Long time ago we discussed that good clustered index needs to be unique, static and narrow. I mentioned that internally SQL Server needs to be able to unique identify every row in the table (think about non-clustered index key lookup operation) and in case, if clustered index is not defined as unique, SQL Server adds 4 bytes uniquifier to the row. Today I want us to talk about that case in much more details and see when and how SQL Server maintains that uniquifier and what overhead it introduces.

In order to understand what happens behind the scene we need to look at the physical row structure and data on the page. First, I have to admit, that general comment about 4 bytes uniquifier overhead was not exactly correct. In some cases overhead could be 0 bytes but in most cases it would be 2, 6 or 8 bytes. Let’s look at that in more details. Click on the images below to open them in the new window.

First, let’s create 3 different tables. Each of them will have only fixed-width columns 1000 bytes per row + overhead. So it gives us an ability to put

• UniqueCI – that table has unique clustered index on KeyValue column
• NonUniqueCINoDups – that table has non unique clustered index on KeyValue column. Although we don’t put any KeyValue duplicates to that table
• NonUniqueCIDups – that table has non unique clustered index on KeyValue column and will have a lot of duplicates.

Now let’s add some data to those tables.

First, let’s take a look at the physical stats on the clustered index. Two things are interesting. First – Min/Max/Avg record size, and second is the Page Count.

As you can see, best case scenario in UniqueCI table has 1007 bytes as Min/Max/Avg record size (again – all columns are fixed width) and uses 12500 pages. Each page can store 4 rows (1,007 bytes per row * 8 = 8,056 bytes < 8,060 bytes available on the page.

Next, let’s take a look at NonUniqueCINoDups table. Even if clustered index is not unique, Min/Max/Avg/Page Count are the same with UniqueCI clustered index. So as you can see, in this particular case of non-unique clustered index, SQL Server keeps null value for uniquifier for the first (unique) value of the clustered index. And we will look at it in more details later.

The last one – NonUniqueDups table, is more interesting. As you can see, if Min record size is the same (1,007 bytes), Maximum is 1,015 bytes. And Average record size is 1,014.991 – very similar to the maximum record size. Basically, uniquifier value added to all rows with exception of the first row per unique value. Interestingly enough that even if uniquifier itself is 4 bytes the total overhead is 8 bytes.

Another thing is worth to mention is the page count. As you can see, there are 1,786 extra pages (about extra 14M of the storage space). 8 rows don’t fit on the page anymore. Obviously this example does not represent real-life scenario (no variable with columns that can go off-row, etc) although if you think about non-clustered indexes, the situation is very close to the real-life. Let’s create non-clustered indexes and checks the stats.

As you can see, in the latter case, we almost doubled the size of the non-clustered index leaf row and storage space for the index. That makes non-clustered index much less efficient.

Now let’s take a look at the actual row data to see how SQL Server stores uniquifier. We will need to take a look at the actual data on the page. So the first step is to find out what is the page number. We can use DBCC IND command below. Let’s find the first page on the leaf level (the one that stores very first row from the table). Looking at DBCC IND result set, we need to select PagePID for the IndexLevel = 0 and PrevPageFID = 0.

Next, we need to run DBCC PAGE command and provide that PagePID. Both DBCC IND and DBCC PAGE are perfectly save to run on the production system. One other thing you need to do is to enable trace flag 3604 to allow DBCC PAGE to display result in the console rather than put it to SQL Server error log.

So let’s take a look at the row itself. First, we need to remember that DBCC PAGE presents multi-byte values with least-significant bytes first (for example int (4 bytes) value 0x00000001 would be presented as 0x01000000). Let’s take a look at the actual bytes.

• Byte 0 (TagByteA) (green underline) – this is the bit mask and in our case 0x10 means that there is NULL bitmap
• Byte 1 (TagByteB) – not important in our case
• Byte 2 and 3 (yellow underline) – Fixed width data size
• Bytes 4+ stores the actual fixed width data. You can see values 0x01 (0x10000000 in reverse order) for KeyValue and ID and ‘a..’ for the CharData columns.

