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This is the first in a series of labs in which you'll build a fault-tolerant key/value storage system. In this lab you'll implement Raft, a replicated state machine protocol. In the next lab you'll build a key/value service on top of Raft. Then you will “shard” your service over multiple replicated state machines for higher performance.
A replicated service achieves fault tolerance by storing complete copies of its state (i.e., data) on multiple replica servers. Replication allows the service to continue operating even if some of its servers experience failures (crashes or a broken or flaky network). The challenge is that failures may cause the replicas to hold differing copies of the data.
Raft organizes client requests into a sequence, called the log, and ensures that all the replica servers see the same log. Each replica executes client requests in log order, applying them to its local copy of the service's state. Since all the live replicas see the same log contents, they all execute the same requests in the same order, and thus continue to have identical service state. If a server fails but later recovers, Raft takes care of bringing its log up to date. Raft will continue to operate as long as at least a majority of the servers are alive and can talk to each other. If there is no such majority, Raft will make no progress, but will pick up where it left off as soon as a majority can communicate again.
In this lab you'll implement Raft as a Go object type with associated methods, meant to be used as a module in a larger service. A set of Raft instances talk to each other with RPC to maintain replicated logs. Your Raft interface will support an indefinite sequence of numbered commands, also called log entries. The entries are numbered with index numbers. The log entry with a given index will eventually be committed. At that point, your Raft should send the log entry to the larger service for it to execute.
You should follow the design in the extended Raft paper, with particular attention to Figure 2. You'll implement most of what's in the paper, including saving persistent state and reading it after a node fails and then restarts. You will not implement cluster membership changes (Section 6).
You may find this guide useful, as well as this advice about locking and structure for concurrency. For a wider perspective, have a look at Paxos, Chubby, Paxos Made Live, Spanner, Zookeeper, Harp, Viewstamped Replication, and Bolosky et al. (Note: the student's guide was written several years ago, and part 2D in particular has since changed. Make sure you understand why a particular implementation strategy makes sense before blindly following it!)
Keep in mind that the most challenging part of this lab may not be implementing your solution, but debugging it. To help address this challenge, you may wish to spend time thinking about how to make your implementation more easily debuggable. You might refer to the Guidance page and to this blog post about effective print statements.
We also provide a diagram of Raft interactions that can help clarify how your Raft code interacts with the layers on top of it.
This lab is due in four parts. You must submit each part on the corresponding due date.
If you have done Lab 1, you already have a copy of the lab source code. If not, you can find directions for obtaining the source via git in the Lab 1 instructions.
We supply you with skeleton code src/raft/raft.go. We also supply a set of tests, which you should use to drive your implementation efforts, and which we'll use to grade your submitted lab. The tests are in src/raft/test_test.go.
When we grade your submissions, we will run the tests without the -race flag. However, you should check that your code does not have races, by running the tests with the -race flag as you develop your solution.
To get up and running, execute the following commands. Don't forget the git pull to get the latest software.
$ cd ~/6.5840
$ git pull
...
$ cd src/raft
$ go test
Test (2A): initial election ...
--- FAIL: TestInitialElection2A (5.04s)
config.go:326: expected one leader, got none
Test (2A): election after network failure ...
--- FAIL: TestReElection2A (5.03s)
config.go:326: expected one leader, got none
...
$
Your implementation must support the following interface, which the tester and (eventually) your key/value server will use. You'll find more details in comments in raft.go.
// create a new Raft server instance:
rf := Make(peers, me, persister, applyCh)
// start agreement on a new log entry:
rf.Start(command interface{}) (index, term, isleader)
// ask a Raft for its current term, and whether it thinks it is leader
rf.GetState() (term, isLeader)
// each time a new entry is committed to the log, each Raft peer
// should send an ApplyMsg to the service (or tester).
type ApplyMsg
A service calls Make(peers,me,…) to create a Raft peer. The peers argument is an array of network identifiers of the Raft peers (including this one), for use with RPC. The me argument is the index of this peer in the peers array. Start(command) asks Raft to start the processing to append the command to the replicated log. Start() should return immediately, without waiting for the log appends to complete. The service expects your implementation to send an ApplyMsg for each newly committed log entry to the applyCh channel argument to Make().
raft.go contains example code that sends an RPC (sendRequestVote()) and that handles an incoming RPC (RequestVote()). Your Raft peers should exchange RPCs using the labrpc Go package (source in src/labrpc). The tester can tell labrpc to delay RPCs, re-order them, and discard them to simulate various network failures. While you can temporarily modify labrpc, make sure your Raft works with the original labrpc, since that's what we'll use to test and grade your lab. Your Raft instances must interact only with RPC; for example, they are not allowed to communicate using shared Go variables or files.
