![]() Using Parquet files will enable you to fetch only the required columns and their values, load those in memory and answer the query. For example, if you have a table with 1000 columns, which you will usually only query using a small subset of columns. This structure is well-optimized both for fast query performance, as well as low I/O (minimizing the amount of data scanned). The same columns are stored together in each row group: Each row group contains data from the same columns. Parquet files are composed of row groups, header and footer. To understand this, let’s look a bit deeper into how Parquet files are structured.Īs we mentioned above, Parquet is a self-described format, so each file contains both data and metadata. Moreover, the amount of data scanned will be way smaller and will result in less I/O usage. When running queries on your Parquet-based file-system, you can focus only on the relevant data very quickly. Parquet implements a combined version of bit packing and RLE, in which the encoding switches based on which produces the best compression results.Īs opposed to row-based file formats like CSV, Parquet is optimized for performance. Run length encoding (RLE): when the same value occurs multiple times, a single value is stored once along with the number of occurrences.This allows more efficient storage of small integers. Bit packing: Storage of integers is usually done with dedicated 32 or 64 bits per integer.Dictionary encoding: this is enabled automatically and dynamically for data with a small number of unique values.Parquet data can be compressed using these encoding methods: In Parquet, compression is performed column by column and it is built to support flexible compression options and extendable encoding schemas per data type – e.g., different encoding can be used for compressing integer and string data. Compressionįile compression is the act of taking a file and making it smaller. Let’s look at some of them in more depth. The above characteristics of the Apache Parquet file format create several distinct benefits when it comes to storing and analyzing large volumes of data. Advantages of Parquet Columnar Storage – Why Should You Use It? Each file stores both the data and the standards used for accessing each record – making it easier to decouple services that write, store, and read Parquet files. Self-describing : In addition to data, a Parquet file contains metadata including schema and structure. To quote the project website, “Apache Parquet is… available to any project… regardless of the choice of data processing framework, data model, or programming language.”ģ. Open-source: Parquet is free to use and open source under the Apache Hadoop license, and is compatible with most Hadoop data processing frameworks. Columnar: Unlike row-based formats such as CSV or Avro, Apache Parquet is column-oriented – meaning the values of each table column are stored next to each other, rather than those of each record:Ģ. Basic Definition: What is Apache Parquet?Īpache Parquet is a file format designed to support fast data processing for complex data, with several notable characteristics:ġ. Now, let’s take a closer look at what Parquet actually is, and why it matters for big data storage and analytics. You can execute sample pipeline templates, or start building your own, in Upsolver for free. It can input and output Parquet files, and uses Parquet as its default storage format. In fact, Parquet is one of the main file formats supported by Upsolver, our all-SQL platform for transforming data in motion. It’s clear that Apache Parquet plays an important role in system performance when working with data lakes. Converting data to columnar formats such as Parquet or ORC is also recommended as a means to improve the performance of Amazon Athena. When AWS announced data lake export, they described Parquet as “2x faster to unload and consumes up to 6x less storage in Amazon S3, compared to text formats”. Since it was first introduced in 2013, Apache Parquet has seen widespread adoption as a free and open-source storage format for fast analytical querying. ![]() Don’t pass up the chance to broaden your understanding by obtaining the full version, where you’ll explore intricate technical details and gain profound insights. This blog post acts as a preview of our extensive and meticulously crafted guide on big data formats. Apache Parquet Use Cases – When Should You Use It?.Column-Oriented vs Row-Based Storage for Analytic Querying.Advantages of Parquet Columnar Storage – Why Should You Use It?.Basic Definition: What is Apache Parquet?. ![]()
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