# Time in Distributed Systems: Lamport Timestamps

What I do in my day job is working on infrastructure systems at dropbox. That means that I’m neck-deep in distributed systems programming – that’s my bread and butter. One of the problems that comes up over and over again in distributed systems is time.

In distributed systems, time is a problem. Each computer has a clock built in, but those clocks are independent. The clocks on different machines can vary quite a bit. If a human being is setting them, then they’re probably at best accurate to one second. Even using a protocol like NTP, which synchronizes clocks between different computers, you can only get the clocks accurate to within about a millisecond of each other.

That sounds pretty good. In human timescales, a millisecond is a nearly imperceptible time interval. But to a modern computer, which can execute billions of instructions per second, it’s a long time: long enough to execute a million instructions! To get a sense of how much time that is to a computer, I just measured the time it took to take all of the source code for my main project, compile it from scratch, and execute all of its tests: it took 26 milliseconds.

That’s a lot of work. On the scale of a machine running billions of instructions per second, a millisecond is a long time.

Why does that matter?

For a lot of things that we want to do with a collection of computers, we need to know what event happened first. This comes up in lots of different contexts. The simplest one to explain is a shared resource locked by a mutex.

A mutex is a mutual exclusion lock. It’s basically a control that only allows one process to access some shared resource at a time. For example, you could think of a database that a bunch of processes all talk to. To make an update, a process P needs to send a request asking for access. If no one is using it when the server receives the request, it will give a lock to P, and and then block anyone else from accessing it until P is done. Anyone else who asks for access to the the database will have to wait. When P is done, it releases the lock on the mutex, and then if there’s any processes waiting, the database will choose one, and give it the lock.

Here’s where time comes into things. How do you decide who to give the lock to? You could give it to whoever you received the request from first, using the time on the database host. But that doesn’t always work well. It could easily end up with hosts with a lower-bandwidth connection to the server getting far worse service than a a closer host.

You get better fairness by using “send time” – that is, the time that the request was sent to the server by the client. But that’s where the clock issue comes up. Different machines don’t agree perfectly on the current time. If you use their clocktime to determine gets the lock first, then a machine with a slow clock will always get access before one with a fast clock. What you need is some fair way of determining ordering by some kind of timestamp that’s fair.

There are a couple of algorithms for creating some notion of a clock or timestamp that’s fair and consistent. The simplest one, which we’ll look at in this post, is called Lamport timestamps. It’s impressively simple, but it works really well. I’ve seen it used in real-world implementations of Paxos at places like Google. So it’s simple, but it’s serious.

The idea of Lamport timestamps is to come up with a mechanism that defines a partial order over events in a distributed system. What it defines is a causal ordering: that is, for any two events, A and B, if there’s any way that A could have influenced B, then the timestamp of A will be less than the timestamp of B. It’s also possible to have two events where we can’t say which came first; when that happens, it means that they couldn’t possible have affected each other. If A and B can’t have any affect on each other, then it doesn’t matter which one “comes first”.

The way that you make this work is remarkably simple and elegant. It’s based on the simplest model of distributed system, where a distributed system is a collection of processes. The processes only communicate by explicitly sending messages to each other.

1. Every individual process $p$ in the distributed system maintains an integer timestamp counter, $\tau_p$.
2. Every time a process $p$ performs an action, it increments $\tau_p$. Actions that trigger increments of $\tau_p$ include message sends.
3. Every time a process $p$ sends a message to another process, it includes the current value of $\tau_p$ in the message.
4. When a process $p$ receives a message from a process $q$, that message includes the value of $\tau_q$ when the message was sent. So it updates its $\tau_q$ to the $\max(\tau_p, \tau_q)+1$ (one more than the maximum of its current timestamp and the incoming message timestamp).

For any two events A and B in the system, if $A \rightarrow B$ (that is, if A causally occurred before B – meaning that A could have done something that affected B), then we know that the timestamp of A will be smaller than the timestamp of B.

The order of that statement is important. It’s possible for timestamp(A) to be smaller than timestamp(B), but for B to have occurred before A by some wallclock. Lamport timestamps provide a causal ordering: A cannot have influenced or caused B unless $A \rightarrow B$; but A and B can be independent.

Let’s run through an example of how that happens. I’ll write it out by describing the clock-time sequence of events, and following it by a list of the timestamp counter settings for each host. We start with all timestamps at 0: [A(0), B(0), C(0), D(0).

