Nightshade: Near Protocol Sharding Design 2.0
Alex Skidanov
7/AlexSkidanov
alex@near.org
Illia Polosukhin
7/ilblackdragon
illia@near.org
Bowen Wang
7/BowenWang18
bowen@near.org
July 2019
Updated February 2024
Contents
1
Sharding Basics
4
1.1
Validator partitioning and beacon chains . . . . . . . . . . . . . .
4
1.2
Quadratic sharding . . . . . . . . . . . . . . . . . . . . . . . . . .
5
1.3
State sharding
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.4
Cross-shard transactions . . . . . . . . . . . . . . . . . . . . . . .
7
1.5
Malicious behavior . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.5.1
Malicious forks . . . . . . . . . . . . . . . . . . . . . . . .
8
1.5.2
Approving invalid blocks . . . . . . . . . . . . . . . . . . .
9
2
State Validity and Data Availability
10
2.1
Validators rotation . . . . . . . . . . . . . . . . . . . . . . . . . .
11
2.2
State validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2.3
Fishermen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
2.4
Stateless Validation . . . . . . . . . . . . . . . . . . . . . . . . . .
16
2.5
Succinct Non-interactive Arguments of Knowledge
. . . . . . . .
17
2.6
Data Availability . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
2.6.1
Proofs of Custody
. . . . . . . . . . . . . . . . . . . . . .
20
2.6.2
Erasure Codes
. . . . . . . . . . . . . . . . . . . . . . . .
21
2.6.3
Polkadot’s approach to data availability . . . . . . . . . .
21
2.6.4
Long term data availability . . . . . . . . . . . . . . . . .
22
3
Nightshade
23
3.1
From shard chains to shard chunks . . . . . . . . . . . . . . . . .
23
3.2
Consensus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
3.3
Block production . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
3.4
Ensuring data availability . . . . . . . . . . . . . . . . . . . . . .
26
3.4.1
Dealing with lazy block producers
. . . . . . . . . . . . .
26
3.5
State transition application . . . . . . . . . . . . . . . . . . . . .
27
3.5.1
State Representation . . . . . . . . . . . . . . . . . . . . .
28
1
3.6
Cross-shard transactions and receipts . . . . . . . . . . . . . . . .
29
3.6.1
Receipt transaction lifetime . . . . . . . . . . . . . . . . .
29
3.6.2
Handling too many receipts . . . . . . . . . . . . . . . . .
30
3.7
Chunks validation
. . . . . . . . . . . . . . . . . . . . . . . . . .
32
3.7.1
Zero-knowledge proofs . . . . . . . . . . . . . . . . . . . .
33
3.8
State Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
3.9
Snapshots Chain . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
4
Conclusion
34
2
Introduction
The past several years have seen significant progress in scaling blockchains. Back
in 2018, when the early team first started to build NEAR Protocol, Ethereum
was one of the only smart contract platforms available to builders and it suffered
from high transaction costs and low throughput. Today, Ethereum is scaling
through rollups (a.k.a. L2s1). Quite a few other layer 1 blockchains such as
Polkadot, Solana, Sui, and Aptos that promise different kinds of scaling have
launched in the past few years.
In general, most layer-one blockchains use one of two scaling approaches:
vertical scaling and horizontal scaling. For vertical scaling, a few approaches
are explored, including each validator operating more expensive hardware, par-
allelizing transaction execution, separating consensus from execution, etc. Hor-
izontal scaling has two categories: heterogeneous and homogeneous.
In the
heterogeneous approach, there are different chains or execution environments
that operate independently and there is usually a main chain that orchestrates
everything and provides security guarantees. In the homogeneous approach, a
blockchain is divided into multiple parallel shards that have the same execution
environment and the protocol dictates how different shards communicate with
each other. Each shard maintains its own state, which allows the blockchain to
scale linearly by adding more shards.
NEAR Protocol chooses a homogeneous sharding approach to build a highly
scalable blockchain. This document outlines the general approach to blockchain
sharding as it exists today, the major problems that need to be overcome (in-
cluding state validity and data availability problems), and presents Nightshade,
the solution NEAR Protocol is built upon that addresses those issues. As of
this writing of the revised version in February 2024, NEAR has been live for
three and half years. There have been a total of 750M transactions and 57M
accounts on chain since the mainnet launch in October 2020.
Since the original publication of the Nightshade whitepaper in July 2019,
there have been a lot of new developments in blockchain protocol research, most
notably zero-knowledge proofs. In the case of NEAR, during the implementation
of Nightshade across different phases over three-plus years, we gained a better
understanding of blockchain scalability and also came to realize some limitations
of the original design. In short, we underestimated the engineering complexity
of fraud proofs in a sharded blockchain and started to question whether that
was still the most promising direction for the protocol. Recent advancements in
stateless validation research provided an alternative path, which not only elim-
inates the critical dependency on fraud proofs, but also significantly increases
per-shard throughput by enabling nodes to hold state in memory. As a result,
we decided to update the Nightshade design to incorporate stateless validation.
This is a revised edition of the Nightshade paper, version 2.0, reflecting this
change in the Nightshade roadmap. We will also elaborate on the power of zero-
knowledge proofs, and how they may be combined with stateless validation to
1Technically a rollup is a type of L2, but the success of rollups has made the two terms
almost synonymous today.
3
produce a future-proof sharding design that is both highly scalable and highly
decentralized. New or heavily modified text is included in section 2.4, section
2.5, section 3.5, section 3.7, and section 3.8.
1
Sharding Basics
Let’s start with the simplest approach to sharding. In this approach, instead
of running one blockchain we will run multiple, and call each such blockchain a
“shard.” Each shard will have its own set of validators. Here, and below, we use
a generic term “validator” to refer to participants that verify transactions and
produce blocks — either by mining, such as in Proof of Work, or via a voting-
based mechanism. For now, let’s assume that the shards never communicate
with each other.
This design, though simple, is sufficient to outline some of the initial major
challenges in sharding.
1.1
Validator partitioning and beacon chains
Say that the system comprises 10 shards. The first challenge is that with each
shard having its own validators, each shard is now 10 times less secure than the
entire chain. So if a non-sharded chain with X validators decides to hard-fork
into a sharded chain, and splits X validators across 10 shards, each shard now
only has X/10 validators, and corrupting one shard only requires corrupting
5.1% (51% / 10) of the total number of validators (see figure 1),
Figure 1:
Splitting the validators across shards
which brings us to the second point: who chooses the validators for each shard?
Controlling 5.1% of validators is only damaging if all those 5.1% of validators
are in the same shard. If validators can’t choose which shard they get to validate
4
in, a participant controlling 5.1% of the validators is highly unlikely to get all
their validators in the same shard, heavily reducing their ability to compromise
the system.
Almost all sharding designs today rely on some source of randomness to
assign validators to shards. Randomness on a blockchain is in itself a very chal-
lenging topic and is out of scope for this document. For now let’s assume there’s
some source of randomness we can use. We will cover validator assignment in
more detail in section 2.1.
Both randomness and validator assignment require computation that is not
specific to any particular shard. For that computation, practically all existing
designs have a separate blockchain that is tasked with performing operations
necessary for the maintenance of the entire network. Besides generating random
numbers and assigning validators to the shards, these operations often also
include receiving updates from shards and taking snapshots of them, processing
stakes and slashing in Proof-of-Stake systems, and rebalancing shards (when
that feature is supported). Such chain is called a Beacon chain in Ethereum, a
Relay chain in Polkadot, and the Cosmos Hub in Cosmos.
