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High Scalability:Denomalizing data for Billions of files for scaling snapshot times

We store metadata about billions of files in Mysql shards and each shard has Folder,File and version table. The schema looked like  this.



Now each file can have n versions and some customers want infinite versions. Problem with scaling snapshot query is that the file system snapshot required information about latest version only. To get latest version you need to join folder, file, version table and discard older rows. I had described the consistent scaling challenge and the improvements we had done to improve snapshot times for our cloud file system in http://neopatel.blogspot.com/2014/02/a-journey-of-scaling-generation-of-file.html.

The latest improvement we did is to denormalize the information about latest entry on to file table itself.
 

Now to generate a snapshot we just need to join Folder and File table. And the improvements are huge. We use box ananometer to log slow queries over all databases and the second query is normalized query and third query is denormalized query. Across all databases we are tracking the time has reduced 3 times  (486 sec to 159 sec), also no of rows examined and rows sent has reduced by half.







Here is the normalized query graph



Here is the denormalized query graph
 
Now denormalizing billions of rows have unique challenges and you need to do it without impacting customers. We spread billions of rows across 28 master and there are 28 slaves for these masters. 

To do performance testing we took the biggest database with customers having 27M rows and imported it in a test environment and migrated it.

27M versions snapshot times before denormalization = 1.2 hours
27M versions snapshot times from denormalized tables = 6.5 minutes constant with or without caching

Off course as we are doubling the data we need to optimize the database tables after denormalization else we were running into row chaining problem.

For production go live as usual we started with feature flags and we added 2 flags latest_entry_migrated and latest_entry_active field on customer model. Then we wrote code that on basis of latest_entry_active flag would execute normalized or denormalized query. Once the code was live in all services then we began migration for few workgroups to test for any bugs. We migrated each data centre every weekend and within a month we had all databases upgraded with denormalized rows and snapshot queries have even gone from slow query logs from many databases.

One sideeffect of this denormalization as this opens up gateway for us to implement infinite versions because now we can sub shard versions on different tables and even in different databases.


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