• [Event 1] A sends to C; sending trigger a timestamp increment. [A(1), B(0), C(0), D(0)].
• [Event 2] C receives a message from A, and sets its counter to 2. [A(1), B(0), C(2), D(0).
• [Event 3] C sends a message to A (C increments to 3, and sends.) [A(1), B(0), C(3), D(0).
• [Event 4] A recieves the message from C, and sets its clock to 4. [A(4), B(0), C(3), D(0)]
• [Event 5] B sends a message to D. [A(4), B(1), C(3), D(0)]
• [Event 6] D receives the message. [A(4), B(1), C(3), D(2)].
• [Event 7] D sends a message to C. [A(4), B(1), C(3), D(3)].
• [Event 8] C receives the message, and sets its clock to 4.

According to the Lamport timestamps, in event 5, B sent its message to D at time 1. But by wallclock time, it sent its message after C’s timestamp was already 3, and A’s timestamp was already 4. We know that in our scenario, event 5 happened before event 3 by wallclock time. But in a causal ordering, it didn’t. In causal order, event 8 happened after event 4, and event 7 happened before event 8. In causal comparison, we can’t say whether 7 happened before or after 3 – but it doesn’t matter, because which order they happened in can’t affect anything.

The Lamport timestamp is a partial ordering. It tells us something about the order that things happened in, but far from everything. In effect, if the timestamp of event A is less than the timestamp of event B, it means that either A happened before B or that there’s no causal relation between A and B.

The Lamport timestamp comparisons only become meaningful when there’s an actual causal link between events. In our example, at the time that event 5 occurs, there’s no causal connection at all between the events on host A, and the events on host B. You can choose any arbitrary ordering between causally unrelated events, and as long as you use it consistently, everything will work correctly. But when event 6 happens, now there’s a causal connection. Event 5 could have changed some state on host D, and that could have changed the message that D sent in event 7. Now there’s a causal relationship, timestamp comparisons between messages after 7 has to reflect that. Lamport timestamps are the simplest possible mechanism that captures that essential fact.

When we talk about network time algorithms, we say that what Lamport timestamps do is provide weak clock consistency: If A causally happened before B, then the timestamp of A will be less than the timestamp of B.

For the mutex problem, we’d really prefer to have strong clock consistency, which says that the timestamp of A is smaller than the timestamp of B if and only if A causally occurred before B. But Lamport timestamps don’t give us enough information to do that. (Which is why there’s a more complex mechanism called vector clocks, which I’ll talk about in another post.

Getting back to the issues that this kind of timestamp is meant to solve, we’ve got a partial order of events. But that isn’t quite enough. Sometimes we really need to have a total order – we need to have a single, correct ordering of events by time, with no ties. That total order doesn’t need to be real – by which I mean that it doesn’t need to be the actual ordering in which events occured according to a wallclock. But it needs to be consistent, and no matter which host you ask, they need to always agree on which order things happened in. Pure lamport timestamps don’t do that: they’ll frequently have causally unrelated events with identical timestamps.

The solution to that is to be arbitrary but consistent. Take some extra piece of information that uniquely identifies each host in the distributed system, and use comparisons of those IDs to break ties.

For example, in real systems, every host has a network interface controller (NIC) which has a universally unique identifier called a MAC address. The MAC address is a 48 bit number. No two NICs in the history of the universe will ever have the same MAC address. (There are 281 trillion possible MAC codes, so we really don’t worry about running out.) You could also use hostnames, IP addresses, or just random arbitrarily assigned identifiers. It doesn’t really matter – as long as it’s consistent.

This doesn’t solve all of the problems of clocks in distributed systems. For example, it doesn’t guarantee fairness in Mutex assignment – which is the problem that I used as an example at the beginning of this post. But it’s a necessary first step: algorithms that do guarantee fairness rely on some kind of consistent event ordering.

It’s also just a beautiful example of what good distributed solutions look like. It’s simple: easy to understand, easy to implement correctly. It’s the simplest solution to the problem that works: there is, provably, no simpler mechanism that provides weak clock consistency.

# Simpler Consensus with Raft

A few weeks ago, I wrote about Paxos, which is (at least in my experience), the most widely used algorithm for consensus in distributed systems. I’m a huge fan of Paxos – I think that it’s a remarkably elegant system.

But Paxos does have its problem.

