10 KiB
Storage proofs & erasure coding
Erasure coding is used for multiple purposes in Codex:
- To restore data when a host drops from the network; other hosts can restore the data that the missing host was storing.
- To speed up downloads
- To increase the probability of detecting missing data on a host
For the first two items we'll use a different erasure coding scheme than we do for the last. In this document we focus on the last item; an erasure coding scheme that makes it easier to detect missing or corrupted data on a host through storage proofs.
Storage proofs
Our proofs of storage allow a host to prove that they are still in possession of the data that they promised to hold. A proof is generated by sampling a number of blocks and providing a Merkle proof for those blocks. The Merkle proof is generated inside a SNARK to compress it to a small size to allow for cost-effective verification on a blockchain.
These storage proofs depend on erasure coding to ensure that a large part of the data needs to be missing before the original dataset can no longer be restored. This makes it easier to detect when a dataset is no longer recoverable.
Consider this example without erasure coding:
-------------------------------------
|///|///|///|///|///|///|///| |///|
-------------------------------------
^
|
missing
When we query a block from this dataset, we have a low chance of detecting the missing block. But the dataset is no longer recoverable, because a single block is missing.
When we add erasure coding:
--------------------------------- ---------------------------------
| |///| |///| | | |///| |///|///| | |///|///| | |
--------------------------------- ---------------------------------
original data parity data
In this example, more than 50% of the erasure coded data needs to be missing before the dataset can no longer be recovered. When we now query a block from this dataset, we have a more than 50% chance of detecting a missing block. And when we query multiple blocks, the odds of detecting a missing block increase dramatically.
Erasure coding
Reed-Solomon erasure coding works by representing data as a polynomial, and then sampling parity data from that polynomial.
__
__ / \ __ __
/ \ / \ / \ / \
/ \ / \__/ \ __ /
-- / \__/ \ __/
\__/ \ /
^ \ / |
| --- |
^ ^ | ^ | |
| | ^ | | ^ | | | | |
| | ^ | | | | | | | | | |
| | | ^ | | | | | | | | | | | |
| | | | | | | | | | | | | | | |
| | | | | | | | v v v v v v v v
------------------------- -------------------------
|//|//|//|//|//|//|//|//| |//|//|//|//|//|//|//|//|
------------------------- -------------------------
original data parity
This only works for small amounts of data. When the polynomial is for instance defined over byte sized elements from a Galois field of 2^8, you can only encode 2^8 = 256 bytes (data and parity combined).
Interleaving
To encode larger pieces of data with erasure coding, interleaving is used. This works by taking larger shards of data, and encoding smaller elements from these shards.
data shards
------------- ------------- ------------- -------------
|x| | | | | | |x| | | | | | |x| | | | | | |x| | | | | |
------------- ------------- ------------- -------------
| / / |
\___________ | _____________/ |
\ | / ____________________________/
| | | /
v v v v
--------- ---------
data |x|x|x|x| --> |p|p|p|p| parity
--------- ---------
| | | |
_____________________________/ / | \_________
/ _____________/ | \
| / / |
v v v v
------------- ------------- ------------- -------------
|p| | | | | | |p| | | | | | |p| | | | | | |p| | | | | |
------------- ------------- ------------- -------------
parity shards
This is repeated for each element inside the shards. In this manner, we can employ erasure coding on a Galois field of 2^8 to encode 256 shards of data, no matter how big the shards are.
Adversarial erasure
The disadvantage of interleaving is that it weakens the protection against adversarial erasure that Reed-Solomon provides.
An adversary can now strategically remove only the first element from more than half of the shards, and the dataset will be damaged beyond repair. For example, with a dataset of 1TB erasure coded into 256 data and parity shards, an adversary could strategically remove 129 bytes, and the data can no longer be fully recovered.
