Michael J. Swart

May 20, 2015

It’s Hard To Destroy Data

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 8:00 am

Had I been born later, I probably would have gone with Wreck-It Ralph It is surprisingly difficult to delete data permanently. SQL Server has sophisticated features that prevent all kinds of data loss, but the opposite is not true. SQL Server has very few features that help you destroy data.

But why would anyone need to destroy data? Lots of reasons.

  • Some laws and data retention policies require that data older than a certain age gets destroyed.
  • Developers often ask for production data, or a subset. A DBA could decide to give them a backup having first dropped certain tables or columns that contain personally identifiable information.

There are countless other reasons why you might want to destroy data and I’m going to focus on three general use cases: destroying tables, destroying columns and destroying rows.

Straight Up Deleting Is Not Enough

I’m going to demonstrate that deleting data is not equivalent to destroying data. Deleting data is just hiding it.

First create a table, populate it and see what it looks like on disk:

USE DeletedDataDemo;
SELECT col1, col2, col3 
INTO dbo.Table1
  VALUES (1, 'aaaaaaaaaaaa', 'bbbbbbbbbbbb'),
         (2, 'cccccccccccc', 'dddddddddddd'),
         (3, 'eeeeeeeeeeee', 'ffffffffffff')
) data (col1, col2, col3)
DBCC IND(DeletedDataDemo, 'dbo.Table1', 1) -- file 1, page 216 for example
--update the following command to look at that page
DBCC PAGE (DeletedDataDemo, 1, 216, 2);

Notice that the last command DBCC PAGE shows the contents of the table as it appears on disk.

Data dump

That data is still there

And now drop the table that was just created:

DROP TABLE dbo.Table1;
-- or
-- ALTER TABLE dbo.Table1 DROP COLUMN col3;
-- or
-- DELETE dbo.Table1 WHERE col1 IN (2,3);
DBCC PAGE (DeletedDataDemo, 1, 216, 2);

Run the DBCC PAGE command and notice that the data values are still there! That data cannot be queried but it remains on disk. Even though this data is in deallocated space, this data can survive backups and restores (even after checkpoints).

Repeat the demo from the beginning. But this time, instead of dropping the table, try deleting rows or dropping a column. Just like the dropped table the data remains on disk.

Try to Destroy Data by Cleaning Pages

So SQL Server doesn’t actually delete data, it hides it and deallocates the space. A ghost cleanup job can eventually clean up this data but you can’t depend on how frequently it runs. That leads to stackoverflow questions like this one: How can I securely destroy some data using sql server 2008?

One of the answers there mentions that sp_clean_db_free_space can force the cleanup to run early. Cleaning pages is exactly what’s required to get rid of that data in deallocated space. But cleaning pages consumes a lot of I/O and maybe that’s why SQL Server 2008 introduced a lot of control over the granularity of cleaning pages:

  • Use sp_clean_db_free_space for a whole database.
  • Use sp_clean_db_file_free_space for a single data file.
  • Use DBCC CLEANPAGE(@dbid, @fileid, @pageid) after a CHECKPOINT for a single page.

These commands force SQL Server to write zeros to any deallocated space.


Dropped-Column Data Sticks Around

Columns are a special case. SQL Server doesn’t reclaim the space that is freed by dropped columns. And this dropped column data is not touched by any of the page cleaning methods. In order to destroy this data, it’s necessary to add a few manual steps. These steps can destroy column data by either overwriting column values or by rebuilding its table’s indexes.

How To Destroy Data

Here’s a table that shows the syntax to use when trying to destroy data.

When destroying … Use this syntax
DROP TABLE dbo.Table1;
EXEC sp_clean_db_free_space 'DeletedDataDemo';
DELETE dbo.Table1 WHERE col1 = @col1;
EXEC sp_clean_db_free_space 'DeletedDataDemo';
(except columns in heaps)
EXEC sp_clean_db_free_space 'DeletedDataDemo';
(alternative syntax)
UPDATE dbo.Table1 SET col3 = N'';
EXEC sp_clean_db_free_space 'DeletedDataDemo';
Other data structures
(Columnstore, Full-text, XML indexes, Service Broker Queues etc…)
Not evaluated. I don’t know how long this data sticks around.

