Apache Parquet vs JSON

Overview

JSON

Stacks2.0K

Followers1.6K

Votes9

Apache Parquet

Stacks97

Followers190

Votes0

Apache Parquet vs JSON: What are the differences?

Introduction

Apache Parquet and JSON are both file formats used for storing and exchanging data. However, there are several key differences between the two that make them suitable for different use cases. In the following paragraphs, we will explore these differences in detail.

Schema-based vs. Schema-less: One of the major differences between Apache Parquet and JSON is their approach to data schema. Parquet is a schema-based file format, which means it requires a predefined schema that specifies the structure of the data. On the other hand, JSON is a schema-less format, allowing for more flexibility as data can be stored without a predefined schema.
Compression: Another key difference between Parquet and JSON is the way they handle data compression. Parquet uses columnar compression, which compresses each column independently. This allows for high compression ratios and efficient query performance, especially in scenarios where only a subset of columns needs to be read. JSON, on the other hand, does not provide built-in compression and the data is usually stored in a verbose manner, leading to larger file sizes.
Data Types: When it comes to data types, Parquet supports a wider range of data types compared to JSON. Parquet includes support for complex data types like arrays, maps, and nested structures, whereas JSON has limited support for these types. JSON primarily relies on string, numeric, boolean, and null types for data representation.
Query Performance: Due to its columnar storage and compression techniques, Parquet generally offers better query performance compared to JSON. Parquet allows for efficient column pruning, where only the required columns are read during query execution, leading to faster data retrieval. JSON, on the other hand, requires parsing the entire document to retrieve specific fields, which can result in slower query performance.
Serialization: Apache Parquet uses a binary format for serialization, which provides a compact representation of data and makes it suitable for use in distributed systems. JSON, being a text-based format, has a larger footprint and may require additional parsing during serialization and deserialization.
Tooling Support: Parquet has extensive tooling support in the Apache Hadoop ecosystem, making it easier to integrate with existing big data processing frameworks like Apache Spark and Apache Hive. JSON, being a widely adopted and simple format, also has good tooling support across various programming languages and platforms.

In summary, Apache Parquet and JSON differ in their approach to data schema, compression techniques, supported data types, query performance, serialization format, and tooling support. Choosing between the two formats depends on the specific requirements of the use case, with Parquet providing better performance and efficiency for structured data, while JSON offers flexibility and simplicity for schema-less data storage.

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Advice on JSON, Apache Parquet

Dhinesh

architect

Jun 16, 2020

Needs adviceon

JSON

Python

Hi. Currently, I have a requirement where I have to create a new JSON file based on the input CSV file, validate the generated JSON file, and upload the JSON file into the application (which runs in AWS) using API. Kindly suggest the best language that can meet the above requirement. I feel Python will be better, but I am not sure with the justification of why python. Can you provide your views on this?

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Detailed Comparison

JSON	Apache Parquet
JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language.	It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
-	Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Statistics
Stacks 2.0K	Stacks 97
Followers 1.6K	Followers 190
Votes 9	Votes 0
Pros & Cons
Pros 5 Simple 4 Widely supported	No community feedback yet
Integrations
MongoDB PostgreSQL MySQL JavaScript JSON Server JSONlite	Hadoop Java Apache Impala Apache Thrift Apache Hive Pig

What are some alternatives to JSON, Apache Parquet?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

Related Comparisons

Apache Parquet vs JSON: What are the differences?

Introduction

Schema-based vs. Schema-less: One of the major differences between Apache Parquet and JSON is their approach to data schema. Parquet is a schema-based file format, which means it requires a predefined schema that specifies the structure of the data. On the other hand, JSON is a schema-less format, allowing for more flexibility as data can be stored without a predefined schema.
Compression: Another key difference between Parquet and JSON is the way they handle data compression. Parquet uses columnar compression, which compresses each column independently. This allows for high compression ratios and efficient query performance, especially in scenarios where only a subset of columns needs to be read. JSON, on the other hand, does not provide built-in compression and the data is usually stored in a verbose manner, leading to larger file sizes.
Data Types: When it comes to data types, Parquet supports a wider range of data types compared to JSON. Parquet includes support for complex data types like arrays, maps, and nested structures, whereas JSON has limited support for these types. JSON primarily relies on string, numeric, boolean, and null types for data representation.
Query Performance: Due to its columnar storage and compression techniques, Parquet generally offers better query performance compared to JSON. Parquet allows for efficient column pruning, where only the required columns are read during query execution, leading to faster data retrieval. JSON, on the other hand, requires parsing the entire document to retrieve specific fields, which can result in slower query performance.
Serialization: Apache Parquet uses a binary format for serialization, which provides a compact representation of data and makes it suitable for use in distributed systems. JSON, being a text-based format, has a larger footprint and may require additional parsing during serialization and deserialization.
Tooling Support: Parquet has extensive tooling support in the Apache Hadoop ecosystem, making it easier to integrate with existing big data processing frameworks like Apache Spark and Apache Hive. JSON, being a widely adopted and simple format, also has good tooling support across various programming languages and platforms.

Apache Parquet vs JSON

Overview