Py txt to CSV - 02

The provided code is a well-structured script for converting a large text file containing concatenated JSON objects into a CSV file using Python, pandas, and a streaming approach to handle memory efficiently. Below is a detailed review of the code, including its strengths, potential issues, and suggestions for improvement.

Strengths

1. Memory Efficiency with Streaming:

   • The stream_json_objects function reads the file in chunks (64KB) and processes JSON objects incrementally, avoiding loading the entire file into memory. This is critical for handling large files.
   • The use of a buffer and regex (separator_re) to split concatenated JSON objects is robust for handling objects split across chunk boundaries.

2. Batch Processing:

   • The json_to_csv_optimized function processes JSON objects in batches (batch_size=10000), normalizing them into a pandas DataFrame and writing to CSV incrementally. This balances memory usage and performance.

3. Error Handling:

   • The code includes comprehensive error handling for:
     • File not found (FileNotFoundError).
     • JSON parsing errors (json.JSONDecodeError).
     • General exceptions during file processing or normalization.
   • Warnings are printed with useful context (e.g., buffer snippets) to aid debugging.

4. Column Consistency:

   • The script ensures consistent column headers across batches by determining columns from the first valid batch and reindexing subsequent batches to match. This prevents misaligned CSV output.

5. Clean File Management:

   • Removes the default Excel file (result.xlsx) if it exists, avoiding confusion from previous runs.
   • Uses 'a' (append) mode for CSV writing after the header is written, ensuring efficient file operations.

6. Modularity:

   • The code is split into two clear functions: stream_json_objects for parsing and json_to_csv_optimized for conversion, making it reusable and maintainable.

Potential Issues and Suggestions

While the code is robust, there are a few areas where it could be improved or where edge cases might cause issues.

1. Edge Case: Malformed JSON Objects

• Issue: If the input file contains malformed JSON objects or unexpected separators (e.g., }{ inside a string value), the stream_json_objects function skips the invalid segment but might lose data. The warning message helps, but it doesn’t allow for recovery of partial valid objects.

• Suggestion: Add an option to log skipped segments to a separate file for post-processing or manual inspection. For example:
  

   def stream_json_objects(filepath, log_file=None):
       if log_file:
           log = open(log_file, 'w', encoding='utf-8')
       try:
           # ... existing code ...
           except json.JSONDecodeError as e:
               print(f"Warning: JSONDecodeError at position {match.start()+1}: {e}")
               if log_file:
                   log.write(f"Error at position {match.start()+1}: {potential_obj_str}\n")
               last_processed_index = match.end()
       finally:
           if log_file:
               log.close()
  

  Usage: stream_json_objects(json_file_path, log_file='skipped_segments.txt').

2. Performance with Large JSON Objects

• Issue: If individual JSON objects are very large (e.g., megabytes each), the 64KB chunk size may result in frequent buffer resizing and incomplete object parsing, slowing down processing.

• Suggestion: Make the chunk size configurable to allow tuning based on the expected JSON object size:
  

   def stream_json_objects(filepath, chunk_size=65536):
       # ... use chunk_size in f.read(chunk_size) ...
  

  Then call: stream_json_objects(json_file_path, chunk_size=1048576) for larger objects (e.g., 1MB).

3. Separator Regex Limitations

• Issue: The regex }[ \t\r\n]*{ assumes JSON objects are separated by optional whitespace. If the file uses a different separator (e.g., commas, newlines only, or no separator), parsing will fail or produce incorrect splits.

• Suggestion: Add flexibility to handle different separator patterns or detect them dynamically. For example:
  

   def stream_json_objects(filepath, separator_pattern=r"}[ \t\r\n]*{"):
       separator_re = re.compile(separator_pattern)
       # ... rest of the function ...
  

  Alternatively, add a preprocessing step to detect the separator by scanning the first few KB of the file.

4. Empty or Invalid File Handling

• Issue: If the input file is empty or contains no valid JSON objects, the script correctly reports “No valid JSON objects were parsed” but still creates an empty result.csv if any batch was attempted. This could be confusing.

• Suggestion: Check if any valid objects were processed before creating the CSV file, or clean up the empty CSV file:
  

   if not header_written and all_columns is None:
       print("No valid JSON objects were parsed. No output CSV was created.")
       if os.path.exists(csv_file):
           os.remove(csv_file)
           print(f"Removed empty CSV file: {csv_file}")
  

5. CSV Encoding and Delimiter Flexibility

• Issue: The CSV output is hardcoded to use UTF-8 encoding and the default pandas delimiter (,). Some systems or datasets may require different encodings (e.g., UTF-16) or delimiters (e.g., ;, \t).

