A Deep Dive into Parquet: The Data Format Engineers Need to Know

TL;DR
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Parquet is an open-source file format that became an essential tool for data engineers and data analytics due to its column-oriented storage and core features, which include robust support for compression algorithms and predicate pushdown. 

For OLAP (Online Analytical Processing) workloads, data teams focus on two main factors — storage size and query performance. Parquet can significantly reduce storage requirements and boost query response times compared to other formats like CSV.

In this article, we will explain Parquet, the key features of the file format, and how it can benefit data professionals. We have also listed steps for successfully implementing Parquet-based workflows.

Understanding Parquet

Apache Parquet is a columnar storage file format widely used in big data processing and analytics. 

It was designed as a joint effort between Cloudera and Twitter and was launched in 2013. It has since been widely adopted and is now a part of the Apache Hadoop ecosystem.

Parquet is built to optimize analytical operations on large datasets containing complex data. It supports highly efficient data compression methods, schema evolution, encoding schemes, and other functionalities that can speed up analytical querying.

The Parquet file format stores data in a column-oriented manner, where values from each column are stored together. This differs from row-based file formats, like CSV.

Columnar storage formats offer better performance by enabling better compression and faster data retrieval. This makes them more suitable for query-intensive workloads.

Parquet is also a flexible open-source file format that seamlessly integrates with big data processing tools, including Apache’s Hadoop, Spark, and Hive, along with others like Presto.

Key features of the Parquet format

Here are some key characteristics and concepts to understand about Parquet:

• Columnar storage: Parquet stores data in a columnar format. This data organization makes it easier to fetch specific column values when running queries and boosts performance.
• Compression: Parquet supports many compression algorithms, like Snappy, Gzip, and LZO. This decreases storage requirements and minimizes the amount of data that needs to be read from the disk while running queries.
• Metadata: Parquet files store column metadata and statistics, like minimum and maximum values, data types, and encoding information. Query engines and analytics tools can leverage this metadata for query optimization, automatic schema inference, and data processing.
• Predicate pushdown: Predicate pushdown allows query engines to push filters to the storage layer. Using the feature, Parquet enables users to skip reading irrelevant data during query execution.
• Data types: Parquet supports primitive data types (e.g., integer, float, string) but can also handle complex data structures (e.g., arrays, maps, structs) and advanced nested data structures efficiently.
• Portability: Although Parquet works best with serverless architectures like Amazon Redshift and BigQuery, Parquet files are portable across many other frameworks and languages. They make it easier to exchange and share data between different systems.

The Benefits of Parquet for Data Engineering

Using Parquet in data engineering workflows provides five major benefits:

1. Efficient IO operations

Apache Parquet’s columnar storage format minimizes I/O operations. 

Instead of reading entire rows, Parquet allows for selective column reading. This means that when an operation only requires specific columns, Parquet can efficiently read and retrieve those columns, reducing the overall amount of data scanned and improving I/O performance.

This makes it highly efficient for data engineering workflows and tasks like data transformation, filtering, and aggregation, where working with specific columns can significantly improve performance.

2. Better compression

Columnar storage speeds up compression since every column contains similar data types.

Parquet also supports several compression algorithms and encoding schemes, each offering different trade-offs between compression ratio and CPU overhead. 

Choosing the right algorithm allows users to balance compression ratios with the desired level of performance.

Parquet also uses advanced compression techniques like run-length encoding and bit-packing to minimize storage requirements, even when storing and processing complex data.

3. Improved query performance

Parquet’s columnar storage and predicate pushdown capability enhance performance. By storing data column by column, Parquet minimizes the amount of data scanned during query execution, improving query response times. 

Predicate pushdown allows filtering at the storage layer, skipping irrelevant data and further accelerating query processing.

4. Schema evolution support

Apache Parquet supports schema evolution so engineers can easily manage changing data schemas in their workflows. They can add, remove, or modify columns in Parquet files without having to rewrite the entire dataset.