1. Paxos has a lot of roles: client, proposer, learner, acceptor, leader, follower. When you want to implement Paxos, you need to figure out all of those roles, and how you’re going to implement them. In general, you end up merging roles – but there are lots of ways of doing that merge. Each particular way of setting up the roles has its own properties, and thus its own tradeoffs that you need to understand.
2. Paxos, as we normally talk about it, is really a single-consensus protocol – that is, the basic protocol is designed to get a group of agents to come to consensus just once. If you want to be able to repeatedly seek new consensus values, you’re actually going to be using an extension to the basic paxos protocol. There are a ton of Paxos extensions that work to add repeated consensus. Paxos itself is simple and elegant, with well-defined formal properties that we care about – the moment we start modifying it, we can no longer count on those properties unless we can also prove them in our extension!
3. Paxos was originally described in a truly awful paper. Leslie Lamport was trying to write a paper that would be less dull than the typical bone-dry technical snoozer – but the way that he wrote it actually makes it much harder to understand.

In short: Paxos has more complexity than it needs, and despite that, it needs to be tweaked to be really useful, and getting those tweaks right is hard. There are, sadly, a lot of incorrect Paxos implementations – and their incorrectness has all-too-often come as a surprise to the people who rely on them.

To avoid those problems, there are other consensus algorithms out there. In this post, we’re going to look at one of the Paxos competitors – a consensus algorithm/protocol called raft.

Raft does away with the role complexity of Paxos. In Raft, you have a collection of cooperating agents. There are no distinct proposers, acceptors, or learners: there are just servers. Communication between the servers in raft is done entirely with synchronous remote procedure calls.

In Raft, the target of consensus is a log containing a sequence of events. The log is the history of the distributed system. The goal of raft is that the log be maintained in a consistent state throughout the raft network. Just like in Paxos, if we have $2n+1$ servers, up to $n$ can fail without the network losing its consistency.

Raft is designed in terms of remote procedure calls between the elements of the network. In Raft, we never talk about single messages – every communication between servers is a pair of messages: a request from caller to callee; and a response from callee to caller. When a message gets lost, we’ll just talk about it as a failed remote procedure call.

Within a Raft network, at any given time, each server has a state. It can be a follower, a leader, or a candidate. Within the network, there is at most one leader. When there is a leader, all of the other servers are in the follower state. The followers are almost entirely passive. Followers don’t talk to clients at all – they just wait for RPCs from the leader. The leader is the only participant that’s allowed to talk to clients: any client request must go through the current leader. The leader is also the only server that’s allowed to add new entries to the consensus log.

Raft divides time into a sequence of terms. In each term, the servers in the raft network need to select a leader using a process called an election. Raft is a strong leader protocol – no interactions with a client can take place except through a leader. If there’s no leader, then we can’t process client requests without a leader.

So, to understand Raft, there’s three processes that we need to
understand:

2. Transitions between terms
3. Appending an entry to a log.

In those processes, the servers have a collection of variables
that they use for the Raft protocol:

currentTerm
the current term for the server.
votedFor
the serverID that this server voted for in the current term, or “none”.
log
the list of entries in the log.
commitIndex
The index of the highest log entry known to be committed by the server.
lastApplied
The index of the highest log entry that’s been added to the log – but not necessarily committed. (It doesn’t become committed until a majority of servers accept it.)

In each term, the Raft cluster needs to have a leader. The way that a leader is selected called election.

Elections are triggered by a term transition. When a server in the cluster decided that it needs to start a new term, it increments its term number, puts itself into the candidate state, and sends a RequestVote(term, candidateId) RPC to each of the other servers in the cluster. This request asks the other servers in the cluster to select it as the leader. If it receives enough “yes” votes, it will become the leader.

When a server receives a RequestVote RPC, it checks the term. If it’s smaller than the server’s current term, then it replies “No” – meaning that it cannot support the requestor as leader.

If the term in the request is greater that the receiver’s term, then the receiver cannot have voted in the new term. So it updates to the term from the request, and then it replies “Yes”.

If the term in the request equals the receiver’s term, then the receiver has already updated its term. If it’s already voted for someone else as leader, then it can’t support the requestor, so it replies “No”. If it hasn’t voted for a leader in the term, then it votes for the requestor, and replies “Yes”.

