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results = await ProcessConcurrently(async (item) => {\n  return item * 2;\n}, [1, 2, 3, 4, 5, 6]);\n// [2, 4, 6, 8, 10, 12]\n```\n\nCommonJS:\n\n```js\nconst { ProcessConcurrently } = require('iterate-async');\n```\n\n## API\n\n```ts\nProcessConcurrently(fn, iterable, options?) => ProcessConcurrentlyInstance<Result>\n```\n\n`ProcessConcurrentlyInstance` extends `Promise<Result[]>` - you can `await` it directly.\n\n### Parameters\n\n#### `fn`\n\nThe function called for each item. Receives `(item, commonArgs, meta)`.\n\n```js\nconst fn = async (item, common, meta) => {\n  // item    — current item from the iterable\n  // common  — whatever you passed as options.commonArgs\n  // meta    — live progress info (see Meta below)\n  return processedValue;\n};\n```\n\n`fn` can also be a **class** or a **function with static lifecycle hooks** — see [Lifecycle hooks](#lifecycle-hooks).\n\n#### `iterable`\n\nAny sync or async iterable: `Array`, `Set`, `Map`, `string`, generator, async generator, or any object implementing `[Symbol.iterator]` or `[Symbol.asyncIterator]`.\n\n#### `options`\n\nAll options are optional.\n\n| Option | Type | Default | Description |\n|---|---|---|---|\n| `concurrency` | `number` | `4` | Max items processed simultaneously. Must be > 0. |\n| `commonArgs` | `any` | `undefined` | Passed as the second argument to `fn` on every call. |\n| `log` | `'log'`\\|`'info'`\\|`'warn'`\\|`'error'`\\|`fn` | `console.log` | Progress logger. Pass a no-op `() => {}` to silence. |\n| `applyArgs` | `(item, common, meta) => [...args]` | — | Transform item into the spread argument list for `fn`. See [applyArgs](#applyargs). |\n\n### Meta\n\nThe third argument to `fn` provides live progress information. All properties are read live at the moment you access them.\n\n```ts\ntype Meta = {\n  idx: number;       // index of this item\n  idxx: number;      // count of items dispatched so far\n  done: number;      // count of successfully completed items\n  active: number;    // count of items currently in-flight\n  worker: number;    // 0-based index of the worker slot handling this item\n  idxArg: any;       // the original iterable passed to ProcessConcurrently\n  results: Result[]; // snapshot of results collected so far\n  signal: AbortSignal; // aborted if this worker slot is scaled down or on error\n  waiting?: number;  // items not yet started (only present when total is known)\n  total?: number;    // total item count (only present for sized iterables such as arrays, Sets and strings)\n}\n```\n\n`waiting` and `total` are absent for unsized iterables (generators, Maps, custom iterators).\n\n### Instance properties\n\nThe returned instance is a `Promise<Result[]>` with additional live-readable properties:\n\n```js\nconst job = ProcessConcurrently(fn, items, options);\n\njob.concurrency  // current number of worker slots (writable — see Dynamic concurrency)\njob.result       // snapshot of results collected so far\njob.done         // count of completed items\njob.active       // count of in-flight items\njob.idx          // count of items dispatched so far\njob.running      // true until all items are processed\njob.errors       // array of errors (indexed by item position)\njob.waiting      // items not yet started (sized iterables only)\njob.total        // total item count (sized iterables only)\n\nawait job;       // resolves to Result[]\njob.valueOf()    // returns the underlying Promise directly\n```\n\n### Dynamic concurrency\n\nYou can change the number of concurrent workers at any time by assigning to `.concurrency`:\n\n```js\nconst job = ProcessConcurrently(fn, largeDataset, { concurrency: 4 });\n\n// Scale up\njob.concurrency = 10;\n\n// Pause processing (scale to 0)\njob.concurrency = 0;\n\n// Resume\njob.concurrency = 4;\n\nawait job;\n```\n\nScaling down aborts the excess worker slots via their `AbortSignal`. Scaling up to 0 pauses until concurrency is increased again.