But much more interesting is what we have after Fixed-Width data block. Let’s take a look:

Here we have:

• Number of columns – 2 bytes (Red underline): 0x0300 in reverse order – 3 columns that we have in the table.
•  Null bitmap (Blue underline): 1 byte in our case – no nullable columns – 0.

So everything is simple and straightforward. Now let’s take a look at NonUniqueCINoDups data. Again, first we need to find the page id with DBCC IND and next – call DBCC PAGE.

I’m omitting first part of the row – it would be exactly the same with UniqueCI row. Let’s take a look at the data after fixed-width block.

As you can see, number of columns (Red underline) is now 4 that includes uniquifier which does not take any physical space. And if you thinking about it for a minute – yes, uniquifier is nullable int column that stores in the variable-width section of the row. SQL Server omits data for nullable variable width columns that are the last in the variable-width section which is the case here. If we had any other variable width columns with the data, uniquifier would use two bytes in the offset array even if value itself would be null.

And now let’s take a look at NonUniqueCIDups rows. Again, DBCC IND, DBCC PAGE.

If we look at the variable width section of the first row in the duplication sequence), it would be exactly the same with NonUniqueCINoDups. E.g. uniquefier does not take any space.

But let’s look at the second row.

Again we have:

• Number of columns – 2 bytes (Red underline): 4
• Null bitmap (Blue underline)
• Number of variable-width columns – 2 bytes (Green underline) – 0x0100 in reverse order – 1 variable width column
• Offset of variable-width column 1 – 2 bytes (Black underline)
• Uniquefier value – 4 bytes (purple underline)

As you can see, it introduces 8 bytes overhead total.

To summarize storage-wise – if clustered index is not unique then for unique values of the clustered key:

• There is no overhead if row don’t have variable-width columns or all variable-width columns are null
• There are 2 bytes overhead (variable-offset array) if there is at least 1 variable-width column that stores not null value

For non-unique values of the clustered key:

• There are 8 extra bytes if row does not have variable-width columns
• There are 6 extra bytes if row has variable-width columns

This applies not only to the clustered indexes but also to non-clustered index that references clustered index key values. Well, storage is cheap but IO is not..

Source code is available for download

P.S. Happy Thanksgiving! 🙂

We all already know that in most part of the cases deadlocks happen due non-optimized queries. Today I’d like to show another pattern that could lead to the deadlocks. It’s not something that happens very often but it’s worth to mention.

Let’s think about the following scenario. Assuming you have the system that collects some data from the users. Assuming the data has a few parts that can be processed and saved independently from each other. Also let’s assume that there is some processing involved – let’s say there is a raw data part and something system needs to calculate based on that.

One of the approaches to architect the system is separating those updates and processing to the different threads/sessions. It could make sense in some cases – data is independent, threads and sessions would update different columns so even if they start updating the row simultaneously, in the worst case one session would be blocked for some time. Nothing terribly wrong as long as there are no multiple updates of the same row involved. Let’s take a look.

First, let’s create the table and populate it with some data:

Now let’s run the first session, open transaction and do the update of RawData1 column. Also, let’s check the plan. This update statement used non-clustered index seek/key lookup – keep this in mind, it would be important later.

Now let’s run the second session that updates different column on the same row. Obviously this session is blocked – first session holds (X) lock on the row.

Now let’s come back to the first session and try to update another column on the same row. This is the same session that holds (X) row so it should not be the problem.

But.. We have the deadlock.

Why? Let’s take a look at deadlock graph (click to open the new window)

So on the right we have the first session. This session holds the (X) lock on the clustered index row (PK_Users). When we ran the session 2 statement, that session obtained (U) lock on non-clustered index row (IDX_Users_ExternalID), requested (U) lock on the clustered index and was blocked because of the first session (X) lock. Now, when we ran the second update statement from the first session, it tries to request the (U) lock on the non-clustered index and obviously was blocked because the second session still holds (U) lock there. Classic deadlock.

As you can see, it happened because SQL Server uses non-clustered index seek/key lookup as the plan. Without non-clustered index seek everything would work just fine.

