Database migration on a production database is never simple. Depending on the volume of requests some teams schedule database migration to off hours. You can run your alter statements with zero or minimal downtime following the steps mentioned below with a practical example.
As an example for this post, let’s assume you work for an E-commerce company which has both recurring and new customers. You have partners who publicize a discount code valid for a given date range and get a percent of the order’s total tracked by their discount code. Below is the schema for the partner and coupon code. Below is the initial schema before any change:
Now the new requirement is to track different commission percent for the new and recurring customer. New and recurring customers are evaluated by a different system/microservice and it is also responsible for commission calculation based on order total which is out of the scope of this focused example.
To do commission tracking by customer type we would decide to add 2 new columns commission_percent_new_customer, commission_percent_recurring_customer, the difficult question is how to roll it out without downtime.
The following migration script will update the DB schema to be in the above state for MYSQL DB:
ALTER TABLE `partner_discount_code` ADD `commission_percent_new_customer` DECIMAL NULL AFTER `commission_percent`, ADD `commission_percent_recurring_customer` DECIMAL NULL AFTER `commission_percent_new_customer` ,algorithm=inplace,lock=none;
Notice the algorithm=inplace,lock=none it is discussed below.
In the above example there can be 2 types of downtime as follows:
Tables locked while migration runs
Downtime because of newly introduced columns and code not matching to it
For the first downtime issue depending on the database, it can be mitigated to a significant level with executing proper alter SQL statements.
For example in MYSQL if, algorithm=inplace, lock=none is suffixed with your alter it will run with 0 to minimum table lock allowing reads and writes while the migration runs.
This is especially important when altering tables with millions of rows as the alter can take minutes depending on the structure and data volume of the tables involved.
For the second issue, if the DB alter and code release is deployed in a specific sequence it can be handled much better. The deployment steps are listed below:
Add the two new columns to the partner_discount_code, let’s say the code is in v 1.1 now
Deploy code v 1.2 which adds and edits all 3 columns — commission_percent, commission_percent_new_customer and commission_percent_recurring_customer.
Test that all the things work as expected, even if you need to roll back nothing breaks and it’s fully backward compatible
When everything is fine, deploy code v 1.3 that adds or edits only on the 2 new columns
Test it for a day or two, then as per need you can drop the commission_percent column on the partner_discount_code table as it’s not used anymore
Always be careful with database migration. It’s is surely safe to take a backup of the table you will run the alter statement on before executive it.
Don’t deploy the code first that write to new columns then run the migration, it will result in errors as the code will try to access non-existing column(s).
Always think of backward compatibility usually without a revert migration. Generally, access to the production database is only given to a select few.
Run drop or rename columns only after you are fully satisfied that the new changes are not breaking anything.
It is better to run migrations (alter SQL) manually than part of the deployment to keep things segregated and more predictable.
Database migration is not difficult if it is done the right way. Hope this post helps you run your DB migrations in a smoother fashion.
Originally published at geshan.com.np.