4 ms·

We don't currently have a comparison with SeaweedFS. For the record, we have been developping Garage for almost two years now, and we hadn't heard of SeaweedFS

by lxpz 5y ago

We don't currently have a comparison with SeaweedFS. For the record, we have been developping Garage for almost two years now, and we hadn't heard of SeaweedFS at the time we started. To me, the two key differentiators of Garage over its competitors are as follows: - Garage contains an evolved metadata system that is based on CRDTs and consistent hashing inspired by Dynamo, solidly grounded in distributed system's theory. This allows us to be very efficient as we don't use Raft or other consensus algorithms between nodes, and we also do not rely on an external service for metadata storage (Postgres, Cassandra, whatever) meaning we don't pay an additionnal communication penalty. - Garage was designed from the start to be multi-datacenter aware, again helped by insights from distributed system's theory. In practice we explicitly chose against implementing erasure coding, instead we spread three full copies of data over different zones so that overall availability is maintained with no degradation in performance when one full zone goes down, and data locality is preserved at all locations for faster access (in the case of a system with three zones, our ideal deployment scenario).

atombender 5y ago
Can you go into a bit more detail about how using CRDTs avoids the need for consensus-based replication?
- lxpz 5y ago
  Sure! CRDTs are often presented as a way of building asynchronous replication system with eventual consistency. This means that when modifying a CRDT object, you just update your local copy, and then synchronize in background with other nodes. However this is not the only way of using CRDTs. At there core, CRDTs are a way to resolve conflicts between different versions of an object without coordination: when all of the different states of the object that exist in the network become known to a node, it applies a local procedure known as a merge that produces a deterministic outcome: all nodes do this, and once they have all done it, they are all in the same state. In that way, nodes do not need to coordinate before-hand when doing modifictations, in the sense where they do not need to run a two-phase commit protocol that ensures that operations are applied one after the other in a specific order that is replicated identically at all nodes. (This is the problem of consensus which is theoretically much harder to solve from a distributed system's theory perspective, as well as from an implementation perspective). In Garage, we have a bit of a special way of using CRDTs. To simplify a bit, each file stored in Garage is a CRDT that is replicated on three known nodes (deterministically decided). While these three nodes could synchronize in the background when an update is made, this would mean two things that we don't want: 1/ when a write is made, it would be written only on one node, so if that node crashes before it had a chance to synchronize with other nodes, data would be lost; 2/ reading from a node wouldn't necessarily ensure that you have the last version of the data, therefore the system is not read-after-write consistent. To fix this, we add a simple synchronization system based on read/write quorums to our CRDT system. More precisely, when updating a CRDT, we wait for the value to be known to at least two of the three responsible nodes before returning OK, which allows us to tolerate one node failure while always ensuring durability of stored data. Further when performing a read, we ask for their current state of the CRDT to at least two of the three nodes: this ensures that at least one of the two will know about the last version that was written (due to the intersection of the quorums being non-empty), making the system read-after-write consistent. These are basically the same principles that are applied in CRDT databases such as Riak.
  - eternalban 5y ago
    I'll also admit to having difficulty understanding how is all this distinct from non-CRDT replication mechanisms. Great mission and work by DeuxFluers team btw. Bonne chance! Edit: Blogs on Riak's use of CRDTs: http://christophermeiklejohn.com/erlang/lasp/2019/03/08/monotonicity.html http://christophermeiklejohn.com/erlang/lasp/2019/03/08/mono... https://web.archive.org/web/20170801114916/http://docs.basho.com/riak/kv/2.2.3/learn/concepts/crdts/ https://web.archive.org/web/20170801114916/http://docs.basho... https://naveennegi.medium.com/rendezvous-with-riak-crdts-part-1-e94cfc8fe091 https://naveennegi.medium.com/rendezvous-with-riak-crdts-par...)
    - lxpz 5y ago
      Thanks! I'll give a bit more details about CRDTs, then. The thing is, if you don't have CRDT, the only way to replicate things over nodes in such a way that they end up in consistent states, is to have a way of ordering operations so that all nodes apply them in the same order, which is costly. Let me give you a small example. Suppose we have a very simple key-value storage, and that two clients are writing different values at the same time on the same key. The first one will invoke write(k, v1), and the second one will invoke write(k, v2), where v1 and v2 are different values. If all nodes receive the two write operation but don't have a way to know which one came first, some will receive v1 before v2, and end up with value v2 as the last written values, and other nodes will receive v2 before v1 meaning the will keep v1 as the definitive value. The system is now in an inconsistent state. There are several ways to avoid this. The first one is Raft consensus: all write operations will go through a specific node of the system, the leader, which is responsible for putting them in order and informing everyone of the order it selected for the operations. This adds a cost of talking to the leader at each operation, as well as a cost of simply selecting which node is the leader node. CRDT are another way to ensure that we have a consistent result after applying the two writes, not by having a leader that puts everything in a certain order, but by embedding certain metadata with the write operation itself, which is enough to disambiguate between the two writes in a consistent fashion. In our example, now, the node that does write(k, v1) will for instance generate a timestamp ts1 that corresponded to the (approximate) time at which v1 was written, and it will also generate a UUID id1. Similarly, the nodes that does write(k, v2) will generate ts2 and id2. Now when they send their new values to other nodes in the network, they will send along their values of ts1, id1, ts2 and id2. Nodes now know enough to always deterministcally select either v1 or v2, consistently everywhere: if ts1 > ts2 or ts1 = ts2 and id1 > id2, then v1 is selected, otherwise v2 is selected (we suppose that id1 = id2 has a negligible probability of happening). In terms of message round-trips between nodes, we see that with this new method, nodes simply communicate once with all other nodes in the network, which is much faster than having to pass through a leader. Here the example uses timestamps as a way to disambiguate between v1 and v2, but CRDTs are more general and other ways of handling concurrent updates can be devised. The core property is that concurrent operations are combined a-posteriori using a deterministic rule once they reach the storage nodes, and not a-priori by putting them in a certain order.