Throughout this document we will refer to such a chain as a Beacon chain.
The existence of the Beacon chain brings us to the next interesting topic:
quadratic sharding.
1.2
Quadratic sharding
Sharding is often advertised as a solution that scales infinitely with the number
of nodes participating in the network operation. While it is possible in theory
to design such a sharding solution, any solution that includes the concept of
a beacon chain doesn’t have infinite scalability. To understand why, note that
the beacon chain has to do some bookkeeping computation, such as assigning
validators to shards, or snapshotting shard chain blocks, that is proportional to
the number of shards in the system. Since the beacon chain is itself a single
blockchain, with computation bounded by the computational capabilities of its
operating nodes, the number of shards is naturally limited.
However, the structure of a sharded network does bestow a multiplicative
effect on any improvements to its nodes. Consider the case in which an arbitrary
improvement is made to the efficiency of nodes in the network, which will allow
them to process transactions faster.
If the nodes operating the network, including the nodes in the beacon chain,
become four times faster, then each shard will be able to process four times more
transactions, and the beacon chain will be able to maintain 4 times more shards.
The throughput across the system will increase by the factor of 4 × 4 = 16 —
thus the name quadratic sharding.
It is hard to provide an accurate measurement for how many shards are
viable today, but it is unlikely that in any foreseeable future the throughput
needs of blockchain users will outgrow the limitations of quadratic sharding.
5
1.3
State sharding
Until now, we haven’t defined clearly what exactly is and is not separated when
a network is divided into shards. Specifically, nodes in a blockchain perform
three important tasks: not only do they 1) process transactions, they also 2)
relay validated transactions and completed blocks to other nodes and 3) store
the state and the history of the entire network ledger. Each of these three tasks
imposes a growing requirement on the nodes operating the network:
1. The task of processing transactions requires more compute power with the
increasing number of transactions being processed;
2. The task of relaying transactions and blocks requires more network band-
width with the increasing number of transactions being relayed;
3. The task of storing data requires more storage as the state grows. Im-
portantly, unlike the needs for processing power and network bandwidth,
the storage requirement grows even if the transaction rate (number of
transactions processed per second) remains constant.
From the above list it might appear that the storage requirement would be
the most pressing, since it is the only one that is being increased over time
even if the number of transactions per second doesn’t change, but in practice
the most pressing requirement today is the compute power. The entire state
of Ethereum as of this writing is 300GB — easily manageable by most of the
nodes.
But the number of transactions Ethereum can process per second is
around 20 - orders of magnitude less than what is needed for many practical
use cases.
Zilliqa is the most well-known project that shards processing but not storage.
Sharding of processing is an easier problem because each node keeps the entire
state, meaning that contracts can freely invoke other contracts and read any data
from the blockchain. Some careful engineering is needed to make sure updates
from multiple shards updating the same parts of the state do not conflict. In
those regards Zilliqa takes a relatively simplistic approach2.
While sharding of storage without sharding of processing was proposed, it is
extremely uncommon. Thus in practice sharding of storage, or State Sharding,
almost always implies sharding of processing and sharding of network band-
width.
Practically, under State Sharding, the nodes in each shard are building their
own blockchain that contains transactions affecting only the local part of the
global state that is assigned to that shard. Therefore, the validators in each
shard only need to store their local part of the global state and only execute
(and as such only relay) transactions that affect their part of the state. This
partition linearly reduces the requirement on all compute power, storage, and
network bandwidth, but introduces new problems, such as data availability and
cross-shard transactions, both of which we will cover below.
2Our analysis of their approach can be found here: https://medium.com/nearprotocol/
8f9efae0ce3b
6
1.4
Cross-shard transactions
The sharding model we have described so far is not very useful, because if in-
dividual shards cannot communicate with each other, they are no better than
multiple independent blockchains that can’t efficiently communicate. The pro-
liferation of bridges in today’s multichain ecosystem is a clear signal that cross-
chain (or cross-shard) communication is crucial.
For now let’s only consider simple payment transactions, where each partici-
pant has an account on exactly one shard. If someone wishes to transfer money
from one account to another within the same shard, the transaction can be
processed entirely by the validators in that shard. If, however, Alice resides on
shard #1, and wants to send money to Bob who resides on shard #2, neither the
validators on shard #1 (who can’t credit Bob’s account) nor the validators on
shard #2 (who can’t debit Alice’s account) can process the entire transaction.
There are two families of approaches to cross-shard transactions:
• Synchronous: whenever a cross-shard transaction needs to be executed,
all blocks in the relevant shards that contain state transitions related to
the transaction get produced at the same time, and the validators of the
relevant shards collaborate to execute such transactions.3
• Asynchronous: a cross-shard transaction that affects multiple shards is
executed in those shards asynchronously, the “Credit” shard executing its
half once it has sufficient evidence that the “Debit” shard has executed its
portion. This approach tends to be more prevalent due to its simplicity
and ease of coordination. This system has been proposed today in Cosmos,
Ethereum Serenity, NEAR, Kadena, and others.
A problem with this
approach is that if blocks are produced independently, there’s a non-zero
chance that one of the multiple blocks will be orphaned, thus making the
transaction only partially applied. Consider figure 2 depicting two shards,
both of which encounter a fork, and a cross-shard transaction that was
recorded in blocks A and X’ correspondingly. If the chains A-B and V’-X’-
Y’-Z’ end up being canonical in the corresponding shards, the transaction
is fully finalized.
If A’-B’-C’-D’ and V-X become canonical, then the
transaction is fully abandoned, which is acceptable. But if, for example, A-
B and V-X become canonical, then one part of the transaction is finalized
and one is abandoned, creating an atomicity failure. We will cover how
this problem is addressed in the proposed protocols in the second part,
when covering changes to the fork-choice rules and consensus algorithms
proposed for sharded protocols.
Note that communication between chains is useful outside of sharded blockchains
too. Interoperability between chains is a complex problem that many projects
are trying to solve.
In sharded blockchains the problem is somewhat more
3The
most
detailed
proposal
known
to
the
authors
of
this
doc-
ument
is
Merge
Blocks,
described
here:
https://ethresear.ch/t/
merge-blocks-and-synchronous-cross-shard-state-execution/1240
7
Figure 2:
Asynchronous cross-shard transactions
straightforward since the block structure and consensus are the same across
shards, and there’s a beacon chain that can be used for coordination. However,
in a sharded blockchain, all the shard chains are the same, while in the global
blockchain ecosystem there are lots of different blockchains, with different target
use cases, levels of decentralization, and privacy guarantees.
Building a system in which a set of chains have different properties but use
sufficiently similar consensus and block structure and have a common beacon
chain could enable an ecosystem of heterogeneous blockchains with a working
interoperability subsystem. Such a system is unlikely to feature validator rota-
tion, so some extra measures need to be taken to ensure security. Both Cosmos
and Polkadot are effectively these types of systems.4
1.5
Malicious behavior
In this section we will review the adversarial behaviors malicious validators can
exercise if they manage to corrupt a shard. We will review classic approaches
to avoiding corrupting shards in section 2.1.
