python - Stream Parse Huge JSON file into small files

Python - Stream Parse Huge JSON file into small files

To parse a huge JSON file and stream its contents into smaller files using Python, you can use the ijson library along with standard file handling techniques. This approach allows you to efficiently handle large JSON files without loading the entire file into memory.

Steps to Stream Parse a Huge JSON File into Small Files:

1. Install Required Libraries

First, make sure you have the necessary libraries installed. You'll need ijson for streaming JSON parsing:

pip install ijson

2. Example Code

Here's an example script that demonstrates how to stream parse a large JSON file and split its contents into smaller files based on your requirements:

import ijson import json import os # Function to parse and split JSON file def split_json_file(input_file, output_dir, batch_size=1000): # Create output directory if it doesn't exist if not os.path.exists(output_dir): os.makedirs(output_dir) # Open the input JSON file for streaming parsing with open(input_file, 'r') as f: # Create an iterator over the JSON objects in the file objects = ijson.items(f, 'item') current_batch = [] batch_count = 0 file_count = 1 # Iterate over each JSON object for obj in objects: current_batch.append(obj) batch_count += 1 # If batch size is reached, write to a new file if batch_count >= batch_size: output_file = os.path.join(output_dir, f'output_{file_count}.json') with open(output_file, 'w') as out: json.dump(current_batch, out) # Reset batch variables current_batch = [] batch_count = 0 file_count += 1 # Write any remaining objects to a final file if current_batch: output_file = os.path.join(output_dir, f'output_{file_count}.json') with open(output_file, 'w') as out: json.dump(current_batch, out) # Example usage input_file = 'huge_data.json' output_directory = 'output_files' batch_size = 1000 # Number of JSON objects per output file split_json_file(input_file, output_directory, batch_size)

Explanation:

  • split_json_file Function: This function takes three parameters:

    • input_file: Path to the input JSON file to be parsed.
    • output_dir: Directory where the smaller JSON files will be saved.
    • batch_size (optional): Number of JSON objects to include in each smaller file (default is 1000).

  • Using ijson.items: This function creates an iterator over the JSON objects in the input file (f). It iterates over each JSON object one-by-one without loading the entire file into memory.

  • Batching and Writing Files: Objects are accumulated in current_batch until batch_size is reached. Then, it writes current_batch to a new JSON file in the output_dir. It continues this process until all objects from the input file are processed.

  • Output Files: Each batch of JSON objects is written to a separate file (output_1.json, output_2.json, etc.) in the output_dir.

Notes:

  • Adjust Batch Size: Depending on your system's memory and processing capabilities, you may adjust batch_size to optimize performance.

  • Error Handling: Add appropriate error handling for file operations and ensure paths are correctly set.

  • JSON Structure: Ensure your input JSON file has a structure suitable for streaming parsing. The example assumes a JSON array of objects ([{}, {}, ...]).

By using ijson and iterating over the JSON objects in the input file, you can efficiently handle large JSON files in Python without loading the entire file into memory, making it suitable for handling big data scenarios.

Examples

  1. Python parse huge JSON file and write to multiple smaller files

    • Description: This query seeks methods to efficiently parse a large JSON file in Python and split its contents into smaller files.
    • Code:
      import json import os def parse_and_split_large_json(input_file, output_dir, chunk_size=1000): with open(input_file, 'r') as f: data = json.load(f) for i in range(0, len(data), chunk_size): chunk = data[i:i + chunk_size] output_filename = os.path.join(output_dir, f'output_{i // chunk_size}.json') with open(output_filename, 'w') as f: json.dump(chunk, f) # Example usage parse_and_split_large_json('large_data.json', 'output_directory')

  2. Python read large JSON file line by line

    • Description: This query focuses on reading a large JSON file line by line in Python to avoid loading the entire file into memory.
    • Code:
      import json def read_large_json_line_by_line(input_file): with open(input_file, 'r') as f: for line in f: data = json.loads(line) # Process each line of JSON data here # Example usage read_large_json_line_by_line('large_data.json')

