6.824 Lab 2: Raft

Introduction

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!)

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.

Getting Started

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.

To get up and running, execute the following commands. Don't forget the git pull to get the latest software.

$ cd ~/6.824
$ git pull
...
$ cd src/raft
$ go test -race
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
...
$

The code

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.

Part 2A: leader election

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 -race to test your 2A code.

• You can't easily run your Raft implementation directly; instead you should run it by way of the tester, i.e. go test -run 2A -race.
• Follow the paper's Figure 2. At this point you care about sending and receiving RequestVote RPCs, the Rules for Servers that relate to elections, and the State related to leader election,
• Add the Figure 2 state for leader election to the Raft struct in raft.go. You'll also need to define a struct to hold information about each log entry.
• Fill in the RequestVoteArgs and RequestVoteReply structs. Modify Make() to create a background goroutine that will kick off leader election periodically by sending out RequestVote RPCs when it hasn't heard from another peer for a while. This way a peer will learn who is the leader, if there is already a leader, or become the leader itself. Implement the RequestVote() RPC handler so that servers will vote for one another.
• To implement heartbeats, define an AppendEntries RPC struct (though you may not need all the arguments yet), and have the leader send them out periodically. Write an AppendEntries RPC handler method that resets the election timeout so that other servers don't step forward as leaders when one has already been elected.
• Make sure the election timeouts in different peers don't always fire at the same time, or else all peers will vote only for themselves and no one will become the leader.
• The tester requires that the leader send heartbeat RPCs no more than ten times per second.
• The tester requires your Raft to elect a new leader within five seconds of the failure of the old leader (if a majority of peers can still communicate). Remember, however, that leader election may require multiple rounds in case of a split vote (which can happen if packets are lost or if candidates unluckily choose the same random backoff times). You must pick election timeouts (and thus heartbeat intervals) that are short enough that it's very likely that an election will complete in less than five seconds even if it requires multiple rounds.
• The paper's Section 5.2 mentions election timeouts in the range of 150 to 300 milliseconds. Such a range only makes sense if the leader sends heartbeats considerably more often than once per 150 milliseconds. Because the tester limits you to 10 heartbeats per second, you will have to use an election timeout larger than the paper's 150 to 300 milliseconds, but not too large, because then you may fail to elect a leader within five seconds.
• You may find Go's rand useful.
• You'll need to write code that takes actions periodically or after delays in time. The easiest way to do this is to create a goroutine with a loop that calls time.Sleep(); (see the ticker() goroutine that Make() creates for this purpose). Don't use Go's time.Timer or time.Ticker, which are difficult to use correctly.
• The Guidance page has some tips on how to develop and debug your code.
• If your code has trouble passing the tests, read the paper's Figure 2 again; the full logic for leader election is spread over multiple parts of the figure.
• Don't forget to implement GetState().
• The tester calls your Raft's rf.Kill() when it is permanently shutting down an instance. You can check whether Kill() has been called using rf.killed(). You may want to do this in all loops, to avoid having dead Raft instances print confusing messages.
• Go RPC sends only struct fields whose names start with capital letters. Sub-structures must also have capitalized field names (e.g. fields of log records in an array). The labgob package will warn you about this; don't ignore the warnings.

Be sure you pass the 2A tests before submitting Part 2A, so that you see something like this:

$ go test -run 2A -race
Test (2A): initial election ...
  ... Passed --   4.0  3   32    9170    0
Test (2A): election after network failure ...
  ... Passed --   6.1  3   70   13895    0
PASS
ok      raft    10.187s
$

Each "Passed" line contains five numbers; these are the time that the test took in seconds, the number of Raft peers (usually 3 or 5), 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.

Part 2B: log

Implement the leader and follower code to append new log entries, so that the go test -run 2B -race tests pass.