• Suggestion: Add parameters for encoding and delimiter:
  

   def json_to_csv_optimized(json_file, csv_file='result.csv', batch_size=5000, encoding='utf-8', delimiter=','):
       # ... in df.to_csv calls ...
       df.to_csv(csv_file, index=False, mode='w', header=True, encoding=encoding, sep=delimiter)
       # ... and in append mode ...
       df.to_csv(csv_file, index=False, mode='a', header=False, encoding=encoding, sep=delimiter)
  

  Usage: json_to_csv_optimized(json_file_path, csv_output_path, encoding='utf-16', delimiter=';').

6. Progress Feedback

• Issue: For very large files, the user only gets feedback per batch (e.g., every 10,000 objects). Long-running processes could benefit from more granular progress updates.

• Suggestion: Add a counter for total objects processed and print progress every N objects:
  

   total_objects = 0
   for i, obj in enumerate(stream_json_objects(json_file)):
       total_objects += 1
       batch_data.append(obj)
       if total_objects % 1000 == 0:
           print(f"Processed {total_objects} objects...")
   # ... rest of the function ...
   print(f"Total objects processed: {total_objects}")
  

7. Commented-Out Excel Code

• Issue: The commented-out json_to_excel function and openpyxl import are remnants of the original script. While harmless, they add clutter and could confuse maintainers.
• Suggestion: Remove the commented-out code and the openpyxl import unless there’s a plan to reintroduce Excel support. If Excel output is needed, consider implementing it as an optional output format in json_to_csv_optimized with a parameter (e.g., output_format='csv' or 'excel').

8. Batch Size Tuning

• Issue: The default batch_size=10000 may be too large for systems with limited memory or too small for very simple JSON objects, affecting performance.

• Suggestion: Provide guidance in the docstring or add a dynamic batch size adjustment based on memory usage or object complexity. Alternatively, make it easier to tune via a command-line argument or config:
  

   json_to_csv_optimized(json_file_path, csv_output_path, batch_size=5000)  # Smaller batch for low-memory systems
  

9. Unused io Import

• Issue: The io module is imported but not used in the provided code. This is minor but could indicate an oversight or leftover from earlier versions.
• Suggestion: Remove the import io line unless there’s a specific plan to use it (e.g., for in-memory buffering).

10. Documentation and Type Hints

• Issue: The docstrings are clear, but they could be enhanced with return types and parameter types for better IDE support and maintainability.

• Suggestion: Add type hints and improve docstrings:
  

   from typing import Generator, Dict, List, Union
   import pandas as pd
  
   def stream_json_objects(filepath: str, chunk_size: int = 65536) -> Generator[Union[Dict, List], None, None]:
       """
       Streams JSON objects from a text file containing concatenated objects.
       Args:
           filepath: Path to the input text file.
           chunk_size: Size of chunks to read from file (in bytes).
       Yields:
           Parsed Python dictionary or list per JSON object.
       """
       # ... function body ...
  
   def json_to_csv_optimized(json_file: str, csv_file: str = 'result.csv', batch_size: int = 5000, encoding: str = 'utf-8') -> None:
       """
       Converts a text file with concatenated JSON objects to a CSV file.
       Args:
           json_file: Path to the input text file.
           csv_file: Path for the output CSV file.
           batch_size: Number of JSON objects to process per batch.
           encoding: Encoding for the output CSV file.
       """
       # ... function body ...
  

Specific Code Fixes

Here’s a consolidated version of the suggested changes applied to the code, keeping it concise:

import pandas as pd
import json
import re
import os
from typing import Generator, Dict, List, Union

def stream_json_objects(filepath: str, chunk_size: int = 65536) -> Generator[Union[Dict, List], None, None]:
    """
    Streams JSON objects from a text file containing concatenated objects.
    Args:
        filepath: Path to the input text file.
        chunk_size: Size of chunks to read from file (in bytes).
    Yields:
        Parsed Python dictionary or list per JSON object.
    """
    buffer = ""
    separator_re = re.compile(r"}[ \t\r\n]*{")
    last_processed_index = 0

    try:
        with open(filepath, 'r', encoding='utf-8') as f:
            while True:
                chunk = f.read(chunk_size)
                if not chunk:
                    break
                buffer += chunk
                for match in separator_re.finditer(buffer, last_processed_index):
                    potential_obj_str = buffer[last_processed_index : match.start() + 1]
                    try:
                        obj = json.loads(potential_obj_str)
                        yield obj
                        last_processed_index = match.end()
                    except json.JSONDecodeError as e:
                        print(f"Warning: JSONDecodeError at position {match.start()+1}: {e}")
                        print(f"Segment snippet (first 200 chars): {potential_obj_str[:200]}...")
                        last_processed_index = match.end()
                buffer = buffer[last_processed_index:]
                last_processed_index = 0
            buffer = buffer.strip()
            if buffer:
                try:
                    obj = json.loads(buffer)
                    yield obj
                except json.JSONDecodeError as e:
                    print(f"Warning: Could not parse remaining buffer: {e}")
                    print(f"Remaining buffer snippet (first 200 chars): {buffer[:200]}...")
    except FileNotFoundError:
        print(f"Error: File not found at {filepath}")
    except Exception as e:
        print(f"An error occurred: {e}")

def json_to_csv_optimized(json_file: str, csv_file: str = 'result.csv', batch_size: int = 5000, encoding: str = 'utf-8') -> None:
    """
    Converts a text file with concatenated JSON objects to a CSV file.
    Args:
        json_file: Path to the input text file.
        csv_file: Path for the output CSV file.
        batch_size: Number of JSON objects to process per batch.
        encoding: Encoding for the output CSV file.
    """
    if os.path.exists(csv_file):
        try:
            os.remove(csv_file)
            print(f"Removed existing CSV file: {csv_file}")
        except OSError as e:
            print(f"Error removing {csv_file}: {e}")

    all_columns = None
    header_written = False
    batch_data = []
    total_objects = 0

    print(f"Starting conversion of '{json_file}' to '{csv_file}'...")
    try:
        for i, obj in enumerate(stream_json_objects(json_file)):
            total_objects += 1
            batch_data.append(obj)
            if total_objects % 1000 == 0:
                print(f"Processed {total_objects} objects...")

            if len(batch_data) >= batch_size:
                try:
                    df = pd.json_normalize(batch_data)
                    if all_columns is None:
                        all_columns = list(df.columns)
                        df.to_csv(csv_file, index=False, mode='w', header=True, encoding=encoding)
                        header_written = True
                        print(f"Processed batch {i // batch_size + 1}. Header written.")
                    else:
                        df = df.reindex(columns=all_columns, fill_value=None)
                        df.to_csv(csv_file, index=False, mode='a', header=False, encoding=encoding)
                        print(f"Processed batch {i // batch_size + 1}.")
                    batch_data = []
                except Exception as e:
                    print(f"Warning: Error normalizing batch starting at object {i - len(batch_data) + 1}: {e}")
                    batch_data = []

        if batch_data:
            try:
                df = pd.json_normalize(batch_data)
                if all_columns is None:
                    all_columns = list(df.columns)
                    df.to_csv(csv_file, index=False, mode='w', header=True, encoding=encoding)
                    header_written = True
                    print(f"Processed remaining {len(batch_data)} objects. Header written.")
                else:
                    df = df.reindex(columns=all_columns, fill_value=None)
                    df.to_csv(csv_file, index=False, mode='a', header=False, encoding=encoding)
                    print(f"Processed remaining {len(batch_data)} objects.")
            except Exception as e:
                print(f"Warning: Error normalizing final batch: {e}")

        if not header_written and all_columns is None:
            print("No valid JSON objects were parsed. No output CSV created.")
            if os.path.exists(csv_file):
                os.remove(csv_file)
                print(f"Removed empty CSV file: {csv_file}")
        elif header_written:
            print(f"Data written successfully to '{csv_file}'")
        print(f"Total objects processed: {total_objects}")

    except Exception as e:
        print(f"An unexpected error occurred: {e}")

# Example usage
json_file_path = "sample2.txt"
csv_output_path = "result.csv"
json_to_csv_optimized(json_file_path, csv_output_path, batch_size=10000, encoding='utf-8')

Key Changes in the Fixed Version

1. Removed unused io and commented-out openpyxl code.
2. Added type hints for better code clarity.
3. Made chunk size configurable in stream_json_objects.
4. Added progress feedback every 1000 objects.
5. Ensured empty CSV files are removed if no valid objects are parsed.
6. Added encoding parameter to json_to_csv_optimized.
7. Improved docstrings with type information.
8. Removed the Excel file cleanup logic since it’s no longer relevant (replaced with CSV cleanup).

Testing Recommendations

To ensure the code works as expected, test it with the following scenarios:

1. Valid Input: A large file with correctly formatted JSON objects separated by }{ or whitespace.
2. Malformed JSON: A file with some invalid JSON objects to verify error handling and skipping.
3. Empty File: An empty file to check that no CSV is created.
4. Single Large Object: A file with one very large JSON object to test chunk size handling.
5. Different Separators: A file with JSON objects separated by commas or newlines to test regex robustness (may require modifying the separator pattern).
6. Low Memory: Run on a system with limited memory to tune batch_size and chunk_size.

Conclusion

The code is well-designed for its purpose, with strong memory efficiency and error handling. The suggested improvements enhance its flexibility, robustness, and usability without significantly altering its core functionality. The fixed version incorporates these changes and is ready for use with large JSON files. If you have specific requirements (e.g., support for Excel output, different separators), let me know, and I can tailor the code further!