Subsequent labs build on this lab, so it is important to give yourself enough time to write solid code.
Implement Raft leader election and heartbeats (AppendEntries RPCs with no log entries). The goal for Part 2A is for a single leader to be elected, for the leader to remain the leader if there are no failures, and for a new leader to take over if the old leader fails or if packets to/from the old leader are lost. Run go test -run 2A to test your 2A code.
Be sure you pass the 2A tests before submitting Part 2A, so that you see something like this:
$ go test -run 2A Test (2A): initial election ... ... Passed -- 3.5 3 58 16840 0 Test (2A): election after network failure ... ... Passed -- 5.4 3 118 25269 0 Test (2A): multiple elections ... ... Passed -- 7.3 7 624 138014 0 PASS ok 6.5840/raft 16.265s $
Each "Passed" line contains five numbers; these are the time that the test took in seconds, the number of Raft peers, the number of RPCs sent during the test, the total number of bytes in the RPC messages, and the number of log entries that Raft reports were committed. Your numbers will differ from those shown here. You can ignore the numbers if you like, but they may help you sanity-check the number of RPCs that your implementation sends. For all of labs 2, 3, and 4, the grading script will fail your solution if it takes more than 600 seconds for all of the tests (go test), or if any individual test takes more than 120 seconds.
When we grade your submissions, we will run the tests without the -race flag. However, you should make sure that your code consistently passes the tests with the -race flag.
Implement the leader and follower code to append new log entries, so that the go test -run 2B tests pass.
The tests for upcoming labs may fail your code if it runs too slowly. You can check how much real time and CPU time your solution uses with the time command. Here's typical output:
$ time go test -run 2B Test (2B): basic agreement ... ... Passed -- 0.9 3 16 4572 3 Test (2B): RPC byte count ... ... Passed -- 1.7 3 48 114536 11 Test (2B): agreement after follower reconnects ... ... Passed -- 3.6 3 78 22131 7 Test (2B): no agreement if too many followers disconnect ... ... Passed -- 3.8 5 172 40935 3 Test (2B): concurrent Start()s ... ... Passed -- 1.1 3 24 7379 6 Test (2B): rejoin of partitioned leader ... ... Passed -- 5.1 3 152 37021 4 Test (2B): leader backs up quickly over incorrect follower logs ... ... Passed -- 17.2 5 2080 1587388 102 Test (2B): RPC counts aren't too high ... ... Passed -- 2.2 3 60 20119 12 PASS ok 6.5840/raft 35.557s real 0m35.899s user 0m2.556s sys 0m1.458s $The "ok 6.5840/raft 35.557s" means that Go measured the time taken for the 2B tests to be 35.557 seconds of real (wall-clock) time. The "user 0m2.556s" means that the code consumed 2.556 seconds of CPU time, or time spent actually executing instructions (rather than waiting or sleeping). If your solution uses much more than a minute of real time for the 2B tests, or much more than 5 seconds of CPU time, you may run into trouble later on. Look for time spent sleeping or waiting for RPC timeouts, loops that run without sleeping or waiting for conditions or channel messages, or large numbers of RPCs sent.
If a Raft-based server reboots it should resume service where it left off. This requires that Raft keep persistent state that survives a reboot. The paper's Figure 2 mentions which state should be persistent.
A real implementation would write Raft's persistent state to disk each time it changed, and would read the state from disk when restarting after a reboot. Your implementation won't use the disk; instead, it will save and restore persistent state from a Persister object (see persister.go). Whoever calls Raft.Make() supplies a Persister that initially holds Raft's most recently persisted state (if any). Raft should initialize its state from that Persister, and should use it to save its persistent state each time the state changes. Use the Persister's ReadRaftState() and Save() methods.
Complete the functions persist() and readPersist() in raft.go by adding code to save and restore persistent state. You will need to encode (or "serialize") the state as an array of bytes in order to pass it to the Persister. Use the labgob encoder; see the comments in persist() and readPersist(). labgob is like Go's gob encoder but prints error messages if you try to encode structures with lower-case field names. For now, pass nil as the second argument to persister.Save(). Insert calls to persist() at the points where your implementation changes persistent state. Once you've done this, and if the rest of your implementation is correct, you should pass all of the 2C tests.