This is quite interesting scenario and you can argue that it does not happen often in the real life. Well, yes and no. If we think about 2 update statements in the row – yes – usually we don’t write code that way. But think about stored procedures. If the processing can be done/called from a few different places, you can decide to put the update to the stored procedure. And here you go.

But most importantly – there are the triggers. What if you have AFTER UPDATE trigger and want to update some columns from there. Something like that:

Now let’s run update statement in the first session.

And in the second session.

Deadlock again. You can notice that I used ExternalId and as result non-clustered index seek/key lookup plan there. It does not make a lot of sense in this scenario – I could use UserId there and avoid the problem. So if you have to update original row from the trigger – be careful and write the query in the way that introduces clustered index seek.

Source code is available for download

Part 12 – Lock Escalation

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Wrapping up.. Only remaining question is “What isolation level is good for me”? And there is no right answer to that question. As usual, it depends. I’ll try to summarize a few things but again, think about specific details of your system and make decision based on them, not on generic advices below.

When you start to think what isolation level should you use in your system, there are 2 questions you need to answer. First, what is the blueprint of your system – is it OLTP or Data Warehouse type system? OLTP systems usually handles operational activity of the company and serves high volume of short identical queries and short transactions. Data Warehouse blueprint described reporting type system with low volume of long transactions and complex queries. In OLTP systems data is constantly changing, in Data Warehouse systems it rarely the case – data usually updates on the batches based on some schedule.

As the example, let’s think about online store. The database that handles customer facing shopping cart web site is OLTP. There are always new orders coming, existing order updating, customers check the status of existing orders, search the article lists, etc – those are done within short transactions and simple queries. Assuming same time company executives want to run some reports, see some trends and other analysis – there is the good chance that it would be another database for that purpose. This database would be optimized for complex reporting and data in that database could be refreshed every night or perhaps on the weekly basis. That database is Data Warehouse.

In real life, of course, it’s rarely the case. There is always some reporting activity against OLTP database but in any case, you can see what blueprint fits better. And it leads to the second question you need to answer: “How much data consistency do I really need?”. And even if the answer “I don’t need any consistency” is quite popular, it rarely the case in the real life. So let’s dive a little bit more in “no-consistency” mode.

No-consistency in terms of transaction isolation levels mean read uncommitted. Either directly as “set transaction isolation level” or with (NOLOCK) hints. There are some cases when you can decide to use that isolation level – for example, in our system we have a few transaction entities where data has been inserting to our system and never ever updating/deleting after that. Same time, clients are constantly downloading the data and we don’t really care if clients get the data from uncommitted transaction. This is the case when we can downgrade to read uncommitted isolation level to reduce the blocking for the client downloading sessions.

But if you think about that example – it’s rather exception than the rule. In most part of the systems you want to have consistency. Think about reporting for executives – how happy they would be if you provided them incorrect data or, even better, if they get different results running the same report twice? And the real problem that in the real life people often switches to read uncommitted isolation level to solve the blocking issues. Right, it could help in the cases when select queries (readers) are blocked by update queries (writers). But it’s rather masking the problem than solving it. As we already know, in most part of the cases, locking and blocking issues triggered by non-optimized queries. So, if you had a chance, you’d better spend some time on query optimization. Of course, this advice would not help if you are in fire-drill mode and need to fix the blocking issues in production system but in such case there is another option that could work even better – use read committed snapshot instead of
read uncommitted. In that mode writers also don’t block readers and same time give you statement level consistency. Of course there are some performance implications but in most part of the cases you can live with them.

Read committed, which is also default isolation level, in most part of the cases (again, assuming the system is more or less optimized) – this is the good choice especially for OLTP systems. It gives you acceptable compromise between consistency and concurrency. Again, there is blocking involved but when system is optimized – it’s minimal. Speaking of higher isolation levels – repeatable reads and serializable – those are typically bad choice for OLTP. For reporting and data warehouse systems those could be acceptable but same time for such systems optimistic isolation levels (read committed snapshot and snapshot) are better.

And speaking of optimistic isolation levels. For Data Warehouse type systems – use them. I don’t think about any single reason why you would like to avoid them in such systems. For OLTP – consider them. If you can live with performance overhead – it could be the good choice. Be careful – don’t forget about extra 14 bytes and don’t use fillfactor = 100 though.

So, the bottom line. For Data Warehouse – use optimistic isolation levels whenever possible. Only case with Data Warehouse systems when I would suggest to consider different options is when data in the system updates on the real time. And even in such case give optimistic isolation levels the try. For OLTP – if you can use optimistic isolation levels – use them (start with read committed snapshot). If not, use read committed and optimize the queries. Don’t use read uncommitted and (nolock) hints unless you don’t care at all about consistency. And definitely don’t use read uncommitted to reduce blocking. This is the bad choice.

Last, but not least, don’t forget that you can, and often need to use multiple different isolation levels in the system. Use them wisely! 🙂

Part 11 – Deadlocks due multiple updates of the same row

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Two week ago we discussed 2 “optimistic” transaction isolation levels – Snapshot and Read Committed Snapshot. We already saw how those isolation levels can provide us transaction and statement level consistency reducing blocking issues same time. Sounds too good to be true..

So does it make sense to switch to optimistic isolation levels? Well, the answer is typical – “it depends”. There are a few things you need to keep in mind before you make the decision. Let’s talk about them from both – DBA and Database Developer standpoints.

First of all, as we already know, optimistic isolation levels use tempdb for the version store. When you modified the row, one or more old versions of the row is stored in tempdb. This leads to the higher tempdb load as well as to the larger tempdb size. Would it be the issue for your system? Again, it depends. As the bare minimum you should reserve enough space for tempdb and closely monitor the load. There are a few performance counters under <SqlInstance>:Transactions section that related to the version store – they will show you version store size, generation and cleanup rates and a few other parameters. Those are very useful and I recommend to add them to your baseline.

Second thing you need to keep in mind is that SQL Server needs to store additional 14 bytes pointer to the version store in the. It’s not only increasing the row size, it also can introduce page splits. Let’s take a look. Let’s use the same database from the last blog post – you can download it from the last post.

Let’s rebuild the index (with default FILLFACTOR=100) and look at the index statistics (click on the image to open it in the different window). As you can see, it has 0% fragmentation. Row size is 215 bytes.

Now let’s run transaction in snapshot isolation level and update Value column. This is integer (fixed width column) so this update by itself should not increase row size. If you look at the index statistics now, you can see that there were heavy page splits and row size increased by 14 bytes. Those 14 bytes is the pointer to the version store.

The bottom line – if you use optimistic isolation level, don’t use 100% fillfactor for the indexes.

Speaking of development challenges – well, it’s become a little bit more interesting. First potential program is referential integrity based on triggers. Let’s take a look. Let’s create 2 tables – MasterData and DetailData and after insert trigger on DetailData. In this trigger let’s check that master data exists and rollback transaction in case of referential integrity violation. Let’s test that:

Now let’s move to more complex scenario and 2 sessions. First, let’s start transaction in the 1st session and check if we have MasterData row.

Let’s keep transaction open and in the second session let’s delete master data row. As you see everything is just fine.

Next, lets come back to the first session and insert detail row that references the master row – as you can see there is no errors but referential integrity has been compromised.

It happens because inside the trigger we still are in the context of the old transaction where we reference old version of MasterData row from the version store. This could be easily fixed in the trigger by using (READCOMMITTED) query hint but of course you should remember it and modify the code. It worth to mention that regular referential integrity based on foreign keys uses read committed isolation level by default.

Another issue is the update of the same row. Let’s take a look. first let’s reset Master and Detail table data. Now let’s start transaction in the first session and query the data

Next, let’s update data row in another transaction from another session

And now let’s try to update the same row from the first session and commit the transaction.

As you can see, behavior is completely different from the regular pessimistic isolation levels – it raises the exception. So obviously the client application needs to catch those errors and either notify users or implement some sort of retry logic to handle it.

