https://twitter.com/kanzure/status/1091460316288868352

I have one announcement to make before we start the session. If you feel like after all of these talks that the thing you really need is a drink, then there's help. Chainlink is hosting the Stanford Blockchain Conference happy hour at The Patio which is the most famous bar in Palo Alto. It doesn't mean that much, but it's good fun. It's at 412 Evans St in Palo Alto, from 5-8pm and it's an open bar. Thank you very much to Chainlink for hosting this.

There seems to be a lot of excitement for this next session. We'll be talking about the future of ethereum. We will have Vlad and Vitalik. We'll do it slightly differently this time. Both will present and then we will have a Q&A and perhaps a tiny debate. Please give Vlad a warm welcome. We're very excited for the session.

Hey everyone, how's it going? I hope you're enjoying yourself. Thank you very much for having me. Today I am going to be talking about sharded consensus, an approach to scalability known as sharding but applied to consensus protocols. I'll try to get through my talk in about 20 minutes. I'll give a primer on consensus protocols and sharing, and then we talk about sharded consensus protocols in theory in the abstract. Then we will give some concrete examples for sharded consensus protocols we worked on in CBC casper research. Then we will talk about other topics that I didn't talk about, then we will conclude.

Consensus protocols are about making decisions in a distributed system in a consistent way. This could include things like faults of various kinds. Normally we talk about safety and liveness, the idea that these nodes running these systems don't make inconsistent decisions and they do eventually make decisions. Also there are other properties we want.

Consensus protocols decide on values or predicates of values or properties. For example, a binary consensus protocol can decide on a 0 bit or a 1 bit. Another one is blockchain consensus on predicates "the chain has this block at height 110". Usually nodes commit to this in the protocol.

A decision on the value is consistent by all the nodes. They should have the same values. Decisions on predicates or properties of values are consistent if there are values that jointly satisfy the predicates. The question I'm answering here is what does it mean for decisions to be consistent. This means something slightly different in the question of deciding values and deciding predicates of values. In sharding, we're not able to decide on the whole value, only the predicates of values which lets us say things about part of the value.

Consensus protocols are about making decisions on a predefined domain of consensus values. Every consensus protocol has a different notion of what it is deciding on. And these protocols are about making decisions on this domain of consensus values either directly or on predicates of value.

Here we have a sharded glass image. It's very appropriate. Sharding is all about splitting up the work of a distributed system in order to scale its capacity. Fundamentally the idea is that we are going to scale the capacity by splitting up the system. Sharding was either coined by the Computer Corporation of America 1988 or by Ultima Online in 1997. They used it to mean "using more computers as part of a system" in order to replicate or distribute the load.

Fundamentally, something sharded has more than one part or component. But a bit is not sharded. This is a really true thing I hope you believe me. A bit can't be sharded. There's no way to shard a single bit. There's no way to have a binary consensus protocol that is sharded.

We know a consensus protocol that can't be sharded (binary consensus protocols). Sharding means that there's more than one thing that we're deciding on, at the end of the day. So we can't have a sharded binary consensus protocol. What we really need is a sharded consensus value. This is a prerequisite for having a sharded consensus protocol. The consensus protocol needs multiple parts.

Decisions are on consensus values or on predicates of values. But because we want to increase the throughput of the protocol, we're going to insist that we are deciding on predicates instead of the whole value. If we decided on the whole value, it would have to be decided for every shard. Nodes should be interested in predicates that describe the value on some shard.

So nodes are not interested in the whole consensus value, but part of it. Hopefully this seems straightforward. Somehow we have to have a notion of consistency of decisions, and it's the same as before- basically, these predicates are considered consistent if there's a value in the domain of consensus values that satisfies all of them. So if you decide this shard of the broken glass is a teacup and you decide it's a wine glass, and that's inconsistent because there's no cup that satisfies both.

In a sharded protocol, we have the opportunity to make inconsistent decisions baout different parts of the consensus value or shards. So we have to choose different shards, things for it to be consistent to be a safe consensus protocol.

Consensus protocols that are sharded require sharded consensus values. Clients are interested in predicates of those values, and only certain shards. I want to tell you that sharding clients are lite clients. You may have heard of light clients before. They are consensus protocol implementations that have to do less work than an actual consensus protocol implementation.

We will imagine that sharding clients only have to do the work that pertains to decisions that pertain to the shards that they are interested in, otherwise we don't get scalability. Also, we have to imagine that this is less work than otherwise.

So we will imagine sharding clients are lite clients. It turns out that this is perfectly appropriate as a decision.

There are two kinds of lite clients.

• There are lite clients that don't look at all of the consensus value.

• There are lite clients that don't look at all of the protocol messages.

