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Expert T-SQL Window Functions in SQL Server takes you from any level of knowledge of windowing functions and turns you into an expert who can use these powerful functions to solve many T-SQL queries. Replace slow cursors and self-joins with queries that are easy to write and fantastically better performing, all through the magic of window functions.
First introduced in SQL Server 2005, window functions came into full blossom with SQL Server 2012. They truly are one of the most notable developments in SQL in a decade, and every developer and DBA can benefit from their expressive power in solving day-to-day business problems. Begin using windowing functions like ROW_NUMBER and LAG, and you will discover more ways to use them every day. You will approach SQL Server queries in a different way, thinking about sets of data instead of individual rows. Your queries will run faster, they will be easier to write, and they will be easier to deconstruct and maintain and enhance in the future.
Just knowing and using these functions is not enough. You also need to understand how to tune the queries. Expert T-SQL Window Functions in SQL Server explains clearly how to get the best performance. The book also covers the rare cases when older techniques are the best bet. Stop using cursors and self-joins to solve complicated queries. Become a T-SQL expert by mastering windowing functions.
- Teaches you how to use all the window functions introduced in 2005 and 2012.
- Provides real-world examples that you can experiment with in your own database.
- Explains how to get the best performance when using windowing functions.
- Sales Rank: #540592 in Books
- Published on: 2015-03-20
- Released on: 2015-03-21
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .35" w x 6.10" l, .49 pounds
- Binding: Paperback
- 152 pages
About the Author
strongKathi Kellenberger/strong is a consultant with Linchpin People. She enjoys writing and speaking on SQL Server topics, having written over two dozen articles, contributed to four books, and presented at many SQL Server events. In her spare time, Kathi enjoys spending time with family and friends, singing and cycling.
Most helpful customer reviews
4 of 5 people found the following review helpful.
Excellent framework, useful for data scientists
By Mark Tabladillo (MarkTab)
This book provides a unique and valuable framework for Window functions in T-SQL (SQL Server). The material is relevant for people doing analysis through advanced analytics. Window functions provide a way to generate multiple features (aka variables, attributes) for use in data mining and machine learning. In this review, I provide a brief description of the topics and pay special attention to areas which I believe are important for data scientists (in their data preparation for further advanced analytics).
The chapters:
Chapter 1: Looking Through the Window: the basics on OVER and PARTITION BY, including tricky ways to handle ROW_NUMBER()
Chapter 2: Discovering Ranking Functions: ROW_NUMBER(), RANK, DENSE RANK, and NTILE. Many data scientists will have use for NTILE. I created an advanced concept based on NTILE for a client last year.
Chapter 3: Summarizing with Window Aggregates: Classic statisticians will enjoy having these statistical functions including AVG and STDEV. Only trained statistical people can explain the difference between STDEV and STDEVP (similar to Excel’s functions).
Chapter 4: Tuning for Better Performance: Looking at the execution plans is important. There are often ways to refactor code to achieve the same result in less time. This type of activity is important for “big data”, tables or joins which are large in size. These performance-measuring techniques are also the way to measure the merit of window functions over other ways to accomplish these same results in SQL Server.
Chapter 5: Calculating Running and Moving Aggregates: ORDER BY allows for creating a metric based on a subset. Any of the statistical functions can be used for these metrics, not just COUNT but MAX and others too. The book has a few examples, and data scientists could use these same techniques to determine the subpopulations which have the maximum variance (in a dataset which has lots of nulls or even zeros to filter out).
Chapter 6: Adding Frames to the Window: ROWS and RANGE are often needed for cumulative totals. Again, a data scientist may need to apply a variance to a range of values, and it’s possible to have a running variance. Tracking variance changes could be a metric of changing consistency.
Chapter 7: Taking a Peek at Another Row: LAG, LEAD, FIRST_VALUE, LAST_VALUE – these functions allow for looking ahead or behind rows. Many people write Excel functions which have similar calculations. Having the ability to reference previous or future rows within a variable-sized window is something which is harder to accomplish in Excel.
Chapter 8: Understanding Statistical Functions: These functions either compute actual or projected (estimated) values corresponding to percentiles. These types of functions are important for statistical calculations, and are another source of features for data scientists.
Chapter 9: Time Range Calculations and Trends: I consider the last chapter to be two-chapters’ worth of value in one. There are a number of complex, but understandable ways to leverage window functions and time calculations. The chapter has some examples of rate of change calculations, which could be modified by trained statisticians to leverage either the standard deviation or variance functions. Changes in standard deviation will continue to be the units of measure (in financial applications, units of money).
The value of this book includes a progressive and understandable track of explaining how window functions work. If you have been an experienced Excel user, you can make the goal of translating your set-based formula work completely into SQL (the way to know what would translate is to read this book). I am generally preferring using SQL for these tasks, where I would have (in Excel) used the Fill-Down command with relative formulas.
