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!)

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.

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.

When we grade your submissions, we will run the tests without the -race flag. However, you should make sure that your code does not have race conditions because race conditions can cause the tests to fail. So, it's highly recommended to also run 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 ~/6.824
$ 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
...
$

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 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 .
• 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
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  	6.824/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 but you should also make sure that your code consistently passes the tests with the -race flag.

Part 2B: log

Implement the leader and follower code to append new log entries, so that the go test -run 2B 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. Send each newly committed entry on applyCh on each peer.
• 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 --   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  	6.824/raft	35.557s

real	0m35.899s
user	0m2.556s
sys	0m1.458s
$

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, and if the rest of your implementation is correct, you should pass all of the 2C tests.

• Run git pull to get the latest lab software.
• The 2C tests are more demanding than those for 2A or 2B, and failures may be caused by problems in your code for 2A or 2B.
• 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 lecture notes.

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  	6.824/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

Part 2D: log compaction

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 persister.SaveStateAndSnapshot(), which takes separate arguments for the Raft state and the corresponding snapshot. If there's no snapshot, pass nil as the snapshot argument.

When a server restarts, the application layer reads the persisted snapshot and restores its saved state.

Previously, this lab recommended that you implement a function called CondInstallSnapshot to avoid the requirement that snapshots and log entries sent on applyCh are coordinated. This vestigal API interface remains, but you are discouraged from implementing it: instead, we suggest that you simply have it return true.

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).

• git pull to make sure you have the latest software.
• A good place to start is to modify your code to so that it is able to store just the part of the log starting at some index X. Initially you can set X to zero and run the 2B/2C tests. Then make Snapshot(index) discard the log before index, and set X equal to index. If all goes well you should now pass the first 2D test.
• You won't be able to store the log in a Go slice and use Go slice indices interchangeably with Raft log indices; you'll need to index the slice in a way that accounts for the discarded portion of the log.
• Next: have the leader send an InstallSnapshot RPC if it doesn't have the log entries required to bring a follower up to date.
• Send the entire snapshot in a single InstallSnapshot RPC. Don't implement Figure 13's offset mechanism for splitting up the snapshot.
• Raft must discard old log entries in a way that allows the Go garbage collector to free and re-use the memory; this requires that there be no reachable references (pointers) to the discarded log entries.
• Even when the log is trimmed, your implemention still needs to properly send the term and index of the entry prior to new entries in AppendEntries RPCs; this may require saving and referencing the latest snapshot's lastIncludedTerm/lastIncludedIndex (consider whether this should be persisted).
• A reasonable amount of time to consume for the full set of Lab 2 tests (2A+2B+2C+2D) without -race is 6 minutes of real time and one minute of CPU time. When running with -race, it is about 10 minutes of real time and two minutes of CPU time.

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      6.824/raft      293.456s

Again, a reminder that when we grade your submissions, we will run the tests without the -race flag but you should also make sure that your code consistently passes the tests with the -race flag.