The Lessons

Microsoft warns you that these data cleaning procedures are very I/O intensive so I would be reluctant to suggest these methods when alternatives exist.

The clumsiness of the dropped column scenario makes me wonder about other scenarios where SQL Server deletes data but doesn’t destroy it.

And this leads directly to this bit of advice:

Don’t distribute backups of databases that have contained sensitive information.

April 27, 2015

The Appeal Of Lightning Talks

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 11:32 am

Lightning talks are quick presentations, usually five-ten minutes long, and I really enjoy them.

But it’s not (only) about indulging short-attention-span habits. Lightning talks tend to be really dense with interesting information. The speaker is forced to say one thing and nothing else. Speakers need to make a choice between important content and not-so important content. It’s very difficult for the speaker, but it’s great for us in the audience. 
It’s possible to get dense information into a hour long session, but those sessions are more prone to contain filler.

The best lightning talk I’ve ever watched is this one, Fighting Dirty In Scrabble by Mehal Shah.

He just crushes that talk. I use him as a model for a really good content-driven lightning talk.

That’s why I’m excited about attending the third annual member presentations at Toronto PASS.

Lightning Talks About SQL in Toronto

It’s basically like an open mic night. If you’re going to attend one user group meeting in Toronto, this is the one. I plan on giving a talk about my favorite nemesis, tempdb.

The Toronto PASS user group meeting is tomorrow (April 28, 2015) at 5:30 pm at the Northern District Library (near Eglinton and Yonge).
By the way, if any local friends from KW want to catch a ride to Toronto, I’ll be leaving KW in the afternoon.

April 6, 2015

Finding Scalar Aggregate Indexed Views in Your Database

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:35 am

Fellow MVP Paul White recently blogged about a SQL Server bug. His blog post is called An Indexed View Bug with Scalar Aggregates

It’s a really well-written article (as always). After reading it, it’s natural to wonder whether your databases have any such indexed views?

Chances are that you don’t have any. These indexed views aren’t too common, but if you do have indexed views that use scalar aggregates, here’s a query that can help you find them.
The following query finds indexed views without GROUP BY that have exactly one row in any partition.

with IndexedViewIds as
  SELECT [object_id] from sys.indexes
  SELECT [object_id] from sys.views
IndexedViewInfo as 
    OBJECT_SCHEMA_NAME([object_id]) as SchemaName,
    OBJECT_NAME([object_id]) as ViewName,
    OBJECT_DEFINITION([object_id]) as [Definition]
  FROM IndexedViewIds
FROM IndexedViewInfo v
    SELECT * 
    FROM sys.partitions
    WHERE [object_id] = v.[object_id]
    AND [rows] <> 1
  AND v.[definition] NOT LIKE '%GROUP BY%'


The query isn’t perfect. It’s actually possible (but rare) to get false positives here. This query doesn’t look for any aggregate keywords. So look for them in the SELECT list. Also make sure no GROUP BY clause exists.

It’s also possible (but rare) to miss some indexed views when GROUP BY gets mentioned, but not used. For example, if an indexed view definition contains the phrase GROUP BY in a comment, it won’t show up in this list.

(For my curious co-workers, none of our indexed views use scalar aggregates)

April 2, 2015

Look at Blocking By Index

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:33 am

This post is for me. It’s a script I find useful so I’m putting it in a place where I know to go look for it, my blog. You may find it useful too.

The script below extends the DMV sys.dm_db_index_operational_stats by focusing on lock waits and supplying index and table names. If you want to know about blocking by index, these queries can help.