1.5.1
Malicious forks
A set of malicious validators might attempt to create a fork.
Note that it
doesn’t matter if the underlying consensus is byzantine fault tolerant (BFT) or
not; corrupting a sufficient number of validators will always make it possible to
create a fork.
It is significantly more likely that more that 50% of a single shard will be
corrupted, than that more than 50% of the entire network will be corrupted (we
4Refer to this writeup by Zaki Manian from Cosmos: and this Tweet-storm by the first
author of this document: for a detailed comparison of the two.
8
will dive deeper into these probabilities in section 2.1). As discussed in section
1.4, cross-shard transactions involve certain state changes in multiple shards,
and the corresponding blocks in such shards that apply such state changes must
either all be finalized (i.e. appear in the selected chains on their corresponding
shards), or all be orphaned (i.e. not appear in the selected chains on their cor-
responding shards). Since in general the probability of shards being corrupted
is non-negligible, we can’t assume that forks won’t happen, even if byzantine
consensus had been reached among the shard validators, or if many blocks were
produced on top of the block with the state change.
This problem has multiple potential solutions, the most common being oc-
casional cross-linking of the latest shard chain block to the beacon chain. The
fork choice rule in the shard chains is then changed to always prefer the chain
that is cross-linked, and only apply the shard-specific fork-choice rule for blocks
published since the last cross-link.
1.5.2
Approving invalid blocks
A set of validators might attempt to create a block that applies the state tran-
sition function incorrectly. For example, starting with a state in which Alice
has 10 tokens and Bob has 0 tokens, the block might contain a transaction that
sends 10 tokens from Alice to Bob, but ends up with a state in which Alice has
0 tokens and Bob has 1000 tokens, as shown in figure 3.
Figure 3:
An example of an invalid block
In a classic non-sharded blockchain, such an attack is not possible, since all
the participants in the network validate all the blocks. A block with such an
invalid state transition will be rejected both by other block producers and non-
block-producing participants in the network. Even if the malicious validators
continue creating blocks on top of the invalid block faster than honest validators
build the correct chain (thus making the chain with the invalid block longer than
9
the chain without it), every participant using the blockchain for any purpose
validates all the blocks, and will discard all blocks built on top of the invalid
block.
Figure 4:
Attempt to create an invalid block in a non-sharded blockchain
In figure 4 there are five validators, three of whom are malicious.
They
created an invalid block A’, and then continued building new blocks on top
of it.
Two honest validators have discarded A’ as invalid and built on top
of the last valid block known to them, creating a fork. Since there are fewer
validators in the honest fork, their chain is shorter. However, in a classic non-
sharded blockchain, every participant using the blockchain for any purpose is
responsible for validating all the blocks they receive and recomputing the state.
Thus any person with any interest in the blockchain would observe that A’ is
invalid, and thus also immediately discard B’, C’ and D’, taking the chain A-B
as the current longest valid chain.
In a sharded blockchain, however, no participant can validate all the trans-
actions on all the shards, so they need to have some way to confirm that at no
point in the history of any shard of the blockchain was an invalid block included.
Note that unlike with forks, cross-linking to the beacon chain is not a suf-
ficient solution for proving that the blockchain’s history contains no invalid
blocks, since the beacon chain doesn’t have the capacity to validate the blocks.
It can only validate that a sufficient number of validators in that shard signed
the block (and as such have attested to its correctness).
We will discuss solutions to this problem in section 2.2 below.
2
State Validity and Data Availability
The core idea in sharded blockchains is that most participants operating or
using the network cannot validate all blocks in all shards. As such, whenever
10
any participant needs to interact with a particular shard, they generally cannot
download and validate the entire history of the shard.
The partitioning aspect of sharding, however, raises a significant potential
problem: without downloading and validating the entire history of a particular
shard, a participant cannot be certain that the state they interact with is the
result of some valid sequence of blocks and that this sequence of blocks is indeed
the canonical chain in the shard. This is a problem that doesn’t exist in non-
sharded blockchains.
We will first present a simple solution to this problem that has been previ-
ously proposed by many protocols, and then analyze the way this solution can
break and the attempts that have been made to address it.
2.1
Validators rotation
The naive solution to state validity is shown on figure 5: let’s say we assume
that the entire system has on the order of thousands of validators, out of which
no more than 20% are malicious or will otherwise fail (such as by failing to be
online to produce a block). Then if we sample 200 validators, the probability
of more than 1/3 failing can (for practical purposes) be assumed to be zero.
Figure 5:
Sampling validators
1/3 is an important threshold.
There’s a family of consensus protocols,
called byzantine fault tolerant (BFT) consensus protocols, that guarantees that
as long as fewer than 1/3 of participants fail, either by crashing or by acting in
some way that violates the protocol, consensus will be reached.
With this assumed honest validator percentage, if the current set of valida-
tors in a shard provides us with some block, the naive solution assumes that
the block is valid and that it is built on what the validators believed to be
the canonical chain for that shard when they started validating. The validators
learned the canonical chain from the previous set of validators, who by the same
11
assumption built on top of the block which was the head of the canonical chain
before that. By induction the entire chain is valid, and since no set of validators
at any point produced forks, the naive assumption is also that the current chain
is the only chain in the shard. See figure 6 for a visualization.
Figure 6:
A blockchain with each block finalized via BFT consensus
This simple solution doesn’t work if we assume that validators can be cor-
rupted adaptively, which is not an unreasonable assumption.5 Adaptively cor-
rupting a single shard in a system with 1000 shards is significantly cheaper than
corrupting the entire system. Therefore, the security of the protocol decreases
linearly with the number of shards. To be certain about the validity of a block,
we must know that at any point in history no shard in the system has had a
colluding validator majority; taking into consideration adaptive adversaries, we
no longer have such certainty. As we discussed in section 1.5, colluding valida-
tors can produce two basic malicious behaviors: creating forks, and producing
invalid blocks.
Malicious forks can be addressed by blocks being cross-linked to the beacon
chain, which is generally designed to have significantly higher security than
the shard chains.
Producing invalid blocks, however, is a significantly more
challenging problem to tackle.
2.2
State validity
Consider figure 7, in which Shard #1 is corrupted and a malicious actor produces
invalid block B. Suppose that in this block B 1000 tokens were minted out of thin
air and deposited in Alice’s account. The malicious actor then produces valid
5Read
this
article
for
details
on
how
adaptive
corruption
can
be
carried
out:
https://medium.com/nearprotocol/d859adb464c8.
For
more
details
on
adap-
tive
corruption,
read
https://github.com/ethereum/wiki/wiki/Sharding-FAQ#
what-are-the-security-models-that-we-are-operating-under.
12
block C (in a sense that the transactions in C are applied correctly) on top of B,
obfuscating the invalid block B, and initiates a cross-shard transaction to Shard
#2 that transfers those 1000 tokens to Bob’s account. From this moment, the
improperly created tokens reside on an otherwise completely valid blockchain in
Shard #2.
Figure 7:
A cross-shard transaction from a chain that has an invalid block
Some simple approaches to tackling this problem are:
1. For validators of Shard #2 to validate the block from which the transaction
is initiated. This won’t work even in the example above, since block C
appears to be completely valid.
2. For validators in Shard #2 to validate some large number of blocks pre-
ceding the block from which the transaction is initiated. Naturally, for
any number of blocks N validated by the receiving shard, the malicious
validators can create N+1 valid blocks on top of the invalid block they
produced.
