Yes! We can significantly speed up the process using multi-threading, chunked processing, and optimized I/O operations. Here's a high-performance version of your script:

Optimized Version (Using ThreadPool + Chunked Processing) 

import pandas as pd
import json
import re
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm  # For progress bar (install with `pip install tqdm`)

def json_to_excel_optimized(json_file, excel_file='result.xlsx', chunk_size=5000, max_workers=4):
    def parse_json_chunk(chunk):
        parsed_data = []
        for line in chunk:
            try:
                data = json.loads(line)
                parsed_data.append(data)
            except json.JSONDecodeError:
                continue
        return parsed_data

    # Step 1: Preprocess file into valid JSON lines
    print("Preprocessing JSON file...")
    with open(json_file, 'r') as f:
        content = f.read()
    # Fix concatenated JSON objects (e.g., "}{" -> "}\n{")
    content = re.sub(r'}\s*{', '}\n{', content)
    lines = content.splitlines()

    # Step 2: Process in parallel chunks
    print("Processing chunks in parallel...")
    chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)]

    all_data = []
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [executor.submit(parse_json_chunk, chunk) for chunk in chunks]
        for future in tqdm(as_completed(futures), total=len(chunks)):
            all_data.extend(future.result())

    # Step 3: Write to Excel in one go (fastest for OpenPyXL)
    print("Writing to Excel...")
    df = pd.json_normalize(all_data)
    df.to_excel(excel_file, index=False, engine='openpyxl')
    print(f"Processed {len(lines)} lines. Output saved to {excel_file}")

# Usage
json_to_excel_optimized("large_file.txt", max_workers=8)  # Adjust workers based on CPU cores

Key Optimizations:

1. Multi-threading with ThreadPoolExecutor

  • Parallelizes JSON parsing (CPU-bound task) across multiple threads.
  • max_workers=4 (default) can be increased if your CPU has more cores.

2. Chunked Processing

  • Splits the file into smaller chunks (e.g., 5,000 lines per chunk).
  • Each chunk is processed independently, reducing memory overhead.

3. Batched I/O Operations

  • Reads the entire file once (content = f.read()), then splits it into lines.
  • Writes to Excel in a single operation (faster than incremental writes).

4. Progress Tracking with tqdm

  • Shows a real-time progress bar for monitoring.

5. Regex Optimization

  • Preprocesses the entire file once to fix malformed JSON (e.g., }{ → }\n{).

Alternative: Multi-processing (For CPU-Intensive Tasks)

If parsing is very CPU-intensive (e.g., complex JSON), replace ThreadPoolExecutor with ProcessPoolExecutor:

from concurrent.futures import ProcessPoolExecutor

with ProcessPoolExecutor(max_workers=4) as executor:  # Instead of ThreadPoolExecutor
    ...

Note: Multi-processing has higher overhead but avoids Python's GIL limitations.

Performance Comparison (Estimated)

Method Time (20MB File) Memory Usage
Original ~5-10 mins High
Optimized (Threads) ~1-2 mins Medium
Optimized (Processes) ~30-60 secs Low

Further Optimizations (If Needed):

  1. Use ujson instead of json (2-3x faster parsing): 
pip install ujson
import ujson as json
  1. Write to CSV First (Then convert to Excel): 
df.to_csv("temp.csv", index=False)
   pd.read_csv("temp.csv").to_excel(excel_file, index=False)
  1. Dask for Out-of-Core Processing (For 100MB+ files): 
import dask.dataframe as dd
   ddf = dd.read_json(json_file, lines=True)
   ddf.compute().to_excel(excel_file, index=False)

Let me know if you'd like to explore any of these alternatives!