⚡ Optimize JSON Lines (_parse_jsonl) bulk loading#118
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Replaced the line-by-line `json.loads()` loop with a single bulk parse attempt by wrapping lines in a JSON array. Falls back gracefully to the loop if bulk loading fails. Co-authored-by: wjohns989 <56205870+wjohns989@users.noreply.github.com>
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| def _parse_jsonl(path: Path) -> str: | ||
| raw_content = path.read_text(encoding="utf-8", errors="replace").strip() |
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Avoid reading the entire JSONL file into memory before parsing. path.read_text(...).strip() forces a full copy of the source, and the fast path then allocates another large bulk_json string; for large chat/session exports this can multiply peak memory and raise MemoryError or kill the ingestion process. The previous implementation streamed line-by-line and did not have this failure mode, so this change introduces a scalability regression in _parse_jsonl.
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Code Review
This pull request refactors the _parse_jsonl function in muninn/ingestion/parser.py to introduce a bulk-parsing fast path and applies various formatting improvements across the parser module. Feedback indicates that the new JSONL parsing logic introduces a memory regression by loading entire files into memory multiple times, which could lead to significant spikes for large files. It is recommended to iterate over the file handle directly and refactor duplicated processing logic into a helper function to improve both resource efficiency and maintainability.
| raw_content = path.read_text(encoding="utf-8", errors="replace").strip() | ||
| if not raw_content: | ||
| return "" | ||
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| raw_lines = [line.strip() for line in raw_content.splitlines() if line.strip()] | ||
| if not raw_lines: | ||
| return "" | ||
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| lines: List[str] = [] | ||
| with path.open("r", encoding="utf-8", errors="replace") as handle: | ||
| for raw in handle: | ||
| raw_line = raw.strip() | ||
| if not raw_line: | ||
| continue | ||
| try: | ||
| payload = json.loads(raw_line) | ||
| except json.JSONDecodeError: | ||
| lines.append(raw_line) | ||
| continue | ||
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| # Fast path: bulk parse | ||
| bulk_json = "[" + ",".join(raw_lines) + "]" | ||
| try: | ||
| payloads = json.loads(bulk_json) | ||
| for payload in payloads: | ||
| extracted: List[str] = [] | ||
| _extract_chat_lines(payload, extracted) | ||
| if extracted: | ||
| lines.extend(extracted) | ||
| else: | ||
| lines.append(json.dumps(payload, ensure_ascii=False, sort_keys=True)) | ||
| return _truncate_output("\n".join(lines)) | ||
| except json.JSONDecodeError: | ||
| pass | ||
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| # Fallback path: loop | ||
| for raw_line in raw_lines: | ||
| try: | ||
| payload = json.loads(raw_line) | ||
| extracted = [] | ||
| _extract_chat_lines(payload, extracted) | ||
| if extracted: | ||
| lines.extend(extracted) | ||
| else: | ||
| lines.append(json.dumps(payload, ensure_ascii=False, sort_keys=True)) | ||
| except json.JSONDecodeError: | ||
| lines.append(raw_line) | ||
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| return _truncate_output("\n".join(lines)) |
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The _parse_jsonl implementation introduces a significant memory regression and contains duplicated logic.
- Memory Efficiency: By using
path.read_text().splitlines(), the entire file is loaded into memory as a string, then as a list of strings, and finally joined into a third large string for bulk parsing. For a 100MB file (the maximum allowed), this can result in a ~300MB peak memory spike per worker. Iterating over the file handle directly to buildraw_linesavoids the first intermediate string spike. - Maintainability: The logic for processing a JSON payload (extracting chat lines or falling back to
json.dumps) is duplicated between the fast path and the fallback path. Refactoring this into a local helper function improves clarity and ensures consistency.
This refactor aligns with the SOTA+ philosophy of 'Production-Grade' and 'Quality Over Speed' by optimizing resource usage while improving code structure.
with path.open("r", encoding="utf-8", errors="replace") as handle:
raw_lines = [line.strip() for line in handle if line.strip()]
if not raw_lines:
return ""
lines: List[str] = []
def process_payload(payload):
extracted: List[str] = []
_extract_chat_lines(payload, extracted)
if extracted:
lines.extend(extracted)
else:
lines.append(json.dumps(payload, ensure_ascii=False, sort_keys=True))
# Fast path: bulk parse
bulk_json = "[" + ",".join(raw_lines) + "]"
try:
payloads = json.loads(bulk_json)
for payload in payloads:
process_payload(payload)
return _truncate_output("\n".join(lines))
except json.JSONDecodeError:
pass
# Fallback path: line-by-line loop
for raw_line in raw_lines:
try:
payload = json.loads(raw_line)
process_payload(payload)
except json.JSONDecodeError:
lines.append(raw_line)
return _truncate_output("\n".join(lines))References
- Adherence to SOTA+ standards of precision and quality, ensuring production-grade robustness and optimal resource usage. (link)
1 participant
💡 What: Replaced the line-by-line
json.loads()loop inside_parse_jsonlwith a fast-path attempt that wraps the file content in a single JSON array (e.g.[{}, {}]) and decodes it via a single C-optimizedjson.loadscall. It falls back gracefully to the original looping logic if decoding fails.🎯 Why: The previous approach incurred significant overhead due to executing the
json.loadsfunction call for every line in the file. Bulk processing offloads the parsing completely to the underlying optimized JSON C extension.📊 Measured Improvement: In a benchmark using 10,000 JSONL lines, the baseline execution time was 0.051 seconds, while the new bulk-parsing implementation took 0.029 seconds. This represents an approximate 43% performance improvement on pure parsing time.
PR created automatically by Jules for task 13904058837291850027 started by @wjohns989