If you want something more comprehensive, I’d suggest Kendra Little’s http://www.brentozar.com/blitzindex/

Blocking Wait Stats

-- Get index blocking wait stats
  t.name as tableName, 
  i.name as indexName, 
from sys.dm_db_index_operational_stats(db_id(), null, null, null) ios
join sys.indexes i
  on i.object_id = ios.object_id
  and i.index_id = ios.index_id
join sys.tables t
  on ios.object_id = t.object_id
where ios.row_lock_wait_in_ms + ios.page_lock_wait_in_ms > 0
order by ios.row_lock_wait_in_ms + ios.page_lock_wait_in_ms desc

Create Snapshot Of Stats

begin try
    drop table #IndexBlockingWaitStats
end try
begin catch
-- swallow error
end catch
into #IndexBlockingWaitStats
from sys.dm_db_index_operational_stats(db_id(), null, null, null)

Get Waits Since Last Snapshot

-- Get delta results
  t.name as tableName, 
  i.name as indexName, 
  ios.row_lock_wait_count  - iossnapshot.row_lock_wait_count    as row_lock_wait_count, 
  ios.row_lock_wait_in_ms  - iossnapshot.row_lock_wait_in_ms    as row_lock_wait_in_ms, 
  ios.page_lock_wait_count - iossnapshot.page_lock_wait_count   as page_lock_wait_count,
  ios.page_lock_wait_in_ms -  iossnapshot.page_lock_wait_in_ms  as page_lock_wait_in_ms
from sys.dm_db_index_operational_stats(db_id(), null, null, null) ios
join #IndexBlockingWaitStats iossnapshot
  on iossnapshot.[object_id] = ios.[object_id]
  and iossnapshot.index_id = ios.index_id
join sys.indexes i
  on i.[object_id] = ios.[object_id]
  and i.index_id = ios.index_id
join sys.tables t
  on ios.[object_id] = t.[object_id]
cross apply ( 
  ( ios.row_lock_wait_in_ms + ios.page_lock_wait_in_ms ) -
  ( iossnapshot.row_lock_wait_in_ms + iossnapshot.page_lock_wait_in_ms )
) as calc(totalwaittime)
where totalwaittime > 0
order by totalwaittime desc


  • There are many kinds of lock waits, this script focuses on waits on pages or rows. Other kinds of waits not shown here include objects (i.e. locks on tables), latches and IO latches.
  • This is only one small focused tool in a troubleshooting tool belt. Don’t depend on it too much
  • If you’re keen, you’ll notice I didn’t give info on schemas or on partitions, sounds like a fun exercise doesn’t it?
  • No illustration? Nope, or at least not yet. If I continue to find this script useful, then I plan on adding an illustration, because I use Browse By Illustration as my main navigation tool

April 1, 2015

Some Tweets I Drew

Filed under: SQLServerPedia Syndication,Tongue In Cheek — Michael J. Swart @ 8:00 am

Last week I asked for people to give me illustration ideas on twitter using the hashtag #SwartDrawsTweets. Thanks for everyone who gave me suggestions. Here are a few of them.

The Stig

Brent Ozar tweeted:

(Sorry Richie. Use the hashtag!)
What will he do next. The world wants to know

The Drive-thru

Then Aaron Bertrand tweeted:

@SQLBrit and other left-side drivers are probably wondering about that sign.

M. C. Escher

Ken Fisher had this idea

He probably had a different idea in mind, but I’ve always loved Escher’s Bond of Union.
Maybe not impossible, but linked servers are rarely part of a good solution

Bonus Regional Illustration

And of course I saved the best for last which all Canadians will get:
Regional filtering is a bizarre youtube feature. It's even more bizarre that people use it.

Using These Images

By the way, I really had fun doing these images, so share away. I waive any copyright I have on these three images. Copy them, modify them without attribution wherever you like. Profit if you can. Go nuts.

