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The high level design of the Machi “chain manager” has moved to the Machi chain manager high level design document.
We try to discuss the network partition simulator that the algorithm runs in and how the algorithm behaves in both symmetric and asymmetric network partition scenarios. The symmetric partition cases are all working well (surprising in a good way), and the asymmetric partition cases are working well (in a damn mystifying kind of way). It’d be really, really great to get more review of the algorithm and the simulator.
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Much of the text previously appearing in this document has moved to the Machi chain manager high level design document.
The definitive text for this section has moved to the Machi chain manager high level design document.
Due to the ranking algorithm’s preference for author node names that are large (lexicographically), nodes with larger node names should execute the algorithm more frequently than other nodes. The reason for this is to try to avoid churn: a proposal by a “small” node may propose a UPI list of L at epoch 10, and a few moments later a “big” node may propose the same UPI list L at epoch 11. In this case, there would be two chain state transitions: the epoch 11 projection would be ranked higher than epoch 10’s projection. If the “big” node executed more frequently than the “small” node, then it’s more likely that epoch 10 would be written by the “big” node, which would then cause the “small” node to stop at state A40 and avoid any externally-visible action.
Assume a chain of three nodes, A, B, and C. In a projection at epoch E. For all nodes, the P_current projection at epoch E is:
UPI=[A,B,C], Repairing=[], Down=[]
Now assume that C crashes during epoch E. The failure detector running locally at both A & B eventually notice C’s death. The new information triggers a new iteration of the self-management algorithm. A calculates its P_newprop (call it P_newprop_a) and writes it to its own public projection store. Meanwhile, B does the same and wins the race to write P_newprop_b to its own public projection store.
At this instant in time, the public projection stores of each node looks something like this:
| Epoch | Node A | Node B | Node C |
|---|---|---|---|
| E | UPI=[A,B,C] | UPI=[A,B,C] | UPI=[A,B,C] |
| Repairing=[] | Repairing=[] | Repairing=[] | |
| Down=[] | Down=[] | Down=[] | |
| Author=A | Author=A | Author=A | |
| E+1 | UPI=[A,B] | UPI=[A,B] | C is dead, |
| Repairing=[] | Repairing=[] | unwritten | |
| Down=[C] | Down=[C] | ||
| Author=A | Author=B |
If we use the CORFU-style projection naming convention, where a projection’s name is exactly equal to the epoch number, then all participants cannot tell the difference between the projection at epoch E+1 authored by node A from the projection at epoch E+1 authored by node B: the names are the same, i.e., E+1.
Machi must extend the original CORFU protocols by changing the name of the projection. In Machi’s case, the projection is named by this 2-tuple:
{epoch #, hash of the entire projection (minus hash field itself)}
This name is used in all relevant APIs where the name is required to make a wedge state transition. In the case of the example & table above, all of the UPI & Repairing & Down lists are equal. However, A & B’s unanimity is due to the symmetric nature of C’s partition: C is dead. In the case of an asymmetric partition of C, it is indeed possible for A’s version of epoch E+1’s UPI list to be different from B’s UPI list in the same epoch E+1.
Building on the first example, let’s assume that A & B have reconciled their proposals for epoch E+2. Nodes A & B are running under a unanimous proposal at E+2.
| E+2 | UPI=[A,B] | UPI=[A,B] | C is dead, |
| Repairing=[] | Repairing=[] | unwritten | |
| Down=[C] | Down=[C] | ||
| Author=A | Author=A |
|---|
Now assume that C restarts. It was dead for a little while, and its code is slightly buggy. Node C decides to make a proposal without first consulting its failure detector: let’s assume that C believes that only C is alive. Also, C knows that epoch E was the last epoch valid before it crashed, so it decides that it will write its new proposal at E+2. The result is a set of public projection stores that look like this:
| E+2 | UPI=[A,B] | UPI=[A,B] | UPI=[C] |
| Repairing=[] | Repairing=[] | Repairing=[] | |
| Down=[C] | Down=[C] | Down=[A,B] | |
| Author=A | Author=A | Author=C |
|---|
Now we’re in a pickle where a client C could read the latest projection from node C and get a different view of the world than if it had read the latest projection from nodes A or B.
If running in AP mode, this wouldn’t be a big problem: a write to node C only (or a write to nodes A & B only) would be reconciled eventually. Also, eventually, one of the nodes would realize that C was no longer partitioned and would make a new proposal at epoch E+3.
If running in CP mode, then any client that attempted to use C’s version of the E+2 projection would fail: the UPI list does not contain a quorum majority of nodes. (Other discussion of CP mode’s use of quorum majority for UPI members is out of scope of this document. Also out of scope is the use of “witness servers” to augment the quorum majority UPI scheme.)
The function machi_chain_manager1_test:convergence_demo_test() executes the following in a simulated network environment within a single Erlang VM:
Test the convergence behavior of the chain self-management algorithm for Machi.
- Set up 4 FLUs and chain manager pairs.
- Create a number of different network partition scenarios, where (simulated) partitions may be symmetric or asymmetric. (At the Seattle 2015 meet-up, I called this the “shaking the snow globe” phase, where asymmetric network partitions are simulated and are calculated at random differently for each simulated node. During this time, the simulated network is wildly unstable.)
- Then halt changing the partitions and keep the simulated network stable. The simulated may remain broken (i.e. at least one asymmetric partition remains in effect), but at least it’s stable.
- Run a number of iterations of the algorithm in parallel by poking each of the manager processes on a random’ish basis to simulate the passage of time.
- Afterward, fetch the chain transition histories made by each FLU and verify that no transition was ever unsafe.
The simulator has yet to find an error. This is both really cool and really terrifying: is this really working? No, seriously, where are the bugs? Good question. Both the algorithm and the simulator need review and futher study.
In fact, it’d be awesome if I could work with someone who has more TLA+ experience than I do to work on a formal specification of the self-management algorithm and verify its correctness.
Text has moved to the Machi chain manager high level design document.
I’ve finished moving the chain manager plus the inner/nested projection code into the top-level ‘src’ dir of this repo. The idea is working very well under simulation, more than well enough to gamble on for initial use.
Stronger validation work will continue through 2015, ideally using a tool like TLA+.
I’ve come to realize that the property that causes the nice property of “Were my last 2L proposals identical?” also requires that the proposals be stable. If a participant notices, “Hey, there’s flapping happening, so I’ll propose a different projection P_different”, then the very act of proposing P_different disrupts the “last 2L proposals identical” cycle the enables us to detect flapping. We kill the goose that’s laying our golden egg.
I’ve been working on the idea of “nested” projections, namely an “outer” and “inner” projection. Only the “outer projection” is used for cycle detection. The “inner projection” is the same as the outer projection when flapping is not detected. When flapping is detected, then the inner projection is one that excludes all nodes that the outer projection has identified as victims of asymmetric partition.
This inner projection technique may or may not work well enough to use? It would require constant flapping of the outer proposal, which is going to consume CPU and also chew up projection store keys with the flapping churn. That churn would continue as long as an asymmetric partition exists. The simplest way to cope with this would be to reduce proposal rates significantly, say 10x or 50x slower, to slow churn down to proposals from several-per-second to perhaps several-per-minute?