However, there are also some inefficiencies with InnoDB. For example, although flash storage devices are widely used for OLTP workloads, we are limited to less storage. While our database engineering team worked to make compressed InnoDB performant for read-write workloads, which reduced the size of our UDB tier by approximately 40 percent, we wanted to make further improvements beyond what was possible with InnoDB.
Flash is also limited by write endurance. In the worst case, updating one row requires a number of page reads, makes several pages dirty, and forces many dirty pages to be written back to storage. In addition, InnoDB writes dirty pages to storage twice to support recovery from partial page writes on unplanned machine failure. We also implement online defragmentation , which periodically scans InnoDB leaf pages where indexes are only 50 percent full and rewrites them, which further increases write amplification.
Finally, InnoDB is constrained by a fixed compressed page size. The fragmentation and compression alignment results in extra unused space because the leaf nodes are not full. Assume a table with a compressed page size of 8 KB. In short, InnoDB is great for performance and reliability, but there are some inefficiencies on space and write amplification with flash.
To help optimize for more storage efficiency, we decided to investigate an alternative space- and write-optimized database technology. The MyRocks instance size was 3. MyRocks is heavily optimized for space efficiency; fragmentation, compression size alignment, prefix encoding, and metadata space optimization were some of the factors between the space differences.
This graph shows how many bytes were relatively written from OS metrics iostat and flash device metrics depending on storage devices.
This does not include binary logs and relay logs. MyRocks wrote orders of magnitude less than InnoDB. Flash-written bytes were larger than iostat bytes written, because flash internally does garbage collection and needs extra writes, which is not visible from operating systems. The write amplification difference also affects flash storage. Lower write amplification allows you to use more affordable flash, or to use the same flash for a longer period of time.
Sign in. Forgot your password? Get help. Privacy Policy. Password recovery. Home Explanatory. Hive inside Facebook is used to convert SQL queries to a sequence of map-reduce jobs that are then executed on Hadoop. The DB has a rich set of capabilities enabling data engineers, scientists, business analysts process Tera to Petabytes of data. Facebook uses PrestoDB to process data via a massive batch pipeline workload in their Hive warehouse.
The project has also been adopted by other big guns such as Netflix, Walmart, Comcast etc. The client sends SQL query to the Presto co-ordinator. The data is then pulled back by the client at the output stage for results. The entire system is written in Java for speed. Also, it makes it really easy to integrate with the rest of the data infrastructure as they too are written in Java. Facebook uses the storage engine to store system measurements such as product stats like how many messages are sent per minute, the service stats, for instance, the rate of queries hitting the cache vs the MySQL database.
Ideally in the industry Grafana is used to create custom dashboards for running analytics. It is intelligent enough to handle failures ranging from a single node to entire regions with little to no operational overhead. The below figure shows how gorilla fits in the analytics infrastructure. Since deployment Gorilla has almost doubled in size twice in the month period without much operational effort which shows the system is pretty scalable.
No system can run without logs. And a system of the size of Facebook where so many components are plugged in together generates a crazy amount of logs. In comparison to a file system which stores data as files, LogDevice stores data as logs.
The kind of workloads supported by LogDevice are event logging, stream processing, ML training pipelines, transaction logging, replicated state machines etc. Folks, this is pretty much it. If you liked the article, share it with your folks.
You can follow scaleyourapp. I am Shivang, you can read about me here! What Database Does Twitter Use? What is Google Cloud Storage? Explained in Simple Words. Learn to build scalable distributed systems from Educative. Username or Email Address. Remember Me. About Me. Trending News. They also have a lot of binlog consumers.
Binlog is a replication mechanism of MySql that independent of a storage type. HPHP is a frontend application, cluster and region caches are instances of memcache that use leases instead of locks to avoid race conditions. Cluster cache keeps very hot information that can have 10 — million accesses per second and these data are cached for each region.
Region cache keeps less accessed data and they keep only single copy of the data. TAO is graph service that is based on memcache server but understands the facebook graph. It allows to optimize the performance of the cache. It also has read after write consistency that means that if you update something, you must always see the result of this update however other users may not see the most recent update. Wormhole is publisher-subscriber system. MySql publishes binlog changes to this system so other applications can consume the data.
Hadoop and Hive are used for aggregation and reporting. LogDevice is a distributed data store for logs based on RocksDb.
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