\n\nYou can use this as a control feature, e.g., you can scale the concurrency down where there are many errors.\n\n```js\nif (errorRate > 0.1) job.concurrency = 2;\nelse job.concurrency = 10;\n```\n\n### applyArgs\n\nUse `applyArgs` when `fn` expects a different argument signature than `(item, common, meta)`. It receives the same three arguments and must return an array that will be spread into `fn`.\n\n```js\n// fn expects (url, method, body) instead of (item, common, meta)\nconst fn = async (url, method, body) => { ... };\n\nawait ProcessConcurrently(fn, requests, {\n  applyArgs: (item, common, meta) => [item.url, item.method, item.body],\n});\n```\n\n### Lifecycle hooks\n\n`fn` can expose `init` and `destroy` static methods (or class methods).\n\n```js\n// As a plain function with statics\nasync function fn(item, common, meta) { ... }\nfn.init    = ({ signal, worker }) => { /* set up worker-local state */ };\nfn.destroy = ({ worker, error }) => { /* tear down; error is set if the worker threw */ };\n\n// As a class — constructor receives { signal, worker }, run() processes each item\nclass fn {\n  constructor({ signal, worker }) { /* set up */ }\n  init({ signal, worker })    { /* called before the worker loop starts */ }\n  destroy({ worker, error })  { /* called when the worker loop ends */ }\n  async run(item, common, meta)      { /* processes each item */ }\n}\n\nawait ProcessConcurrently(fn, items, { concurrency: 4 });\n// init/constructor called 4 times (once per slot), destroy called 4 times at the end\n```\n\nThey are called once per worker slot, not once per item.\n\nThis means, this is really useful for setting up shared, but limited resources, such as:\n- one DB connection per worker\n- one browser tab per worker\n- one GPU context per worker\n\n```\n\nclass fn {\n  constructor({ signal, worker }: { signal: AbortSignal; worker: number }) { console.log(\"CONSTRUCTOR\", worker) }\n  init({ signal, worker }: { signal: AbortSignal; worker: number })    { console.log(\"INIT\", worker) }\n  destroy({ worker, error }: { worker: number; error?: Error })  { console.log(\"DESTROY\", worker) }\n  async run(item: number, common: any, meta: any)      { console.log(\"RUN\", item) }\n}\nProcessConcurrently(fn, [1,2, 3, 4], { concurrency: 2, log: () => {} }).then(it => console.log(it))\n\n/*\nLogs: \nCONSTRUCTOR 0\nCONSTRUCTOR 1\nINIT 0\nINIT 1\nRUN 1\nRUN 2\nRUN 3\nRUN 4\nDESTROY 0\nDESTROY 1\n*/\n```\n\nNote that call order is only guaranteed within slots/instances. That is, within a slot the constructor is guaranteed called first. If any runs happen on this instance, then init is called next followed by one invocation of `run` per task to be execute. Once no more runs are planned for the instance `destroy` is called. Note that destroy is only called if `init` was called! Also note that across instances the order is not guaranteed, e.g., `init` of slot 1 may be called before `constructor` of slot 2. \n\n\n#### Constructor vs init\n\nWhen passing a class as the iterator function, the library may instantiate up to `<concurrency>` many instances, even when the number of tasks is smaller. This is due to the number of tasks not being limited (or even countable, e.g., in the case of an infinite iterator). Therefore, you should do heavy setup logic in `init` rather than the constructor. `init` is only called once the first task is allocated to the instance. This way you won't waste any resources.\n\n\n### Pipelining model\n\nOpposed to many other implementations, this library will \"pipeline\" items. That is, as soon as one item has been processed, the next one is started.\nThis ensures that there are always <concurrency> number of items being processed in parallel.\nThis is distinctly different from chunking. To understand more, see the practical example below.\n\n#### Setup\n\n```\nconst now = Date.now();\nasync function process(i) {\n  const sleep = (i % 2) * 100 + 100;\n  await new Promise(res => setTimeout(res, sleep));\n  console.log(`Processed ${i} at ${Date.now() - now}`);\n  return i;\n}\n```\n\n#### One item after another\n\nIn this example, every item is strictly processed after the previous one is already finished. This is the same as `concurrency: 1` in `ProcessConcurrently`.