19 KiB
Storage proofs & erasure coding
Authors: Codex Team
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.
The amount of original data shards is typically called K, the amount of parity shards M, and the total amount of shards N.
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.
More dimensions
Another thing that we could do is to keep using existing erasure coding implementations, but perform erasure coding in more than one dimension. For instance with two dimensions you would encode first in rows, and then in columns:
original data row parity
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|///|///|///|///|///|///|///|///| -> | p | p | p |
--------------------------------- -------------
|///|///|///|///|///|///|///|///| -> | p | p | p |
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|///|///|///|///|///|///|///|///| -> | p | p | p |
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|///|///|///|///|///|///|///|///| -> | p | p | p |
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|///|///|///|///|///|///|///|///| -> | p | p | p |
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|///|///|///|///|///|///|///|///| -> | p | p | p |
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|///|///|///|///|///|///|///|///| -> | p | p | p |
--------------------------------- -------------
| | | | | | | | | | |
v v v v v v v v v v v
--------------------------------- -------------
| p | p | p | p | p | p | p | p | | p | p | p |
--------------------------------- -------------
| p | p | p | p | p | p | p | p | | p | p | p |
--------------------------------- -------------
| p | p | p | p | p | p | p | p | | p | p | p |
--------------------------------- -------------
column parity
This allows us to use the maximum amount of shards for our rows, and the maximum amount of shards for our columns. When we erasure code using a Galois field of 2^16 in a two-dimensional structure, we can now have a maximum of 2^16 x 2^16 = 2^32 shards. Or we could go up another two dimensions and have a maximum of 2^64 shards in a four-dimensional structure.
Note that although we now have multiple dimensions of erasure coding, we do not need multiple dimensions of Merkle trees. We can simply unfold the multi-dimensional structure into a one-dimensional one (like you would do when writing the structure to disk), and then construct a Merkle tree on top of that.
There are however a number of drawbacks to adding more dimensions.
Data corrupted sooner
In a one-dimensional scheme, corrupting an amount of shards just larger than the amount of parity shards ( M + 1 ) will render data lost:
<--------- missing: M + 1---------------->
--------------------------------- ---------------------------------
|///|///|///|///|///|///|///| | | | | | | | | | |
--------------------------------- ---------------------------------
<-------- original: K ----------> <-------- parity: M ------------>
In a two-dimensional scheme, we only need to lose an amount much smaller than the total amount of parity before data is lost:
<-------- original: K ----------> <- parity: M ->
--------------------------------- ------------- ^
|///|///|///|///|///|///|///|///| |///|///|///| |
--------------------------------- ------------- |
|///|///|///|///|///|///|///|///| |///|///|///| |
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|///|///|///|///|///|///|///|///| |///|///|///| |
--------------------------------- ------------- |
|///|///|///|///|///|///|///|///| |///|///|///| |
--------------------------------- -------------
|///|///|///|///|///|///|///|///| |///|///|///| K
--------------------------------- -------------
|///|///|///|///|///|///|///|///| |///|///|///| |
--------------------------------- ------------- |
|///|///|///|///|///|///|///|///| |///|///|///| |
--------------------------------- ------------- | ^
|///|///|///|///|///|///|///| | | | | | | |
--------------------------------- ------------- v |
--------------------------------- ------------- ^ M
|///|///|///|///|///|///|///| | | | | | | +
--------------------------------- ------------- 1
|///|///|///|///|///|///|///| | | | | | M
--------------------------------- ------------- |
|///|///|///|///|///|///|///| | | | | | | |
--------------------------------- ------------- v v
<-- missing: M + 1 -->
This is only (M + 1)² shards from a total of N² blocks. This gets worse when you go to three, four or higher dimensions. This means that our chances of detecting whether the data is corrupted beyond repair go down, which means that we need to check more shards in our Merkle storage proofs. This is exacerbated by the the need to counter parity blowup.
Parity blowup
When we perform a regular one-dimensional erasure coding, we like to use a ratio of 1:2 between original data (K) and total data (N), because it gives us a >50% chance of detecting critical data loss by checking a single shard. If we were to use the same K and M in a 2-dimensional setting, we'd get a ratio of 1:4 between original data and total data. In other words, we would blow up the original data by a factor of 4. This gets worse with higher dimensions.
To counter this blow-up, we can choose an M that is smaller. For two dimensions, we could chose M = N / √2. This ensures that the total amount of data N² is double that of the original data K². For three dimensions we'd choose K / ∛2, etc. This however means that the chances of detecting critical data loss in a row or column go down, which means that we'd again have to sample more shards in our Merkle storage proofs.
Larger encoding times
Another drawback of multi-dimensional erasure coding is that we now need to erasure code the original data multiple times, and we also need to erasure code some of the parity data. For a two-dimensional code this means that encoding times go up by a factor of at least 2, and for a three-dimensional a factor of at least 3, etc.
Complexity
The final drawback of multi-dimensional erasure coding is its complexity. It is harder to reason about its correctness, and implementations must take great care to ensure that cornercases when the data is not exactly K² shaped (or K³, or...) are handled correctly. Decoding is also more involved because it might require restoring parity data before it is possible to restore the original data.
The good news
Despite these drawbacks, the multi-dimensional approach allows us to make the shards almost arbitrarily small. This allows us to compensate for the need to sample more shards in our Merkle proofs. For example, using a 2 dimensional structure of erasure coded shards in a Galois field of 2^16, we can handle 1TB of data with shards of size 256 bytes. When we allow parity data to take up to half of the total data, we would need to sample 160 shards to have a 0.999999 chance of detecting critical data loss. This is much more than the amount of shards that we need in a one-dimensional setting, but the shards are much smaller. This leads to less hashing in a SNARK, just 40 KB.
The numbers for multi-dimensional erasure coding schemes can be found in the accompanying spreadsheet
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. There are two design directions that allow an increase of slot size. One is to extend or implement an erasure coding implementation to use a larger field size. The other is to use existing erasure coding implementation in a multi-dimensional setting.
Two concrete options are:
- Erasure code with a field size that allows for 2^28 shards. Check 20 shards per proof. For 1TB this leads to shards of 4KB. This means the SNARK needs to hash 80KB plus the Merkle paths for a storage proof. Requires custom implementation of Reed-Solomon, and requires at least 1 GB of memory while performing erasure coding.
- Erasure code with a field size of 2^16 in two dimensions. Check 160 shards per proof. For 1TB this leads to a shards of 256 bytes. This means that the SNARK needs to hash 40KB plus the Merkle paths for a storage proof. We can use the leopard library for erasure coding and keep memory requirements for erasure coding to a negligable level.