February 26, 2015

When Parameter Sniffing Caused Deadlocks

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:06 am

Last week I was asked to troubleshoot some deadlocks in production. I was surprised to find out that parameter sniffing was one of the causes. I describe the investigation below.

Parameter Sniffing

SQL Server does this neat trick when you give it a query with parameters. The query optimizer will take the parameter values into account when making cardinality estimates. It finds the best query plan it can for these values. This is called parameter sniffing.

But parameter sniffing combined with query plan caching means that SQL Server seems to only care about parameter values the first time it sees a query. So ideally the first parameter values should be typical parameter values.

When people talk about parameter sniffing problems, it’s usually because this assumption doesn’t hold. Either the query was compiled with atypical values, or maybe the data has an uneven distribution (meaning that “typical” parameter values don’t exist).

But remember that…

Does this smell funny to you?

The Problem

The problem I saw in production involved some fairly typical looking tables. They looked something like this:


This is how they were defined.

create table dbo.Collections
  CollectionId bigint identity primary key, 
  Name nvarchar(20) default ('--unnamed--') not null,
  Extrastuff char(100) not null default ('')
create table dbo.CollectionItems
  CollectionItemId bigint identity primary key,
  CollectionId bigint not null references dbo.Collections(CollectionId),
  Name nvarchar(20) default ('--unnamed--') not null,
  ExtraStuff char(100) not null default ('')
create index ix_CollectionItems
on dbo.CollectionItems(CollectionId);

The errors we were getting were deadlock errors and the deadlock graphs we collected were always the same. They looked something like this:


See that procedure called s_CopyCollection? It was defined like this:

create procedure dbo.s_CopyCollection 
  @CollectionId bigint
  set nocount on;
  declare @NewCollectionId bigint;
  if @CollectionId = 0
  if not exists (select 1 from dbo.Collections where CollectionId = @CollectionId)
  set xact_abort on;
  begin tran;
    insert dbo.Collections (Name, Extrastuff)
    select Name, ExtraStuff
    from dbo.Collections
    where CollectionId = @CollectionId;
    set @NewCollectionId = SCOPE_IDENTITY();
    insert dbo.CollectionItems (CollectionId, Name, ExtraStuff)
    select @NewCollectionId, Name, ExtraStuff
    from dbo.CollectionItems
    where CollectionId = @CollectionId;

It’s a pretty standard copy procedure right? Notice that this procedure exits early if @CollectionId = 0. That’s because 0 is used to indicate that the collection is in the “recycle bin”. And in practice, there can be many recycled collections.

Some Digging

I began by reproducing the problem on my local machine. I used this method to generate concurrent activity. But I couldn’t reproduce it! The procedure performed well and had no concurrency issues at all.

This meant more digging. I looked at the procedures behavior in production and saw that they were performing abysmally. So I grabbed the query plan from prod and here’s what that second insert statement looked like:


This statement inserts into CollectionItems but it was scanning Collections. That was a little confusing. I knew that the insert needed to check for the existence of a row Collections in order to enforce the foreign key, but I didn’t think it had to scan the whole table. Compare that to what I was seeing on my local database:


I looked at the compilation parameters (SQL Sentry Plan Explorer makes this easy) of the plan seen in production and saw that the plan was compiled with @CollectionId = 0. In this case, the assumption about parameter sniffing I mentioned earlier (that the compilation parameters should be typical parameters) did not hold.

This procedure was performing poorly in production (increasing the likelihood of overlapping executions times) but also, each one was taking shared locks on the whole Collections table right after having inserted into it. The whole procedure uses an explicit transaction and that’s a recipe for deadlocks.

Doing Something About It

Here are things I considered and some actions I took. My main goal was to avoid the bad plan shown above.