\n\n`1 ─200─> 2 ─100─> 3 ─200─> 4 ─100─> 5 ─200─> 6 ─100─> 7 ─200─> 8 ─100─> DONE`\n\n```\nconst items = [1,2,3,4,5,6,7,8];\nfor(const i of items) {\n  await process(i);\n}\n/*\nProcessed 1 at 203\nProcessed 2 at 311\nProcessed 3 at 527\nProcessed 4 at 637\nProcessed 5 at 842\nProcessed 6 at 951\nProcessed 7 at 1156\nProcessed 8 at 1265\n*/\n```\n\n#### All items at once\n\nIn this example, no item waits for another - they are all running in parallel. This is the same as `concurrency: Infinity` in `ProcessConcurrently`.\n\n```\nt=0:\n1(200) |\n2(100) |\n3(200) |\n4(100) |\n5(200) |\n6(100) |\n7(200) |\n8(100) |\n\nt=100: 2,4,6,8 done\nt=200: 1,3,5,7 done → DONE\n```\n\n```\nconst items = [1,2,3,4,5,6,7,8];\nawait Promise.all(items.map(async i => process(i)));\n/*\nProcessed 2 at 114\nProcessed 4 at 114\nProcessed 6 at 114\nProcessed 8 at 115\nProcessed 1 at 208\nProcessed 3 at 208\nProcessed 5 at 208\nProcessed 7 at 208\n*/\n```\n\nNote that in the real world, this will go sideways real fast!\nThink:\n- APIs rate limiting\n- DB connection pools\n- file descriptor limits\n- memory pressure\n\nConcurrency is not only about speed - it's about not crashing your system.\n\n#### One chunk at a time\n\nIn this example, items are split into chunks. All items within a chunk are processed in parallel. Each chunk waits for the prvious one to finish before starting the next.\n\nChunking is like shipping pallets - items are grouped and processed together.\n\n```\nBatch 1: [1,2]\n1(200) |\n2(100) |   → done at 200\n\nBatch 2: [3,4]\n3(200) |\n4(100) |   → done at 200\n\nBatch 3: [5,6]\n5(200) |\n6(100) |   → done at 200\n\nBatch 4: [7,8]\n7(200) |\n8(100) |   → done at 200\n\nTOTAL: 4 batches × 200ms = 800ms\n```\n\n```\nfunction chunkArray(arr, size) {\n  const chunkedArr = [];\n  for (let i = 0; i < arr.length; i += size) {\n    chunkedArr.push(arr.slice(i, i + size));\n  }\n  return chunkedArr;\n}\nconst items = [1,2,3,4,5,6,7,8];\nconst chunks = chunkArray(items, 2); \nfor(const chunk of chunks) {\n  await Promise.all(chunk.map(async i => process(i)));\n}\n/*\nProcessed 2 at 107\nProcessed 1 at 215\nProcessed 4 at 325\nProcessed 3 at 418\nProcessed 6 at 527\nProcessed 5 at 620\nProcessed 8 at 728\nProcessed 7 at 822\n*/\n```\n\n#### Pipelined\n\nIn this example, 2 pipelines are created. Each pipeline will pull items one after the other from the pool until all are processed. Pipelines don't wait for one another but are always keeping busy. This ensures consistent throughput without spikes or drops.\n\nPipelineing is like assembly lines - each item moves station to station continuously\n\n```\nt=0:\nP1: 1(200)\nP2: 2(100)\n\nt=100:\nP2 picks next → 3(200)\n\nt=200:\nP1 finishes → picks 4(100)\nP2 still running 3\n\nt=300:\nP1 finishes → picks 5(200)\nP2 finishes → picks 6(100)\n\nt=400:\nP2 finishes → picks 7(200)\n\nt=500:\nP1 finishes → picks 8(100)\n\nt=600:\nP1 finishes\nt=700:\nP2 finishes → DONE\n```\n\n```\nP1: 1 ─200─> 4 ─100─> 5 ─200─> 8 ─100─> DONE (600ms)\nP2: 2 ─100─> 3 ─200─> 6 ─100─> 7 ─200─> DONE (700ms)\n```\n\n```\nconst items = [1,2,3,4,5,6,7,8];\n await ProcessConcurrently(\n  i => process(i),\n  items,\n  { concurrency: 2 }\n);\n/*\nProcessed 2 at 104\nProcessed 1 at 212\nProcessed 3 at 306\nProcessed 4 at 322\nProcessed 6 at 433\nProcessed 5 at 511\nProcessed 8 at 619\nProcessed 7 at 634\n*/\n```\n\n#### Comparison\n\n| Mode                               | Peak Running | Avg Running | Total Time |\n|------------------------------------|-------------|------------|------------|\n| Sequential (for-loop)              | 1           | 1.00       | 1200ms     |\n| Full Parallel (Promise.all)        | 8           | 6.00       | 200ms      |\n| Chunking (batch size = 2)          | 2           | 1.50       | 800ms      |\n| Pipeline (concurrency = 2)         | 2           | 1.71       | 700ms      |\n| Chunking (batch size = 3)          | 3           | 1.71       | 700ms      |\n| Pipeline (concurrency = 3)         | 3           | 2.18       | 550ms      |\n\n#### Example 2\n\nAssume workloads are vastly uneven - this is how, in reality, many workloads (network requets, competing for physical resources, file-processing, ...) behave.