And finally let’s look at the different update behavior in snapshot isolation mode. Let’s start the transaction assuming we have 2 rows in the table

Next, in another session let’s run update that changes DetailDataId to 2 for the first row.

Now in the first session let’s do the opposite action and check results

As you see, because of the row versioning it simply swaps the values. It would be completely different with regular pessimistic isolation levels when one session would be blocked and next update either 0 or 2 rows (depend on what session acquires the lock first). The bottom line – if you move your application to snapshot isolation level, you need to test how it would behave in environment with the multiple users. Otherwise you’d have a few nice side effects.

So to summarize – optimistic isolation levels are great but you have to keep a few things in mind:

• Extra tempdb load
• Possible page splits/fragmentations due bigger row size
• Referential integrity based on triggers does not work unless read committed hint is used
• There are different behaviors for updates when multiple sessions update the same rows and when scan is involved.

Source Code is available for download

Part 10 – What isolation level should I choose?

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There was some time since we discussed locking and blocking in Microsoft SQL Server. One big area we did not cover is optimistic transaction isolation levels. So what exactly are those?

As you remember, standard “pessimistic” isolation levels – read uncommitted, read committed and repeatable read – all of them can have anomalies. Dirty and non-repeatable reads, ghost rows and duplicated reads. Higher isolation levels reduce anomalies in price of reduced concurrency. Most restrictive – Serializable isolation level guarantees no anomalies although with such level concurrency greatly suffers. Before SQL Server 2005 it was virtually impossible to have consistent reporting in the systems with heavy reading and writing activities.

Obviously in quite competitive market Microsoft had to do something to solve that problem. As the result, one of the features Microsoft introduced in SQL Server 2005 was new “optimistic” transaction isolation levels. With those isolation levels readers use row versions rather than locking. When you start the transaction and modify some rows, old version of the rows are copied to the version store in tempdb. Actual row holds 14 bytes pointer to the version store.

There are 2 different isolation levels – well, actually one and half. First one is called Read Committed Snapshot and not really the level but more or less the mode of Read Committed. This level implements statement level consistency – readers would not be blocked by writers but in transaction scope it could have same anomalies as regular read committed (with the exception of duplicated reads). Let’s take a look. If you don’t have test database, create it first. First, let’s enable read committed snapshot isolation level. It worth to mention that when you switch the mode, you should not have any users connected to the database.

Next, let’s create the table and populate it with some data

Now let’s start one session and update one of the rows. As you can see, this session starts in read committed isolation level

Let’s look at the locks we have in the system. As you can see, we have exclusive lock on the row. This is expected and should happen with any isolation level. Click to the image to open it in the different window

Now let’s start another session and try to select the same row.

And here is the big difference with regular read committed. Instead of being blocked as when we have pessimistic isolation level, it returns old version of the row with placeholder equal ‘a’. So far, so good. Now let’s run another update.

As you can see, with read committed snapshot we still have writers acquiring U and X locks – so those statements would be blocked as with regular pessimistic isolation levels. What does it really mean for us? If your system suffers because of the blocking between readers and writers, read committed snapshot helps in such situation – blocking would be reduced. This is especially good if you need to deal with 3rd party vendors databases – you can change the database option and (partially) solve the problem. Although read committed snapshot would not help if writers block each other. you’ll still have typical update and exclusive locks behavior.

One other thing I already mentioned – read committed snapshot does not provide you consistency on the transaction scope. Consistency is guaranteed only on the statement scope. Let’s check that:

Now let’s commit the session 1 (I don’t put screen shot here) and run select statement again. We’re on the same transaction context.

As you can see, select returns new (updated) row value. Again, no transaction level consistency in such case. If you need full consistency on transaction level, you need to use SNAPSHOT isolation level. With such isolation level SQL Server stores multiple “old” versions of the row – as long as there are transactions that can reference them. When session references the row, it reads the version that was valid on the moment when transaction started. It has “snapshot” of the data for the transaction.

Let’s take a look. First, you need to enable that option on database level. Again, you need to be on single user mode in order to do so.

