Danksharding explained: data sharding for rollups with sampling
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Describes Ethereum's rollup-centric data sharding: splitting the validator set into shards that each download part of the data, with DAS checking other shards, e.g. four shards of 250 validators quadruple data space.
Key claims
Splitting 1000 validators into four shards quadruples data space for rollups
Validators use DAS to verify data on other shards without downloading it
Sharding in the Rollup-Centric Roadmap
In Ethereum’s rollup-centric roadmap for ETH 2.0, Ethereum will implement a limited form of sharding referred to as data sharding, or more specifically danksharding.
Data sharding will split Ethereum’s validator set into validator sub-committees (shards) that provide data space for rollups to post data to.
I will cover this mechanism in detail in my upcoming research report on data availability in the context of blockchains and modular scaling.
But on a high level and in simplified form, it works as follows:
Suppose we have 1000 validators. Today, each validator downloads full data (that for example rollup sequencers dump onto L1). In the danksharding paradigm, These validators are split into sub-sets (shards).
If we for example create four shards (of 250 validators each) which each downloads only part of the data, we have suddenly quadrupled the data space available to rollup sequencers.
However, validators still need to ensure that the full data is available. To do so, they will use data availability sampling, ensuring data on other shards is available, without having to download & store the full data.
This is how Ethereum will be transformed into a powerful data availability layer for scalable execution layers (rollups)! 🚀
Want to learn more? Stay tuned for my in depth report! 👀
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