\n\nWe will assume 1000 items to be processed. As an example formular we take `(i % 100)^2 + 1`, that is item 1 takes 2ms, item 2 takes 5ms, item 3 takes 10ms, ..., item 96 takes 9410ms, item 97 takes 9605ms, item 98 takes 9802ms, item 99 takes 1ms, item 100 takes 2ms and so on.\n\nWith this modelling, the table from above looks as follows.\n\n| Mode                              | Peak Running | Avg Running | Total Time |\n|-----------------------------------|-------------|------------|------------|\n| Sequential                        | 1           | 1.00       | 3,284,500ms |\n| Full Parallel                     | 1000        | 335.00     | 9,802ms     |\n|-----------------------------------|-------------|------------|------------|\n| Chunking (size = 2)               | 2           | 1.00       | 3,284,500ms |\n| Chunking (size = 5)               | 5           | 2.50       | 1,313,800ms |\n| Chunking (size = 10)              | 10          | 5.00       | 656,900ms   |\n| Chunking (size = 20)              | 20          | 10.00      | 328,450ms   |\n| Chunking (size = 50)              | 50          | 25.00      | 131,380ms   |\n| Chunking (size = 100)             | 100         | 50.00      | 65,690ms    |\n| Chunking (size = 200)             | 200         | 100.00     | 32,845ms    |\n|-----------------------------------|-------------|------------|------------|\n| Pipeline (concurrency = 2)        | 2           | 2.00       | 1,642,250ms |\n| Pipeline (concurrency = 5)        | 5           | 5.00       | 656,900ms   |\n| Pipeline (concurrency = 10)       | 10          | 10.00      | 328,450ms   |\n| Pipeline (concurrency = 20)       | 20          | 20.00      | 164,225ms   |\n| Pipeline (concurrency = 50)       | 50          | 50.00      | 65,690ms    |\n| Pipeline (concurrency = 100)      | 100         | 100.00     | 32,845ms    |\n| Pipeline (concurrency = 200)      | 200         | 200.00     | 16,423ms    |\n\nEach chunk is only as fast as its _slowest_ item. Thus each chunking step is almost always blocked. Chunking cares about worst case in batch.\n\nIn contrast, the pipeline remains stable. Pipeline cares about average.\n\n`Pipeline time ≈ total_work / concurrency`\n\nThis means, the gap between chunking and pipeline becomes extreme: In the above example, chunking is consistently ~2x slower!\n\n#### Takeaway\n\nSequential is simplest but slow.\n\nFull parallel is as fast as it gets, but unsafe due to uncontrolled parallelism.\n\nChunking is controlled, but inefficient - item-processors within a chunk will sit idle until each item in the chunk is done!\n\nPipelining is controlled and efficient. Every item-processors is always busy.\n\n*Chunking is dominated by the slowest item in each group; pipelines smooth out variance by continuously redistributing work.*\n\n## Examples\n\n### Fetch with concurrency limit\n\n```js\nconst results = await ProcessConcurrently(\n  (id, { baseUrl }) => fetch(`${baseUrl}${id}`).then(r => r.json()),\n  [1, 2, 3, 4, 5],\n  { commonArgs: { baseUrl: 'https://api.example.com/items/' }, concurrency: 2 }\n);\n```\n\n### Read files in Node.js\n\n```js\nimport { readFile } from 'node:fs/promises';\n\nconst buffers = await ProcessConcurrently(\n  (filename, { dir }) => readFile(`${dir}/${filename}`),\n  ['a.txt', 'b.txt', 'c.txt'],\n  { commonArgs: { dir: './data' } }\n);\n```\n\n### Walk a directory tree with a generator\n\n```js\nimport { readdirSync, statSync } from 'node:fs';\n\nfunction* walk(dir) {\n  for (const entry of readdirSync(dir)) {\n    const path = `${dir}/${entry}`;\n    if (statSync(path).isDirectory()) yield* walk(path);\n    else yield