Time series feature designThe Consensus has dRafted a decision
time to read 2 min | 317 words

So, after reaching the conclusion that replication is going to be hard, I went back to the office and discussed those challenges and was in general pretty annoyed by it. Then Michael made a really interesting suggestion. Why not put it on RAFT?

And once he explained what he meant, I really couldn’t hold my excitement. We now have a major feature for 4.0. But before I get excited about that (we’ll only be able to actually start working on that in a few months, anyway), let us talk about what the actual suggestion was.

Raft is a consensus algorithm. It allows a distributed set of computers to arrive into a mutually agreed upon set of sequential log records. Hm… I wonder where else we can find sequential log records, and yes, I am looking at you Voron.Journal.

The basic idea is that we can take the concept of log shipping, but instead of having a single master/slave relationship, we change things so we can put Raft in the middle. When committing a transaction, we’ll hold off committing the transaction until we have a Raft consensus that it should be committed. The advantage here is that we won’t be constrained any longer by the master/slave issue. If there is a server down, we can still process requests (maybe need to elect a new cluster leader, but that is about it).

That means that from an architectural standpoint, we’ll have the ability to process write requests for any quorum (N/2+1). That is a pretty standard requirement for distributed databases, so that is perfectly fine.

That is a pretty awesome thing to have, to be honest, and more importantly, this is happening at the low level storage layer. That means that we can apply this behavior not just to a single database solution, but to many database solutions.

I’m pretty excited about this.

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More posts in "Time series feature design" series:

  1. (04 Mar 2014) Storage replication & the bee’s knees
  2. (28 Feb 2014) The Consensus has dRafted a decision
  3. (25 Feb 2014) Replication
  4. (20 Feb 2014) Querying over large data sets
  5. (19 Feb 2014) Scale out / high availability
  6. (18 Feb 2014) User interface
  7. (17 Feb 2014) Client API
  8. (14 Feb 2014) System behavior
  9. (13 Feb 2014) The wire format
  10. (12 Feb 2014) Storage