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Stephane Faroult first discovered relational databases and the SQL language back in 1983. He joined Oracle France in their early days (after a brief spell with IBM and a bout of teaching at the University of Ottawa) and soon developed an interest in performance and tuning topics. After leaving Oracle in 1988, he briefly tried to reform and did a bit of operational research, but after one year, he succumbed again to relational databases. He has been continuously performing database consultancy since then, and founded RoughSea Ltd in 1998.
Pascal L'Hermite has been working with relational databases in OLTP, production and development environments on Oracle Databases for the past 12 years and on Microsoft SQL Server for the past 5 years.
Faroult and Hermite focus their attention on relatively classical SQL optimizations. The gist of their advice is that developers running SQL code need to leverage the database engine's optimizer. To do that, they offer relatively common advice: use set operations, avoiding procedural code in your sql code whenever possible; minimize the number of visits to a table; and minimize the number of times your code has to scan a given table. Most of the content of the book is spent offering techniques for achieving these objectives. For developers without a lot of experience writing SQL intensive applications, the authors provide a relatively accessible discussion of these techniques.
Outside of that, the authors include a chapter called `Testing Framework' that addresses one of the key requirements of any refactoring effort: creating and maintaining a library of unit tests that allows us to prove that our code is correct. When writing database code, and particular code whose performance may vary based on the amount of data being processed, unit testing can be a bit of a challenge due to the typical case where developers are developing locally on databases that contain small data sets. In this chapter Faroult and Hermite offer some data generation techniques and some mechanisms for automating the comparison of resulting outputs.
What I like about this book is that unlike books on optimizing the performance of a particular database product, this book tries to elevate the discussion to the level of optimizing the performance of an application that contains a substantial amount of realistic database code. It should enable developers to analyze their code in the context of the business objectives that it is trying to fulfill, rather than the context of the database engine in which it is executing. For that reason alone, I am recommending it to the database developers on my team.
I'm at odds with the first two reviewers, but I think it depends on what you're looking for. This book is NOT about classical tuning. Classical tuning is "tune the server", and "tune the query".
The emphasis - in the preface and the excellent Chapter 6 - is that the real gains are usually elsewhere, when you have older code.
I work with a 25 year heritage of fairly well written apps. Many of them have the described situation - a single query that's been broken into two or more parts, with an outer loop and (at least one) inner loop. When server memories were 64K, or 1 Meg, or 4 Meg, and CPU's only came in packages of 1, and disk channels were slow, and networks were slower, this was often the only practical way to get a result.
The interplay changed over the years, but the old code worked. In the past few years, with 64-bit processors, cheap 64-CPU servers, and multi-io disk channels, a wierd thing happened. We found that moving to newer systems and faster hardware made things run SLOWER.
The answer time and again was in those "split loop queries". If we turned them back into one big query - the kind that you couldn't run before, we would see performance improvements of hundreds or thousands of times. In the end, the math proved - on powerful machines, most of the overhead is sending the query, compiling it, and sending it back. If one monster query takes a full second, but every query in the loop takes 1/4 second - if that inner loop runs 1000 times, you lose.
Meantime, that 64-core machine has every CPU working full blast - recompiling the same stupid statement over and over! The problem is, you try to tell this to a developer, they don't beleive you "I didn't change anything in the code".Read more ›
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Any application that accesses a database will eventually present you with queries that run much slower than is desirable. This book provides you with a guide to how to resolve such problems and get your database accesses to run faster.
The most often suggested solutions are not always the correct ones and this book clearly demonstrates several situations where doing what appears to be an obvious fix (such as adding an index) may actually make things worse.
The author of this book has an in-depth understanding of how databases really work and as a result of the explanations that are provided in this book you can leverage on that to improve the performance of your SQL without needing as complete an understanding as the author has. If anything the book will actually help to give you a greater understanding of how databases work.
Are you going to know where to look when you next have a progr
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Here is what I was looking for in a book and did not find here: A set of SQL refactoring patterns - like what Martin Fowler offers in his book for oo languages.
I want to know stuff like: How do i remove duplication in SQL. How to make the code more readable/clear and decoupled how do i change the structure of queries without changing the results. how do i turn a set of subqueries with similar/same criteria into another structure
What I got instead a book about tuning and performance improvements a book about embedded sql app-dev refactoring
I'm moderately disappointed, but I think this book will give me some knowledge that may help me invent 'real' t-sql refactoring patterns.
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