Zilliqa is a new high-throughput blockchain platform that is designed to scale in an open, permissionless distributed network securely. In the most recent experiments, the Zilliqa private testnet network recorded a peak throughput of 2,488 transactions with 3,600 nodes. The underlying technology that makes Zilliqa scale is that of sharding — — dividing the mining network into smaller groups called shards, each capable of processing transactions in parallel. In fact, the throughput in Zilliqa increases as the network expands which makes Zilliqa truly scalable. Zilliqa has been featured in esteemed news outlets like CNBC, Cointelegraph, BlockchainNews, IBT, The Merkle, Digital Journal, etc.
Existing cryptocurrencies and smart contract platforms are known to have scalability issues, i.e., the number of transactions they are capable of processing per second is limited, usually less than 10. As the number of applications utilizing public cryptocurrencies and smart contract platforms grow, the demand for processing high transaction rates in the order of hundreds and thousands of Tx/s is increasing. In this work, we present ZILLIQA— a new blockchain platform that is designed to scale in transaction rates. As the number of miners in ZILLIQA increases, its transaction rates are expected to increase. At Ethereum’s present network size of 30,000 miners, ZILLIQA would expect to process about a thousand times the transaction rates of Ethereum. The cornerstone in ZILLIQA’s design is the idea of sharding — dividing the mining network into smaller shards each capable of processing transactions in parallel. ZILLIQA further proposes an innovative special-purpose smart contract language and execution environment that leverages the underlying architecture to provide a large scale and highly efficient computation platform. The smart contract language in ZILLIQA follows a dataflow programming style which makes it ideal for running large-scale computations that can be easily parallelized. Examples include simple computations such as search, sort and linear algebra computations, to more complex computations such as training neural nets, data mining, financial modeling, scientific computing and in general any MapReduce task.