path;\n  }\n}\n\nconst results = await ProcessConcurrently(uploadFile, walk('./src'), { concurrency: 8 });\n```\n\n### Walk a directory tree with an async generator\n\n```js\nimport { readdir, stat } from 'node:fs/promises';\n\nasync function* walk(dir) {\n  for (const entry of await readdir(dir)) {\n    const path = `${dir}/${entry}`;\n    if ((await stat(path)).isDirectory()) yield* walk(path);\n    else yield path;\n  }\n}\n\nconst results = await ProcessConcurrently(uploadFile, walk('./src'), { concurrency: 8 });\n```\n\n### Progress indicator\n\n```js\nconst fn = (item, { el }, meta) => {\n  el.textContent = `${meta.done} / ${meta.total}`;\n  return processItem(item);\n};\n\nawait ProcessConcurrently(fn, items, {\n  commonArgs: { el: document.querySelector('#progress') },\n  concurrency: 4,\n});\n```\n\n### Silence logging\n\n```js\nawait ProcessConcurrently(fn, items, { log: () => {} });\n```\n\n### Use a custom logger\n\n```js\nawait ProcessConcurrently(fn, items, { log: 'warn' });       // console.warn\nawait ProcessConcurrently(fn, items, { log: myLogger.info }); // custom function\n```\n\n## Browser\n\nUse your bundler's ESM import. For direct browser use without a bundler:\n\n```html\n<script type=\"module\">\n  import ProcessConcurrently from './node_modules/iterate-async/build/index.mjs';\n</script>\n```\n\nFor legacy global-namespace use:\n\n```html\n<script src=\"./node_modules/iterate-async/build/index.js\"></script>\n<!-- window.ProcessConcurrently is now available -->\n```\n\n## Error handling\n\nIf `fn` throws or rejects, the error propagates out of `await job` and the iterator is closed. The error is also recorded in `job.errors` at the item's index position. Other in-flight items complete normally before the rejection settles.\n\n```js\ntry {\n  await ProcessConcurrently(fn, items);\n} catch (e) {\n  console.error(job.errors); // sparse array — only errored positions are set\n}\n```\n\nLikewise, if you throw in any life-cycle hooks, the iteration will be cancelled and the iterator closed.\n\nAs general guidance, avoid throwing errors and rather return results of form `[OK, data]` and `[ERROR, e]` from `fn`.\nYou can additionally push failed items back into the task-iterable for re-try.\n\n```\nconst items = [{i: 1, r: 0}, {i: 2, r: 0}, {i: 3, r: 0}, {i: 4, r: 0}];\nconst OK = Symbol('OK');\nconst ERROR = Symbol('ERROR');\nawait ProcessConcurrently(async (item) => {\n  try {\n    const result = await taskThatMightFail(item);\n    return [OK, result]   \n  } catch(e) {\n    if(item.r < 10)\n      items.push({...item, r: item.r + 1});\n    return [ERROR, e];\n  }\n}, items);\n```\n\n## Typescript support\n\nThis library supports TypeScript natively.\nMake sure you type your iterator function well!\n\n```\nimport ProcessConcurrently from 'iterate-async';\n\nProcessConcurrently(async (arg, com, meta): Promise<string> => {\n    console.log(com.hello); // type { hello: string }\n    console.log({...meta}); // type as documented above\n    return arg.toFixed(); // arg is number\n}, [1, 2, 3, 4], {\n    commonArgs: {\n        hello: 'world'\n    }\n}).then((results) => { // results is string[] as the function returned string\n  console.log(results);\n});\n\nProcessConcurrently(async (arg, _, meta): Promise<string> => { // arg is number, _ is undefined (as we pass no commonArgs), meta is as above\n    console.log({...meta})\n    return arg.toFixed();\n}, [1, 2, 3, 4], {\n    concurrency: 2\n}).then((results) => { // results is string\n  console.log(results);\n});\n\nProcessConcurrently(async (arg, common: {\n    hello: string;\n}, meta): Promise<string> => {\n    console.log(common.hello);\n    console.log({...meta})\n    return arg.toFixed();\n}, [1, 2, 3, 4], {\n    concurrency: 2,\n    commonArgs: {\n        hello: 'world'\n    }\n}).then((results) => {\n  console.log(results);\n});\n\nProcessConcurrently(async (a: string, b: string): Promise<string> => { // arg-signature must match the return type of applyArgs\n    console.log(a, b);\n    return a + ', ' + b;\n}, [1, 2, 3, 4], {\n    commonArgs: {\n        foo: 'bar'\n    },\n    concurrency: 1,\n    applyArgs: (arg: number, common: { foo: string }) => [\"\"+arg, common.foo] as [string, string]\n}).then((results) => {\n  console.log(results);\n});\n```\n\n## Why iterate-async?