Map, Filter & Reduce
Transform, filter, and compute aggregate metrics over arrays without manual for-loop iteration.
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📋 Copy Functional Array Pipelines Snippet
const transactions = [
{ type: "credit", amount: 150 },
{ type: "debit", amount: 40 },
{ type: "credit", amount: 200 }
];
// Calculate Net Balance with Reduce
const netBalance = transactions.reduce((acc, t) => {
return t.type === "credit" ? acc + t.amount : acc - t.amount;
}, 0);⚠️ 5 Fatal Traps & JavaScript Pitfalls
Trap #1: Reduce of Empty Array Without Initial Value TypeError
Calling
[].reduce((acc, x) => acc + x) without an initial value throws an immediate TypeError: Reduce of empty array with no initial value. Always provide the initial accumulator argument.Trap #2: Chaining Multiple Iterations on Massive Arrays
Chaining
arr.map().filter().map() creates multiple intermediate arrays in memory. On large datasets (100k+ items), combine into a single reduce() or for-loop.Trap #3: Mutating the Accumulator In-Place in Reduce
Mutating objects or arrays in
reduce() without returning them breaks pipeline expectations and causes side-effects across iterations.Trap #4: Using map() Without Consuming the Return Value
Using
arr.map(x => doSomething(x)) purely for side-effects allocates a throwaway array. Use for...of or forEach.Trap #5: Falsy Element Removal Confusion in filter()
Writing
arr.filter(Boolean) removes all falsy items (0, false, null, undefined, ""), which may unintentionally strip valid numeric zeros.💬 Frequently Asked Questions
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