* nes-datagen: stream large JSON array inputs to avoid 2 GiB readFile limit Reading the whole input via fs.readFile fails for files larger than 2 GiB (and exceeds V8's max string length). Add a streaming JSON-array parser and use it in both the sequential and parallel pipeline paths so multi-GB recordings can be processed with bounded memory. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: also accept JSON Lines (NDJSON) input Auto-detect the input format from the first non-whitespace character: a leading '[' is parsed as a single JSON array, otherwise the file is parsed as JSON Lines (one JSON object per line). Both formats are streamed so multi-GB inputs work regardless of shape. Rename streamJsonArray -> streamJsonRecords to reflect the broader purpose. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: infer JSON vs JSON Lines input format from file extension Use the file extension (.jsonl/.ndjson -> JSON Lines, otherwise JSON array) to select the streaming parser instead of sniffing the content. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: validate streamed JSON arrays for truncation and malformed input The new streaming parser previously accepted any prefix of a JSON array silently: a truncated file (no closing ']'), a missing element between commas, a trailing comma or trailing data after the array all produced zero or fewer records rather than an error. That is especially dangerous for the multi-GB inputs this parser was introduced for, because the underlying file is much more likely to be incomplete. Tighten the state machine to surface these as errors, matching what the old whole-file JSON.parse would have done, and add tests for each case. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: stream worker-result merging and final output write For multi-GB inputs the parent process was still hitting V8's ~512 MiB max-string-length limit in two places after the input-side fix: 1. Merging worker result files used fs.promises.readFile + JSON.parse on each result, but with a 5+ GB input split N ways each per-worker file is hundreds of MB of similar-shaped data and easily exceeds the string limit. 2. writeSamples serialized the entire validSamples array via a single JSON.stringify(arr, null, 2) before writing, which has the same problem on output. Switch both to stream over individual records: - A new shared openWriteStream(filePath) helper wraps fs.createWriteStream, attaches an 'error' listener immediately (so async write failures don't surface as uncaughtException and skip cleanup), awaits backpressure via the per-write callback, and exposes an idempotent close(). - writeChunkFiles uses the helper inside a try/finally so any mid-stream ENOSPC/EIO bubbles up cleanly and the tmp dir is still removed. - The merge step now uses streamJsonRecords<ISample>(resultPath), so the parent never materializes a single worker's output as one string. - writeSamples emits the output JSON array incrementally: per-sample JSON.stringify(..., null, 2) (indented two spaces to match the previous layout) joined with ',\n'. Byte size is accumulated for the existing IWriteResult.fileSize. Also documents that single-process loadAndParseInput still buffers the full row set in memory and that --parallelism is required for very large inputs (workers each only load their slice). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: switch output to JSON Lines Both the final user-facing output and the per-worker intermediate result files now use JSON Lines (one record per line) instead of a pretty-printed JSON array. JSONL is dramatically simpler to write and read incrementally: no surrounding brackets/commas to track, no multi-line per-element indentation, just JSON.stringify + '\n' per record on the write side and split-on-newline + JSON.parse per non-empty line on the read side (this is what streamJsonRecords already does when it detects the .jsonl extension). Changes: - writeSamples emits one JSON.stringify(sample) + '\n' per validated sample via no array wrapper, no pretty-printing.openWriteStream - resolveOutputPath defaults the implicit output to <input>_output.jsonl (was <input>_output.json). - Per-worker result files in runInputPipelineParallel are now result_${w}.jsonl, so the merge step's streamJsonRecords auto-picks the JSONL parser from the extension. - E2e tests updated to read JSONL (split on newline, JSON.parse per line) and to use .jsonl output paths. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * nes-datagen: surface worker-result parse errors and update --out help text Two review follow-ups: 1. The merge step that streams each worker's result file used to wrap the iteration in a try/catch that downgraded any parse error to a console.error warning. With the new streaming reader that is unsafe: streamJsonRecords yields N valid records first and then throws on a malformed/truncated tail, leaving those N partial records already in allSamples. The pipeline would then quietly emit a truncated training-data output. Drop the swallowing try/catch so a corrupt worker result aborts the run non-zero. 2. The --out help text in simulationOptions.ts still advertised the old JSON-array default (<input>_output.json). Update it to reflect the JSONL output, and also note in --input that the format is inferred from the .jsonl/.ndjson extension. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
GitHub Copilot - Your autonomous AI peer programmer
GitHub Copilot is an AI peer programming tool that transforms how you write code in Visual Studio Code.
GitHub Copilot agents handle complete coding tasks end-to-end, autonomously planning work, editing files, running commands, and self-correcting when they hit errors. You can also leverage inline suggestions for quick coding assistance and inline chat for precise, focused edits directly in the editor.
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Let AI agents implement complex features end-to-end. Give an agent a high-level task and it breaks the work into steps, edits multiple files, runs terminal commands, and self-corrects when it hits errors or failing tests. Agents excel at building new features, debugging and fixing failing tests, refactoring codebases, and collaborating via pull requests.
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Licensed under the MIT license.



