Scaling Issues in Blockchain Networks and How Data Availability Sampling Can Help

Data Availability Sampling is a method used to verify the validity of block data in a blockchain network. It involves collecting small chunks of data from a node while it is still in operation, enabling real-time analysis of the node's performance and identification of potential issues or areas for improvement. This technique is commonly used in distributed systems to monitor the health and stability of the network.

In the context of the blockchain, full nodes verify the validity of transactions by downloading and verifying the content of blocks. However, increasing the number of transactions per second through upgrading hardware requirements can lead to scaling issues and reduce decentralization and trust in the network. Data availability sampling allows nodes to sample data from a sequencer and use erasure coding to maintain confidence levels while reducing the amount of data that needs to be downloaded. This helps to mitigate scaling issues and maintain the decentralization and trust of the network. Read more on Erasure Coding here.

Erasure coding reduces the likelihood of full nodes being fooled by the sequencer in the event of misbehavior. This is because the sequencer must hold more than 50% of the data in order to successfully fool the full nodes. As a result, if this process is repeated over time, the likelihood of full nodes being fooled by the sequencer will decrease by 50% with each iteration. After the seventh iteration, the likelihood of being fooled will be less than 1%. Here is the permutation:

A concept illustration on how data availability sampling works

Eventually, the nodes can confidently download a small amount of data published from the sequencer since it is almost guaranteed to be identical to download and check the entire block.

Faiz Adnan
Faiz Adnan
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