  3. Python stream large JSON file parsing

    • Description: This query looks for methods to stream-parse a large JSON file in Python, processing each chunk or line as it's read.
    • Code:
      import json def stream_parse_large_json(input_file): with open(input_file, 'r') as f: for line in f: data = json.loads(line) # Process each line of JSON data here # Example usage stream_parse_large_json('large_data.json')

  4. Python split JSON file into smaller files based on size

    • Description: This query seeks ways to split a large JSON file into smaller files based on size criteria in Python.
    • Code:
      import json import os def split_json_into_smaller_files(input_file, output_dir, max_size_bytes=1048576): with open(input_file, 'r') as f: data = json.load(f) current_chunk = [] current_size = 0 chunk_count = 0 for item in data: item_json = json.dumps(item) item_size = len(item_json.encode('utf-8')) if current_size + item_size > max_size_bytes: output_filename = os.path.join(output_dir, f'output_{chunk_count}.json') with open(output_filename, 'w') as f: json.dump(current_chunk, f) current_chunk = [] current_size = 0 chunk_count += 1 current_chunk.append(item) current_size += item_size if current_chunk: output_filename = os.path.join(output_dir, f'output_{chunk_count}.json') with open(output_filename, 'w') as f: json.dump(current_chunk, f) # Example usage split_json_into_smaller_files('large_data.json', 'output_directory')

  5. Python read large JSON array file

    • Description: This query focuses on reading a large JSON array file efficiently in Python, handling memory constraints.
    • Code:
      import json def read_large_json_array(input_file): with open(input_file, 'r') as f: data = json.load(f) for item in data: # Process each item in the JSON array pass # Example usage read_large_json_array('large_data.json')

  6. Python parse large JSON file lazily

    • Description: This query looks for methods to lazily parse a large JSON file in Python, minimizing memory usage.
    • Code:
      import json def parse_large_json_lazily(input_file): with open(input_file, 'r') as f: for line in f: data = json.loads(line) # Process each line of JSON data lazily # Example usage parse_large_json_lazily('large_data.json')

  7. Python iterate through large JSON file

    • Description: This query seeks ways to iterate through a large JSON file in Python efficiently.
    • Code:
      import json def iterate_through_large_json(input_file): with open(input_file, 'r') as f: data = json.load(f) for item in data: # Process each item in the JSON file pass # Example usage iterate_through_large_json('large_data.json')

  8. Python process large JSON file in chunks

    • Description: This query looks for methods to process a large JSON file in chunks in Python, handling data in manageable portions.
    • Code:
      import json def process_large_json_in_chunks(input_file, chunk_size=1000): with open(input_file, 'r') as f: data = json.load(f) for i in range(0, len(data), chunk_size): chunk = data[i:i + chunk_size] # Process each chunk of JSON data here # Example usage process_large_json_in_chunks('large_data.json')

  9. Python split large JSON file into smaller parts

    • Description: This query focuses on splitting a large JSON file into smaller parts or chunks in Python.
    • Code:
      import json import os def split_large_json_file(input_file, output_dir, chunk_size=1000): with open(input_file, 'r') as f: data = json.load(f) for i in range(0, len(data), chunk_size): chunk = data[i:i + chunk_size] output_filename = os.path.join(output_dir, f'output_{i // chunk_size}.json') with open(output_filename, 'w') as f: json.dump(chunk, f) # Example usage split_large_json_file('large_data.json', 'output_directory')

  10. Python read large JSON file efficiently

    • Description: This query seeks efficient methods to read and handle large JSON files in Python without loading the entire file into memory.
    • Code:
      import json def read_large_json_file(input_file): with open(input_file, 'r') as f: for line in f: data = json.loads(line) # Process each line of JSON data here # Example usage read_large_json_file('large_data.json')

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