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from typing import Iterator, List, Dict, TypeVar |
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import json |
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import argparse |
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from concurrent.futures import ThreadPoolExecutor |
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from itertools import islice |
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import smart_open |
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from tqdm import tqdm |
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import jmespath |
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import gcld3 |
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T = TypeVar("T") |
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detector = gcld3.NNetLanguageIdentifier(min_num_bytes=20, max_num_bytes=1000) |
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def batch_iterator(iterator: Iterator[T], batch_size: int = 50) -> Iterator[List[T]]: |
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""" |
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Batch an iterator into an iterator over lists of batch size. |
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""" |
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iterator = iter(iterator) |
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while True: |
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batch = list(islice(iterator, batch_size)) |
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if not batch: |
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return |
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yield batch |
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def process_chunk(chunk: List, extractor: jmespath.parser.ParsedResult) -> List[Dict]: |
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""" |
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Apply JMESPath to a chunk of JSONL data. |
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""" |
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results: List[Dict] = [] |
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for line in chunk: |
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data = json.loads(line) |
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extracted = extractor.search(data) |
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if extracted is not None: |
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if not isinstance(extracted, list): |
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extracted = [extracted] |
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try: |
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extracted_str = " ".join( |
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set([ex for ex in extracted if isinstance(ex, str)]) |
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).strip() |
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except: |
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print(json.dumps(data, ensure_ascii=False)) |
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raise |
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lang = detector.FindLanguage(extracted_str) |
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url = data["url"] |
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schema_type = data["schema_type"] |
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del data["url"] |
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del data["schema_type"] |
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wrapped_data = { |
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"payload": data, |
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"language": lang.language, |
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"language_is_reliable": lang.is_reliable, |
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"text_length": len(extracted_str), |
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"data_length": len(line), |
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"text_to_data_ratio": len(extracted_str) / len(line), |
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"url": url, |
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"schema_type": schema_type, |
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} |
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results.append(wrapped_data) |
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return results |
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def process_file( |
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input_files: str, |
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output_file: str, |
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chunk_size: int, |
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num_threads: int, |
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extractor: jmespath.parser.ParsedResult, |
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): |
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""" |
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Apply JMESPath to a large JSONL file in parallel. |
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input_file: path to input JSONL file |
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output_file: path to output JSONL file |
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chunk_size: number of lines to process at a time |
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num_threads: number of threads to use |
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extractor: compiled JMESPath expression to apply |
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""" |
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with smart_open.open(output_file, "wt", encoding="utf-8") as writer: |
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for input_file in input_files: |
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with smart_open.open(input_file, "rt", encoding="utf-8") as reader: |
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with ThreadPoolExecutor(max_workers=num_threads) as executor: |
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for chunk in batch_iterator(tqdm(reader), batch_size=chunk_size): |
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if not chunk: |
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break |
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results = executor.map(process_chunk, [chunk], [extractor]) |
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for result in results: |
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for item in result: |
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writer.write(json.dumps(item, ensure_ascii=False)) |
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writer.write("\n") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser( |
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description="Apply JMESPath to a large JSONL files in parallel." |
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) |
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parser.add_argument("input_files", help="path to input JSONL files", nargs="+") |
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parser.add_argument("output_file", help="path to output JSONL file") |
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parser.add_argument( |
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"--chunk-size", |
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type=int, |
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default=10000, |
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help="number of lines to process at a time (default: 10000)", |
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) |
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parser.add_argument( |
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"--num-threads", |
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type=int, |
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default=4, |
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help="number of threads to use (default: 4)", |
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) |
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args = parser.parse_args() |
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jmespath_expression: jmespath.parser.ParsedResult = jmespath.compile( |
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"[name, description, headline, about, tool, supply, keywords, step[].name, step[].text, step[].itemListElement[].text, " |
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+ "step[].itemListElement[].itemListElement[].text, mainEntity[].name, mainEntity[].acceptedAnswer.text, " |
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+ "mainEntity[].acceptedAnswer.name, mainEntity.name, mainEntity.acceptedAnswer.text, " |
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+ "mainEntity.*.acceptedAnswer[].text, mainEntity[].acceptedAnswer[].text, step.itemListElement[].text, step.itemListElement[].itemListElement[].text][][]" |
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) |
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process_file( |
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args.input_files, |
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args.output_file, |
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args.chunk_size, |
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args.num_threads, |
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jmespath_expression, |
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) |
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