Lodash Support for Large BigInt Arrays

Evaluating large arrays of native BigInt primitives inside Lodash reveals significant architectural limitations despite partial compatibility. While structural and traversal utilities like _.chunk or _.flatten handle BigInt values without issue, Lodash fails entirely when performing mathematical evaluations, aggregations, or numeric coercions. Because Lodash was primarily engineered before ES2020 around standard IEEE 754 floating-point numbers, executing complex mathematical operations on BigInt arrays triggers unhandled runtime exceptions rather than smooth, native execution.

Structural Manipulation vs. Mathematical Evaluation

Lodash distinguishes sharply between operations that inspect or transform array structures and operations that evaluate values numerically:

• Safe Structural Methods: Functions that merely relocate or group values—such as _.chunk, _.compact, _.take, _.drop, _.flatten, and _.reverse—treat BigInt elements as opaque primitive references. These methods succeed smoothly on arrays of arbitrary size because they do not attempt type conversion.
• Failed Mathematical Methods: Functions such as _.sum, _.mean, _.sumBy, _.meanBy, _.max, and _.min fail immediately on BigInt collections. These functions initialize accumulators to the Number type 0 or rely on standard relational comparisons and helper functions (like baseToNumber). In JavaScript, mixing a BigInt with a Number in standard arithmetic operations (+, -, *, /) throws a native TypeError: Cannot mix BigInt and other types, use explicit conversions.

Internal Type Coercion Hurdles

Lodash makes extensive internal use of conversion utilities such as toFinite, toNumber, and toInteger. When a BigInt is passed to internal helpers that perform unary plus conversions (+value), JavaScript throws a TypeError: Cannot convert a BigInt value to a number.

Because these coercions are embedded throughout sorting algorithms and comparative predicates (such as _.sortBy with custom numeric metrics or _.inRange), mathematically evaluating BigInt sequences fails unless explicit mapping to strings or standard numbers is applied beforehand—defeating the precision purpose of BigInt.

Performance and Memory Handling with Large Datasets

When dealing with explicitly large datasets (hundreds of thousands to millions of entries), native TypedArrays like BigInt64Array offer memory compaction and cache locality.

Lodash does not provide dedicated optimizations for BigInt64Array. When processing large arrays:

1. Garbage Collection Pressure: Lodash methods often create intermediate standard arrays instead of mutating in place or returning typed buffers, drastically increasing memory overhead.
2. Execution Overhead: Lodash's defensive iteration wrappers add function-call overhead that slows down heavy loops compared to native for loops or native typed array iterations.

Deep Cloning and Serialization

Lodash handles BigInt primitives cleanly in cloning scenarios. _.clone and _.cloneDeep correctly duplicate BigInt values across nested objects and arrays without attempting coercion. However, users must note that parsing or serializing these datasets to formats like JSON will fail downstream, as JSON.stringify does not support BigInt without a custom replacer.

Conclusion

Lodash cannot natively or smoothly evaluate mathematically complex BigInt arrays. For pure structural partitioning, Lodash is functional; however, for any mathematical reductions, range calculations, or high-throughput numeric parsing of large BigInt arrays, developers must bypass Lodash in favor of native ES2020 array methods (Array.prototype.reduce initialized with 0n) or native typed loops using BigInt64Array.