As an Ethereum developer or a backend/infra engineer, you've likely encountered the challenge of needing faster Ethereum RPC responses for your applications or indexers. Whether you're building a dApp, a data analytics service, or a wallet, the core interaction often involves querying the blockchain for state. But what happens when your application repeatedly asks for the same data, only to find it hasn't changed?
The Challenge of Redundant Ethereum RPC Calls
Consider an application that frequently checks user balances, monitors specific contract states, or tracks logs for certain events. Your code might be executing eth_getBalance in a polling loop, calling eth_call on a read-only contract method every few seconds, or repeatedly querying eth_getLogs for a fixed time range. Each of these interactions translates to a distinct RPC request against your chosen provider, such as Infura or Alchemy.
The pain point is clear: you're making repeated read-only RPC calls — eth_call, eth_getBalance, eth_getLogs — over and over. Many of these calls retrieve data that hasn't actually changed since your last query. This pattern drives up your operational costs, as most providers charge per request, and it quickly pushes you against rate limits, leading to throttled requests and degraded application performance. There's no caching layer sitting in front of the endpoint to intelligently catch these requests for data that hasn't changed.
This issue doesn't merely represent an occasional inefficiency; it's a fundamental scaling challenge. As your application's user base grows or its demand for real-time data increases, the volume of RPC requests often scales linearly. Your application's RPC consumption scales directly with the number of times it asks for data, not necessarily with the actual frequency of state changes on the blockchain. This means you're incurring costs and consuming rate limit quotas based on request volume, rather than the true novelty of the data being retrieved. Without an intelligent caching mechanism, every request is treated as a fresh query to the underlying blockchain node, regardless of whether the data has been fetched moments before.
This approach to data retrieval is particularly inefficient for read-heavy operations where the underlying blockchain state changes relatively infrequently compared to the rate at which applications poll for updates. For instance, a user's token balance might only change when they execute a transaction, but your application might check it every few seconds. Similarly, the code of a deployed contract (eth_getCode) remains static, yet it might be queried multiple times during an application's lifecycle. These patterns create unnecessary load on RPC providers and, consequently, on your budget and application's reliability.