Next, let’s run same update statement and keep transaction uncommitted.

Now let’s go to another session, start transaction in snapshot isolation level and see what happens.

First, let’s select of the row updated by another session that holds X lock. As you can see – same behavior with read committed snapshot – it returns old value.

Now let’s try to update another row with table scan. As you can see – no blocking due U/X lock incompatibility now. Both rows have been updated, 2 X locks held

Now let’s commit transaction in the first session and run select in the second session again. It still returns the data that you had at the time when transaction was just started. Even if that row has been modified.

And now let’s delete that row in the first session.

If you run same select in the second session – it still returns that row.

As you can see, this is consistency on the transaction level. SQL Server guarantees that data within the transaction would be exactly the same during transaction lifetime. Even if it looks similar to Serializable isolation level, there is the key difference. With Serializable level you “lock” the data on the moment you access it. With Snapshot, it “locked” on the moment when transaction started. And obviously, this is the Heaven for reporting – consistent data with no blocking.

Too good to be true. Yes, there are a few things you need to keep in mind from both DBA and Development prospective. And we will talk about them next time.

Source code is available for download

Part 9 – Optimistic transaction isolation levels – TANSTAAFL! 

Also take a look at Part 15 – When Transaction Starts. It gives some additional details about snapshot isolation level

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One of very interesting phenomenon we have not discussed earlier when we covered lock types and isolation levels  is duplicate readings. As we already know, read committed isolation level protects us from reading of modified and uncommitted data but the scope of protection is “this row/right now”. So what happens if row updates during the time when query is executed.

Let’s take a look. First, as usual, let’s create the table and populate it with 100,000 rows:

As you can see, even if we don’t have any unique constraints, that table has unique ID and Value columns (equal to each other) with values from 1 to 100,000. We can even run SQL query and see that there is no duplications.

Now let’s start another session and lock one row:

As you can see, we don’t commit transaction, so exclusive (X) lock is in play. Now let’s start another session, create temporary table and simply copy data from our main table there. When we run this statement in read committed mode, it would be blocked because there it tries to acquire shared (S) lock on the row that held (X) lock from the previous session.

Now let’s come back to the previous session and update another row (value column this time) and commit transaction.

As you can see, first (X) lock has been released and our blocked session resumes and finishes copying the data. And now, let’s see what we have in this table –

Surprise! We have 1 extra row – the one the first session updated. This row appears twice – with old and new values in Values column. In order to understand what happened, let’s take a look at the execution plan for insert/select statement.

As you can see, plan uses index scan. Let’s think what happened – select read first 999 rows (with ID from 1 to 999) and was blocked by (X) lock from the different transaction. Next, that transaction updated Value column for the row with ID = 1. Because Value is the index key, that index row has been moved in the index. As result, when select resumes, it read this row (with ID = 1) second time during remaining part of the index scan. Fun?

With read uncommitted another factor could come in play – sometime SQL Server can decide to use allocation scan. There are a lot of factors that can lead to that decision – but if it happens and if you’re lucky enough to have page split during query execution, you can easily have rows to be read/processed more than once.

Fortunately neither of those cases happen quite often. I’d say I saw duplicate readings in production systems maybe 2-3 times during my career. But it could give you a lot of grey hairs – think what if #Temp table had primary key on ID and you would get primary key violation on the data that guaranteed to be unique? If you want to be protected from that you have to use optimistic isolation levels. I (promise another time) is going to blog about them next time 🙂

Source code is available for download

Part 8 – Optimistic transaction isolation levels

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As we already saw, the reasons why we have blocking issues and deadlocks in the system are pretty much the same. They occur because of non-optimized queries. So, not surprisingly, troubleshooting techniques are very similar. Let’s have a quick look. We’ll use the same scripts I used last time.

The simplest approach is to use SQL Profiler. There is “Deadlock graph” event in the “Locks” event group you can use. Click on the picture to open it in the different window.

Let’s start the trace and trigger deadlock.

