Last Friday, Justin Drake, a researcher at the Ethereum Foundation, said that Sharding will be implemented in 2020. This is a long-awaited network scraping solution for Ethereum’s network.
Drake and co-creator Vitalik Buterin have been discussing scale-up solutions for the project’s future. They have been outlining each of the goals and specific functions of the upcoming new features within the network thanks to Sharding. With regard to escalation, transaction fees are highly dependent on supply and demand.
Discussing scaling, Buterin said that transactions fees depend largely on supply and demand, adding:
“There are a lot of people who are sending transactions and want those transactions to get onto the blockchain. The blockchain is popular enough and people are interested enough in using it that they basically have to compete to get those transactions included into the blocks.”
Justin Drake spoke about their two main priorities at the moment being Casper [the Proof-of-Stake fork], and Sharding. The Ethereum scaling solution is orienting its goal to allow its blockchain to take more users and transactions without it slowing down and clogging the network. Drake said:
“The idea of proof of stake is partly to provide a new superpower to blockchains which is called finality. The other idea of proof of stake is to reduce the cost of consensus.”
While initially the PoS initiative and sharding were being developed separately, Drake said that they are now both being combined into one design due to sharing a common infrastructure, as well as adding beneficial network effects by being implemented together.
“With sharding you have all these different shards and they’re like parallel universes, pretty disconnected from each other and so you want to wait for the shards to be able to communicate with each other and we do that by what we call cross-links or checkpoints.”
Drake strongly believes that the sharding phases one and two will come in 2020 and 2021, respectively.
What is Sharding?
Sharding refers to the splitting of the entire state of the network into many partitions called ‘shards’. Each shard contains its own independent piece of state and transaction history. Since this system allows nodes to process transactions only for particular shards, the result becomes a higher amount of transactions being processed in the same amount of time.