If the requester receives “Yes” votes from more than 1/2 of the cluster (counting itself), then it becomes the leader, and starts both processing requests from clients, and sending heartbeats to the other servers in the cluster.

If it doesn’t receive enough votes, then it waits to see if anyone else becomes the leader and starts sending heartbeats. If it doesn’t get a heartbeat in time, then it starts over: it would increment its term again, and try to start a fresh election.

### Term Transitions

For a given server, term transitions happen in three ways:

1. Timeout: the leading server needs to periodically communicate with each of the followers. This process is called heartbeat: even if the leader has no updates for its followers, it sends RPC calls to the followers just to say “I’m still here”. If a client goes too long without receiving a heartbeat, it decides that the leader was lost, and it will increment the term number, and trigger a new election.
2. Leader resignation: the current leader can, at any time, decide to stop being the leader. (This is typically done by an implementation as part of a system that says that there’s a maximum period between leader elections. For example, in the Aurora scheduler, we had leader elections at least once per day. In a raft consensus, the leader would trigger this by deciding it was time for it to stop being a leader, and triggering an election by starting a new term.)
3. External term change: every RPC received by a server includes a term number. If any RPC to a server ever includes a term number greater than the current term for that server, the server will update its term to the new number. As a special case of this, when a leader server decides to resign, it does that by sending an RPC to the other servers with an incremented term number.

### Appending to the log

We just spent a fair bit of time talking about leaders and elections. That’s almost beside the point. What we really want to do is just maintain a consistent log across the cluster of servers. Everything except creating log entries is just the book-keeping that’s necessary to make the consistent log work. The log itself is maintained using the AppendEntries RPC call.

When a client request does something that alters the state of the cluster, the leader describes that change by adding an entry to the log. It builds a proposed log entry, and sends it to the other members of the cluster using an RPC. If it gets enough “Yes” votes from other cluster members, then the log entry becomes committed, and the leader updates its commitIndex to the index of the new log entry to reflect that.

The RPC request takes a bunch of parameters:

1. term: the leader’s term.
2. leaderId: the id of the leader.
3. prevLogIndex: the index number of the last log entry in the consensus log preceeding this new entry.
4. prevLogTerm: the number of the term where the last log entry was committed.
5. entries: a set of new log entries to be appended to the log.
6. leaderCommit: the index of the commitlog on the leader after this set of entries has been committed.

When an AppendEntries call is received by a follower, what it does is:

• If the receiver’s term is greater than the request term, then the receiver rejects the request by replying “No”.
• If the the receivers commit index is larger than the commit index of the request, then it rejects the request by replying “No”.
• If the receiver’s log doesn’t contain an entry at prevLogIndex, or that entry’s term doesn’t match the request term, then it rejects the request by replying “No”.
• If there’s an entry in the log with the same index as the new log entries, and the term in the request matches the receiver’s term, then the receiver removes all entries after prevLogIndex from its log.
• The receiver then appends the new entries from the request to its log.
• If the leaderCommit is greater than the commitIndex on the receiver, then the receiver updates its commitIndex.
• Finally, the receiver replies “Yes”.

When a majority of the cluster members have accepted an AppendEntries call, then the log entry gets committed.

The one part of this that’s confusing is how the logs get managed. The leader creates a new log entry, and sends it to the other servers. The complexity comes from dealing with cases where something doesn’t reach consensus.

For example, the leader sends entries 5, 6, and 7 to server S. S adds the entries to its copy of log – it now contains [1, 2, 3, 4, 5, 6, 7]. Meanwhile, the leader also sends those entries to server T, but the RPC to T fails due to a network fault. Another client request happens, and now the leader sends [5, 6, 7, 8] to S. S sees that it’s got entry 5 already: so it discards everything after 5, and then re-appends.

So the trailing segment of the log can change! How do we handle consensus?

The next time that the leader sends an AppendEntries to a follower, it contains the leader’s commitIndex. The follower updates its commit index to that value. Once it’s done that, any request from a leader that tries to modify anything that comes before that commit index will be rejected.

The consensus commit thus doesn’t really occur until the next heartbeat call after a log update.

## Raft versus Paxos

That’s the basics of Raft.

In comparison to Paxos, there’s a couple of things to notice:

1. There’s a lot less confusion around roles. Paxos had a ton of different roles, and rules for interactions between the different roles. Raft doesn’t have any of that: it’s just servers, with one of the servers designated as the leader.
2. Raft explicitly manages a log, and it adds complexity around log management. In Paxos, you’re just managing a single consensus value; in Raft, you’ve got a sequence of log entries.
3. Paxos is defined in terms of messages; Raft is designed in terms of remote procedure calls.