  • Never call the procedure with @CollectionId = 0. The early-exit in the procedure was not enough to avoid bad query plans. If the procedure never gets called with @CollectionId = 0, then SQL Server can never sniff the value 0.
  • I began to consider query hints. Normally I avoid them because I don’t like telling SQL Server “I know better than you”. But in this case I did. So I began to consider hints like: OPTIMIZE FOR (@CollectionId UNKNOWN).
  • I asked some experts. I know Paul White and Aaron Bertrand like to hang out at SQLPerformance.com. So I asked my question there. It’s a good site which is slightly better than dba.stackexchange when you want to ask about query plans.
  • Aaron Bertrand recommended OPTION (RECOMPILE). A fine option. I didn’t really mind the impact of the recompiles, but I like keeping query plans in cache when I can, just for reporting purposes (I can’t wait for the upcoming Query Store feature)
  • Paul White recommended a LOOP JOIN hint on the insert query. That makes the INSERT query look like this:
     insert dbo.CollectionItems (CollectionId, Name, ExtraStuff)
      select @NewCollectionId, Name, ExtraStuff
      from dbo.CollectionItems
      where CollectionId = @CollectionId
      option (loop join);

    That was something new for me. I thought LOOP JOIN hints were only join hints, not query hints.

  • Paul White also mentioned some other options, a FAST 1 hint or a plan guide and he also suggested OPTION (RECOMPILE).

So I stopped calling the procedure with @CollectionId = 0 and I used a query hint to guarantee the better plan shown above. The performance improved and the procedure was no longer vulnerable to inconsistent performance due to parameter sniffing.

In general, there seem to be only two ways to avoid deadlocks. The first way minimizes the chance that two queries are executing at the same time. The second way carefully coordinates the locks that are taken and the order they’re taken in. Most deadlock solutions boil down to one of these methods. I was happy with this solution because it did both.

January 23, 2015

Designing Indexed Views for OLTP Workloads

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 8:00 am

When I look at indexed views defined on OLTP databases, I’m encouraged when their join diagrams resemble snowflake schemas.

It must be nice in Vermont this time of the year.

When you create an indexed view, SQL Server will enforce a number of restrictions. These restrictions ensure that your views are deterministic and easy to maintain. The restrictions are more than a recommendation, SQL Server simply won’t let you create the index if your view doesn’t meet those criteria.

Indexed Views Can Sometimes Cause Poor Performance

I once thought that if I followed Microsoft’s prerequisites for indexed views, then the maintenance of those indexed views was guaranteed to always be safe. I thought the restrictions would guarantee performance comparable to the maintenance of a regular index. But I was wrong, sometimes it can be much worse. Let’s look an example I invented for this post. Check out the following UPDATE statement. SQL Server reports eighteen logical reads:

use AdventureWorks2012
-- An update of the Product table (no indexed views defined)
UPDATE Production.Product
SET Color = 'Midnight' 
WHERE Color = 'Black';
-- Table 'Product'. Scan count 1, logical reads 18

But when I create this indexed view:

  SUM(th.Quantity) AS [total quantity],
  COUNT_BIG(*) AS [transaction count]
FROM Production.TransactionHistory th
JOIN Production.Product p
  ON th.ProductID = p.ProductID
GROUP BY p.Color;
  ON dbo.v_AggregateQuantityByColor (Color)

Then the same UPDATE statement becomes significantly more expensive requiring over 1000 reads:

use AdventureWorks2012
-- An update of the Product table (maintenance of an indexed view is required)
UPDATE Production.Product
SET Color = 'Midnight' 
WHERE Color = 'Black';
Table 'v_AggregateQuantityByColor'. Scan count 1, logical reads 6, 
Table 'Workfile'.                   Scan count 0, logical reads 0, 
Table 'Worktable'.                  Scan count 2, logical reads 377, 
Table 'TransactionHistory'.         Scan count 1, logical reads 797, 
Table 'Product'.                    Scan count 1, logical reads 18, 

You can see the extra work caused by the indexed view in the query plan:

Indexed View Maintenance

click to embiggen

The maintenance cost for this UPDATE statement got significantly worse. If statements like this are executed frequently it could be disastrous. That’s one of the reasons that Microsoft promotes indexed views as ideal for read-heavy scenarios such as those seen in data warehousing.

