Batch Processing API Requests with Lodash Chunk

Batch processing API requests is a crucial strategy for preventing network bottlenecks, payload size errors, and third-party rate limiting. The Lodash _.chunk function provides an elegant, reliable solution for this pattern by splitting large datasets into smaller, uniform sub-arrays. This article explains how _.chunk operates, why its deterministic array splitting aligns seamlessly with asynchronous API workflows, and how it enhances application performance and fault tolerance.

Predictable and Controlled Payload Sizes

Many REST and GraphQL APIs enforce strict limits on the number of items that can be processed in a single call or impose maximum request payload sizes. When handling thousands of records, attempting to send the entire dataset at once leads to HTTP 413 (Payload Too Large) errors or timeouts.

The _.chunk function takes an input array and an integer representing the desired chunk size, returning a new two-dimensional array where each nested array contains at most that specified number of elements:

const _ = require('lodash');

const userIds = [1, 2, 3, 4, 5, 6, 7];
const batches = _.chunk(userIds, 3);
// Output: [[1, 2, 3], [4, 5], [6, 7]]

By ensuring that no individual batch exceeds the allowed parameter threshold, _.chunk eliminates the guesswork in payload construction and guarantees compliance with third-party endpoint constraints.

Elimination of Boilerplate Pagination Logic

Native JavaScript requires manual loops and slice calculations to partition arrays:

const chunks = [];
for (let i = 0; i < array.length; i += size) {
  chunks.push(array.slice(i, i + size));
}

While functional, writing this pattern repeatedly introduces boilerplate code and the potential for off-by-one errors. Lodash abstracts this logic into a pure, immutable function that handles edge cases automatically, such as empty arrays, chunk sizes larger than the array length, and odd balances.

Seamless Concurrency and Rate-Limit Management

Managing external rate limits (such as a maximum of 10 requests per second) requires sending calls sequentially or in small parallel groups. Because _.chunk returns an array of arrays, it pairs naturally with asynchronous control flows.

When sequential execution is required to respect API rate limits, _.chunk works cleanly inside a standard for...of loop:

async function updateUsers(allUsers) {
  const userBatches = _.chunk(allUsers, 50);

  for (const batch of userBatches) {
    await apiClient.post('/users/bulk-update', { users: batch });
    // Optional delay can be added here to respect strict rate limits
  }
}

For controlled parallel execution, chunks can be mapped into concurrent groups using Promise.all:

async function fetchProfilesInParallel(ids) {
  const idBatches = _.chunk(ids, 10);

  const results = await Promise.all(
    idBatches.map(batch => apiClient.post('/profiles', { ids: batch }))
  );

  return results.flatMap(response => response.data);
}

Enhanced Error Handling and Partial Failure Recovery

Sending an entire dataset in one monolithic request creates an "all-or-nothing" failure risk. If a single record is malformed, the entire transaction may fail, requiring an expensive retry of the full dataset.

Splitting requests using _.chunk isolates failures to specific subsets. If batch three of ten encounters an error, the previous two batches have already succeeded, and only the failed batch needs to be retried or logged. This modularity simplifies retry mechanisms, reduces wasted compute resources, and improves the overall resilience of the integration.