So is Raft really simpler than Paxos? I think that’s up for discussion. Personally, I prefer Paxos. There’s a lot of complexity hidden under the covers of the RPC system. It looks simple on the surface, but all of the complexity of message passing, lost messages, message duplication – it’s still there. It’s just been swept under the carpet, as if that really makes it easier.

The way that the logs get maintained is confusing. That’s inevitable: getting distributed knowledge is never easy. Raft at least makes that part of things explicit, whereas it’s a common part of Paxos implementations, but it’s not really specified in the protocol.

# Paxos, a really beautiful protocol for distributed consensus

The work that I do in real life is all focused on tools for other developers. In todays environment, that means that I’ve spent a lot of time working on tools that, in one way or another, help other developers deal with distributed systems. In that work, I’ve noticed that there are some really key things that straddle the line between pure math and pure engineering. That’s really interesting to someone like me!

A good example of that is something called paxos. My first exposure to paxos was very interesting. I’d just been hired by Google, and was working on their build tool. At the time, engineers in Google had a problem. Google’s codebase was contained in one massive version control repository. Doing things that way has a ton of really neat advantages – most importantly, the fact that it makes it really convenient to reuse code written by anyone else at the company. The problem was that code reuse can become very confusing. Project A reuses a bit of code written by people from project B. B’s code reused some stuff from C, and C from D, and D from E. So now project A is using code from project E, and they don’t know why!

In this case, I had someone from a storage project coming to me trying to figure out just why his system had a dependency on a plan9 database system called paxos. I had to built a tool that would allow people to ask questions like “Why does A depend on E?”.

As it turned out, paxos was a really important thing, and it was widely reused through the Google codebase. Once I learned about it, I started seeing it everywhere. Since I left Google nearly four years ago, I didn’t stop seeing it. It’s ubiquitous in distributed systems. Outside of Google, we weren’t using that friendly old plan9 paxos implementation – but the paxos model has been reimplemented dozens of times, because it’s so darned useful!

paxos is a system for managing consensus.

In distributed systems, there a collection of hard problems that you constantly need to deal with.

1. Things fail. You can never count on anything being reliable. Even if you have perfectly bug-free software, and hardware that never breaks, you’ve still got to deal with the fact that network connections can break, or messages within a network can get lost, or that some bozo might sever your network connection with a bulldozer. (That really happened while I was at Google!)
2. Given (1), you can never rely on one copy of anything, because that copy might become unavailable due to a failure. So you need to keep multiple copies, and those copies need to be consistent – meaning that at any time, all of the copies agree about their contents.
3. There’s no way to maintain a single completely consistent view of time between multiple computers. Due to inconsistencies in individual machine performance, and variable network delays, variable storage latency, and several other factors, there’s no canonical way of saying that for two events X and Y, “X happened before Y”. What that means is that when you try to maintain a consistent set of data, you can’t just say “Run all of the events in order”, because while one server maintaining one copy might “know” that X happened before Y, another server maintaining another copy might be just as certain that Y happened before X.

In a world where you can’t count on different agents seeing events in the same order, and where you expect things to be constantly failing, how can you make sure that any distributed system you build ends up with a consistent view of reality?

The answer is a consensus protocol. You need to create a mechanism based on communication between the copies of your system that allows them to mantain a consistent consensus of what the current state of the world is, even in the presence of failures of machines, storage systems, and communications.

paxos is a very elegant, reasonably simple consensus protocol.

Let’s get a bit more precise. Paxos is built on a model of storage. The original application of it was a consistent database, so it’s built around the idea of keeping data consistent. In paxos, the state of the storage is modelled as a sequence of transactions. Each transaction is a pair (t, v), where t is a numeric transaction identifier, and a v is a transaction value.

The state of the system being modelled is a sequence of transaction pairs, [(ti, vi), (tj, vj), ..], where the t values are increasing as you progress through the sequence. As time passes, new transaction pairs can be added to the state.

The focus of the paxos protocol is ensuring that in a collection of 2n+1 participants, all surviving participants will agree on the current value of the state, even if up to n participants fail, and even if messages can be delivered in arbitrary order.