As you can see, it shows you very nice picture. There are 2 sessions (ovals) involved. Those sessions compete for the page locks (squares). You can see what locks each session held and you can even track it down to the resources (but that rarely needed). You can even see the statements when you move the mouse over the session oval and wait for the tool tip.

In context menu for “deadlock graph” line in the grid above, you have “Extract event data” menu command that can save this information as the file.

You can open it as the graph in management studio or, technically, simply look at XML which is extremely familiar:

As you can see it’s way more detailed in compare with graphical representation. It’s also extremely familiar with blocking process report – and you can use same technique and query sys.dm_exec_sql_text if you need to obtain sql text from handle. I demonstrated how to do that in post related with blocking troubleshooting.

In case, if you don’t want to use SQL Profiler, there are 2 options you can use. The first one is enabling trace flag 1222 with DBCC TRACEON(1222,-1) command. When you have it enabled, SQL Server put deadlock graph XML to SQL Server log.

Another option is using extended events (SQL Server 2008/2008R2). Again, it’s very powerful method although requires some initial work to set it up. As with the blocking, I’m leaving it out of scope for now.

How to deal with deadlocks? Of course, the best thing is not to have deadlocks at the first place. Again, golden rule is to optimize the queries. Meanwhile, if you need to control (up to degree) what session will be terminated, you can use SET DEADLOCK PRIORITY option. There are 21 priority levels available. When 2 sessions deadlocked, the session with the lower deadlock priority level would be chosen as the victim. In case of the same priority level (default case), SQL Server chooses the session that is less expensive to rollback.

If session is chosen as the victim, it would be aborted with error code 1205. In such case client (or T-SQL code) can catch the exception and re-run the query. But again, the best way is avoiding deadlocks at the first place.

Part 7 – Read Committed – duplicate readings 

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We already know why blocking occurs in the system and how to detect and troubleshoot the blocking issues. Today I’d like us to focus on the deadlocks.First, what is the deadlock? Classic deadlock occurs when 2 processes compete for the resources and waiting on each other. Let’s take a look on that. Click on each picture below to open them in the different window. Let’s assume that Orders table has the clustered index on ID column.Let we have the session 1 that starts transaction and updates the row from the Order table. We already know that Session 1 acquires X lock on this row (ID=1) and hold it till end of transaction

Now let’s start session 2 and have it update another row in the Orders table. Same situation – session acquires and holds X lock on the row with (ID = 2).

Now if session 1 tries to acquire X lock on the row with ID = 2 , it cannot do that because Session 2 already held X lock there. So, Session 1 is going to be blocked till Session 2 rollback or commit the transaction

It worth to mention that it would happen in any transaction isolation level except SNAPSHOT that handles such conditions differently. So read uncommitted does not help with the deadlocks at all.

Simple enough. Unfortunately it rarely happens in the real life. Let’s see it in the small example using the same dbo.Data table like before. Let’s start 2 sessions and update 2 separate rows in the table (acquire X locks).Session 1:

Session 2:

Now let’s run 2 selects.Session 1:

Session 2:

Well, as you can see, it introduces deadlock.

To understand why it happens, let’s take a look at the execution plan of the select statement:

As you can see – there is the table scan. So let’s think what happened

1. First of all, we have 2 X locks on the different rows acquired by both sessions.
2. When session 1 ran select, it introduced table scan. In read committed, repeatable read or serializable isolation levels, readers issue shared S locks, so when session 1 tried to read the row with ID = 40000 (X lock held by session 2) – it was blocked.
3. Same thing happens with session 2 – it’s blocked on the row with ID = 1 (X lock held by session 1).

So this is the classic deadlock even if there are no data updates involved. It worth to mention that read uncommitted transaction isolation level would not introduce deadlock – readers in this isolation level do not acquire S locks. Although you’ll have deadlock in the case if you change select to update even in read uncommitted level. Otimistic isolation levels also behave differently and we will talk about it later.So as you can see, in the real life as other blocking issues deadlocks happen due non-optimized queries.

Next week we will talk how to detect and deal with deadlocks. Source code is available for download

Part 6 – How to troubleshoot deadlocks

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