Pulling Group By Above a Join

One of the transformations available to the SQL Server query optimizer is pulling a logical Group By (and any associated aggregates) above a Join.

Visually, this means transforming a tree of logical operations from:

…to this:

The above diagrams are logical representations. They need to be implemented as physical operators to appear in an execution plan. The options are:

• Group By
  • Hash Match Aggregate
  • Stream Aggregate
  • Distinct Sort
• Join
  • Nested Loops Join
  • Nested Loops Apply
  • Hash Match Join
  • Merge Join

When the optimizer moves a Group By above a Join it has to preserve the semantics. The new sequence of operations must be guaranteed to return the same results as the original in all possible circumstances.

One cannot just pick up a Group By and arbitrarily move it around the query tree without risking incorrect results.

Nested Loops Prefetching

Nested loops join query plans can be a lot more interesting (and complicated) than is commonly realized.

One query plan area I get asked about a lot is prefetching. It is not documented in full detail anywhere, so this seems like a good topic to address in a blog post.

The examples used in this article are based on questions asked by Adam Machanic.

Two Partitioning Peculiarities

Table partitioning in SQL Server is essentially a way of making multiple physical tables (row sets) look like a single table. This abstraction is performed entirely by the query processor, a design that makes things simpler for users, but which makes complex demands of the query optimizer.

This post looks at two examples which exceed the optimizer’s abilities in SQL Server 2008 onward.

Working Around Missed Optimizations

In my last post, we saw how a query featuring a scalar aggregate could be transformed by the optimizer to a more efficient form. As a reminder, here’s the schema again:

Execution Plan Analysis: The Mystery Work Table

I love SQL Server execution plans. It is often easy to spot the cause of a performance problem just by looking at one closely. That task is considerably easier if the plan includes run-time information (a so-called ‘actual’ execution plan), but even a compiled plan can be very useful.

Nevertheless, there are still times when the execution plan does not tell the whole story, and we need to think more deeply about query execution to really understand a problem. This post looks at one such example, based on a question I answered.

Incorrect Results with Indexed Views

If you use MERGE, indexed views and foreign keys, your queries might return incorrect results. Microsoft have released a fix for incorrect results returned when querying an indexed view. The problem applies to:

• SQL Server 2012
• SQL Server 2008 R2
• SQL Server 2008

The Knowledge Base article does not go into detail, or provide a reproduction script, but this blog post does.

Optimizing T-SQL queries that change data

Most tuning efforts for data-changing operations concentrate on the SELECT side of the query plan. Sometimes people will also look at storage engine considerations (like locking or transaction log throughput) that can have dramatic effects. A number of common practices have emerged, such as avoiding large numbers of row locks and lock escalation, splitting large changes into smaller batches of a few thousand rows, and combining a number of small changes into a single transaction in order to optimize log flushes.

This is all good, but what about the data-changing side of the query plan — the INSERT, UPDATE, DELETE, or MERGE operation itself — are there any query processor considerations we should take into account? The short answer is yes.

The query optimizer considers different plan options for the write-side of an execution plan, though there isn’t a huge amount of T-SQL language support that allows us to affect these choices directly. Nevertheless, there are things to be aware of, and things we can look to change.

Deletes that Split Pages and Forwarded Ghosts

Can DELETE operations cause pages to split?

Yes. It sounds counter-intuitive on the face of it. Deleting rows frees up space on a page, and page splitting occurs when a page needs additional space. Nevertheless, there are circumstances when deleting rows causes them to expand before they can be deleted.

Temporary Table Caching Explained

SQL Server (since 2005) caches temporary tables and table variables referenced in stored procedures for reuse, reducing contention on tempdb allocation structures and catalogue tables.

A number of things can prevent this caching (none of which are allowed when working with table variables):

• Named constraints (bad idea anyway, since concurrent executions can cause a name collision)
• DDL after creation (though what is considered DDL is interesting)
• Creation using dynamic SQL
• Table created in a different scope
• Procedure executed using WITH RECOMPILE

Temporary objects are often created and destroyed at a high rate in production systems, so caching can be an important optimization.

Temporary Table Caching in Stored Procedures

Introduction

Ask anyone what the primary advantage of temporary tables over table variables is, and the chances are they will say that temporary tables support statistics and table variables do not.

This is true, of course. The indexes that enforce PRIMARY KEY and UNIQUE constraints on table variables do not have populated statistics associated with them. Neither do any non-constraint table variable indexes (using inline index definitions, available starting with SQL Server 2014). Finally, it is not possible to manually create statistics on table variables.

Intuitively, then, any query that has alternative execution plans to choose from ought to benefit from using a temporary table rather than a table variable. This is also true, up to a point.

Query Optimizer Deep Dive - Part 4

This is the final part in a series of posts based on the content of the Query Optimizer Deep Dive presentations I have given over the last month or so at the Auckland SQL Users’ Group, and SQL Saturday events in Wellington, New Zealand and Adelaide, Australia.

Links to other parts of this series: Part 1 Part 2 Part 3

Beating the Optimizer

Our AdventureWorks test query produces an optimized physical execution plan that is quite different from the logical form of the query.

