.
Ok, so now we understand
the problems, what can we do to fix it? We can address
the hash spilling by forcing a different order for
the joins:
SELECT P.ProductMainID AS PID,
P.Name,
D.OrderQty,
H.SalesOrderNumber,
H.OrderDate,
C.TerritoryID
FROM #Prods P
JOIN #Custs C
JOIN #OrdHeader H
ON H.CustomerID = C.CustomerID
JOIN #OrdDetail D
ON D.SalesOrderID = H.SalesOrderID
ON P.ProductMainID = D.ProductMainID
AND P.ProductSubID = D.ProductSubID
AND P.ProductSubSubID = D.ProductSubSubID
ORDER BY
D.ProductMainID
OPTION (MAXDOP 1, HASH JOIN, FORCE ORDER);
With this plan, each of
the inputs to
the hash joins has a good estimate, and no hash recursion occurs.
The final sort still suffers from
the one-row estimate problem, and we get a single-pass sort warning as it writes rows to disk. Even so,
the query runs to completion in three or four seconds. That’s around half
the time of
the previous hashing solution, but still not as fast as
the nested loops trickery.
Final Thoughts
SQL Server’s optimizer makes cost-based decisions, so it is vital to provide it with accurate information. We can’t really blame
the performance problems highlighted here on anything other than
the decision to use completely unindexed tables, and not to allow
the creation of additional statistics.
I should probably stress that
the nested loops solution shown
above is not one I would normally contemplate in
the real world. It’s there primarily for its educational and entertainment value. I might perhaps use it to demonstrate to
the sceptical that SQL Server itself is crying out for an index.
Be sure to read Brad’s original post for more details. My grateful thanks to him for granting permission to reuse some of his material.
Paul White
Email:
[email protected]
Twitter: @PaulWhiteNZ
