Key Takeaways

• Ethereum is currently undergoing development updates, with the upcoming Dencun upgrade (part of ‘The Surge’ on Ethereum’s roadmap) to achieve higher scalability. The most anticipated feature is the introduction of Proto-Danksharding, a process that aims to further increase Ethereum’s transaction throughput and scalability. But what exactly is sharding in the context of blockchain technology and Ethereum, in particular? Our report explores this and other features of the Dencun upgrade.

• Ethereum co-founder Vitalik Buterin’s blog opines on the advantages of sharding compared to other scaling solutions in addressing the scalability trilemma.

  • Ethereum aims to eventually implement a particular type of sharding called Danksharding. Essentially, instead of splitting into ‘shard chains’, Danksharding uses distributed data sampling across ‘blobs’ to scale Ethereum. However, there are still steps that need to be implemented before full Danksharding is achieved. One of these is Proto-Danksharding (also known as EIP-4844), which introduces ‘data blobs’ and is an important part of the upcoming Dencun upgrade.

  • Rollups are limited in how cheap they can make user transactions since they currently post their transactions in CALLDATA (a data location on Ethereum’s block). This is expensive because it is processed by all Ethereum nodes and lives on-chain forever, even though rollups only need the data for a short time. In fact, over 90% of the transaction costs users pay on rollups is due to this data storage.

• Proto-Danksharding introduces a new transaction type known as a ‘blob-carrying transaction’. Blobs (short for ‘Binary Large Objects’) make way for lower transaction costs (e.g., for both Layer-2 users and operators) and increase the transaction throughput of Ethereum.

  • Blob data operates under a separate market mechanism based on the supply and demand of blob storage. This fee market for blobs is entirely independent of the demand for block space, potentially improving flexibility and efficiency in the allocation of network resources.