Category: Business Intelligence

In the part 1 of this series, I discussed the positioning of Change Data Capture. In part 2, I want to cover how it works.

Log Reading

There are many ways that you can output details of changes that occur in data within SQL Server. Many of those methods require actions to occur at the time the data change is made. This can be problematic.

The first problem with this, is the performance impact on the application that's making the change. If I update a row in a table and there is part of the process that writes details of that change to some type of audit or tracking log, I've now increased the work that needs to happen in the context of the application that's making the change. Generally what this means, is that I've slowed the application down by at least doubling the work that needs to be performed. That might not be well-received.

The second potential problem is even nastier. What if the change tracking part of the work fails even though the original update works? If I've done the change tracking work in the context of the original update (particularly if it's done as part of an atomic process), by adding tracking I might have broken the original application. That certainly wouldn't be well-received.

So what to do?

The best answer seems to be to work with the transaction log that's already built into SQL Server. By default, it does have details of the changes that have been occurring to the data. It can be read asynchronously so delays in reading it mostly won't affect the original data changes at all (there are only rare exceptions to this). If the reading of the logs failed, the problem can be corrected and the reading can be restarted, all again without affecting the main updates that are occurring.

And that's what Change Data Capture does. It uses the same log reader agent that has been part of SQL Server for a very long time. Previously though, it was used for Transactional Replication. In fact if you use both Transactional Replication and Change Data Capture on the same SQL Server system, they share the same instance of the log reader. The SQL Server Agent is used to make them both work.

SQL Server Agent – Isn't that missing?

When we're working with Azure SQL Database, things are a bit different. Currently, we don't have any concept of Transactional Replication. That could change but right now, it's not there. So sharing the log reader isn't an issue.

But I also mentioned that with SQL Server, it was the SQL Server Agent that kicks off the log reading agent. And with Azure SQL Database, we don't have SQL Server Agent either !

The Azure SQL Database team have instead provided a scheduler that runs the log reader (called the capture), and also runs the required clean-up tasks. SQL Server had another agent to perform clean-up. This is all automated and requires no maintenance from the user.

Change Data Capture (CDC) Data Flow

The data flow with CDC is basically like the following:

1. The original app sends a change (insert, update, delete) to a table in the Azure SQL Database.
2. The change is recorded in the transaction log.
3. Some time later (usually not long though), the change is read by the capture process and stored in a change table.
4. The Data Warehouse (DW) or target system or ETL system makes a call to a set of CDC functions to retrieve the changes.

Everything in the dotted box above is part of, and contained within, the Azure SQL Database.

Upcoming

In the next section, I'll show you the code that's required, and show you the changes that occur to the Azure SQL Database when you enable CDC.

1. Why use Change Data Capture for Azure SQL Database?
2. How Change Data Capture works in Azure SQL Database
3. Enabling and using Change Data Capture in Azure SQL Database
4. Change Data Capture and Azure SQL Database Service Level Objectives
5. Accessing Change Data Capture Data from Another Azure SQL Database

 

I often need to capture the changes from one database into another. The most common reason is that I'm wanting to bring changes from a transactional system across into a data warehouse that's part of a BI setup.

So which technology is best to use for this?

That's not a trivial question to answer but here are some thoughts:

Replication?

Unfortunately, this one's not available for Azure SQL DB as yet. Azure SQL DB can be a subscriber in Transactional Replication. We often use it this way. If we have an on-premises SQL Server, one of our favourite ways to get data into the cloud is by using Transactional Replication. (If you need to get your head around Replication with SQL Server, just head to our course here).

There are many advantages to replication, including the lack of impact on the source system, however Azure SQL DB can't currently be a publisher, so it doesn't help here.

And other forms of replication aren't really useful here, or an available option. So if the source DB is an Azure SQL DB, we need to find something else.

Azure SQL Data Sync

Azure SQL Data Sync is an odd technology. It basically grew out of Merge Replication based ideas. It's not built on Merge Replication, but it's very similar in concept. It was in a preview state so long, and the team had so long since stopped posting information about it, that most of us never thought it would ever reach GA.

You create a setup similar to this:

The sync metadata lives in a DB in Azure, and a copy of the DB that you want to sync is created as an Azure SQL DB. The Azure Data Sync engine then synchronizes the data between the HUB and the other DBs. If any of the DBs are on-premises, then an on-premises agent does the work.

Azure Data Sync (like Merge Replication) is trigger-based. Triggers are used to capture the changes ready for synchronization.

I wasn't a fan of Merge, and I can't say I'm a great fan of Azure SQL Data Sync. While it's conceptually simple, you would not want to use it for anything except very low volume applications.

Change Tracking

Change Tracking is another technology that's come directly from SQL Server land. When it's enabled, a set of change tracking tables are created. As data is changed in the tables of interest, changes are recorded in the change tracking tables.

One positive aspect of Change Tracking is that it isn't based on triggers and it outperforms trigger-based solutions. There are two downsides:

• The changes are written synchronously, and in the context of the transaction that writes the change to the tracked table. This can impact the performance of the changes to the tracked table i.e. usually two writes are happening for each one that would have happened.
• You don't get to see all the changes, and not in the order that they happened. Change Tracking lets you know which rows have changed, based upon the table's primary key. You can also ask to have a summary of which columns were changed). This can be a challenge for dealing with referential integrity, and other issues.

Queues (and Service Broker)

Another interesting option is to write to a queue. With an on-premises SQL Server, we can use Service Broker. If you haven't seen Service Broker, it's a transacted queue that lives inside the database. (To learn about this, look here).

With SQL CLR code or with External Activation for Service Broker, we could write to other types of queue like RabbitMQ.

At the current time, Azure SQL Database doesn't currently support writing to external queues. However, I do expect to see this change, as so many people have voted to have this capability added.

Change Data Capture

Change Data Capture (CDC) is another technology direct from SQL Server land. CDC is based on reading changes from a database's transaction log.

When you use it with SQL Server, it shares the same transaction log reader that Transactional Replication (TR) does. If you enable either CDC or TR, a log reader is started. If you have both enabled, they use a single log reader.

A key upside of using a log reader is that it doesn't slow down the initial updates to the target table. The changes are read asynchronously, separately.

Until recently, though, you could not use CDC with Azure SQL Database. The log reader agent ran from within SQL Server Agent, and with Azure SQL Database, you didn't have a SQL Server Agent.

The product team have recently done the work to make CDC work with Azure SQL Database.  It is an interesting option for extracting changes from a database, so this is the first blog post in a series of posts about using CDC with Azure SQL Database. Links to other posts will be added here as they are available:

1. Why use Change Data Capture for Azure SQL Database?
2. How Change Data Capture works in Azure SQL Database
3. Enabling and using Change Data Capture in Azure SQL Database
4. Change Data Capture and Azure SQL Database Service Level Objectives
5. Accessing Change Data Capture Data from Another Azure SQL Database