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A copy of my content originally hosted on SQLBlog.com
Showing posts with label Trace Flags. Show all posts
Showing posts with label Trace Flags. Show all posts

Wednesday, 21 August 2013

Incorrect Results Caused By Adding an Index

Incorrect Results Caused By Adding an Index

Say you have the following two tables, one partitioned and one not:

CREATE PARTITION FUNCTION PF (integer)
AS RANGE RIGHT
FOR VALUES (1000, 2000, 3000, 4000, 5000);

CREATE PARTITION SCHEME PS
AS PARTITION PF
ALL TO ([PRIMARY]);

-- Partitioned
CREATE TABLE dbo.T1
(
    T1ID    integer NOT NULL,
    SomeID  integer NOT NULL,

    CONSTRAINT [PK dbo.T1 T1ID]
        PRIMARY KEY CLUSTERED (T1ID)
        ON PS (T1ID)
);

-- Not partitioned
CREATE TABLE dbo.T2
(
    T2ID    integer IDENTITY (1,1) NOT NULL,
    T1ID    integer NOT NULL,

    CONSTRAINT [PK dbo.T2 T2ID]
        PRIMARY KEY CLUSTERED (T2ID)
        ON [PRIMARY]
);

Tuesday, 11 June 2013

Hello Operator, My Switch Is Bored

Hello Operator, My Switch Is Bored

This post is in two parts. The first part looks at the Switch execution plan operator. The second part is about an invisible plan operator and cardinality estimates on filtered indexes.

Friday, 8 March 2013

Execution Plan Analysis: The Mystery Work Table

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.

Saturday, 26 January 2013

Optimizing T-SQL queries that change data

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.

Monday, 10 December 2012

MERGE Bug with Filtered Indexes

MERGE Bug with Filtered Indexes

A MERGE statement can fail, and incorrectly report a unique key violation when:

  • The target table uses a unique filtered index; and
  • No key column of the filtered index is updated; and
  • A column from the filtering condition is updated; and
  • Transient key violations are possible

Tuesday, 1 May 2012

Query Optimizer Deep Dive - Part 4

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.

Optimizer plan

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.

Sunday, 29 April 2012

Query Optimizer Deep Dive – Part 3

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.

Saturday, 28 April 2012

Query Optimizer Deep Dive – Part 2

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

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.

Friday, 23 December 2011

Forcing a Parallel Query Execution Plan

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.

Wednesday, 21 September 2011

Finding the Statistics Used to Compile an Execution Plan

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.