Query Batching

By sending one HTTP request instead of many, batching minimizes round-trip time (RTT). This is critical for applications requiring data from multiple sources (e.g., wallet balances, token metadata, contract states) as it eliminates the cumulative latency of sequential requests.

• Example: Fetching 10 user balances goes from ~10 network round trips to 1.
• Impact: Dramatically improves user-facing application responsiveness.

Batching reduces the total number of requests processed by RPC providers, which directly lowers costs for services with usage-based pricing. For high-throughput applications like dashboards or indexers, this can result in substantial operational savings.

• Mechanism: One batched request often costs the same as a single standard request.
• Efficiency: Enables more data queries per credit or subscription tier.

Processing a batched request is generally more efficient for a node than handling many separate connections. This reduces overhead from connection establishment, request parsing, and context switching.

• Benefit for Providers: Allows nodes to serve more users with the same resources, improving overall network scalability.
• Benefit for Users: Can lead to more consistent performance during peak load times.

All queries in a batch are executed against the same blockchain state (same block height/hash). This guarantees data consistency, which is vital for applications performing atomic calculations or snapshots.

• Use Case: A DeFi dashboard calculating total portfolio value needs all balance queries to reflect the same moment in time.
• Contrast: Sequential individual requests may be executed against slightly different states, leading to calculation errors.

Developers can manage a single request/response cycle instead of orchestrating multiple asynchronous calls, error handlers, and retry logic. This leads to cleaner, more maintainable code.

• Implementation: Libraries like Ethers.js and Web3.js provide built-in batching support.
• Result: Reduced complexity in fetching disparate on-chain data points.

For APIs with rate limits (requests per second/minute), batching allows applications to retrieve more data within the same limit. This is a key strategy for staying within free tiers or avoiding throttling.

• Practical Effect: 1 batched call containing 50 eth_getBalance queries counts as 1 request against the limit, not 50.
• Outcome: Enables data-intensive applications without requiring premium plans.

Beyond generic tools, many DeFi and NFT protocols build batching directly into their smart contracts and APIs for user convenience and gas efficiency.

• Uniswap V3: The NonfungiblePositionManager uses multicall for complex position management (mint, add liquidity) in one transaction.
• Compound: The Comptroller can check liquidity for multiple assets in a single call.
• ERC-1155: The balanceOfBatch function is a standard for querying balances of multiple token IDs for multiple addresses in one call. These designs reduce the number of interactions needed for common user flows.

While powerful, query batching has important technical constraints:

• Gas Limits: On-chain multicall transactions are bound by block gas limits, capping the number of operations per batch.
• Error Handling: A single failing call in a batch can cause the entire operation to revert, requiring robust error isolation strategies.
• Response Size: Large batched queries can hit RPC response size limits, necessitating pagination or query splitting.
• Complexity: Debugging batched calls is more difficult, requiring tools that can decode and trace individual component requests.

A critical security consideration is that the order of responses in a batch must match the order of requests. Malicious or buggy RPC providers could return responses out of order, causing a client application to misinterpret data (e.g., associating a user's balance with the wrong address). This necessitates client-side validation to ensure response integrity.

• Mitigation: Implement strict response order validation. Cryptographically link requests and responses where possible.

Batch requests are typically executed sequentially by the node but are not atomic. If one query in a batch fails (e.g., due to a revert or invalid parameter), subsequent queries in the same batch may still execute, potentially returning state from different points in time.

• Implication: Applications cannot assume all data in a batch response reflects a single, consistent blockchain state. This is crucial for transactions that depend on multiple, interdependent state reads.

Batching can create disproportionate load on RPC nodes. A single batch containing 100 eth_getLogs queries over large block ranges is far more computationally expensive than 100 separate eth_getBalance calls. This can lead to:

• Node Degradation: Timeouts or crashes for the node serving the request.
• Resource Exhaustion: Impact on other users sharing the node infrastructure.
• Mitigation: Implement client-side rate limiting, query complexity heuristics, and consider using dedicated node services for heavy batch workloads.

Designing robust error handling for batched calls is complex. A batch may partially fail, returning a mix of successful results and errors. The client must:

• Parse Heterogeneous Responses: Distinguish between successful JSON-RPC results and error objects within the same response array.
• Implement Retry Logic: Decide whether to retry the entire batch or only the failed individual requests, which can affect idempotency.
• Partial Data Usability: Determine if the application can proceed with the successful subset of data.

RPC providers enforce their own limits on batch size (e.g., max requests per batch) and total computational work. Exceeding these limits results in rejected requests. Key variations include:

• Size Limits: Common limits range from 50 to 1000 requests per batch.
• Execution Models: Some providers execute batches in parallel for speed, which can further affect state consistency guarantees.
• Best Practice: Design batching logic to be configurable per provider and include fallback to sequential requests.

It's important to distinguish JSON-RPC batching (a client-side aggregation technique) from protocol-level batching (e.g., EIP-4337 Bundler's eth_sendBundle).

• JSON-RPC Batching: Purely a transport optimization for read queries. No effect on blockchain state.
• Protocol Batching: A consensus-critical operation that groups write operations (transactions) for execution and inclusion in a block. This involves MEV, ordering, and fee market considerations far beyond simple query aggregation.