Before I go further into the description of paxos, we need to look at the basic assumptions that underlie it. Like any formal model, it’s not defined in terms of real computers. It’s defined in terms of an abstraction that approximates reality. In this case, the approximation is quite good, but we still need to go through the basic assumptions that make up its model of the universe.

1. Processors (aka participants, servers, computers):
1. operate at any speed. No two processors necessarily operate at the same speed.
2. may fail without warning.
3. may rejoin after recovering from a failure.
4. are cooperative (in the sense that they do not attempt to cause failures).
2. Network:
1. Delivers messages between any pair of processors.
2. Transmits messages asynchronously.
3. Delivers messages at arbitrary speeds.
4. Does not guarantee that messages will be delivered in the order in which they were transmitted.
5. Does guarantee that a message, if delivered, will be delivered correctly, without any changes.
6. May fail to deliver a message.
7. May deliver multiple copies of the same message.

In short, everything can fail at any time; after failure, participants can recover and rejoin the system; any no part of the system acts in an actively adversarial way.

The protocol describes the behavior of the system in terms of a collection of roles. A participant can have more than one role in the system – in fact, in most implementations of paxos, all partipants do have multiple roles. The roles are:

Client
The client is not part of the paxos cluster. It’s an external entity whose actions trigger state changes by making requests to the paxos system. Each state update in paxos is initiated by a client request, and completed by a reply to the client.
Acceptor
An acceptor (also called a voter) is a participant in the maintanence of distributed storage. A state change in a paxos cluster does not occur until a majority (quorum) of acceptors agree upon it.
Proposer
A proposer recieves a request from the client, and attempts to get a quorum of acceptors to agree on it.
One of the proposers is special. It is the single proposer who most recently had a proposal accepted. In many paxos implementations, there is only one active proposer serving client requests: the only time the other proposers send proposals is when the current leader fails, and a new one needs to be selected.
Learner
The learner is the real service provided by the paxos cluster. Once a proposal is accepted, a learner processes the request from the client, and sends it the result.

In a typical paxos cluster, the client sends requests to a proposer. The proposer sends a proposal to update the state with the new client request, and attempts to convince a majority of the acceptors to accept it. Once a majority accepts it, the client request is processed by the learner, and a result is returned to the client.

The meat of paxos the protocol that the proposer gets a majority of acceptors to agree on a proposal, and how that protocol process ensures that the collection of acceptors maintains a consistent state.

The protocol itself is pretty simple. Each round is effectively independent, and consists of a process of attempting to reach consensus. Within each round, finding consensus is a two-phase process, where each phase consists of a message sent from a proposer to a group of acceptors, and a reply from the acceptors to the proposer.

1. Phase One: Prepare/Promise
• Proposer: A proposer attempts to start setting a new consensus by sending a Prepare(N) message to a quorum of acceptors. It can send to any group of acceptors, so long as that group forms a majority of the acceptors. The prepare message specifies a numeric identifier N for its proposal, which is larger than any proposal that’s been sent by this proposer.
• Acceptors:
Each acceptor, upon receiving the proposal, checks if the N-value from the prepare message is greater than any proposal from the current round that it has accepted. If so, it sends a reply called a Promise to the proposer, promising that it will never accept any proposal with a number less that N. If the acceptor has accepted a proposal with number less than $N$ in the current round, then it includes the pair $(v, n_v)$ consisting of the proposed consensus value $v$ and the number $n_v$ of the accepted proposal that proposed $v$.
The acceptor thus sends a message Promise(N, (v, nv)) (if it has accepted a proposal this round) or Promise(N, null) (if it has not yet accepted a proposal with number less than N).

Once it’s sent a promise message, it must not accept any request for a proposal with number less that N. Note though that this does not mean that the acceptor promises to accept the proposal: all it’s doing is promising not to accept any proposal with number less than N! If in receives a message Prepare(N+1), it’s free to promise that – but if it does, it will no longer be able to accept the proposal for N.

(If N is smaller that the number of any proposal promised or accepted by the acceptor, then in the original version of paxos, the acceptor does nothing; in some optimizations of the protocol, it replies Reject(nv).)

What this phase does is allow a proposer to determine whether or not a new proposal is even worth considering. If a quorum (majority) of acceptors send promises, then it can move on to phase 2.