But I think that indexed views still have a place in OLTP systems. It’s just that extra care must be taken so that no SQL statement causes indexed view maintenance to be significantly worse than the regular table index maintenance. I want to talk about some things I look for when I evaluate views meant for OLTP databases.

Look For A Join Diagram Like a Snowflake Schema

Look at your view’s select statement. Specifically focus on the tables in the FROM clause and draw a “join diagram” for yourself. I’ve got a shortcut for that work. I start by running a query like this:

SELECT * FROM dbo.v_AggregateQuantityByColor OPTION (EXPAND VIEWS);

This gets me the query plan for the statement. I open the query plan in SQL Sentry’s Plan Explorer which has a handy dandy Join Diagram tab.

Using your join diagram, ask yourself these questions:

  • Does the join diagram look like a snowflake schema with one “fact” table?
  • Do the joins correspond to defined indexed foreign keys?
  • Are the columns included in the “dimension” tables modified infrequently?

Whenever I’ve dealt with poor performance caused by indexed views, these views have always given a “no” to at least one of these questions.


Let’s apply these questions to dbo.v_AggregateQuantityByColor from my example. Here’s the join diagram:

Join Diagram

This diagram does in fact look like a snowflake schema with the TransactionHistory table acting as the fact table and the Product table acting as the dimension table. The one join follows an actual foreign key FK_TransactionHistory_Product_ProductID. And this foreign key is indexed (IX_TransactionHistory_ProductID).

Now lets answer the last question “Are the columns included in the dimension tables modified infrequently?”. In the context of this question, that’s the Color column in the Product table. Now it’s impossible to actually tell how frequently colors get updated in the Product table because this is a hypothetical example. But it’s unlikely that any OLTP workload would update product colors that often. So lets give the answer: “infrequently updated”.

So according to my criteria, this indexed view gets the green light. Even though it seems like it could be expensive to maintain, I don’t have any automatic objections with it because product colors are rarely updated.


Q: Is it possible to ignore these rules and still create effective indexed views?
A: Yes!

Q: Is it possible to follow these rules and still create indexed views that cause performance problems?
A: Yes!

Q: If I follow these rules, can I skip any performance testing steps?
A: No!

Q: So why the heck am I reading this post?
A: Lots of reasons. At the very least, it is useful when you want to identify indexed views and testing scenarios that deserve extra scrutiny.

December 12, 2014

Obvious and Not-So-Obvious Writing Tips

Filed under: SQLServerPedia Syndication — Michael J. Swart @ 10:54 am

Takeaway: I leave SQL Server behind this week and I give two tips for technical bloggers,

  1. An obvious tip: Practice a lot
  2. A not-so-obvious tip: Help your readers skip reading your article

First the obvious tip.

Practice in Volume

As far as tips go, practice makes perfect is kind of obvious, and ultimately a little disappointing. Just like “Eat right and exercise”, the phrase “Go practice more” is one of those things that is easier said than done.

I first heard about a Composition Derby when I read The Underachieving School by John Holt. John Holt was an English teacher and author and he describes the Composition Derby as a device he used to help kids practice writing. The kids in his English class get divided into teams and they are asked to write about anything they want (spelling and grammar doesn’t count). At the end of the competition, the team who has written the most words wins. That’s the only criteria, number of words. When kids don’t worry about making mistakes they feel free to practice more. And that frees them to improve faster.

But I think the volume of practice is the key here. I believe in Malcolm Gladwell’s 10,000 hours rule. The rule claims that it takes 10,000 hours to become an expert at something. I like the idea of the 10,000 hour rule, but the one thing I don’t like is that it gives a definite number. Eight hours of writing practice can yield results and 10,000 hours implies a finish line. For example, compare these two illustrations I drew. They both use the same reference photo but they’re spaced apart by about 1,000 hours of practice.
An upset looking E. F. CoddTed Codd

It’s easy to compare illustrations when presented side by side. It’s not as easy to compare writing but feel confident that with practice, you’ll improve and your readers will notice.

