Currently we are dealing with quite a few deployment processes. For a company that enables DevOps culture, deployment happens many many times a day. Tiny fraction of code change goes to deployment, and as the change size is so small it gets easier to spot a bug and if the bug is crucial maybe it is time to rollback to an older version and to be able to have a database that accepts rollback, yet we have to do it with zero downtime so that the user do not understand a thing. It is often is not as easy as it sounds in principal.
Before describing about few key idea to solve this common problem lets discuss few of our most common deployment architectures.
In a blue/green deployment architecture, it consists of two different version of application running concurrently, one of them can be the production stage and another one can be development platform, but we need to note that both of the version of the app must be able to handle 100% of the requests. We need to configure the proxy to stop forwarding requests to the blue deployment and start forwarding them to the green one in a manner that it works on-the-fly so that no incoming requests will be lost between the changes from blue deployment to green.
Canary Deployment is a deployment architecture where rather than forwarding all the users to a new version, we migrate a small percentage of users or a group of users to new version. Canary Deployment is a little bit complicated to implement, because it would require smart routing Netflix’s OSS Zuul can be a tool that helps. Feature toggles can be done using FF4J and Togglz.
As we can see that most of the deployment processes requires 2 version of the application running at the same time but the problem arises when there is database involved that has migration associated with it because both of the application must be compatible with the same database.So the schema versions between consecutive releases must be mutually compatible.
Now how can we achieve zero downtime on these deployment strategies?
So we can’t do database migrations that are destructive or can potentially cause us to lose data. In this blog we will be discussing how can we approach database migrations:
One of the most common problem that we face during UPDATE TABLE is that it locks up the database. We don’t control the amount of time it will take to ALTER TABLE but most popular DBMSs available in the market, issuing an ALTER TABLE ADD COLUMN statement won’t lead to locking. For example if we want to change the type of field of database field rather than changing the field type we can add a new column.
When adding column we should not be adding a NOT NULL constraint at the very beginning of the migration even if the model requires it because this new added column will only be consumed by the new version of the application where as the new version still doesn’t provide any value for this newly added column and it breaks the INSERT/UPDATE statements from current version. We need to assure that the new version reads values from the old column but writes on both. This is to assure that all new rows will have both columns populated with correct values. Now that new columns are being populated in a new way, it is time to deal with the old data, we need to copy the data from the old column to the new column so that all of your current rows also have both columns populated, but the locking problem arises when we try to UPDATE.
Instead of just issuing a single statement to achieve a single column rename, we’ll need to get used to breaking these big changes into multiple smaller changes. One of the solution could be taking baby steps like this:
ALTER TABLE customers ADD COLUMN correct VARCHAR(20); UPDATE customers SET correct = wrong WHERE id BETWEEN 1 AND 100; UPDATE customers SET correct = wrong WHERE id BETWEEN 101 AND 200; ALTER TABLE customers DELETE COLUMN wrong;
When we are done with old column data population. Finally when we would have enough confidence that we will never need the old version, we can delete a column, as it is a destructive operation the data will be lost and no longer recoverable.
As a precaution, we should delete only after a quarantine period. After quarantined period when we are enough confident that we would no longer need our old version of schema or even a rollback that does require that version of schema then we can stop populating the old column. If you decide to execute this step, make sure to drop any NOT NULL constraint or else you will prevent your code from inserting new rows.