Flexible Paxos: Reaching agreement without majorities
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Flexible Paxos introduces a generalization of the Paxos algorithm that allows for reaching consensus without requiring majority quorums. The key insight is that only the intersection between replication quorums and leader election quorums needs to be considered, rather than all quorums intersecting. This opens up many new quorum schemes that can provide more scalable and available consensus. Majority quorums are not necessarily optimal and different quorum designs may better suit practical systems with heterogeneous resources. Ongoing work aims to adopt these ideas into production and further expand the algorithms to new failure models.
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Flexible Paxos: Reaching agreement without majorities
- 1. Flexible Paxos: Reaching agreement without majorities Heidi Howard, Dahlia Malkhi, Alexander Spiegelman University of Cambridge, VMware Research, Technion heidi.howard@cl.cam.ac.uk @heidiann360 hh360.user.srcf.net fpaxos.github.io 1
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- 4. TL;DR Majorities are not necessary to safety reach distributed consensus. 4
- 5. Aims 1. Understand the widely adopted Paxos algorithm and how Flexible Paxos safely generalizes it 2. Introduce a broad spectrum of possibilities for reaching consensus and 3. Promote the idea that Paxos is not the best point on this spectrum for most practical systems 5
- 6. Defining Consensus Reaching agreement in an asynchronous distributed system in the face of crash failures. More specifically: • Compromising safety is never an option • No clock synchronization is assumed • Participants fail by crashing and messages may be lost but neither can act arbitrarily 6
- 7. Consensus is not a solved problem • Consensus is not scalable - often limited to 3, 5 or 7 participants. • Consensus is slow - each decision requires at least majority agreement, more when failures occur. • Consensus is not available - cannot handle majority failures or many network partitions. You pay a high price even if no failures occur. Consider by many as “best avoided” 7
- 8. Back in the good old days … 8
- 16. Distributed systems came to the rescue 16
- 22. How many copies is enough? We refer to any group of nodes which is ‘enough’ as a replication quorum More copies, More resilient Fewer copies, Faster replication 22
- 23. Now what happens when a server fails? 23
- 28. What happens when the leader fails? 28
- 31. Recall that we never compromise safety Thus the new leader has two jobs: 1. To stop the old leader. We cannot say for sure that they have failed. 2. To learn about which log entries have already been agreed and not overwrite them. The order is important here! 31
- 32. How to stop past leaders? They may come back to haunt us. • Ask them to stop • Use leases and wait for them to expire • Ignore them by having nodes promise to stop replicating for them 32
- 33. How many promises are enough? Leaders cannot wait forever. We need a minimum of one node from each replication quorum to promise not to replicate new entries. We refer to this as the leader election quorum. 33
- 34. Promises are tricky to safely break Let’s assume each leadership term has a unique term number. Problem: We don’t always know who the past leaders are. We need a deterministic scheme for breaking promises. Solution: Each node stores the promise as “ignore all smaller terms”. 34
- 35. Learn all committed entries The new leader must never overwrite committed entries. It must learn about the committed entries before it starts adding new entries without leaving holes. 35
- 36. Safely handle in-progress commands Any in-progress entries already replicated on the leader election quorum nodes must be finished by the new leader. If there are conflicting in-progress entries from different leaders, we choose the log entry with the highest view. 36
- 37. Context This mechanism of views and promises is widely utilised. This core idea is commonly referred to as Paxos. It’s a recurring foundation in the literature: Viewstamped Replication [PODC’88], Paxos [TOCS’98], Zab/Zookeeper [ATC’10] and Raft [ATC’14] and many more. 37
- 38. What’s changed? Traditionally, it was thought that all quorums needed to intersect, regardless of whether the quorum was used for replication or leader election. Majorities are the widely adopted approach to this. We have just seen that the only requirement is that leader election quorums intersect with replication quorums. 38
- 39. Implications In theory: • Helps us to understand Paxos • Generalisation & weakening of the requirements • Orthogonal to any particular algorithm built upon Paxos In practice: • Many new quorum schemes now become feasible • Introduces a new breed of scalable, resilient consensus algorithms. 39
- 41. Majorities Replication quorum Leader election quorum 41
- 42. Majorities - 1 Replication quorum Leader election quorum 42
- 43. Counting Replication quorum + Leader election quorum = Number of nodes + 1 More copies, More resilient Fewer copies, Faster replication 43
- 44. Replication quorum = 2/6 Replication quorum Leader election quorum 44
- 45. Replication quorum = 3/8 Replication quorum Leader election quorum 45
- 46. Not all members were created equal • Machines and the networks which connect them are heterogeneous • Failures are not independent: members are more likely to simultaneously fail if they are located in the same host, rack or even datacenter 46
- 47. Groups Replication quorum Leader election quorum 47
- 48. Grids Replication quorum Leader election quorum 48
- 49. Any many more… Weighed Voting 2 2 3 3 1 1 Grid Paths Combined Schemes Hierarchy 49
- 50. Majorities are no longer ‘one size fits all’ Majorities are unlikely to be optimal for most practical systems • Requirements of real systems differ. Performance really matters. • Leader election is rare so often we optimize for the common replication phase. 50
- 51. Summary • The only quorum intersection requirement is that leader election quorums must intersect with the replication quorums. • Majorities are not the only option for safely reaching consensus and are often not best suited to practical systems. • Don’t give up on strong consistency guarantees. Distributed consensus can be performant and scalable. 51
- 52. Ongoing Work • Adoption into existing production systems • Building new algorithms offering interesting compromises • Practical analysis of quorum systems for consensus • Extending to other algorithms and different failures models e.g. Flexible Fast Paxos and Flexible Byzantine Paxos 52
- 53. Question? Let’s continue the discussion: Heidi Howard heidi.howard@cl.cam.ac.uk @heidiann360 53