Make Your Article Skippable

The second tip is a little counter-intuitive. Make it easy for your readers to skim your article or even skip reading your article all together.

You have something important to write, and I get that. But when thinking about the reader-writer relationship, your article is all about your readers. Their need to read actually outweighs your need to write and ultimately your readers will decide what’s important. I’m notoriously bad at predicting whether a post of mine will be well received or not. And so I make my blog posts skippable. The readers who find what I write important will stick around.

Here are some methods I use that help readers stop reading. Consider using these methods in your own writing

  • Topic sentence (which I frame as a takeaway). Condense your whole blog into a tweet-sized sentence. Give everything away as quickly and clearly as you can. Leave suspense-building for mystery writers. For example, if you only read SQL Server articles, you probably haven’t made it this far. You probably didn’t make it past the first sentence.
  • Organize your article into sections with headings that can stand alone as an outline. It improves skimmability.
  • In general, put a high value on your reader’s time. Make every word count in helping you say the one thing you want to say and don’t say anything else.

Now here’s the crazy part, when you make your article skippable it actually has the opposite effect. These methods I use actually help readers stick around. Readers have a better mental roadmap of the content and they stay (see, you’ve stuck around this far!).

December 3, 2014

Materialized Views in SQL Server

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 9:28 am

What’s the difference between Oracle’s “materialized views” and SQL Server’s “indexed views”? They both persist the results of a query, but how are they different? Sometimes it’s difficult to tell.

I'm on the left (or am I?)

I’m on the left (or am I?)

One difference is that SQL Server’s indexed views are always kept up to date. In SQL Server, if a view’s base tables are modified, then the view’s indexes are also kept up to date in the same atomic transaction.

Let’s take a look at Oracle now. Oracle provides something similar called a materialized view. If Oracle’s materialized views are created without the REFRESH FAST ON COMMIT option, then the materialized view is not modified when its base tables are. So that’s one major difference. While SQL Server’s indexed views are always kept current, Oracle’s materialized views can be static.

Static Materialized Views In SQL Server?

Yeah, we just call that a table. You can use a SELECT INTO statement and it’s pretty easy. In fact, for fun I wrote a procedure that does the work for you. Given the name of a view it can create or refresh a table:

/* This is a proof-of-concept and is written for illustration purposes, don't use this in production */
create procedure dbo.s_MaterializeView
  @viewName nvarchar(300),
  @yolo bit = 0 -- use @yolo = 1 to execute the SQL immediately
declare @persistedViewName nvarchar(300);
if not exists (select 1 from sys.views where object_id = object_id(@viewName))
  THROW 50000, N'That @viewName does not exist', 1;
  @viewName = QUOTENAME(object_schema_name(object_id)) 
  + N'.'
  + QUOTENAME(object_name(object_id)),
  @persistedViewName = QUOTENAME(object_schema_name(object_id)) 
  + N'.'
  + QUOTENAME(N'persisted_' + object_name(object_id))
from sys.views
where object_id = object_id(@viewName);
set xact_abort on;
begin tran
  declare @sql nvarchar(2000);
  set @sql = N'
    IF OBJECT_ID(''' + @persistedViewName + N''') IS NOT NULL
      DROP TABLE ' + @persistedViewName + N';
    SELECT *
	INTO ' + @persistedViewName + N'
    FROM ' + @viewName + N';'
  if (@yolo = 1)
    exec sp_executesql @sql;  
    print @sql;

Which can be used to generate sql something like this:

    IF OBJECT_ID('[dbo].[persisted_vSomeView]') IS NOT NULL
      DROP TABLE [dbo].[persisted_vSomeView];
    SELECT *
	INTO [dbo].[persisted_vSomeView]
    FROM [dbo].[vSomeView];

Are Such Static Materialized Views Useful?