A promising idea to resolve this issue would be to arrange shards into an
un-directed graph in which each shard is connected to several other shards, and
only allow cross-shard transactions between neighboring shards (for example,
this is how Vlad Zamfir’s sharding essentially works6, and a similar idea is used
in Kadena’s Chainweb [1]). If a cross-shard transaction is needed between shards
that are not neighbors, such a transaction is routed through multiple shards.
In this design, a validator in each shard is expected to validate all the blocks
in their shard as well as all the blocks in all the neighboring shards. Consider
the figure below with 10 shards, each with four neighbors, where no two shards
require more than two hops for a cross-shard communication — shown on figure
8.
6Read more about the design here: https://medium.com/nearprotocol/37e538177ed9.
13
Figure 8:
An invalid cross-shard transaction in a Chainweb-like system that
will get detected
Shard #2 is not only validating its own blockchain, but also the blockchains
of all the neighbors, including Shard #1. So if a malicious actor on Shard #1
attempts to create an invalid block B, then build block C on top of it and initiate
a cross-shard transaction, such a cross-shard transaction will not go through
since Shard #2 will have validated the entire history of Shard #1 which will
have caused it to identify invalid block B.
While corrupting a single shard is no longer a viable attack, corrupting a
few shards remains a potential problem. On figure 9 an adversary corrupting
both Shard #1 and Shard #2 successfully executes a cross-shard transaction to
Shard #3 with funds from an invalid block B by corrupting both shard #1 and
shard #2.
Shard #3 validates all the blocks in Shard #2, but not in Shard #1, and
has no way to detect the malicious block.
There are a few directions towards properly solving state validity: fraud
proofs, stateless validation, and cryptographic computation proofs.
2.3
Fishermen
The idea behind the first approach is the following: whenever a block header
is communicated between chains for any purpose (such as cross-linking to the
beacon chain, or a cross-shard transaction), there is a period of time during
which any honest validator can provide a proof that the block is invalid. There
are various constructions that enable very succinct proofs that the blocks are
invalid, so the communication overhead for the receiving nodes is much smaller
than that of receiving a full block.
With this approach, as long as there is at least one honest validator in the
shard, the system is secure.
14
Figure 9:
An invalid cross-shard transaction in a Chainweb-like system that
will not get detected
Figure 10:
Fisherman
This is a well-known approach to the state validity problem. This approach,
however, has a few major disadvantages:
1. The challenge period needs to be sufficiently long for the honest validator
to recognize a block was produced, download it, fully verify it, and prepare
the challenge (if the block is in fact invalid). Introducing such a period
would significantly slow down cross-shard transactions.
2. The existence of the challenge protocol creates a new vector of attack as
malicious nodes could spam the network with invalid challenges. An obvi-
15
ous solution to this problem is to make challengers deposit some amount
of tokens that are returned if the challenge is valid. This is only a par-
tial solution, as it might still be beneficial for the adversary to spam the
system with invalid challenges (and burn their deposits) — for example,
to prevent a valid challenge from an honest validator from going through.
These attacks are called Grieving Attacks.
3. The implementation is quite complex and difficult to test. The complexity
comes from two aspects: 1) making sure that a challenge can be properly
processed by validators who do not have the state of the shard and 2)
rolling back the state of the blockchain after a challenge is successfully
processed and slashing the offending validator. The first one is not easy,
as it involves building a lot of the machinery described in section 2.4. The
second one is even more difficult, especially in a sharded blockchain. When
an invalid state transition is found, we cannot just roll back the state of the
affected shard because it is possible for an invalid state transition to mint,
say, 1 billion native or fungible tokens, and send them to other shards.
As a result, the state of all shards needs to be rolled back simultaneously,
which leads to a multitude of problems, especially in regards to consensus
and validator assignment.
Testing challenges is even more daunting. This is a mechanism that is
never expected to trigger in practice yet is crucial to the design – if some-
one finds a vulnerability in the implementation, it is very likely that the
exploit could cause someone to lose money. This is likely the reason why
after quite a few years of the idea being proposed, there is still no full (i.e.,
permissionless) implementation of challenges in any blockchain at the time
of writing.7
2.4
Stateless Validation
One of the core issues mentioned in section 2.1 is the adaptive corruption of
validators in a single shard. This is why the simple solution proposed above
does not work. One possible way to address the issue is to rotate validators very
frequently, e.g. every block. Since we assume that each validator assignment
is the result of a shuffling based on the on-chain randomness beacon, adaptive
corruption won’t work in this case.
However, a core challenge of rotating validators with each block is determin-
ing how validators will retrieve the state to verify the validity of shard blocks,
since they may be assigned to a different shard at every single block. Assuming
fast blocks (˜1s) and a reasonable state size (>10GB), it is not feasible to expect
that a node could download the state of a new shard and validate the new block
within a few seconds.
Stateless validation provides an elegant solution. Instead of maintaining the
full state to execute transactions and validate blocks, a validator is provided a
7Arbitrum implemented fraud proofs but as of this writing, only whitelisted entities can
submit them.
16
state witness to validate a block. State Witness refers to the state touched
during the execution of a chunk alonside with proof that they belong to the
state as of before the chunk. More specifically, assuming that a validator needs
to validate block h and it knows the prev state root sprev, i.e, state root as of
before block h, then a state witness is the state touched during the execution of
h and the proof that they indeed belong to the state specified by sprev. Using
the state witness, a validator can execute and verify blocks without maintaining
the state of that shard locally.
In order for stateless validation to work, some entity needs to be able to
provide state witness. Usually they are part of the overall validator set so that
they have an incentive to maintain the full state. It is worth noting that the state
witness provider, while necessary for the network to function, could not single-
handedly corrupt the network. This is because the state witness is validated
against the state root and even though a single node may be responsible for
producing the state witness, it has no way to produce an invalid state witness
without getting caught.
This property enables a design where most validators are lightweight and
only a few validators need to operate more expensive hardware. This is beneficial
for the decentralization of a blockchain network as it lowers the barrier to entry
to become a validator.
2.5
Succinct Non-interactive Arguments of Knowledge
Another solution to the multiple-shard corruption problem is to use a crypto-
graphic construction that allow participants to prove that a certain computation
(such as computing a block from a set of transactions) was carried out correctly.
Such constructions do exist, e.g. zkSNARKs, zkSTARKs, and a few others, and
some are actively used in blockchain protocols today for private payments —
most notably ZCash. The primary problem with such primitives is that they are
notoriously slow to compute. Despite significant progress made in this area in
the past few years with the emergence of new proving systems and engineering
optimizations, the state-of-the-art zero-knowledge (ZK) proving systems today
such as Polygon Hermez and Risc Zero are about 10,000 times slower than native
execution.
Interestingly, a proof doesn’t need to be computed by a trusted party, since
the proof not only attests to the validity of the computation it is validating, but
also to the validity of the proof itself. Thus, the computation of such proofs
can be split among a set of participants with significantly less redundancy than
would be necessary to perform some trustless computation. It also allows for
participants computing zk-SNARKs to run their processes on special hardware
without reducing the decentralization of the system.
