All About Parquet Part 02 - Parquet's Columnar Storage Model

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In the first post of this series, we introduced the Apache Parquet file format and touched upon one of its key features—columnar storage. Now, we’ll take a deeper dive into what this columnar storage model is, how it works, and why it’s so efficient for big data analytics. Understanding Parquet's columnar architecture is key to leveraging its full potential in optimizing data storage and query performance.

What is Columnar Storage?

Columnar storage means that instead of storing rows of data together, the data for each column is stored separately. This might seem counterintuitive at first, but it has major benefits for certain types of workloads, particularly those where you’re analyzing or aggregating specific columns rather than accessing entire rows.

In a row-based format like CSV or JSON, data is written and read one row at a time. Each row stores all fields together in sequence. On the other hand, in a columnar format like Parquet, all values for a single column are stored together. For instance, if you have a dataset with columns for Name, Age, and Salary, all the values for the Name column are stored in one block, all the values for the Age column are stored in another, and so on.

Why is Columnar Storage Efficient?

The efficiency of columnar storage becomes clear when we consider the type of operations typically performed on large datasets in analytics. Let’s break down the advantages.

1. Faster Query Performance

Columnar storage shines when your queries focus on a subset of columns. For example, if you want to calculate the average salary of employees in a large dataset, Parquet allows you to scan just the Salary column without reading the entire dataset.

In a row-based format, even though you're only interested in one column, the system has to read all the data in every row to retrieve the values for that column. This results in a lot of unnecessary I/O operations, slowing down query performance. With Parquet, only the columns you need are read, making queries significantly faster.

2. Better Compression

Parquet's columnar structure also improves compression. Since similar data types are stored together, compression algorithms can be applied more effectively. For example, if a column contains repeated values or data that follows a consistent pattern (such as dates or integers), it can be compressed more efficiently.

By grouping similar values together, columnar formats enable algorithms like dictionary encoding or run-length encoding to achieve high compression ratios. This leads to smaller file sizes, which means reduced storage costs and faster data transfers.

3. Efficient Aggregation

Columnar storage is ideal for aggregation queries, such as calculating sums, averages, or counts. These types of operations often focus on specific columns. With Parquet, only the relevant columns need to be read into memory, which not only improves query speed but also reduces the overall resource usage.

4. Batch Processing and Parallelization

Another benefit of Parquet’s columnar model is that it enables better parallel processing. Since columns are stored independently, data processing engines like Apache Spark can read different columns in parallel, further speeding up query execution. This makes Parquet a great fit for distributed computing environments, where parallelism is key to achieving high performance.

How Parquet Organizes Data

Understanding how Parquet organizes data internally can help you fine-tune how you store and query your datasets.

• Columns and Row Groups: Parquet organizes data into row groups, which contain chunks of column data. A row group contains all the data for a subset of rows, but the data for each column is stored separately. This allows for efficient I/O when reading subsets of rows or columns.
• Pages: Within each column chunk, data is further divided into pages. Parquet uses pages to store column data more granularly, which helps optimize compression and read performance. Each page is typically a few megabytes in size, and Parquet stores statistics about the data in each page, making it easier to skip irrelevant pages during query execution.

Use Cases for Columnar Storage

Columnar storage formats like Parquet are most effective in the following scenarios:

• Analytics-Heavy Workloads: If your workload involves a lot of analytical queries (e.g., calculating averages, filtering by certain columns), columnar formats will provide significant performance gains.
• Big Data Environments: Parquet is commonly used in distributed data environments where large datasets are stored in cloud data lakes (e.g., AWS S3, Google Cloud Storage). It works seamlessly with frameworks like Apache Spark and Presto, which are built to process data at scale.
• Data Warehousing: When designing data warehouses, storing data in Parquet allows you to run complex analytical queries efficiently while reducing storage costs due to Parquet’s high compression.

When Not to Use Columnar Storage

While columnar storage offers significant advantages for read-heavy, analytical workloads, it may not be the best option for all use cases. For example, transactional systems that involve frequent, small updates to data (like an online store's transaction log) may perform better with row-based formats, which are optimized for write-heavy operations. In such cases, the overhead of reading and writing data in columnar format may outweigh its benefits.

Conclusion

Parquet’s columnar storage model is what makes it a powerful tool for big data analytics. By organizing data by columns, Parquet allows for faster query performance, better compression, and more efficient aggregation. It’s designed to excel in environments where read-heavy workloads dominate and when your queries often target specific columns rather than entire datasets.

In the next blog post, we’ll dive deeper into the file structure of Parquet, exploring how data is organized into row groups, pages, and columns to optimize both storage and retrieval.

Stay tuned for part 3: Parquet File Structure: Pages, Row Groups, and Columns.