2020-7-30

TAO's architecture

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TAO is a multi-regional system that has a two-layered cache layer and a storage layer. The Storage layer uses a MySQL cluster. Before TAO was introduced social graph data was apparently stored in MySQL in Facebook, which makes MySQL a natural choice for the storage layer.

In the MySQL servers, there is both a table for objects and associations and both are stored as records. For both objects and associations, a single table stores all the same type of data as a principle. Key-value data is stored in a data column. The association tables have (id1, atype, time) as an index for fast response times. Also, they store the number of associations in a different table to avoid using COUNT queries.

Because the data that TAO deals with is very large, object and association data are distributed in shards and thus are divided amongst various servers throughout a MySQL cluster. Usually, the number of shards is much larger than the number of servers, it's possible to respond to server load adjusting the division of shards. Because the shard id is buried within the object id , as long as one sees the id one knows what shard that object is in. Associations come in the triplet (id1, atype, id2), and are stored in the same shard as the object of id1, in most cases a single server will respond to an association query.

TAO's cache layer is a two-layer system. It is divided into a leader and followers. The leader is a cache server that directly communicates with the storage layer and forms a tier within a region. Within a tier, the shards it is in charge of are known and it can deal with object and association requests for the entire tier.

Followers are cache servers that are directly connected to clients. They form multiple tiers. Followers are linked to their own leaders and forward cache misses and write requests to that leader. Because of the two layers of the cache layer, they can contain the number of servers within a tier and by increasing the tier number and the cache server count, they can even deal with severe workloads. Because very big tiers are likely to become hot spots, it is very important to keep the number of servers down within a tier.

TAO is an eventually consistent distributed system. When the leader is written to, the followers associated with it are asynchronously notified and the cache is updated. But, to the original follower that executed the write request, the cache is synchronously updated along with the leader's response. Because of this, read-after-write consistency occurs. Since the asynchronous cache update notifications are assigned version numbers, if an old update arrives, it will simply be ignored.

Since a write for a specific id will always arrive at the same leader, the leader's writes are serializable. And because of that, it protects MySQL from the thundering herd effect. A single shard will have the minimum number of queries sent to it.

TAO is set up to be multi-regional and is thus globally distributed. Every region has a leader tier, multiple follower tiers, and a MySQL cluster as discussed earlier. Of the many regions, a single region is the master region and the rest are slave regions. Write requests are sent from the slave leader to the master leader, which then forwards them to the master DB cluster. If the write succeeds, the response is synchronously transmitted to and updates the cache of the slave leader and slave follower. In addition, the slave DB cluster is replicated asynchronously and the caches of the other cache servers are sent notifications. From the perspective of the slave region, because writes require crossing regions, the latency is quite high. On the other hand, because reads are completed within a single region, the latency is low. In Facebook's workload, read requests happen 500 times more often than write requests. Even considering only cache misses, there is a 25x fewer reads than writes. Because of this, they decided on this multi-region architecture.

Facebook chose to have every region possess a copy of the same social graph. In other words, they chose not to distribute the social graph based on regions. They explain the reasoning for this as follows: The internals of the social graph are tightly bound together so distributing it would be quite hard.


Any errata or comments can be made by sending me a pull request.

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