2. Phase Two: Accept!/Accepted

When a proposer recieves promises from a quorum of acceptors, then it moves forward to try to actually commit the proposal. In order to do this, it needs to choose a value for the proposal. If any of the Promise messages contained a value, then the value of this proposal must be set to the value of the highest proposal number in any of the promises. If all of the promises were empty, then the proposer can choose any value that it wants for the proposal.

Once the proposer has chosen a value, then it sends a message Accept!(N, V) to a quorum of acceptors. This is typically written with the exclamation point, because it’s really a command to the acceptors: they’re being told to accept the proposal, if they can.

When an acceptor receives an Accept!(N, v) message, if it has not issued a promise for a proposal with number greater than N, then it must accept the message. It accepts the proposal by sending a message Accepted(N, V) to both the original proposer, and all of the learners.

When Accepted messages have been received from a quorum of acceptors, the new value V becomes the consensus value for the paxos cluster, and the new proposal number N is fully committed.

As with so many things, this is easier to understand when you think about an example. One use of paxos that I’ve worked with is in a cluster scheduling service. In that system:

• a client is a user attempting to run a new job on the cluster. It sends a request to the scheduler detailing the set of resources that it wants to request.
• Each duplicate of the scheduler is a proposer, an acceptor, and a learner. There’s one active instance of the scheduler, which is the leader. When a client wants to schedule a job, its request gets sent to the leading scheduler.
• In the normal non-error case, this works as follows:
1. When a scheduling request is received, the leader proposes scheduling the job, by sending a message to all of the other schedulers saying that it wants to schedule job N.
2. The other schedulers, if they haven’t seen a proposal for a job with number greater than i, make promises to accept that proposal.
3. The leading scheduler chooses resources for the job, and then sends an Accept! message to the other schedulers.
4. The other schedulers reply accepting the scheduling. The non-leader schedulers, acting as learnings, record the scheduling information, and the leader actually starts the job.
• Errors occur when there was some kind of failure. In that case, we don’t necessarily know who the leader is – so we get multiple schedulers trying to act as if they’re the leader. So they each send proposals. Whichever proposal had the largest proposal number will eventually get accepted, and its proposer becomes the new leader.

It’s a pretty simple thing – the core concept is simply that no consensus proposal is considered “committed” until it’s been accepted by a majority of the participants. And if it’s been accepted by a majority of the participants, that means that no conflicting proposal can ever reach consensus – because that would require at least one participant to accept 2 conflicting proposals.

But there’s still a bit of formality that’s working looking at. Exactly what guarantees does paxos give? What properties does paxos-style consensus have?

Even the formal properties of paxos are easy to understand. Paxos provides two key properties: validity, and agreement.

Validity
No value ever reaches consensus without first being proposed, and having its proposal accepted.
Agreement
No two distinct values ever reach consensus at the same time.

You an easily prove those two properties. In fact, the proof is completely obvious once you recognize that the paxos protocol has two invariants (and those invariants are themselves clear from the definition of the protocol!):

1. An acceptor can only accept a proposal $p$ if and only if it has not yet made a promise
for a proposal $n$ where is the consensus value of the
highest numbered proposal that has been accepted before this proposal.

Getting back to the beginning: the point of all of this is to have a system in which we can be sure that things work correctly even in the presence of failures. In paxos, as long as at some point there was a quorum of machines that come to agreement, then any failure that leaves a surviving quorum of machines must have overlapped with the previous quorum – which means that the previous consensus still remains in effect, and will be propagated to the remaining participants. If you’ve got 5 machines, then two can fail, and you won’t lose consistency among the remaining ones.

# DNS and Yesterday's Outage

As I’m sure at least some of you noticed, Scientopia – along with a huge number of other sites – was unreachable for most of yesterday. I say “unreachable” instead of “down” because the site was up. But if you wanted to get to it, and you didn’t know it’s numeric address, you couldn’t reach it. That’s because your computer uses a service called DNS to find things.

So what’s this DNS thing? And what brought down so much of the net yesterday?

DNS stands for “domain name service”. What it does is provide a bridge between the way that you think of the name of a website, and the way that your computer actually talks to the website. You think of a website by a URL, like http://scientopia.org/blogs/goodmath. That URL basically says “the thing named ”blogs/goodmath” on a server named ”scientopia.org”, which you can access using a system called ”http”.”.

