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A couple of years ago, we decide to migrate our customer's data from one data center to another, this is the story of how it goes well.

The product was a Facebook canvas and mobile game with 200M users, that represent approximately 500Gibi of data to move stored in MySQL and Redis. The source was stored in Dallas, and the target was New York.

Because downtime is responsible for preventing users to spend their money on our "free" game, we decide to avoid it as much as possible.

In our MySQL main table (manually sharded 100 tables) , we had a modification TIMESTAMP column. We decide to use it to check if a user needs to be copied on the new database. The rest of the data consist of a savegame stored as gzipped JSON in a LONGBLOB column.

A program in Go has been developed to continuously track if a user's data needs to be copied again everytime progress has been made on its savegame. The process goes like this: First the JSON was unzipped to detect bot users with no progress that we simply drop, then data was exported in a custom binary file with fast compressed data to reduce the size of the file. Next, the exported file was copied using rsync to the new servers, and a second Go program do the import on the new MySQL instances.

The 1st loop takes 1 week to copy; the 2nd takes 1 day; a couple of hours for the 3rd, and so on. At the end, copying the latest versions of all the savegame takes roughly a couple of minutes.

On the Redis side, some data were cache that we knew can be dropped without impacting the user's experience. Others were big bunch of data and we simply SCAN each Redis instances and produces the same kind of custom binary files. The process was fast enough to launch it once during migration. It takes 15 minutes because we were able to parallelise across the 22 instances.

It takes 6 months of meticulous preparation. The D day, the process goes smoothly, but we shutdowns our service for one long hour because of a typo on a domain name.

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