Reading Group. Strong and Efficient Consistency with Consistency-Aware Durability

In the 62nd reading group session, we covered the “Strong and Efficient Consistency with Consistency-Aware Durability” paper from FAST’20. Jesse did an excellent presentation for the group that explains the core of the paper rather well:

This paper describes a problem with many leader-based replication protocols. It specifically focuses on ZooKeper and Zab, but similar issues arise in Multi-Paxos, and Raft systems as well. Linearizable reads are expensive, as they need to go through a lease-protected leader (or carry out a full consensus round in consensus-based replication if leases are to be avoided). This puts a single leader node in the middle of everything, which is bad for throughput. 

But what if we allow reading from followers? Systems like ZooKeeper allow such reads, but these have a few problems. For one, followers may be stale, so such reads do not have the same recency guarantees as the linearizable solutions. Secondly, reading from followers violates monotonic reads if the client is to jump between the followers it reads from without having a special session/recency token. The third problem, the one the paper is specifically solving, is that there is no monotonicity between clients — when one client reads a value of version n, nothing prevents another client at a later time to read version n-1 from a different node. 

So, the goal of the paper is to allow reading from followers while sacrificing as little consistency as possible. The system introduced in the paper, called ORCA, solves two consistency-related problems. First, it ensures that local followers return to clients only durable data that cannot be lost in the face of failures. Second, ORCA enforces some recency guarantees in the form of cross-client monotonic reads.

To achieve both goals, the system introduces a globally replicated durable-index. The purpose of this index is to let the nodes know up to which point in the log the data has been durably (here durability means persistence to some non-volatile storage) replicated to some quorum of nodes (traditionally, this would be a majority quorum). This durability marker allows the followers to avoid returning a value that can be lost due to a crash. For example, consider some object k with the initial value k1 and a corresponding durable-index=1. Another value k2 is being replicated in log position 2. If a local node returns k2 before it is durable, there is a chance that k2 was not replicated and persisted at the majority of nodes yet, and a minority partition or failure can erase k2. To prevent this, the leader maintains a durable-index that gets updated once the quorum of replicas acknowledged the persistence of data to storage. In the example, once the k2 is durable and followers acked, the leader updates its durable-index:=2, and sends it to the followers. A follower can only return a value k2 if it has received a durable-index>=2 (i.e the index that has moved to or past the value k2). Naturally, there are plenty of situations when the follower may be aware of k2 but has not received the durable-index=2 update just yet. In this case, the follower cannot return the data just yet. It cannot return k2, since it is not durable yet, but it also cannot return k1, as some other follower may have already returned k2 to a client. One solution would be to wait out and see whether the durable-index eventually catches up. A more proactive approach would forward the read to the leader, and let the leader synchronously replicate k2 (and tell followers to flush it to disk) before returning the value to the client. The paper favors the proactive approach, as it acts somewhat as “read-repair” applicable to a variety of leader-based replication schemes. 

The paper presents this durable-index as a big contribution, although I am a bit skeptical about this. See, existing systems have been doing the whole durable-index business for a very long time. I will focus on just one example, however — Raft. In Raft, the leader must communicate the leader’s commitIndex with the followers after a quorum has been reached so that all nodes can mark all log items or updates up to commitIndex as committed. If we assume that Raft persists operations to disk at each node upon receiving them, then the leader’s commitIndex in the “commit” message (i.e next AppendEntry RPC) is the same as durable-index from this paper. A similar global commit progress marker can be implemented in Paxos-style systems despite a few challenges with out-of-order commits in Multi-Paxos. 

For paper’s defense, it makes the durable-index a bit more general than Raft’s approach. For example, the paper allows for more relaxed primary backup implementations, asynchronous replication, and implementations with delayed disk persistence. Consider adding durable-index to an asynchronous leader-driven system to allow durable reads. The system can check whether the value to be returned is durable, allowing it to do synchronous “read-repair” only when needed, and preserving the benefits of async replication as often as possible.

Ok, so we have a durable-index that marks the global progress of the system, and the leader distributes the durable-index to all nodes. This is enough to ensure the data read once won’t ever get lost due to a failure, but this is still not enough to ensure monotonicity. If one server in the cluster of 3 is slow, it may not have received some updates and the new durable-index as 2 other faster nodes have successfully applied some more operations. This can lead to the slow node returning older data than the other two nodes may have already returned. Similar can happen when one replica is network partitioned. For a single client, this monotonicity problem can be solved with some sort of session token to make recency of data observed by the client, but there is no elegant solution for the cross-client case. 

The naive solution to the monotonicity problem, which Jesse describes in great detail in the presentation, expands the durable-index requirement to all nodes. In this case, a follower can only return a value when its state matches the durable state agreed upon by all nodes, so there can be no slow or partitioned replicas. For instance, we may have 3 nodes, all having a persisted history of k1, k2, k3. Since all nodes have the value k3, then it is clearly durable. A leader node can then send the durable-index=3 to signal the rest of the cluster that a 3rd item (i.e. k3) is durable. Let’s say one follower f1 has received this durable-index. We can allow this replica to return k3 locally. At the same time, other follower f2 may not have received the durable-index update just yet, so it is aware of k3 but does not think it is durable. The node f2 won’t return k2 or k3 and instead will forward the read to the leader, which will ensure that k3 is durable and no other operation is in progress before returning k3.