The challenges of zk-SNARKs, besides performance, are:
1. Dependence on less researched and less time-tested cryptographic primi-
tives;
17
2. “Toxic waste” — zkSNARKs depend on a trusted setup in which a group
of people performs some computation and then discards the intermediate
values of that computation. If all the participants of the procedure collude
and keep the intermediate values, fake proofs can be created. zkSTARKs,
however, do not rely on the same underlying cryptographic primitives such
as KZG commitments and therefore do not suffer from the same problem;
3. Extra complexity introduced into the system design. While there are a
few zkEVM rollups in production today, they rely on a single sequencer
and a single prover, which greatly simplifies the design. How exactly ZK
proofs should be integrated into the design of a scalable blockchain is still
mostly a research topic at this point;
4. Generating zk-SNARK proofs can be computationally intensive, especially
for complex computation such as hashing. The computational resources
required for zk proofs inevitably lead to high cost of proof generation,
which poses a barrier for wide adoption.
Despite these challenges, the field of zero-knowledge research is progressing
very rapidly. When this paper was first written in 2019, few people believed
that a production-grade zkEVM would be possible in the next five years. Today,
however, there are multiple zkEVMs (Polygon, Scroll, zkSync, Linea, etc.) live
on Ethereum mainnet. It is not unreasonable to expect that in the next one or
two years, the overhead of zero-knowledge proofs would go down by one or two
orders of magnitude, which would greatly improve their usability in different
applications, including protocol design.
2.6
Data Availability
The second problem we will touch upon is data availability. Generally nodes
operating a particular blockchain are separated into two groups: Full Nodes,
those that download every full block and validate every transaction, and Light
Nodes, those that only download block headers, and use Merkle proofs for the
parts of state and transactions they are interested in, as shown in figure 11.
Now, if a majority of full nodes collude, they can produce a block (valid
or invalid) and send its hash to the light nodes, while never disclosing the full
contents of the block. There are various ways they can benefit from this. For
example, consider figure 12.
There are three blocks: the previous, A, is produced by honest validators;
the current, B, is produced by colluding validators; and the next, C, also will
be produced by honest validators (the blockchain is depicted in the lower right
corner).
Say you are a merchant. The validators of the current block (B) received
block A from the previous validators, computed a block in which you receive
money, and sent you a header of that block with a Merkle proof of the state
in which you have money (or a Merkle proof of a valid transaction that sends
18
Figure 11:
Merkle Tree
Figure 12:
Data Availability problem
the money to you). Confident that the transaction is finalized, you provide the
service.
However, the validators never distributed the full content of block B to
anyone. As such, the honest validators of block C can’t retrieve the block, and
are either forced to stall the system or to build on top of A — and depriving
you, as a merchant, of money.
When we apply the same scenario to sharding, the definitions of full and
light node generally apply per shard: validators in each shard download every
block in that shard and validate every transaction in that shard, but other
nodes in the system, including those that snapshot shard chains’ state into the
19
beacon chain, only download the headers. Thus the validators in the shard are
effectively full nodes for that shard, while other participants in the system,
including the beacon chain, operate as light nodes.
For the fisherman approach we discussed above to work, honest validators
need to be able to download blocks that are cross-linked to the beacon chain.
If malicious validators cross-linked the header of an invalid block (or used it
to initiate a cross-shard transaction), but never distributed the block, honest
validators have no way to craft a challenge.
We will cover three complementary approaches to address this problem.
2.6.1
Proofs of Custody
The most immediate problem to be solved is whether a block is available once
it is published.
One proposed idea is to have so-called Notaries that rotate
between shards more often than validators whose only job is to download a
block and attest to the fact that they were able to download it. They can be
rotated more frequently because they don’t need to download the entire state of
the shard — unlike validators, who cannot be rotated as frequently, since they
must download the entire state of the shard each time they rotate (as shown in
figure 13).8
Figure 13:
Validators need to download state and thus cannot be rotated
frequently
The problem with this naive approach is that it is impossible to prove later
whether the Notary was or was not able to download the block, so a Notary
can choose to always attest that they were able to download the block without
even attempting to retrieve it. One solution to this is for Notaries to provide
8Here we assume stateless validation is not used, so validators all have to download the
state of a shard.
20
some evidence or to stake some amount of tokens attesting that the block was
downloaded. One such solution is discussed here: https://ethresear.ch/t/
1-bit-aggregation-friendly-custody-bonds/2236.
2.6.2
Erasure Codes
When a particular light node receives a hash of a block, in order to increase the
node’s confidence that the block is available, it can attempt to download a few
random pieces of the block. This is not a complete solution, because unless the
light nodes collectively download the entire block, the malicious block producers
can choose to withhold the parts of the block that were not downloaded by any
light node, thus still making the block unavailable.
One solution is to use a construction called Erasure Codes to make it possible
to recover the full block even if only some part of the block is available, as shown
in figure 14.
Figure 14:
Merkle tree built on top of erasure coded data
Both Polkadot and Ethereum Serenity have designs around this idea that
provide a way for light nodes to be reasonably confident that blocks are available.
The Ethereum Serenity approach has a detailed description in [2].
2.6.3
Polkadot’s approach to data availability
In Polkadot, like in most sharded solutions, each shard (called a parachain) sends
snapshots of its blocks to the beacon chain (called a relay chain). Say there are
2f + 1 validators on the relay chain.
The block producers of the parachain
blocks (called collators) compute an erasure coded version of the block that
consists of 2f + 1 parts, once the parachain block is produced, such that any f
parts are sufficient to reconstruct the block. They then distribute one part to
each validator on the relay chain. A particular relay chain validator would only
21
sign a relay chain block if they have their part of each parachain block that is
snapshotted to the relay chain block. Thus, if a relay chain block has signatures
from 2f + 1 validators, and as long as no more than f of them have violated
the protocol, each parachain block can be reconstructed by fetching the relevant
parts from the validators that followed the protocol. See figure 15.
Figure 15:
Polkadot’s data availability
2.6.4
Long term data availability
Note that all the approaches discussed above only attest to the fact that a block
was published at all, and is available now. Blocks can become unavailable later
for a variety of reasons: nodes going offline, nodes intentionally erasing historical
data, etc.
A whitepaper worth mentioning that addresses this issue is Polyshard’s [3],
which uses erasure codes to make blocks available across shards even if several
shards completely lose their data. Unfortunately their specific approach requires
that all the shards download blocks from all other shards, which is prohibitively
expensive.
Long-term availability is not as pressing an issue; since no participant in the
system is expected to be capable of validating all the chains in all the shards,
the security of a sharded protocol needs to be designed in such a way that the
system is secure even if some of the old blocks in some shards become completely
unavailable.
22
3
Nightshade
3.1
From shard chains to shard chunks
The sharding model with shard chains and a beacon chain is very powerful but
comes with certain complexities. In particular, the fork choice rule needs to be
executed separately in each chain, so therefore the fork choice rule in the shard
chains and the beacon chain must be built differently and tested separately.
In Nightshade we model the system as a single blockchain, in which each
block logically contains all the transactions for all the shards, and changes the
whole state of all the shards. Physically, however, no participant downloads the
full state or the full logical block. Instead, each participant of the network either
maintains the state that corresponds to the shards that they validate transac-
tions for, or relies on others to provide them with state to validate transactions.
The list of all the transactions in the block is split into physical chunks (one
chunk per shard).