This flexibility is particularly valuable when dealing with evolving data sources or updating data models without disrupting existing data pipelines.

5. Compatibility with various data processing frameworks

Parquet is compatible with most major data processing platforms. This ensures that data teams can leverage Parquet’s benefits within their preferred processing environments.

It enables seamless data interchange and integration across tools and frameworks, providing flexibility and interoperability in big data processing workflows.

Working with Parquet: Practical Guide

There are three primary areas that engineers must master so they can work seamlessly with Apache Parquet:

How to create Parquet files

Here are the four steps for creating files in the Parquet file format:

• Select the programming language or data processing framework that supports Parquet file writing.
• Use the corresponding APIs or libraries to write data to Parquet files. For instance, in Python, you can use functions like “pandas.DataFrame.to_parquet()” or “pyspark.DataFrame.write.parquet()” to write data frames or RDDs.
• Specify the output file or directory path where you want to save the files.
• Configure compression algorithms, schema evolution options, and other parameters based on your requirements.

How to read Parquet files

Here are the four steps to read files in the Parquet file format:

• Choose a programming language or data processing framework that supports Parquet file reading. Popular options include Python (with libraries like Pandas or PySpark), Apache Spark, Apache Hive, and Apache Impala.
• Use the appropriate APIs or libraries to read the files. For example, in Python, you can use libraries like “pandas.read_parquet()” or “pyspark.read.parquet()” to read files into data frames or RDDs.
• Specify the file or directory path of the Parquet file(s) you want to read.
• Apply any necessary filtering or transformation operations to the data during the reading process.

Data integration and ecosystem compatibility:

To integrate Apache Parquet into your current data ecosystem:

• Consider Parquet’s compatibility with your data processing tools.
• Ensure that your platforms support Parquet file reading and writing natively or provide integration libraries.
• Leverage Parquet’s portability to seamlessly exchange data between systems and platforms.

Following these guidelines, you can use Parquet’s features for efficient data storage and processing within your workflows.

Best practices when working with Parquet

Use these best practices to maximize performance when working with Apache Parquet:

• Use appropriate compression: Consider factors such as compression ratio, decompression speed, and CPU overhead to choose the best compression algorithms for your workflow.
• Optimize file and row group size: The size of files and row groups must balance efficient data access and storage. Smaller file sizes can improve data locality and reduce I/O latency, but too many small files can impact performance due to increased overhead.
• Partition and bucket data: Design your partitioning and bucketing strategy based on query patterns. This can speed up queries by pruning unnecessary data.
• Leverage predicate pushdown: Apply filtering conditions to the storage layer to minimize the data scanned. This can greatly improve query execution, especially when dealing with large datasets.
• Utilize dictionary encoding: For columns with repetitive or categorical values, enable dictionary encoding in Parquet. This encoding replaces repetitive values with dictionary indices, resulting in efficient storage and improved compression ratios.
• Avoid wide schema evolution: When evolving the schema, try to minimize vast schema changes that affect the data stored in a large number of columns. Wide schema evolution can lead to slower query execution and increased resource usage.
• Data type selection: Choose the most compact data types that accurately represent your data to minimize storage and boost performance.
• Monitor performance: Regularly analyze your Parquet-based workflows. Keep track of query execution times, resource usage, and storage requirements. This will help identify optimization opportunities.

Use Cases and Examples

Parquet’s features and benefits make it a valuable component in modern data engineering pipelines. Here are some real-world examples of how Parquet is used:

• Data warehousing: Parquet is commonly used in data warehousing environments, where large volumes of structured and semi-structured data are stored and analyzed. 

Data warehouses, like those built with Apache Hive or Amazon Redshift, leverage Parquet’s storage and compression features to reduce storage costs and speed up performance.

Netflix used Parquet and other technologies to revolutionize their S3 data warehouse and more efficiently organize the data stored in their warehouse.

• Analytical Workloads: Parquet is a preferred format for analytical workloads, such as data exploration, data visualization, and machine learning. 

