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Category: Transactions

MSDTC and Availability Groups

Ryan Adams provides guidance on using distributed transactions against Availability Groups:

A paramount concept to understand is how to make the DTC highly available.  We can see from the precedence order that SQL Server will use the local DTC out of the box.  This makes it appear that everything is working, and it is, but it is not exactly highly available.

I see a lot of customers leave it configured this way because they either don’t know the ramifications or do not realize they are using the MSDTC (Linked Servers). Since it simply works out of the box, things get left this way until they end up with a suspect database and error messages that look like this:

“SQL Server detected a DTC/KTM in-doubt transaction with UOW  {598B7EDD-F7A1-9DC1-8D3E-303A4C93AAB4}.Please resolve it following the guideline for Troubleshooting DTC Transactions.”

Read the whole thing. There are a lot of small areas between processes where things can fail, and the combination of DTC + AGs is no different.

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Dealing with a Massive Transaction Log File

Kevin Hill takes us through troubleshooting an oversized transaction log file:

I had a customer call in this week after struggling to resolve an issue for a couple of days,  Their Transaction Log file had grown to 400+GB and was filling the drive to the point that had to keep adding space to the drive to keep operations online.  This was for a core internal system.  There are no staff DBAs here, but plenty of very sharp sysadmins, developers, network folks, etc.

Note: I have written about the most common cause of this issue here, but this was not the resolution in this case. The embedded video is far and away number one in hits of all my SQL videos, showing just how common log file growth issues are.

I like the way Kevin does this. He frames the story, takes you through his actions, and gives you a chance to understand his troubleshooting process. Most problems become an order of magnitude easier to solve if you have a reasoned-through process (and enough practice to follow when three levels of management are staring at your screen during a crisis).

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Distributed Transactions on Linux

Tejas Shah and crew announce distributed transactions with SQL Server on Linux:

With SQL Server 2017, a new era was heralded with SQL server being available to deploy on Linux (and Linux based container) systems. While all functionality of the SQL Server engine were brought over as is to SQL Server on Linux, some of the functionality which depended on Windows system processes such as distributed transactions (which relies on MSDTC service) were not brought over immediately.

Well, now your wait is over.

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When the Transaction Begins

Josh Darnell points out that transactions don’t really begin with BEGIN TRAN:

Of course, no one reads the documentation. And even if they do, they certainly don’t continue on to the 4th paragraph of the “General Remarks” section. That’s like going to the second page of Google search results.

If one did trudge on through the docs, they would find this gem:

Although BEGIN TRANSACTION starts a local transaction, it is not recorded in the transaction log until the application subsequently performs an action that must be recorded in the log, such as executing an INSERT, UPDATE, or DELETE statement. An application can perform actions such as acquiring locks to protect the transaction isolation level of SELECT statements, but nothing is recorded in the log until the application performs a modification action.

Well that’s interesting! Let’s check it out.

Four paragraphs? Where’s the tl;dr, Microsoft Docs?

Read the whole thing even though Josh just made a joke about nobody reading the whole thing and I followed along with it.

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SQL Server Isolation Levels

Pamela M. takes us through a humorous journey covering transaction isolation levels in SQL Server:

READ COMMITTED:  One step up (and the default for SQL Server).    A query in the current transaction can’t read data modified by someone else that hasn’t yet committed.  No dirty reads.  BUT….data could be changed by others between statements in the current transaction, so the data may not look the same twice.  READ COMMITTED uses shared locks to prevent dirty reads, but that’s about all you get.  You still get non-repeatable reads and phantom reads here (more on phantom reads below).

Click through for the full list.

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New(ish) VLF Status: 4

Paul Randal points out a new VLF status which can appear if you’re using an Availability Group:

At least since I started working on the SQL Server team (just after 7.0 shipped) and since then there have only been two VLF status codes:

  • 0 = the VLF is not active (i.e. it can be (re)activated and overwritten)
  • (1 = not used and no-one seems to remember what it used to mean)
  • 2 = the VLF is active because at least one log record in it is ‘required’ by SQL Server for some reason (e.g. hasn’t been backed up by a log backup or scanned by replication)

A few weeks ago I learned about a new VLF status code that was added back in SQL Server 2012 but hasn’t come to light until recently (at least I’ve never encountered it in the wild). I went back-and-forth with a friend from Microsoft (Sean Gallardy, a PFE and MCM down in Tampa) who was able to dig around in the code to figure out when it’s used.

Read on to uncover the mysteries of the VLF status of 4.

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The Problems With NOLOCK

Rob Farley demonstrates the downside of the READ UNCOMMITTED isolation level:

I’m going to create a table and insert exactly 1 million rows. This particular table will be a clustered index, and will contain 1 million GUIDs.

Next I prove that there a million rows.

Now without inserting or deleting any rows, I’m going to shuffle them.

And if while this is happening, I count the rows in a different session, I have to wait for that query to finish.

Read on to see what happens when someone gets the idea of running the select query with NOLOCK.

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Understanding Transactions In SQL Server

Jeanne Combrinck explains the purpose and nature of transactions in SQL Server:

Distributed Transactions:
You can use Distributed transactions which specifies the start of a SQL distributed transaction managed by Microsoft Distributed Transaction Coordinator (MS DTC). This is only applicable to SQL Server and no Azure. Transaction-level snapshot isolation does not support distributed transactions.

Arguments:
transaction_name
Is a user-defined transaction name used to track the distributed transaction within MS DTC utilities. transaction_name must conform to the rules for identifiers and must be <= 32 characters.

@tran_name_variable
Is the name of a user-defined variable containing a transaction name used to track the distributed transaction within MS DTC utilities. The variable must be declared with a char, varchar, nchar, or nvarchar data type.

Jeanne also includes the important comment that nested transactions don’t really work the way you’d expect them to.  I probably could have ended that last sentence early with, “don’t really work.”

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Benefits Of Explicit Transactions

Kendra Little talks about explicit transactions and when they’re useful for single-statement operations:

If you do not enable implicit transactions, and you don’t start an explicit transaction, you are in the default “autocommit” mode.

This mode means that individual statements are automatically committed or rolled back as whole units. You can’t end up in a place where only half your statement is committed.

Our question is really about whether there are unseen problems with this default mode of autocommit for single-statement units of work.

By force of habit, I wrap data modification operations in an explicit transaction.  They let me test my changes before committing and the time you’re most likely to spot an error seems to be right after hitting F5.

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Phantom Reads

Arun Sirpal sees not-quite-there-yet transactions:

With Halloween around the corner what better topic to discuss than phantom reads. A phantom read occurs when rows have been inserted after a read operation and becomes visible in a follow-up read operation within the same transaction.

I will show you what this looks like with an example. Please note the code will not work for you because I dropped the foreign key constraint within the person.person table just so I could show you the example.

Arun mentions that the serializable isolation level prevents phantom reads.  So do the repeatable read and snapshot isolation levels.

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