• Run git pull to get the latest lab software.
• Your first goal should be to pass TestBasicAgree2B(). Start by implementing Start(), then write the code to send and receive new log entries via AppendEntries RPCs, following Figure 2.
• You will need to implement the election restriction (section 5.4.1 in the paper).
• One way to fail to reach agreement in the early Lab 2B tests is to hold repeated elections even though the leader is alive. Look for bugs in election timer management, or not sending out heartbeats immediately after winning an election.
• Your code may have loops that repeatedly check for certain events. Don't have these loops execute continuously without pausing, since that will slow your implementation enough that it fails tests. Use Go's condition variables, or insert a time.Sleep(10 * time.Millisecond) in each loop iteration.
• Do yourself a favor for future labs and write (or re-write) code that's clean and clear. For ideas, re-visit our the Guidance page with tips on how to develop and debug your code.
• If you fail a test, look over the code for the test in config.go and test_test.go to get a better understanding what the test is testing. config.go also illustrates how the tester uses the Raft API.

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 --   1.6  3   18    5158    3
Test (2B): RPC byte count ...
  ... Passed --   3.3  3   50  115122   11
Test (2B): agreement despite follower disconnection ...
  ... Passed --   6.3  3   64   17489    7
Test (2B): no agreement if too many followers disconnect ...
  ... Passed --   4.9  5  116   27838    3
Test (2B): concurrent Start()s ...
  ... Passed --   2.1  3   16    4648    6
Test (2B): rejoin of partitioned leader ...
  ... Passed --   8.1  3  111   26996    4
Test (2B): leader backs up quickly over incorrect follower logs ...
  ... Passed --  28.6  5 1342  953354  102
Test (2B): RPC counts aren't too high ...
  ... Passed --   3.4  3   30    9050   12
PASS
ok      raft    58.142s

real    0m58.475s
user    0m2.477s
sys     0m1.406s
$

Part 2C: persistence

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 SaveRaftState() 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.

Insert calls to persist() at the points where your implementation changes persistent state. Once you've done this, you should pass the remaining tests.

In order to avoid running out of memory, Raft must periodically discard old log entries, but you do not have to worry about this until the next lab.

• Run git pull to get the latest lab software.
• Many of the 2C tests involve servers failing and the network losing RPC requests or replies. These events are non-deterministic, and you may get lucky and pass the tests, even though your code has bugs. Typically running the test several times will expose those bugs.
• 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, perhaps with the help of the 6.824 Raft lectures.
• While 2C only requires you to implement persistence and fast log backtracking, 2C test failures might be related to previous parts of your implementation. Even if you pass 2A and 2B tests consistently, you may still have election or log bugs that are exposed on 2C tests.

Your code should pass all the 2C tests (as shown below), as well as the 2A and 2B tests.

$ go test -run 2C -race
Test (2C): basic persistence ...
  ... Passed --   7.2  3  206   42208    6
Test (2C): more persistence ...
  ... Passed --  23.2  5 1194  198270   16
Test (2C): partitioned leader and one follower crash, leader restarts ...
  ... Passed --   3.2  3   46   10638    4
Test (2C): Figure 8 ...
  ... Passed --  35.1  5 9395 1939183   25
Test (2C): unreliable agreement ...
  ... Passed --   4.2  5  244   85259  246
Test (2C): Figure 8 (unreliable) ...
  ... Passed --  36.3  5 1948 4175577  216
Test (2C): churn ...
  ... Passed --  16.6  5 4402 2220926 1766
Test (2C): unreliable churn ...
  ... Passed --  16.5  5  781  539084  221
PASS
ok      raft    142.357s
$ 

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

Part 2D: log compaction

As things stand now with your code, a rebooting service 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 to save space: from time to time a service will persistently store a "snapshot" of its current state, and Raft will discard log entries that precede the snapshot. When a service falls far behind the leader and must catch up, the service first installs a snapshot and then replays log entries from after the point at which the snapshot was created. 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.

To support snapshots, we need an interface between the service and the Raft library. The Raft paper doesn't specify this interface, and several designs are possible. To allow for a simple implementation, we decided on the following interface between service and Raft: