Parquet File Format

• Magic Number: A Parquet file begins and ends with a magic number, PAR1, which helps in recognizing the file format.

• File Metadata: The file contains metadata at the beginning (optional) and at the end (required). This metadata includes information about the schema, the number of rows, columns, and the offsets of the row groups.

• Row Groups: Parquet files are divided into row groups, which are contiguous blocks of rows. Each row group contains column chunks, which store data for a specific column.

• Column Chunks: Each column in a row group is stored separately in a column chunk, making it easier to read specific columns without loading the entire dataset. Within each row group in a Parquet file, the metadata includes statistics that provide information about the data stored in the column chunks. This information is used for various optimizations, including filtering and querying. The relevant statistics include:

  • Min Value: The minimum value in a column chunk.
  • Max Value: The maximum value in a column chunk.
  • Count: The number of values in a column chunk.

• Pages: Each column chunk is further divided into pages. There are different types of pages:

  • Data Pages: Store the actual data
  • Index Pages: Used for indexing.
  • Dictionary Pages: Pages can utilize various encoding schemes, such as dictionary encoding or run-length encoding.

Why Parquet? The Advantages of a Columnar Format

• Efficient Compression: Since similar data is stored together in columns, Parquet can achieve high compression ratios. This reduces the amount of storage space required and speeds up data transfer. This Python script demonstrates how to convert a CSV file into a Parquet file while applying different compression schemes. The script also measures and prints the size of the resulting Parquet files, allowing you to compare the effects of each compression method and the impact of dictionary encoding.

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    import pandas as pd
    import os
    
    # Load the CSV file
    df = pd.read_csv('syntax_file.csv')
    
    # Get the size of the CSV file
    file_size = os.path.getsize('syntax_file.csv')
    print(f"Size of the CSV file '{{syntax_file.csv}}': {{file_size}} bytes")
    
    # Get the size of the Parquet file with different compression schemes
    compression_types = ['snappy', 'gzip', 'brotli', 'zstd']
    for compression in compression_types:
        file_name = f'file_{{compression or "none"}}.parquet'
        
        # Specify the column to use dictionary encoding
        df.to_parquet(file_name, compression=compression, use_dictionary=['Type'])
        
        # Get the size of the Parquet file
        file_size = os.path.getsize(file_name)
        print(f"Size of the Parquet file '{{file_name}}': {{file_size}} bytes")
    

Output

  Size of the CSV file 'syntax_file.csv': 783924 bytes 
  Size of the Parquet file 'file_snappy.parquet': 462097 bytes 
  Size of the Parquet file 'file_gzip.parquet': 246460 bytes 
  Size of the Parquet file 'file_brotli.parquet': 193447 bytes 
  Size of the Parquet file 'file_zstd.parquet': 212575 bytes
  

The performance of different compression schemes can vary depending on the characteristics of the input data.

• Faster Query Performance: Queries that access only a subset of columns can read just the necessary data, avoiding the overhead of processing irrelevant rows.

• Schema Evolution: Parquet supports schema evolution, which allows you to add new columns to your datasets without breaking existing queries.

Comparative Performance Analysis of Data Storage Formats: Parquet vs. CSV, JSON, and Avro

Getting Started with Parquet: Tools and Frameworks

  import pyarrow.parquet as pq
  # Load Parquet file
  parquet_file = pq.ParquetFile('file_brotli.parquet')
  
  # Print Metadata
  print(parquet_file.schema)
  

Future of Parquet: What’s Next?

Conclusion

References

• Documentation | Parquet (apache.org)
• CSV vs Parquet vs JSON vs Avro - datacrump.com
• File Format | Parquet (apache.org)

• Power BI Tips for Star Schema and Dimensional Data Modeling (corebts.com)