Under ideal conditions each block contains exactly one chunk per shard per
block, which roughly corresponds to the model with shard chains in which the
shard chains produce blocks with the same speed as the beacon chain. However,
some chunks might be missing due to network delays, so in practice each block
contains either one or zero chunks per shard. See section 3.3 for details on how
blocks are produced.
Figure 16:
A model with shard chains on the left and with one chain with
blocks split into chunks on the right
3.2
Consensus
The two dominant approaches to consensus in blockchains today are longest (or
heaviest) chain, in which the chain that has the most work or stake used to
23
build it is considered canonical, and BFT, in which some set of validators reach
BFT consensus for each block.
In the recently proposed protocols, the latter is the dominant approach, since
it provides immediate finality. In the longest chain approach, more blocks need
to be built on top of a given block to ensure finality. For meaningful security,
the time it takes for a sufficient number of blocks to be built is often on the
order of hours.
Using BFT consensus on each block also has disadvantages, such as:
1. BFT consensus involves a considerable amount of communication. While
recent advances allow consensus to be reached in an interval of time that
is linear to number of participants (see e.g. [4]), it still adds noticeable
overhead per block;
2. It is not feasible for all network participants to participate in BFT con-
sensus for each block; thus, usually only a randomly sampled subset of
participants reaches consensus. A randomly sampled set can, in princi-
ple, be adaptively corrupted, and in theory a fork can be created. The
system either needs to be modelled to be ready for such an event, and
thus still have a fork-choice rule in addition to BFT consensus, or should
be designed to shut down in such an event. It is worth mentioning that
some designs, such as Algorand [5], significantly reduce the probability of
adaptive corruption.
3. Most importantly, the system stalls if 1
3 or more of all the participants
are offline. Thus, a temporary network glitch or a network split can com-
pletely stall the system. The system ideally should be able to continue
to operate, as long as at least half of the participants are online (heaviest
chain-based protocols continue operating even if fewer than half of the
participants are online, but the desirability of this property is debated
within the blockchain community).
A hybrid model, in which the consensus used is some sort of heaviest chain
approach, but some blocks are periodically finalized using a BFT finality gadget,
maintains the advantages of both models. Such BFT finality gadgets are Casper
FFG [6], used in Ethereum 2.09, Casper CBC, and GRANDPA used in Polkadot.
Nightshade uses heaviest-chain consensus. Specifically, when a block pro-
ducer produces a block (see section 3.3), they can collect signatures from other
block producers and validators, attesting to the previous block. The weight
of a block is then the cumulative stake of all the signers whose signatures are
included in the block. The weight of a chain is the sum of the block weights.
On top of heaviest-chain consensus, Nightshade uses a finality gadget that
uses attestations to finalize the blocks. To reduce the complexity of the system,
we use a finality gadget that doesn’t influence the fork-choice rule in any way.
Instead the gadget only introduces extra slashing conditions such that once a
9Also see the whiteboard session with Justin Drake for an in-depth overview of Casper
FFG, and how it is integrated with GHOST heaviest-chain consensus.
24
block is finalized by the finality gadget, a fork is impossible, unless a very large
percentage of the total stake is slashed. The full details of NEAR’s consensus
algorithm can be found in the Doomslug paper.
3.3
Block production
In Nightshade there are two roles: block producers and validators.
At any
point the system contains w block producers, w = 100 in our models, and wv
validators, in our model v = 100, wv = 10, 000. The system is Proof-of-Stake,
meaning that both block producers and validators have some number of internal
currency (referred to as ”tokens”) locked for a duration of time far exceeding the
time they spend performing their duties of building and validating the chain.
As with all the Proof-of-Stake systems, not all the w block producers and
not all the wv validators are different entities, since that cannot be enforced.
Each of the w block producers and the wv validators, however, does have a
separate stake.
The system contains n shards, where n = 100 in our model. As mentioned
in section 3.1, in Nightshade there are no shard chains; instead all the block
producers and validators are building a single blockchain, which we refer to as
the main chain. The state of the main chain is split into n shards, and each
block producer has at any moment only downloaded locally a subset of the state
that corresponds to some subset of the shards, and only processes and validates
transactions that affect those parts of the state. Validators do not maintain
the state of any shard locally, but they do download and verify all the block
headers. They validate chunks using state witness created by chunk producers.
The details of this mechanism will be discussed in section 3.5.
To become a block producer, a participant of the network locks some large
amount of tokens (a stake). The maintenance of the network is done in epochs,
where an epoch is roughly 16 hours. The participants with the w largest stakes
at the beginning of a particular epoch are the block producers for that epoch.
Each block producer is assigned to sw shards, (say sw = 4, which would make
sww/n = 4 block producers per shard).
The block producer downloads the
state of the shard they are assigned to before the epoch starts, and throughout
the epoch collects transactions that affect that shard, and applies them to the
state.
For each block b on the main chain, and for every shard s, one of the assigned
block producers to s is responsible to produce the part of b related to the shard.
The part of b related to shard s is called a chunk, and contains the list of the
transactions for the shard to be included in b, as well as the Merkle root of the
resulting state. b will ultimately only contain a very small header of the chunk,
namely the Merkle root of all the applied transactions, and the Merkle root of
the final state. When a block producer produces a chunk, they also produce an
associated state witness required to execute the chunk.
Throughout the rest of this document we refer to the block producer that is
responsible for producing a chunk at a particular time for a particular shard as
a chunk producer. A chunk producer is always a block producer and vice versa,
25
this distinction is made to help provide more accurate context when we discuss
block production or chunk production.
Within an epoch, the block and chunk production schedule is determined by
a randomness seed generated at the beginning of the epoch and for each block
height, there is an assigned block producer. Similarly, for each height, there is
an assigned chunk producer for each shard.
Since chunk production, unlike block production, requires maintaining the
state, and for each shard only sww/n block producers maintain the state per
shard, correspondingly only those sww/n block producers are responsible for
producing chunks and associated state witnesses.
3.4
Ensuring data availability
To ensure data availability, Nightshade uses an approach similar to that of
Polkadot described in section 2.6.3. Once a block producer produces a chunk,
they create an erasure coded version of it with an optimal (w, ⌊w/6 + 1⌋) block
code of the chunk. They then send one piece of the erasure coded chunk (we
call such pieces chunk parts, or just parts) to each block producer.
We compute a Merkle tree that contains all the parts as the leaves, and the
header of each chunk contains the Merkle root of such tree.
The parts are sent to the validators via onepart messages. Each such message
contains the chunk header, the ordinal of the part, and the part contents. The
message also contains the signature of the block producer who produced the
chunk and the Merkle path to prove that the part corresponds to the header
and is produced by the proper block producer.
Once a block producer receives a main chain block, they first check if they
have onepart messages for each chunk included in the block. If not, the block
is not processed until the missing onepart messages are retrieved.
Once all the onepart messages are received, the block producer fetches the
remaining parts from the peers and reconstructs the chunks for which they hold
the state.
The block producer doesn’t process a main chain block if, for at least one
chunk included in the block, they don’t have the corresponding onepart mes-
sage, or if for at least one shard for which they maintain the state they cannot
reconstruct the entire chunk.
For a particular chunk to be available, it is enough that ⌊w/6⌋+1 of the block
producers have their parts and serve them. Thus, for as long as the number of
malicious actors doesn’t exceed ⌊w/3⌋no chain that has more than half of the
block producers building it can have unavailable chunks.