The catch is that your computer can’t just send a message to something named “scientopia.org”. Scientopia is a server in a datacenter somewhere, and to send a message to it, it needs to know a numeric address. Numeric addresses are usually written as a set of four numbers, each ranging from 0 to 255. For example, scientopia.org is currently 184.106.221.182.

You don’t want to have to remember those four meaningless numbers in order to connect to scientopia. But there’s more to it than just remembering: those numbers can change. The physical computer that scientopia works on has been moved around several times. It lives in a datacenter somewhere, and as that datacenter has grown, and hardware has needed to be either expanded or replaced, the numeric address has changed. Even though I’m the administrator of the site, there’ve been a couple of times where it’s changed, and I can’t pinpoint exactly when!

The reason that all of that works is DNS. When you tell your computer to connect to another computer by name, it uses DNS to ask “What’s the numeric address for this name?”, and once it gets that numeric address, it uses that to connect to the other computer.

When I first got access to the internet in college, it was shortly before DNS first got deployed. At the time, there was one canonical computer somewhere in California. Every other computer on the (then) arpanet would contact that computer every night, and download a file named “hosts.txt”. hosts.txt contained a list of every computer on the internet, and its numeric address. That was clearly not working any more, and DNS was designed as the replacement. When they decided to replace hosts.txt, they wanted to replace it with something much more resilient, and something that didn’t need to have all of the data in one place, and which didn’t rely on any single machine in order to work.

The result was DNS. It’s a fascinating system. It’s the first real distributed system that I ever learned about, and I still think it’s really interesting. I’m not going to go into all of the details: distributed systems always wind up with tons of kinks, to deal with the complexity of decentralized information in the real world. But I can explain the basics pretty easily.

The basic way that DNS works is really simple. You take a domain name. For example, my personal machine is named “sunwukong”, and my family network is “phouka.net” – so the hostname that I’m typing this on right now is “sunwukong.phouka.net”. To look it up, what happens is you take the host name, and split it at the dots. So for the example, that’s [“sun-wukong”, “phouka”, “net”].

So, to look up sunwukong.phouka.net, what happens is:

1. You send a request to the root nameserver, and ask for the server for “.net”?
2. The root server responds, giving you the “.net” server.
3. You send a request to the .net server address that you just got, asking “Who can tell
4. The .net server responds, and gives you an address for a DNS server that can tell
you where things are in phouka.net.
5. You ask the “phouka.net” server for the address of sunwukong
6. It finally gives you the address of your server.

This distributes the information nicely. You can have lots of root name servers – all they need to be able to do is find DNS servers for the list of top-level domains. And there can be – and are – thousands and thousands of nameservers for addresses in those top-level domains. And then each administrator of a private network can have a top-level nameserver for the computers that they directly manage. In this mess, any nameserver at any level can disappear, and all that will be affected are the names that it manages.

The problem is, there’s no one place to get information; and more importantly, every time you need to talk to another computer, you need to go through this elaborate sequence of steps.

To get around that last issue, you’ve got something called caches, on multiple levels. A cache is a copy of the canonical data, which gets kept around for some period of time. In the DNS system, you don’t need to talk to the canonical DNS server for a domain if someone has the data they need in a cache. You can (and generally do) talk to cacheing DNS servers. With a cacheing DNS server, you tell it the whole domain name that you want to look up, and it does the rest of the lookup process, and gives you the address you’re looking for. Every time it looks something up, it remembers what it did, so that it doesn’t need to ask again. So when you look up a “.com” address, your cacheing service will remember who it can ask for “.com” addresses. So most of the time, you only talk to one server, which does the hard work, and does it in a smart way so that it doesn’t need to unnecessarily repeat requests.

So now, we can get to what happened yesterday. GoDaddy, one of the major american DNS registries, went down. Exactly why it went down is not clear – a group of jerks claimed to have done a denial-of-service attack against them; but the company claims to have just had a configuration glitch. Honestly, I’m inclined to believe that it was the hackers, but I don’t know for sure.

But – the canonical server for scientopia.org was gone. So if you needed to look up scientopia.org and it wasn’t in your cache, then your cacheing nameserver would go to the .org server, and ask for scientopia; the .org nameserver would return the address of GoDaddy’s nameserver, and that wouldn’t respond. So you’d never reach us.

That’s also why some people were able to reach the site, and others weren’t. If your cacheing server had cached the address for scientopia, then you’d get the server address, and since the server was up the whole time, you’d connect right up, and everything would work.