by lxpz 5y ago

atombender 5y ago

Can you go into a bit more detail about how using CRDTs avoids the need for consensus-based replication?

lxpz 5y ago
Sure! CRDTs are often presented as a way of building asynchronous replication system with eventual consistency. This means that when modifying a CRDT object, you just update your local copy, and then synchronize in background with other nodes. However this is not the only way of using CRDTs. At there core, CRDTs are a way to resolve conflicts between different versions of an object without coordination: when all of the different states of the object that exist in the network become known to a node, it applies a local procedure known as a merge that produces a deterministic outcome: all nodes do this, and once they have all done it, they are all in the same state. In that way, nodes do not need to coordinate before-hand when doing modifictations, in the sense where they do not need to run a two-phase commit protocol that ensures that operations are applied one after the other in a specific order that is replicated identically at all nodes. (This is the problem of consensus which is theoretically much harder to solve from a distributed system's theory perspective, as well as from an implementation perspective). In Garage, we have a bit of a special way of using CRDTs. To simplify a bit, each file stored in Garage is a CRDT that is replicated on three known nodes (deterministically decided). While these three nodes could synchronize in the background when an update is made, this would mean two things that we don't want: 1/ when a write is made, it would be written only on one node, so if that node crashes before it had a chance to synchronize with other nodes, data would be lost; 2/ reading from a node wouldn't necessarily ensure that you have the last version of the data, therefore the system is not read-after-write consistent. To fix this, we add a simple synchronization system based on read/write quorums to our CRDT system. More precisely, when updating a CRDT, we wait for the value to be known to at least two of the three responsible nodes before returning OK, which allows us to tolerate one node failure while always ensuring durability of stored data. Further when performing a read, we ask for their current state of the CRDT to at least two of the three nodes: this ensures that at least one of the two will know about the last version that was written (due to the intersection of the quorums being non-empty), making the system read-after-write consistent. These are basically the same principles that are applied in CRDT databases such as Riak.
- eternalban 5y ago
  I'll also admit to having difficulty understanding how is all this distinct from non-CRDT replication mechanisms. Great mission and work by DeuxFluers team btw. Bonne chance! Edit: Blogs on Riak's use of CRDTs: http://christophermeiklejohn.com/erlang/lasp/2019/03/08/monotonicity.html http://christophermeiklejohn.com/erlang/lasp/2019/03/08/mono... https://web.archive.org/web/20170801114916/http://docs.basho.com/riak/kv/2.2.3/learn/concepts/crdts/ https://web.archive.org/web/20170801114916/http://docs.basho... https://naveennegi.medium.com/rendezvous-with-riak-crdts-part-1-e94cfc8fe091 https://naveennegi.medium.com/rendezvous-with-riak-crdts-par...)
  - lxpz 5y ago
    Thanks! I'll give a bit more details about CRDTs, then. The thing is, if you don't have CRDT, the only way to replicate things over nodes in such a way that they end up in consistent states, is to have a way of ordering operations so that all nodes apply them in the same order, which is costly. Let me give you a small example. Suppose we have a very simple key-value storage, and that two clients are writing different values at the same time on the same key. The first one will invoke write(k, v1), and the second one will invoke write(k, v2), where v1 and v2 are different values. If all nodes receive the two write operation but don't have a way to know which one came first, some will receive v1 before v2, and end up with value v2 as the last written values, and other nodes will receive v2 before v1 meaning the will keep v1 as the definitive value. The system is now in an inconsistent state. There are several ways to avoid this. The first one is Raft consensus: all write operations will go through a specific node of the system, the leader, which is responsible for putting them in order and informing everyone of the order it selected for the operations. This adds a cost of talking to the leader at each operation, as well as a cost of simply selecting which node is the leader node. CRDT are another way to ensure that we have a consistent result after applying the two writes, not by having a leader that puts everything in a certain order, but by embedding certain metadata with the write operation itself, which is enough to disambiguate between the two writes in a consistent fashion. In our example, now, the node that does write(k, v1) will for instance generate a timestamp ts1 that corresponded to the (approximate) time at which v1 was written, and it will also generate a UUID id1. Similarly, the nodes that does write(k, v2) will generate ts2 and id2. Now when they send their new values to other nodes in the network, they will send along their values of ts1, id1, ts2 and id2. Nodes now know enough to always deterministcally select either v1 or v2, consistently everywhere: if ts1 > ts2 or ts1 = ts2 and id1 > id2, then v1 is selected, otherwise v2 is selected (we suppose that id1 = id2 has a negligible probability of happening). In terms of message round-trips between nodes, we see that with this new method, nodes simply communicate once with all other nodes in the network, which is much faster than having to pass through a leader. Here the example uses timestamps as a way to disambiguate between v1 and v2, but CRDTs are more general and other ways of handling concurrent updates can be devised. The core property is that concurrent operations are combined a-posteriori using a deterministic rule once they reach the storage nodes, and not a-priori by putting them in a certain order.