Requiring durability/replication to all nodes has been explored before. If I continue to draw parallels with Raft and take into account the flexible quorums result, then configuring Raft with a replication phase requiring a quorum of all nodes will achieve this durable-index of all nodes approach. Needless to say that Raft will provide linearizability and not just monotonic reads (as long as followers block in case of a dirty read until they receive updated commitIndex from the leader). There is an obvious problem with the quorum of all nodes crippling the availability when one node goes down, however, there are also examples of production systems that make this work to some degree.

Anyway, ORCA does not require durability on all nodes due to availability issues. Instead, it introduces the “active-set,” a set of nodes that must achieve durability for the durable-index to progress. Reads are possible against the replicas in the active-set, while reads against nodes outside of the active-set are prohibited (forwarded to leader?). The active-set must be at least the majority of nodes for fault tolerance reasons. The lease protects the active-set to allow failed or partitioned nodes to fall out of the set safely. This idea is largely described in Paxos Quorum Leases paper, which, again, provides linearizability. 

Discussion

1) Linearizable Alternatives

Flexible Quorums in Write All Read One configuration. I already touched on this point in the summary, but I also raised it in our group’s discussion. One thing that is different between just running Raft with write quorum of all nodes and ORCA is that ORCA can do “fast writes” since it is not doing linearizability. These fast writes are just uncommitted writes when clients receive a write acknowledgment before an operation has been quorum-committed. Some systems, like MongoDB, also have them, and they seem to cause more confusion and problems than benefits. So, we do not think this is really a good feature to have in a system with stronger consistency guarantees. In fact, this consistency relaxation seems to be the only big difference between Flexible-Paxos (Raft) approach and the naive ZooKeeper-based ORCA. 

Paxos Quorum Leases. Paxos Quorum Leases (PQL) paper was brought up during the discussion too. At the high level, PQL is similar to lease-protected active-sets of ORCA. However, we were surprised to see a complete lack of comparison between ORCA’s active-set approach and PQL. 

Paxos Quorum Read. The problem of scaling read throughput in strongly consistent systems has been explored by me before. Paxos Quorum Read (PQR) approach allows client-driven reads from a quorum of followers. This is not as scalable as reading from just one node, but PQR avoids any leases and sticks to linearizability. 

2) Safety of durable-index

Lease safety. As with all systems that rely on leases, proper timeouts are essential for safety. Lots of care must be put into establishing proper timeouts. For example, it is essential to have partitioned nodes in active-set to timeout and “kill themselves” before the leader has a chance to timeout and pick a new active-set. If a leader creates a new active-set before the bad node in the old one dies, we may have a situation with two nodes returning different data. Timeouts also need properly working clocks. If one clock ticks too quickly or too slowly, this may also be a problem. 

Replication safety. An interesting point was brought up regarding the safety of durable-index. Since the durable-index update may get delivered faster to some nodes and slower (or be dropped altogether) to others, this creates a situation when a client can read from a more up-to-date node and receive some newer data, let’s say value k3. A client may then contact a different node that has not received the new durable-index and still thinks k2 is durable. This node cannot return k2as it would be a violation of monotonic reads! The node must wait for k3 to become durable, which in ORCA means that the read gets forwarded to the leader. So in other words, if a node knows of some non-durable update to an object, it is prettymuch blocked, and needs to forward to the leader to avoid safety problems. This leads to the next discussion point.

3) Performance cost. The safety of the protocol relies on fixing the durability while doing a read operation. So every time a read is done against the node that knows of some newer update that is not durable yet, the protocol forwards to the leader and potentially re-replicates. Even the forwarding part alone can be costly. It will cost not only extra latency but also the resources needed to support these multi-hop operations. 

ORCA is banking on the fact that we have a system with multiple keys/objects and that the access patterns are such that it is unlikely for us to both have an outstanding non-durable update and a read operation for the same key at the same time. This is generally an ok assumption, and many systems do that. The problem is that the opposite case is a complete disaster for the system. Having just a handful of “hot keys” will cause frequent forwards to the leader and all the extra work and extra latency. At the very best this will create bad latency, but because this creates a lot more work for the system, it actually lowers the maximum throughput achievable in the system. Can this translate to a metastable failure?

4) Practicality of cross-client monotonic reads. The final discussion topic I want to mention is the practicality of such cross-client monotonic read consistency. The paper goes into a few examples. However, monotonic reads are a far cry from more stringent consistency models. ORCA does not explicitly enforce read-your-write property, which, intuitively, is desirable in a system claiming strong consistency. For instance, Facebook goes out of its way to provide read-your-write consistency across its stack of weakly consistent systems to make development easier and prevent user confusion. 

As a last remark, I want to add that consistency properties with the paper’s approach will depend on the underlying leader-based protocol and implementation. As I mentioned a few times, Raft already has durable-index backed in, and solutions like PQL can provide something similar to the active-set described here for a completely linearizable system. However, the durable-index and active-set approach can be applied to weaker-consistency leader-based replication, and in such cases, this can improve consistency, hopefully at a low-enough cost. 

Reading Group

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