3.4.1
Dealing with lazy block producers
If a block producer has a block for which a onepart message is missing, they
might choose to still sign on it, because if the block ends up being on-chain, it
will maximize the reward for the block producer. There’s no risk for the block
26
Figure 17:
Each block contains one or zero chunks per shard, and each chunk
is erasure coded. Each part of the erasure coded chunk is sent to a designated
block producer via a special onepart message
producer since it is impossible to prove later that the block producer didn’t have
the onepart message.
To address the lazy block producer problem, each chunk producer when
creating a chunk must choose a color (red or blue) for each part of the future
encoded chunk, as well as store the bitmask of assigned color in the chunk
before it is encoded. Each onepart message then contains the color assigned to
the part, and the color is used when computing the Merkle root of the encoded
parts. If the chunk producer deviates from the protocol, it can be easily, since
either the Merkle root will not correspond to onepart messages, or the colors
in the onepart messages that correspond to the Merkle root will not match the
mask in the chunk.
When a block producer signs on a block, they include a bitmask of all the
red parts they received for the chunks included in the block. Publishing an
incorrect bitmask is a slashable behavior. If a block producer hasn’t received a
onepart message, they have no way of knowing the color of the message, and
thus have a 50% chance of being slashed if they attempt to blindly sign the
block.
3.5
State transition application
A chunk producer chooses transactions to include in a chunk, applies the state
transition, and generates state witness along the way when they produce a
chunk. More specifically, when a chunk producer applies a chunk they produces,
they record all the trie nodes visited during the execution. This set of trie nodes
is the state witness for this chunk. The chunk header contains a Merkle root
27
of the Merkleized state after the chunk is applied.10 The state witness is then
shared with validators assigned to this shard for the next block so that they
could validate the chunk.
A validator only processes a block if:
1. The previous block was received and processed;
2. For each chunk, it receives the onepart message if it is not assigned to the
corresponding shard.
3. For each chunk, it receives the full chunk if it is assigned to the corre-
sponding shard
Once the block is being processed, a validator applies the state transition
of the shard they are assigned to and checks whether the resulting state root
matches what is posted in the chunk header. If they match, the validator sends
an chunk endorsement to the next block producer.
Block producers would
only include a chunk in a block if they have more than 2/3 endorsements from
validators assigned to the corresponding shard.
3.5.1
State Representation
In section 2.4, we define state witness as the state touched during the execution
of a chunk, along with a proof that the recorded state pieces indeed belong to
the state as specified by a state root. An important question left unanswered
is how the state is represented. In blockchains where state proofs are required,
their state is usually represented as a Merkle Patricia Trie (MPT). However,
even for MPTs, the branching factor matters for the size of state witness — a
critical component of the stateless validation design. A branching factor of 1611
means that when the state is full, each branch node will have an overhead of 15
hashes, which is 480 bytes. In comparison, a branching factor of 2, or binary
Merkle trees, only have an overhead of 1 hash, which is 32 bytes, at each branch
node. An analysis shows that based on Ethereum data, the state witness size
can be reduced 40% by switching the state representation to a binary MPT.
Recent research on Verkle Trees shows even more promising results. Verkle
trees only require one 48-byte KZG commitment [7] per level. Therefore Verkle
trees can have a large branching factor (256 - 1024), which results in much
shallower trees and smaller witness sizes. Verkle tree is currently considered to
be the best state representation for stateless validation.
10This is not exactly what is implemented. To allow for an easier upgrade from the pre-
vious version where the previous state root instead of the post state root is stored in the
chunk headers, the implementation is changed to accommodate that. We describe the design
conceptually here to make it easier for readers to understand.
11This is what Ethereum and many other blockchains use as of this writing.
28
3.6
Cross-shard transactions and receipts
If a transaction needs to affect more than one shard, it needs to be consecutively
executed in each shard separately. The full transaction is sent to the first shard
affected, and once the transaction is included in the chunk for such shard and
is applied after the chunk is included in a block, it generates a so-called receipt
transaction, which is routed to the next shard in which the transaction need to
be executed. If more steps are required, the execution of the receipt transaction
generates a new receipt transaction, and so on.
3.6.1
Receipt transaction lifetime
It is desirable that the receipt transaction should be applied in the block that
immediately follows the block in which it was generated. The receipt transaction
is only generated after the previous block was received and applied by block
producers that maintain the originating shard, and needs to be known by the
time the chunk for the next block is produced by the block producers of the
destination shard. Thus, the receipt must be communicated from the source
shard to the destination shard in the short time frame between those two events.
Let A be the last produced block which contains a transaction t that gener-
ates a receipt r. Let B be the next produced block (i.e. a block that has A as
its previous block) that we want to contain r. Let t be in the shard a and r be
in the shard b.
The lifetime of the receipt, also depicted on figure 18, is the following:
Producing and storing the receipts. The chunk producer cpa for shard
a receives the block A, applies the transaction t, and generates the receipt r. cpa
then stores all such produced receipts in its internal persistent storage indexed
by the source shard ID.
Distributing the receipts. Once cpa is ready to produce the chunk for
shard a within block B, they fetch all the receipts generated by applying the
transactions from block A for shard a, and include them in the chunk for shard
a in block B. Once such a chunk is generated, cpa produces its erasure coded
version and all the corresponding onepart messages. cpa knows which block
producers maintain the full state for which shards. For a particular block pro-
ducer, bp, let Sbp denote the set of shards that bp maintains full state for. Let
R denote the receipts that are results of applying transactions in block A for
shard a that have any shard in Sbp as the destination shard. Then cpa would
include R in the onepart message when they distribute the chunk for shard in
block B to bp.
Receiving the receipts. Remember that the participants (both block pro-
ducers and validators) do not process blocks until they have onepart messages
for each chunk included in the block. Thus, by the time any particular partici-
pant applies the block B, they have all the onepart messages that correspond to
chunks in B, and thus they have all the incoming receipts that have the shards
for which the participant maintains state as their destination. When applying
the state transition for a particular shard, the participant applies both the re-
29
ceipts that they have collected for the shard in the onepart messages, as well as
all the transactions included in the chunk itself.
Figure 18:
The lifetime of a receipt transaction
3.6.2
Handling too many receipts
It is possible that the number of receipts that target a particular shard in a
particular block is too large to be processed. For example, consider figure 19, in
which each transaction in each shard generates a receipt that targets shard 1.
By the next block, the number of receipts that shard 1 needs to process is
comparable to the load that all the shards combined processed while handling
the previous block.
To address it we use a technique similar to that used in QuarkChain 12.
Specifically, for each shard the last block B and the last shard s within that
block from which the receipts were applied is recorded. When the new shard is
created, the receipt are applied in order first from the remaining shards in B,
and then in blocks that follow B, until the new chunk is full. Under normal
circumstances with a balanced load it will generally result in all the receipts
being applied (and thus the last shard of the last block will be recorded for
each chunk), but during times when the load is not balanced, and a particular
shard receives disproportionately many receipts, this technique allows them to
be processed while respecting the limits on the number of transactions included.
Note that if such an imbalanced load remains for a long time, the delay from
the receipt creation until the application can continue growing indefinitely. One
way to address this is to drop any transaction that creates a receipt targeting a
shard that has a processing delay exceeding some constant (e.g. one epoch).