\n\n### Comparisons to libraries and frameworks\n\n#### Quick comparison\n\n- p-map → concurrent mapping over iterables  \n- p-limit → concurrency limiter (primitive)  \n- p-queue → full task queue (scheduler)  \n- **iterate-async** → pipelined iterable processor (work-stealing)\n\n#### Feature comparison\n\n| Feature / Model                | iterate-async | p-map | p-limit | p-queue |\n|-------------------------------|--------------|-------|--------|--------|\n| Core abstraction              | Iterable pipeline | Array map | Function wrapper | Task queue |\n| Works on any iterable         | ✅            | ✅ | ❌ | ❌ |\n| Async generators / infinite   | ✅            | ✅ | ❌ | ❌ |\n| Concurrency limiting          | ✅            | ✅ | ✅ | ✅ |\n| Scheduling model              | Work-stealing pipeline | Batched mapping | Queue + Promise.all | Queue (FIFO / priority) |\n| Dynamic concurrency           | ✅            | ❌ | ⚠️ (manual) | ⚠️ (config-driven) |\n| Backpressure control          | ✅            | ❌ | ❌ | ✅ |\n| Lifecycle hooks (per worker)  | ✅            | ❌ | ❌ | ❌ |\n| Progress / live meta          | ✅            | ❌ | ❌ | ⚠️ events |\n| Result ordering               | Stable        | Stable | Manual | Configurable |\n| Pause / resume                | ✅ | ❌ | ❌ | ✅ |\n| Rate limiting (time-based)    | ❌            | ❌ | ❌ | ✅ |\n\n#### Key differences\n\n##### vs p-map\n\n- Processes a *fixed collection* with concurrency\n- lacks progress reporting and lifecycle hooks\n\n**Bottom line:**  \n`p-map = concurrent map`  \n`iterate-async = streaming pipeline`\n\n##### vs p-limit\n\n- `p-limit` is a **low-level primitive**\n- You manually wrap functions and still rely on `Promise.all`\n\n**Critical difference:**\n\n```js\n// p-limit → YOU manage the loop + scheduling\nawait Promise.all(items.map(item => limit(() => fn(item))));\n\n// iterate-async → scheduling is built-in\nawait ProcessConcurrently(fn, items);\n```\n\n**Bottom line:**  \n`p-limit = building block`\n`iterate-async = system`\n\n##### vs p-queue\n\np-queue is a task queue which focuses on priorities, pause/resume and rate limiting\n\niterate-async:\n- No queue abstraction\n- Focus is data flow, not job orchestration\n\nCore difference:\n- Queue → push tasks into system\n- Pipeline → pull work from iterable\n\n**Bottom line:**  \n`p-queue = scheduler`\n`iterate-async = processor`\n\n##### When to use what\n\nUse iterate-async when:\n- You intend to process _something_ iterable\n- Workloads are uneven or unpredictable\n- One workload may spawn one or more other workload\n- You want to dynamically control concurrency\n- You can't use all-at-once or one-at-a-time\n- You want one abstraction that just works\n\nUse p-map when:\n- You have a fixed-size iterable\n- You just want `Promise.all` with limited concurrency\n\nUse p-limit when:\n- You already have a system\n- You just need to cap concurrency\n\nUse p-queue when:\n- You need priorities / retries / scheduling\n- You are building a job system\n\n### iterate-async's pipelining implementation\n\nThis implementation is a *work-stealing scheduler over an iterable*.\n\nIt's not chunking; it's not like most naive concurrency pools; *there is no pre-partitioning, no fixed assignment - just pull-based scheduling.*\n\n### In a nutshell\n\n- No dependencies - no fuss\n- Native - use with any native type. No fancy pipeline constructors, no wrappers. Just pure javascript\n- Build in typescript\n- Native es6 (.mjs) support\n- One single, simple export - `ProcessConcurrently` - that can be directly awaited\n- Iterate *anything* without prior conversion\n- Small (4.33 kB raw /1.78 kB gzip)\n- Well-tested\n- Uses pipelining rather than chunking\n","readmeFilename":"README.md"}