I told both Kathi and Clayton that their approach toward teaching these topics is the clearest single way I have seen these topics explained. Window functions are the preferred way to accomplish these tasks because they leverage the efficiencies of the SQL engine. We can measure impact using the performance techniques described in the book. These types of calculations are important in data science: I have been on projects which look at over 1,000 features (variables) for modeling purposes. Having a way to generate those features efficiently is important no matter what machine learning software you end up using.
If you do not have a copy of SQL Server, I recommend acquiring the Developer Edition, which is fully functional and will not expire. There is a trial version of SQL Server from the Microsoft website which is free but time limited. Also, anyone enrolled as a student should look at what may be available through the Dreamspark website.
Kellenberger, Kathi; Groom, Clayton (2015-03-20). Expert T-SQL Window Functions in SQL Server (Kindle Locations 48-55). Apress. Kindle Edition.
1 of 1 people found the following review helpful.
Highly recommended
By Jean
Not for first-timers on T-SQL - starting out on this book without even a clue regarding the GROUP BY clause would make for rough seas for a rank beginner. This book is squarely aimed at T-SQL'ers interested in the new "row windowing" features introduced in SS2K5 and SS2K12 who need getting up-to-date following important advances in SQL Server.
This book delivers. In addition to being straightforward and not hard to understand, the authors systematically hit the nail right on the head.
Right off the bat, the authors provide a strong motivation of windowing functions by demonstrating their power with a simple example showing how to list daily stock exchange quotes along with the closing value from the previous day. Not only is the proper solution given using the LAG function, that solution is compared with the older approach of using a derived table from a correlated sub-query. The performance improvement is clearly highlighted.
Further motivation is provided by the "isolated islands" problem, i.e. showing contiguous ranges and gaps in a consecutive series. Remembering the ugly code I wrote when I had just a couple of years of experience with T-SQL, the solution given is so much cleaner.
Not only is the proper usage of windowing functions explained in detail, their limitations are also given. There is also a chapter on tuning the queries to improve performance.
In addition the authors have made available on the web all the source code, which enables experimentation of the solutions given without the drudgery of typing in the code in SSMS. Multiply the time savings by the number of T-SQL'ers trying out the solutions provides an indication to the value offered to the community. And yes, the examples retrieved from the web do correspond to the code used in the book and the results obtained in SSMS also match the results given in the book.
I would have taken out a star because of some confusion regarding the mention of the non-deterministic nature of the ROW_NUMBER function which cannot be influenced in spite of the fact that the OVER ... ORDER By clause actually allows consistently retrieving the results in the same repeatable order. Or that the web source listing misses the first two (very short) T-SQL code used in Chapter 3. But due to the considerations given above in this review, these minor shortcomings can be excused.
The authors mention another book featuring especially clever ways of using windowing functions. I found it a bit much as a starting point and did not go very far with it. Now thoroughly acquainted with these functions I now feel ready to dive into this other book. (I'll report later on that second book when I am done).
Update: Went through the whole book. My initial impressions are confirmed: excellent.
Along the way, the authors covers every component involved in the syntax of the windowing functions. Each component is handled in a T-SQL code to show its exact consequence on the results of a query.
The authors address the monthly subscriptions gains/losses problem with a very simple solution which absolutely rocks in terms of performance. If there was still a need to motivate the use of the windowing functions, this one is it. The author compares it to an SS2K8-era solution (when the OVER clause of a windowing function did not support and ORDER BY component). That SS2K8 solution was devised by a heavy-hitter on the SQL Server Central and Simple Talk web sites in an SQL SPEED PHREAKERY contest (SimpleTalk), achieving a performance of processing 1 million records in less than 500 msec. The solution was so complex that it warranted a follow-up article to explain it. The SS2K12 solution is even faster and orders of magnitude easier to understand.
Chapter 9 gradually builds up on successive additions to a query to produce time-intelligence reports, including year-over-year and month-over-month comparisons. Followed by an excellent summary of why month-over-month measures are not really useful and how the Rate of Change measure is far better at extracting the evolution of a measure and even mentions - so shortly - the addition of foreign data allowing to qualify the corporate sales performance against the stock market performance (leading, lagging, in sync) and even to add leading indicators such as the cost of raw materials to determine the impact of demand for the finished product.
I wish I could add another star to the book rating just over this.
(I found myself hoping the authors would lift the curtain to work out an actual example.)
Throughout the book, performance issues are addressed. Down to even looking at execution plans.
1 of 1 people found the following review helpful.
Great Tutorial and Reference
By W. Kevin Hazzard
Kathi is an excellent teacher and it shows in this book. I've been using the windowing functions in SQL server for a while but never felt as though I had mastered them.125 pages and 3 nights of experimenting with the rich samples in this book later and I can use SQL Server's ranking functions with real confidence. I especially appreciate the depth that Kathi went into for performance tuning. Now that I've used this book to learn the material, it'll stay on my shelf as the definitive reference on this subject for a long, long time.
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