Deep Dive into Apache Parquet: How It Works Internally

Parquet: How It Works Internally

Introduction

Apache Parquet is a popular columnar storage file format used in the big data ecosystem. It is designed for efficient data storage and retrieval, making it a go-to choice for systems like Apache Spark, Hadoop, and AWS Redshift. Parquet's columnar format allows for efficient data compression and encoding schemes, optimizing both storage and query performance. In this article, we'll explore how Parquet works internally, its underlying algorithms, and provide a real-time example to illustrate its benefits.

What is Apache Parquet?

Parquet is an open-source, column-oriented data file format designed for use with data processing frameworks. It supports efficient data compression and encoding, making it highly suitable for analytic workloads. Unlike row-based formats (e.g., CSV), Parquet stores data by columns, enabling efficient querying and aggregation.How Parquet Works InternallyTo understand how Parquet works, it's essential to look at its internal structure and the algorithms it employs for data storage and retrieval.Internal Structure

1. File Layout:
   • File Header: Contains the magic number “PAR1” to identify the file as a Parquet file.
   • Row Groups: A Parquet file is divided into row groups. Each row group contains data for a subset of rows.
   • Column Chunks: Within each row group, data is stored in column chunks, one for each column in the dataset.
   • Page Headers and Pages: Each column chunk is further divided into pages. Pages are the smallest unit of data storage in Parquet. Types of pages include data pages, dictionary pages, and index pages.
   • File Footer: Contains metadata about the schema, row groups, and column chunks, ending with the magic number “PAR1”.
2. Compression and Encoding:
   • Parquet supports various compression algorithms such as Snappy, Gzip, and LZO.
   • It uses different encoding schemes like dictionary encoding, run-length encoding (RLE), and delta encoding to reduce the size of the data.

Algorithms and Techniques

1. Columnar Storage:
   • Data is stored column-wise rather than row-wise. This means all values of a column are stored together, which is advantageous for analytical queries that often involve operations on a few columns rather than entire rows.
2. Dictionary Encoding:
   • Frequently occurring values are stored in a dictionary, and the actual data is replaced with the index of the dictionary entry. This reduces the storage size for columns with repeating values.
3. Run-Length Encoding (RLE):
   • RLE is used to compress sequences of repeated values. Instead of storing each value individually, it stores the value once along with the count of repetitions.
4. Delta Encoding:
   • For columns where values change gradually, delta encoding stores the difference between consecutive values rather than the values themselves. This is particularly effective for time-series data.
5. Page Encoding:
   • Each page within a column chunk can use different encoding methods. Parquet determines the most efficient encoding for each page based on the data it contains.
6. Compression:
   • After encoding, Parquet applies compression algorithms to further reduce the file size. This step is crucial for optimizing storage and improving read performance.

Real-Time Example

Consider a large dataset of sales transactions with the following schema: transaction_id, customer_id, product_id, quantity, and transaction_date. Let's see how Parquet handles this data:

1. Storing Data:
   • Row Groups: Parquet divides the dataset into row groups, each containing a subset of rows. Assume we have 1 million transactions, and we decide to create row groups of 100,000 rows each. Therefore, the file will have 10 row groups.
   • Column Chunks: Within each row group, data for each column is stored together in column chunks. So, for each row group, there will be five column chunks corresponding to our schema.
2. Compression and Encoding:
   • Dictionary Encoding: Columns like product_id and customer_id might have many repeated values. Parquet will create a dictionary for these columns and replace actual values with dictionary indexes.
   • Run-Length Encoding: For the quantity column, if many transactions have the same quantity (e.g., quantity = 1), RLE can be used to store this more efficiently.
   • Delta Encoding: The transaction_date column might have values that increment by one day. Delta encoding will store the difference between consecutive dates instead of full dates.
3. Querying Data:
   • When querying for transactions of a specific product, Parquet reads only the product_id column chunk across relevant row groups. This minimizes the amount of data read from the disk, significantly speeding up query performance.

Implementing Parquet in a Data PipelineLet's implement Parquet in a simple data pipeline using Python and Apache Spark. We'll read a CSV file, transform it, and save it as a Parquet file.

python

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("ParquetExample").getOrCreate()

# Read CSV file into DataFrame
df = spark.read.csv("sales_transactions.csv", header=True, inferSchema=True)

# Perform some transformations (e.g., filter transactions)
filtered_df = df.filter(df["quantity"] > 1)

# Write DataFrame to Parquet file
filtered_df.write.parquet("filtered_sales_transactions.parquet")

# Read Parquet file back into DataFrame
parquet_df = spark.read.parquet("filtered_sales_transactions.parquet")

# Show some data
parquet_df.show()

In this example:

1. We read a CSV file containing sales transactions.
2. We filter the transactions to include only those with quantity > 1.
3. We write the filtered data to a Parquet file.
4. We read the Parquet file back into a DataFrame and display the results.

By using Parquet, we benefit from reduced storage size and faster query performance, especially for large datasets with complex queries.ConclusionApache Parquet's columnar storage format, combined with efficient compression and encoding algorithms, makes it an ideal choice for big data analytics. Its internal structure and use of advanced techniques like dictionary encoding, run-length encoding, and delta encoding allow for significant storage savings and performance improvements. Implementing Parquet in data pipelines can optimize data storage and retrieval, making it a valuable tool for modern data management.