You will probably need the optimization that backs up nextIndex by more than one entry at a time. Look at the extended Raft paper starting at the bottom of page 7 and top of page 8 (marked by a gray line). The paper is vague about the details; you will need to fill in the gaps. One possibility is to have a rejection message include:
XTerm: term in the conflicting entry (if any)
XIndex: index of first entry with that term (if any)
XLen: log length
Then the leader's logic can be something like:
Case 1: leader doesn't have XTerm:
nextIndex = XIndex
Case 2: leader has XTerm:
nextIndex = leader's last entry for XTerm
Case 3: follower's log is too short:
nextIndex = XLen
A few other hints:
Your code should pass all the 2C tests (as shown below), as well as the 2A and 2B tests.
$ go test -run 2C Test (2C): basic persistence ... ... Passed -- 5.0 3 86 22849 6 Test (2C): more persistence ... ... Passed -- 17.6 5 952 218854 16 Test (2C): partitioned leader and one follower crash, leader restarts ... ... Passed -- 2.0 3 34 8937 4 Test (2C): Figure 8 ... ... Passed -- 31.2 5 580 130675 32 Test (2C): unreliable agreement ... ... Passed -- 1.7 5 1044 366392 246 Test (2C): Figure 8 (unreliable) ... ... Passed -- 33.6 5 10700 33695245 308 Test (2C): churn ... ... Passed -- 16.1 5 8864 44771259 1544 Test (2C): unreliable churn ... ... Passed -- 16.5 5 4220 6414632 906 PASS ok 6.5840/raft 123.564s $
It is a good idea to run the tests multiple times before submitting and check that each run prints PASS.
$ for i in {0..10}; do go test; done
As things stand now, a rebooting server replays the complete Raft log in order to restore its state. However, it's not practical for a long-running service to remember the complete Raft log forever. Instead, you'll modify Raft to cooperate with services that persistently store a "snapshot" of their state from time to time, at which point Raft discards log entries that precede the snapshot. The result is a smaller amount of persistent data and faster restart. However, it's now possible for a follower to fall so far behind that the leader has discarded the log entries it needs to catch up; the leader must then send a snapshot plus the log starting at the time of the snapshot. Section 7 of the extended Raft paper outlines the scheme; you will have to design the details.
You may find it helpful to refer to the diagram of Raft interactions to understand how the replicated service and Raft communicate.
Your Raft must provide the following function that the service can call with a serialized snapshot of its state:
Snapshot(index int, snapshot []byte)
In Lab 2D, the tester calls Snapshot() periodically. In Lab 3, you will write a key/value server that calls Snapshot(); the snapshot will contain the complete table of key/value pairs. The service layer calls Snapshot() on every peer (not just on the leader).
The index argument indicates the highest log entry that's reflected in the snapshot. Raft should discard its log entries before that point. You'll need to revise your Raft code to operate while storing only the tail of the log.
You'll need to implement the InstallSnapshot RPC discussed in the paper that allows a Raft leader to tell a lagging Raft peer to replace its state with a snapshot. You will likely need to think through how InstallSnapshot should interact with the state and rules in Figure 2.
When a follower's Raft code receives an InstallSnapshot RPC, it can use the applyCh to send the snapshot to the service in an ApplyMsg. The ApplyMsg struct definition already contains the fields you will need (and which the tester expects). Take care that these snapshots only advance the service's state, and don't cause it to move backwards.
If a server crashes, it must restart from persisted data. Your Raft should persist both Raft state and the corresponding snapshot. Use the second argument to persister.Save() to save the snapshot. If there's no snapshot, pass nil as the second argument.
When a server restarts, the application layer reads the persisted snapshot and restores its saved state.
Implement Snapshot() and the InstallSnapshot RPC, as well as the changes to Raft to support these (e.g, operation with a trimmed log). Your solution is complete when it passes the 2D tests (and all the previous Lab 2 tests).
Your code should pass all the 2D tests (as shown below), as well as the 2A, 2B, and 2C tests.
$ go test -run 2D Test (2D): snapshots basic ... ... Passed -- 11.6 3 176 61716 192 Test (2D): install snapshots (disconnect) ... ... Passed -- 64.2 3 878 320610 336 Test (2D): install snapshots (disconnect+unreliable) ... ... Passed -- 81.1 3 1059 375850 341 Test (2D): install snapshots (crash) ... ... Passed -- 53.5 3 601 256638 339 Test (2D): install snapshots (unreliable+crash) ... ... Passed -- 63.5 3 687 288294 336 Test (2D): crash and restart all servers ... ... Passed -- 19.5 3 268 81352 58 PASS ok 6.5840/raft 293.456s