Batch Processing Pattern

num_batches = 1200000 // batch_size for i in range(num_batches): process_batch(batch_size) if __name__ == "__main__": main() ``` ->-> 3,6 [Turn 4465] Assistant: Certainly! Using Apache NiFi for your ETL workflows can b…

pip install python-dateutil ``` 2. **Run the Script**: Execute the script to see how it handles different date formats. This approach should help you standardize date formats more effectively and handle a wider range of input formats…

- Monitor the system to ensure it achieves the desired performance. - Use monitoring tools to track resource usage and identify any bottlenecks. ### Example Usage Ensure you replace the placeholder documents with your actual data: …

logging.error(f"Failed to vectorize document after {retries} retries: {e}") return None def vectorize_pipeline(docs, max_workers=None): vectors = [] with ThreadPoolExecutor(max_workers=max_workers) a…

Load data only when necessary. This can be particularly useful if you are dealing with large datasets that do not fit into memory all at once. ### 7. **Reduce Redundant Computations** Avoid redundant computations by storing and reusing res…

print(f"Failed to rewrite query '{query}': {e}") ``` ### Explanation 1. **Logging Configuration**: - `filename='error.log'`: Specifies the log file name. - `level=logging.ERROR`: Sets the logging level to `ERROR` to capture …

### Optimized Implementation Here's an optimized version of your code: ```python import hunspell from concurrent.futures import ThreadPoolExecutor, as_completed import time # Load the Hunspell dictionary once hspell = hunspell.HunSpell(…