We're used to a consensus value lite client where instead of the consensus value being the blockchain, you have a header chain instead. Consensus value light clients are about using the commitments or proof systems as consensus values so that clients don't need to know or ask about the full consensus value when they are asking about predicates of consensus values. They look at a merkle root for example and they ask about properties of this merkle root which they can do through a crypto proof system. Instead of putting the whole consensus value in the consensus protocol message, instead you just use a merkle proof or some kind of commitment to some kind of proof system that lets you prove things about the consensus value. Instead of having access to the whole consensus value, you can get proofs or witness data that shows you things about the whole consensus value. The blockheader chain is an example of a lite client, where you don't need to see all the transactions. It's just merkle commitments, and miners can hide information in there. The lite clients are only interested in some parts of the consensus value. Clients are able to have the ability to look at less of the consensus value. The blockchain headerchain is an example, but also any sharding solution where you have headers for every shard in the same chain, but the different headers correspond to changes on different shards. This is a consensus value lite client only approach to sharding.

But there's another kind of lite client which I call protocol message lite client. These clients only receive certain protocol messages. They have some sort of filter on messages, and they only receives ones relevant to their decisions. Maybe they only receive messages from certain shards for exmaple. In the world of blockchain today as a sharded system, it's like the fact that you only download bitcoin if you're interested in bitcoin, or dogecoin chain blocks if you're interested in dogecoin.

There's two kinds of lite clients, the consensus value lite clients which look at less of the consensus value using proof systems that hide inflation, or this other kind of lite client called a protocol client lite client that doesn't need to look at messages. We save by not looking at all the consensus values, and by not looking at all the messages.

I'll talk about how we do this and how we come up with sharded consensus protocols. We start by defining the consensus values- we have a blockchain for every shard, and a shard to identify the shards, and then I define a sent and received message queues for every pair of shards or certain pairs of shards. I can keep track of messages sent and received from shards.

Here's a code snippet from the CBC Casper GitHub. We have a bunch of different things in a block like shard id, send log, receive log, sources like blocks from other shards, virtual machine state, all of the stuff in the data structure of a block. So we first define the domain of consensus values that the protocol is interested in, that nodes will make decisions about and then we define a fork choice rule where we define which validators have influence in which shards, and then we somehow ensure that the communication between different shards are consistent so that nodes on different shards are making consistent decisions. If we allow shards to be completely independent, then there wouldn't be much of a notion of cross-shard consistency. We should say though that a message sent should be received, and we shouldn't have a finalized message not received- it's an atomicity constraint. As soon as you have any constraints about the states that the different shards should be in; this thing that relates consensus values to shards, is going to have to do some work. I'll give an example in a moment.

So we defined sharded consensus values and then we defined a fork choice rule where from a set of protocol messages will give you a fork for every shard. This is something that if you read the CBC Casper Framework you will understand because the estimator is the main parameter of the CBC casper family.

((see video))

At the end of the day, the cool thing about the CBC Casper approach is that we can define rather complex fork choice rules without having to rethink consensus rules because it's all inherited from this framework about making decisions in consensus protocols. In this protocol, a node that is following a shard doesn't need to know everything about what's happening on a few other shards because the fork choice rule in that shard doesn't depend on the fork choice rule in the other shards and they don't need to receive all the messages from the other shards. They get some scalability from not receiving all the messages, which is the second sense of lite client, you don't get all the messages but only the messages that they decide are relevant to the decision you are making.

We want to have a consensus value and fork choice rule that will allow us to have non-deadlocking on the ethereum virtual machine. If you allow the EVM to do cross-shard calls with synchronous calls, you can get into a deadlock where a contract is waiting for a return value and then another contract calls it and hten it waits and then you get into a cycle where everyone is waiting on each other. So we want a version of the EVM that is deadlock free. This will allow synchronous calls on the virtual machine across shards.

We also need a load balancer so that the load on different shards will be balanced over time so you can't overload one shard and make it very expensive to participate or use that shard. Load balancing also reduces the overhead of the routing protocol because we get a lot of scalability from the routing protocol.

You might ask me, what about liveness? Liveness isn't really in scope right now because it's going to be something we work on for consensus protocols, and for sharded consensus protocols a similar solution would apply. Liveness strategies for blockchain will work for shard chains.

Validity and data availability are often problems that we talk about when it comes to sharding. I don't think this is a consensus protocol problem. Consensus protcols are about making decisions in the presence of mutually exclusive options. Availability isn't a consensus protocol problem. You don't need availability for consensus.

Validator rotation would be interesting, proof-of-stake too, and cryptoeconomics. Since these systems are dewsigned to be incentivized, we can leave those conversations for later. Also, you know, cause this talk is really just a distributed systems talk.

Let me wrap up by going over the main takeaways.

Binary consensus protocols cannot be sharded. Sharding consensus requires sharded consensus values. Sharding clients are light clients. Sharded consensus protocols exist. Sharding is a natural way to scale consensus protocols.