12See the whiteboard episode with QuarkChain here: https://www.youtube.com/watch?v=
opEtG6NM4x4, in which the approach to cross-shard transactions is discussed.
30
Figure 19:
If all the receipts target the same shard, the shard may not have
the capacity to process them
Consider figure 20. By block B the shard 4 cannot process all the receipts,
so it only processes receipts originating from up to shard 3 in block A, and
records this. In block C the receipts up to shard 5 in block B are included, and
then by block D, the shard catches up, processing all the remaining receipts in
block B and all the receipts from block C.
Figure 20:
Delayed receipts processing
31
3.7
Chunks validation
As mentioned in section 3.5, a chunk is validated by a set of stateless validators
assigned to the shard. In this section, we provide a more detailed analysis of the
security of the design. Again, we assume the existence of a on-chain randomness
beacon that can be used to generate random seeds with each block, which then
can be used to shuffle validators.
The core problem here can be formulated as follows: let’s assume a total
of n validators (with equal stake) where at most 1/3 of them are malicious.
There are s shards and k validators are assigned to each shard at every block,
so n = sk. What is the probability of a random assignment of validators to a
shard that results in a shard getting corrupted?
First, it is worth noting that a shard being corrupted means that more than
2/3 of validators assigned to that specific shard are malicious and can perform
an invalid state transition. If less than 2/3 of validators are malicious, it is
still possible for the chunk to be skipped due to insufficient endorsement, but
no invalid state transition is possible. In addition, since validators rotate every
single block, it is not possible for a shard to get indefinitely stalled due to a bad
assignment.
Now let’s consider the probability of a single shard getting corrupted. Let
p denote the probability of a randomly chosen validator being malicious (so
p ≤1/3). For a single shard to have l malicious validators, the probability is
hypergeometric distribution:
P(X = l) =
k
l
n−k
k−l

n
k

Therefore, the probability of having more than 2/3 malicious validators for a
single shard is
P(X ≥2k/3) ≤e−D(p+1/3||p)k = e−k
3
by Chernoff bound.
For s shards, the probability that at least one shard is corrupted is
Pbad ≤
s
X
i=1
P(one shard is corrupted) ≤se−n
3s
When n and k are large, i.e, there are many validators assigned to the
same shard, the last term se−n
3s can be made negligbly small. Our numeric
calculation based on multivariate hypergeometric distribution shows that with
800 validators and 4 shards, the probability of the networking getting corrupted
is roughtly 10−29, which means that in expectation it takes 1029 blocks for the
system to fail. Assuming one block per second, that translates to 3×1021 years.
Therefore, while the security of the system is probabilistic, it is safe enough in
practice.
32
3.7.1
Zero-knowledge proofs
One problem that is often ignored in blockchain design is what happens when the
BFT assumption is violated, i.e, if there are more than 1/3 malicious validators
in a blockchain. Obviously the consensus could be broken and the network may
fail to produce blocks. More importantly, the malicious validators could poten-
tially attempt to create an invalid state transition. In a non-sharded blockchain,
doing this would not only require a higher total stake (2/3 of validators being
malicious), but also it is also very likely to be immediately noticed and a social
consensus may ensue to resolve the attack. This is because the likelihood of
every single validator being corrupted in a non-sharded blockchain is extremely
low; there may also be others running RPC nodes who would notice the invalid
state transition, as well.
In a sharded blockchain, however, things are a bit different. It is more likely
that the validators assigned to a shard could all be malicious and it is less likely
that many people would run nodes that track every shard. This gives rise to the
theoretical possibility of an invalid state transition attack occurring with few
or any participants noticing. The result would be disastrous: a billion tokens
could be minted out of thin air and moved to other shards and potentially even
be bridged to other blockchains.
This is where zero-knowledge proofs (ZKPs) can come in. Since ZKPs are
very cheap to verify, if there is a proof generated for the state transition of each
shard, then a very large number of people can independently verify the state
transition without having to rely on the signatures of validators to trust the
validity of a state transition. More specifically, every wallet could run a ZK full
client that verifies the state transition of the entire blockchain and be assured
that no invalid state transition has happened in the history of the blockchain.
ZKPs also enable a stateless validation design that is simpler and more
powerful than the one described in section 3.7. Instead of validators executing
the chunk using state witness and verifying the state transition, they could
verify one ZKP instead – which is much cheaper. The problem, however, is that
generating a ZKP takes a long time. So if we naively swap out state witness
with a ZKP, it won’t work due to the proof generation latency.
Recent advancements in separating consensus and execution [8] provide a
possible solution: blocks and chunks can be produced and optimistically ex-
ecuted while ZK proofs are being generated. Once a ZKP is generated, it is
submitted into a block for all validators to verify. Because ZKPs can be small
in size and quite cheap to verify13, they can be included in the blocks directly
and all validators can validate the state transition of all shards14. In this design,
the validators would be much more lightweight, given that they only need to
verify some ZKP. This means that the cost of operating a validator is going to
be much cheaper and that the validator set could be much larger, which further
improves the decentralization of the design.
13While STARK proofs can be relatively large (a few hundred kilobytes), they can be
compressed again using a SNARK which would result in a proof of a few hundred bytes.
14Alternatively, proofs from different shards can be aggregated into one ZKP for a block.
33
3.8
State Size
Sharding allows us to divide the state of the entire blockchain into those of
individual shards, which provides a solution to the state growth problem that
is becoming more and more imminent today. The stateless validation approach
makes the blockchain even more performant: there are relatively few chunk
producers and they can afford to operate more expensive hardware, which means
that for each individual shard, the state of the shard can be held in memory
when chunk producers produce and process chunks. Validators, on the other
hand, receive state witness, which is small in size, when they need to apply a
chunk and can also have the required state in memory. As a result, the state
reads and writes are very fast.
The cost of operating a chunk producer is not going to be exorbitantly high
either. If we limit the size of each shard to 50GB, then a chunk producer could
be operated with a 64GB RAM machine, which is fairly commonplace today.
This asymmetry between chunk producer and validator also works well when
state witness is replaced by ZKPs. Chunk producer could also operate a prover,
which requires more hardware resources and the validators can run on even
cheaper hardware due to the low cost of verifying ZKPs.
3.9
Snapshots Chain
Since the blocks on the main chain are produced very frequently, downloading
the full history might become expensive very quickly. Since the block producers
set is constant throughout the epoch, validating only the first snapshot blocks
in each epoch is sufficient assuming that at no point a large percentage of block
producers and validators colluded and created a fork.
The first block of the epoch must contain information sufficient to compute
the block producers and validators for the epoch.
We call the subchain of the main chain that only contains the snapshot blocks
a snapshot chain. Such a chain can also be built and validated on Ethereum
inside a smart contract can provides a secure method of cross-chain communi-
cation.
To sync with the NEAR chain, one only needs to download all the snapshot
blocks and confirm that the signatures are correct, and then only have to sync
the main chain blocks from the last snapshot block.
4
Conclusion
In this document, we discussed approaches to building sharded blockchains and
covered two major challenges that come with existing approaches, namely state
validity and data availability. We then presented Nightshade, a sharding design
that powers NEAR Protocol.
The design is a work in progress.
If you have comments, questions, or
feedback on this document, please go to https://github.com/near/neps.
34
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