Analytical tools, like Apache Zeppelin and Tableau, can directly read Parquet files, leveraging its column storage to provide fast query responses and interactive data analysis experiences.

Twitter adopted Parquet to optimize its data analytics and reporting workflows. They used Parquet files as the intermediate storage format during ETL processes and for data analysis in Apache Hive.

• Data lake architecture: Parquet is vital in data lakes, which store large volumes of raw data from diverse sources. 

Parquet’s schema evolution support allows engineers to evolve the data schema without rewriting the entire dataset, so they can easily transform raw data for varying analytical use cases.

Uber uses Parquet as the primary file format in their data lake, built on top of the Hadoop Distributed File System (HDFS). By implementing ZSTD compression algorithms, the company significantly increased storage efficiency and data reading times.

• Big data processing: Parquet plays a crucial role in big data processing. The file format can also be used for streaming data processing of high-velocity data streams.

By storing their data in the Parquet format, Criteo experienced significant performance improvements in their analytical queries. The column-oriented storage and compression techniques supported by Parquet enabled faster data access and processing.

Parquet’s schema evolution support enabled Criteo to seamlessly evolve its data schema without disrupting its analytics pipelines, improving data agility and flexibility.

Apart from these examples, Apache Parquet is also an integral part of cloud data platforms, data integration pipelines, and data archiving processes.

Parquet vs. Other Data Formats

Apache Parquet is just one of many data file formats. Here’s a comparison of Parquet with other popular data formats:

Let’s look at the key differences between Parquet and each of the other data formats:

Suggested read: Avro vs Parquet

Parquet vs. CSV/Text Files

• Parquet is a columnar format, while CSV files use row-based formats.
• Apache Parquet provides more efficient data compression and faster query execution than a CSV file. It is particularly advantageous when dealing with large datasets and complex analytical queries.
• Parquet’s columnar organization results in faster query execution.
• CSV files are human-readable and have widespread support, but they lack Parquet’s storage and performance optimizations.

Parquet vs. ORC (Optimized Row Columnar)

• Parquet and ORC are both columnar storage file formats designed for big data analytics.
• Apache Parquet is widely supported by various data processing tools, including Apache Spark, Hive, and Impala. ORC has strong support in the Hive ecosystem.
• Performance and compression characteristics may vary between Parquet and ORC depending on the specific use case, data patterns, and query workloads. Users can benchmark and evaluate both formats based on their requirements.

Parquet vs. Avro

• Parquet and Avro are both binary data formats but serve different purposes.
• Apache Parquet focuses on effective data organization and compression optimized for analytical workloads. It provides high performance and storage efficiency for query-intensive use cases.
• Avro is a row-based data serialization framework emphasizing data interchange and schema evolution. It is suitable for use cases that require schema flexibility and compatibility across different programming languages.

Parquet vs. JSON

Parquet is a columnar storage format designed for efficient querying and compression of large datasets. On the other hand, JSON is a row-based format that is widely used for data interchange due to its simplicity and readability.

• Parquet and JSON are fundamentally different in terms of data representation and storage.
• Apache Parquet is a highly optimized columnar storage format.
• JSON is a plain text format that provides human-readable data interchange. 
• While JSON is widely supported and easy to work with, it lacks the storage and performance optimizations of Parquet.
• Parquet is used for analytical workloads, whereas JSON is often used for data interchange between systems or semi-structured data storage.

The choice of file format depends on the specific requirements of your use case. Parquet’s column-oriented storage and compatibility with processing frameworks make it a strong choice for analytical workloads. 

However, factors like data interchange, schema flexibility, and ecosystem compatibility may favor other formats in different scenarios.

Conclusion

The Parquet data format is well-suited for analytical processing, data warehousing, and big data analytics. Its features lead to faster query execution, reduced storage costs, and efficient processing of large datasets.

By understanding and exploring Parquet, engineers can build scalable, robust, and cost-effective data pipelines. This empowers them to make informed decisions, optimize performance, and unlock the full potential of the file format for their tasks.

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