GoshawkDB: A distributed, transactional, fault-tolerant object store

Paxos from the ground-up to offer strong transactional guarantees, similar to CockroachDB: http://www.cockroachlabs.com/blog/ CockroachDB is a KV store (that will be released with a SQL layer on top of it) whereas GoshawkDB is a Graph store. GoshawkDB puts data into client caches (much like Datomic) and even attempts transactions there.

Hi. I'm the author of GoshawkDB. Thanks for your questions.

> It isn't true. There're plenty of storages with same strong consistency model (tolerates F failures of 2F+1 nodes), among them are Cassandra with lightweight transactions, Riak with consistent buckets, CockroachDB and ZooKeeper.

The point I was trying to make is the decoupling between cluster size and F. ZK, for example, requires that the cluster size is exactly 2F+1, not is minimum 2F+1. That means that as the cluster size increases, performance will decrease as more nodes have to be contacted.

> Can you please describe the algorithm that you use for implementing distributed transactions?

Yes. In detail, there will be a blog post forthcoming. However the two main points is that 2PC is replaced by Gray and Lamport's paper "Consensus on transaction Commit", and dependencies between transactions are modelled through vector clocks. As I'm visiting lots of family over xmas, I'm afraid I don't have the 12+ hours necessary to document the entire algorithm just at the moment. However, I will certainly get to this asap.

> Do you use the same approach?

I don't know. I don't use the term round-trip because that doesn't make it clear if we're talking individual messages or message-hops, and these sorts of conversation and discussion can often go wrong if we use different terminology. The 3 network-delays that I mention comes directly from the Gray and Lamport paper I mention above.

Yup, on the whole, in order to increase performance, you don't want more and more nodes to be contacted. Remember that every txn has to have a minimum of F+1 replicas vote on each txn, so if F is really large, although they vote in parallel, you have greater chance of network delay and greater load on all machines. So GoshawkDB allows you to increase cluster size without increasing F, so getting you greater performance.

Hi, I'm the author of GoshawkDB. Thanks for your questions - they're certainly not answered on the website so I'll do my best to answer them here and incorporate them into the website later.

> Curious how this works at scale. For example if a node is down and N requests start blocking, is there an upper bound to N? What happens when this is reached? Does N map to a goroutine? A file descriptor? Is there timeouts?

The blocking actually occurs on a per-client basis: when a client submits a txn to the server to which it's connected, that server will calculate which other server nodes need to be contacted in order for the txn to be voted on. If it cannot find enough servers due to failures then it will outright reject the txn and tell the client that there are currently too many failures going on.

However, the server could find the right set of server nodes and send off the txn to be voted on and send of the txn for verification and validation. Right at that point there could be further failures and so it's not known how far those messages got. It is in this case that the txn could block as until some node recover it's impossible to safely abort the txn. But it's important to note that:

1. If a txn starts blocking, that'll block just that txn and that client (as the client is blocked waiting on the outcome of the txn). No other part of the server or any other server is affected.

2. Other clients can be submitting other txns at the same time that need different un-failed nodes to proceed, and they progress just fine.

3. There is no global determination (yet) of whether a node is failed or not. Thus this approach can cope with really weird network issues - eg random connections between different nodes failing. There is no need for the whole remaining cluster to agree that nodes X, Y and Z are "out" - none of the algorithms depend on that sort of thing.

I hope this answers your questions.

> With your situation #1, what if this is very common transaction and therefore you have 100 of these all waiting. What about 1000, 5000 etc. what system resources are used to let these transactions wait indefinitely ( if I understand your semantics with specific regard to blocking )?

So, as each client can only have 1 outstanding txn at a time, this requires there are 100, 1000, 5000 etc clients all submitting the same txn at the same time, and presumably they're all connected to the same server node, and for each of those txns, the set of server nodes that need to be contacted has been calculated, and the necessary messages sent. At that point failures occur so it's not known who has received which messages, so all these txns block.

The only system resources that are held at this point is RAM. Yes, it could get so bad that you eat up a lot of RAM - this is release 0.1 after all. There are a few areas where I have tickets open to make sure that goshawkdb can spill this sort of state to disk as necessary, though it may not be too horrible just relying on swap to start with.

> Some systems handle this as a failure that is communicated to the client rapidly. Other systems let N clients actually wait indefinitely but at the cost of taking up a thread / file descriptor, etc. in systems that have finite amounts of threads for example this would then be communicated in his paradigm as a such of upper bounds as to how many requests one could have waiting.

The problem is that in this particular case, the txn could have committed, it's just the server who initially received the txn from the client and then forwarded it to the necessary server nodes can't learn that it's committed due to failures. Now certainly I could make the client informed that the outcome is delayed, but the client may not be able to do anything with that information: it can't know for sure if the txn has been committed or aborted.

The entire codebase is written using actors and finite state machines. Because Go supports green threads and go-routines can be pre-emptively suspended, there is no problem with eating OS threads. In general, the design is such that the only thing that should block is the client and on the server the actor/state-machine that is talking to that client.