The estimated cost of the execution plan shown below is 0.0295 units.

Since we know the database schema very well, we might wonder why the optimizer did not choose to use the unique nonclustered index on Name in the Product table to filter rows based on the LIKE predicate.

Query Optimizer Deep Dive – Part 3

This is the third in a series of posts based on the content of the Query Optimizer Deep Dive presentations I have given over the last month or so at the Auckland SQL Users’ Group, and SQL Saturday events in Wellington, New Zealand and Adelaide, Australia.

Links to other parts of this series: Part 1 Part 2 Part 4

Storage of Alternative Plans

We saw in part 2 how optimizer rules are used to explore logical alternatives for parts of the query tree, and how implementation rules are used to find physical operations to perform each logical steps.

To keep track of all these options, the cost-based part of the SQL Server query optimizer uses a structure called the Memo. This structure is part of the Cascades general optimization framework developed by Goetz Graefe.

Query Optimizer Deep Dive – Part 2

This is the second in a series of posts based on the content of the Query Optimizer Deep Dive presentations I have given over the last month or so at the Auckland SQL Users’ Group, and SQL Saturday events in Wellington, New Zealand and Adelaide, Australia.

Links to other parts of this series: Part 1 Part 3 Part 4

Cost-Based Optimization Overview

The input to cost-based optimization is a tree of logical operations produced by the previous optimization stages discussed in part one.

Cost-based optimization takes this logical tree, explores logical alternatives (different logical tree shapes that will always produce the same results), generates physical implementations, assigns an estimated cost to each, and finally chooses the cheapest physical option overall.

The goal of cost-based optimization is not to find the best possible physical execution plan by exploring every possible alternative. Rather, the goal is to find a good plan quickly.

Query Optimizer Deep Dive - Part 1

This is the first in a series of posts based on the content of the Query Optimizer Deep Dive presentations I have given over the last month or so at the Auckland SQL Users’ Group, and SQL Saturday events in Wellington, New Zealand and Adelaide, Australia.

Links to other parts of this series: Part 2 Part 3 Part 4

Introduction

The motivation behind writing these sessions is finding that relatively few people have a good intuition for the way the optimizer works. This is partly because the official documentation is rather sparse, and partly because what information is available is dispersed across many books and blog posts.

The content presented here is very much geared to my preferred way of learning. It shows the concepts in what seems to me to be a reasonably logical sequence, and then provides tools to enable the interested reader to explore further, if desired.

Forcing a Parallel Query Execution Plan

This article is for SQL Server developers who have experienced the special kind of frustration that only comes from spending hours trying to convince the query optimizer to generate a parallel execution plan.

This situation often occurs when making an apparently innocuous change to the text of a moderately complex query — a change which somehow manages to turn a parallel plan that executes in ten seconds, into a five-minute serially-executing monster.

Finding the Statistics Used to Compile an Execution Plan

In this post, I show you how to determine the statistics objects used by the query optimizer in producing an execution plan.

Note: This technique only applies to queries compiled using the original (70) cardinality estimation model.

Can a SELECT query cause page splits?

The SQL Server documentation has this to say about page splits:

When a new row is added to a full index page, the Database Engine moves approximately half the rows to a new page to make room for the new row. This reorganization is known as a page split. A page split makes room for new records, but can take time to perform and is a resource intensive operation. Also, it can cause fragmentation that causes increased I/O operations.

Given that, how can a SELECT statement be responsible for page splits?

Well, I suppose we could SELECT from a function that adds rows to a table variable as part of its internal implementation, but that would clearly be cheating, and no fun at all from a blogging point of view.

SQL Server, Seeks, and Binary Search

The following table summarizes the results from my last two articles, Enforcing Uniqueness for Performance and Avoiding Uniqueness for Performance. It shows the CPU time used when performing 5 million clustered index seeks into a unique or non-unique index:

In test 1, making the clustered index unique improved performance by around 40%.

In test 2, making the same change reduced performance by around 70% (on 64-bit systems – more on that later).

Join Performance, Implicit Conversions, and Residuals

Introduction

You probably already know that it’s important to be aware of data types when writing queries, and that implicit conversions between types can lead to poor query performance.

Some people have gone so far as to write scripts to search the plan cache for CONVERT_IMPLICIT elements, and others routinely inspect plans for that type of thing when tuning.

Now, that’s all good, as far as it goes. It may surprise you to learn that not all implicit conversions are visible in query plans, and there are other important factors to consider too.

Undocumented Query Plans: The ANY Aggregate

As usual, here’s a sample table:

CREATE TABLE #Example
(
    pk numeric IDENTITY PRIMARY KEY NONCLUSTERED,
    col1 sql_variant NULL,
    col2 sql_variant NULL,
    thing sql_variant NOT NULL,
);

Some sample data:

And an index that will be useful shortly:

CREATE INDEX nc1 
ON #Example
    (col1, col2, thing);

There’s a complete script to create the table and add the data at the end of this post. There’s nothing special about the table or the data (except that I wanted to have some fun with values and data types).