Achieving No Downtime Through Versioned Service Updates
By: Buck Hodges
With on-premises software like Team Foundation Server (TFS), it often means taking the server offline for updates and upgrades. Azure DevOps and TFS share the same code-base, so operating like TFS would mean downtime. Downtime is a complete nonstarter for global 24×7 service. Azure DevOps is a critical dependency for our customers. They count on it in order to ship their own software. There’s never a good time for everybody. So how did we decide to handle upgrades for Azure DevOps?
Update Layers Separately
We have to be able to upgrade online. With a distributed online service in multiple datacenters and separate data storage, not everything can change simultaneously. If you broadly split the Azure DevOps Service into application code and databases, which are versioned independently of each other, one of those needs to absorb the complexity of handling versioning.
We chose to handle it in the application code. Azure DevOps and TFS have a lot of SQL in the databases. Lots of IF statements are possible with SQL, but never fun. So instead of further complicating our SQL, we handle this complexity in our application code. Specifically, we created a set of factory classes that understand the SQL versioning.
Every sprint we create a new interface with that version. That way there’s always code that matches each database version. This allows for easy rollback of binaries during deployment. The first thing we do is deploy the binaries. If we deploy the new binaries and something goes wrong, we can very quickly roll back binaries. After we deploy the binaries, we kick off the database servicing.
So how does this actually work? Let’s say we’re currently deploying Sprint 123. The binaries understand Sprint 123 database schema and they understand Sprint 122 schema. The general pattern is to work with both versions (sprints) N and N-1 of the SQL Schema. The binaries query the database, determine what schema version they are talking to, and then load the appropriate binding. Then, the application code handles the case when the new data schema is not yet available. Once the new version is available, the application code can start making use of the new functionality that is enabled by the latest database version.
Roll Forward Only with Data Tier
Once we upgrade the databases, we’re in a roll-forward situation if we encounter a problem. Online database migrations are pretty complex and often multi-step – therefore rolling forward is usually the best way to address a problem. To look at it another way, if you can’t get upgrade right, what leads you to believe you could get rollback right as well? On top of that, you’d end up testing code (rollback) that you hope never to use and would almost always be wasted work.
Now let’s look at how we sequence the database changes while remaining online. For example, consider a case where we want to add a set of columns into the database and maybe transform the data. We have to do this invisibly to the user, which means avoiding table locks as much as possible and then holding locks for the shortest time possible so that it is not perceptible to the end user.
The first thing we do is to manipulate the data, possibly in parallel tables using a SQL trigger to keep the data in sync. This also means that large data migrations and transformations sometimes have to be multi-step over several deployments across multiple sprints.
Once we’ve created the extra data or new schema in parallel, we go into what we call “deployment mode” for the application code. In deployment mode, when the code makes a call to the database, it first grabs a lock on the schema and then releases it after running the stored procedure to keep the database from changing between the time the call to the database is issued and when the stored procedure runs.
The upgrade code acts as a schema writer and requests a writer lock on the schema. The application code takes priority in taking a reader lock, and the upgrade code sits in the background trying to acquire the writer the lock. Under the writer lock, only a small number of very fast operations are allowed on the tables. Then the lock is released and the application records the new version of the database is in use and uses the interfaces that match the new database version.
The database upgrades are all performed using a migration pattern – we have a set of code and scripts that look at the version of the database and then make the incremental changes to migrate the schema between from the old to the new version. All migrations are automated and rolled out via the Release Management service in Azure DevOps.
|Buck Hodges is Director of Engineering for Azure DevOps. He's been a member of the team since the beginning of TFS, starting as a developer on Team Foundation Version Control for the first version of TFS. He's helped lead the transition of the team to the cloud and DevOps.|