Implications for storage proofs
This means that when we check for missing data, we should perform our checks on entire shards to protect against adversarial erasure. In the case of our Merkle storage proofs, this means that we need to hash the entire shard, and then check that hash with a Merkle proof. Effectively the block size for merkle proofs should equal the shard size of the erasure coding interleaving. This is rather unfortunate, because hashing large amounts of data is rather expensive to perform in a SNARK, which is what we use to compress proofs in size.
A large amount of input data in a SNARK leads to a larger circuit, and to more iterations of the hashing algorithm, which also leads to a larger circuit. A larger circuit means longer computation and higher memory consumption.
Ideally, we'd like to have small blocks to keep Merkle proofs inside SNARKs relatively performant, but we are limited by the maximum amount of shards that a particular Reed-Solomon algorithm supports. For instance, the leopard library can create at most 65536 shards, because it uses a Galois field of 2^16. Should we use this to encode a 1TB file, we'd end up with shards of 16MB, far too large to be practical in a SNARK.
Design space
This limits the choices that we can make. The limiting factors seem to be:
- Maximum number of shards, determined by the field size of the erasure coding algorithm
- Number of blocks per proof, which determines how likely we are to detect missing blocks
- Capacity of the SNARK algorithm; how many bytes can we hash in a reasonable time inside the SNARK
From these limiting factors we can derive:
- Block size; equals shard size
- Maximum slot size; the maximum amount of data that can be verified with a proof
- Erasure coding memory requirements
For example, when we use the leopard library, with a Galois field of 2^16, and require 80 blocks to be sampled per proof, and we can implement a SNARK that can hash 80*64K bytes, then we have:
- Block size: 64KB
- Maximum slot size: 4GB (2^16 * 64KB)
- Erasure coding memory: > 128KB (2^16 * 16 bits)
Which has the disadvantage of having a rather low maximum slot size of 4GB. When we want to improve on this to support e.g. 1TB slot sizes, we'll need to either increase the capacity of the SNARK, increase the field size of the erasure coding algorithm, or decrease the durability guarantees.
The accompanying spreadsheet allows you to explore the design space yourself
Increasing SNARK capacity
Increasing the computational capacity of SNARKs is an active field of study, but it is unlikely that we'll see an implementation of SNARKS that is 100-1000x faster before we launch Codex. Better hashing algorithms are also being designed for use in SNARKS, but it is equally unlikely that we'll see such a speedup here either.
Decreasing durability guarantees
We could reduce the durability guarantees by requiring e.g. 20 instead of 80 blocks per proof. This would still give us a probability of detecting missing data of 1 - 0.5^20, which is 0.999999046, or "six nines". Arguably this is still good enough. Choosing 20 blocks per proof allows for slots up to 16GB:
- Block size: 256KB
- Maximum slot size: 16GB (2^16 * 256KB)
- Erasure coding memory: > 128KB (2^16 * 16 bits)
Erasure coding field size
If we could perform erasure coding on a field of around 2^20 to 2^30, then this would allow us to get to larger slots. For instance, with a field of at least size 2^24, we could support slot sizes up to 1TB:
- Block size: 64KB
- Maximum slot size: 1TB (2^24 * 64KB)
- Erasure coding memory: > 48MB (2^24 * 24 bits)
We are however unaware of any implementations of reed solomon that use a field size larger than 2^16 and still be efficient O(N log(N)). FastECC uses a prime field of 20 bits, but its decoder isn't released yet, and it is unclear whether its byte encoding scheme allows for a systematic erasure code. The paper "An Efficient (n,k) Information Dispersal Algorithm Based on Fermat Number Transforms" describes a scheme that uses Proth fields of 2^30, but lacks an implementation, and has the same encoding challenges that FastECC has.
If we were to adopt an erasure coding scheme with a large field, it is likely that we'll either have to modify Leopard or FastECC, or implement one ourselves.
Conclusion
It is likely that with the current state of the art in SNARK design and erasure coding implementations we can only support slot sizes up to 4GB. The most promising way to increase the supported slot sizes seems to be to implement an erasure coding algorithm using a field size of around 2^24.