  • They can be used to get around all the constraints placed on regular indexed views. And if you’ve ever implemented indexed views, you understand that that’s a lot of constraints. I think this benefit is what makes this whole blog post worth consideration.
  • Because it’s static, you can avoid all the potential performance pitfalls that accompany the maintenance of an indexed view (more on this next week).
  • Good or bad, the view doesn’t have to be created with SCHEMABINDING.
  • Indexing is strictly do-it-yourself. Chances are you want more than a single heap of data for your materialized view.

… and no:

  • Most obviously, the data is static, which is another way of saying stale. But notice how Microsoft promotes indexed views. They say that indexed views are best suited for improving OLAP, data mining and other warehousing workloads. Such workloads can typically tolerate staleness better than OLTP workloads. And so maybe materialized views are a feasible alternative to indexed views.
  • You have to manage when these views get refreshed. This means scheduling jobs to do extra maintenance work (yuck). For me that’s a really high cost but it’s less costly if I can incorporate it as part of an ETL process.
  • Using Enterprise Edition, SQL Server’s query optimizer can choose to expand indexed views or not. It can’t do that with these materialized views.

I didn’t write the procedure for any important reason, I just wrote it because it was fun. But I have used this materialized view technique in SQL Server at work and I’ve been quite successful with it. It’s not something that should be used often, but it’s always worth considering if you can understand the trade-offs.

October 3, 2014

Watch Out for Misleading Behaviour From SQL Server

Takeaway: To get consistent behaviour from SQL Server, I share a set of statements I like to run when performing tuning experiments.

Inconsistent Behaviour From SQL Server?

I often have conversations where a colleague wants to understand why SQL Server performs faster in some cases and slower in other cases.

The conversation usually starts “Why does SQL Server perform faster when I…” (fill in the blank):

  1. … changed the join order of the query
  2. … added a transaction
  3. … updated statistics
  4. … added a comment
  5. … crossed my fingers
  6. … simply ran it again

What’s Going On?

It can actually seem like SQL Server performs differently based on its mood. Here are some reasons that can affect the duration of queries like the ones above

  • You changed something insignificant in the query. What you may be doing is comparing the performance of a cached plan with a newly compiled plan. Examples 1 – 4 might fall under this scenario. If that’s the case, then you took a broken thing and gave it a good thump. This percussive maintenance may be good for broken jukeboxes, but maybe not for SQL Server.
  • What about those last two? Say you hit F5 to execute a query in Management Studio, and wait a minute for your results. You immediately hit F5 again and watched the same query take fifteen seconds. Then I like to point out that maybe all that data is cached in memory.

In order to do tune queries effectively, we need consistent behaviour from SQL Server, if only to test theories and be able to rely on the results. SQL Server doesn’t seem to want to give us consistent behaviour…

So Is It Possible To Get Straight Answers?

Best line from all Star Wars

But maybe we can get straight answers from SQL Server. Here’s a test framework that I like to use before all experiments when I want consistent behaviour:

-- Only do this on dev sql servers!
-- Ctrl+M in Management Studio to include actual query plan

The first two statements are meant to clear SQL Server’s cache of data. Because of write ahead logging, SQL Server will write data changes to disk immediately, but may take its time writing data changes to disk. Executing CHECKPOINT makes SQL Server do that immediately. After the checkpoint there should be no dirty buffers. That’s why DBCC DROPCLEANBUFFERS will succeed in dropping all data from memory.

The DBCC FREEPROCCACHE command will remove all cached query plans.

These commands give SQL Server a fresh starting point. It makes it easier to compare behaviour of one query with the behaviour of another.

The SET STATISTICS IO, TIME ON and the Ctrl+M are there in order to retrieve better information about the performance of the query. Often CPU time, Logical IO, and the actual query plan are more useful when tuning queries than elapsed time.

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