Avro vs JSON

Overview

JSON

Stacks2.0K

Followers1.6K

Votes9

Avro

Stacks419

Followers178

Votes0

Avro vs JSON: What are the differences?

Avro and JSON are both data serialization formats used for storing and exchanging structured data, but they differ in terms of their schema definition, data size, data typing, and compatibility. Here are the key differences between Avro and JSON:

Schema Definition: Avro requires a schema to be defined before serializing the data. The schema is used to describe the structure of the data, including field names, types, and optional attributes. JSON, on the other hand, does not have a predefined schema. Each JSON document can have a different structure, and the schema is implied based on the data itself.
Data Size: Avro typically produces more compact data compared to JSON. Avro uses a compact binary format and performs schema-based data encoding, which reduces the overall data size. JSON, on the other hand, uses a text-based format that includes field names and values as human-readable strings, resulting in larger data sizes.
Data Typing: Avro supports a rich set of primitive data types, such as integers, floats, strings, booleans, and complex types like arrays and maps. It also allows for defining custom data types through its schema definition. JSON, on the other hand, has a limited set of primitive data types, including strings, numbers, booleans, null, arrays, and objects. JSON does not have built-in support for custom data types.
Compatibility: Avro provides built-in support for schema evolution, which allows for data compatibility across different versions of schemas. It supports forward and backward compatibility, meaning that new or old data can be read using a different version of the schema without loss of information. JSON, however, does not have built-in support for schema evolution. Changes in the structure of JSON data may require manual handling or explicit transformations to ensure compatibility.
Schema Evolution: Avro allows for schema evolution by adding, removing, or modifying fields in a schema without breaking compatibility. It uses a concept called "resolution rules" to handle schema evolution. JSON, on the other hand, does not have a standardized way of handling schema evolution. Changes in the structure of JSON data may require manual adjustments and coordination between producers and consumers of the data.

In summary, Avro and JSON differ in their schema definition, data size, data typing, compatibility, and schema evolution. Avro requires a predefined schema, produces compact binary data, supports a rich set of data types, provides built-in schema evolution capabilities, and allows for forward and backward compatibility. JSON does not have a predefined schema, uses a text-based format, has a limited set of data types, and lacks built-in support for schema evolution.

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Advice on JSON, Avro

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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Comments

Detailed Comparison

JSON	Avro
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 row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
-	Schema Evolution; Code Generation; Untagged Data; Language Support
Statistics
Stacks 2.0K	Stacks 419
Followers 1.6K	Followers 178
Votes 9	Votes 0
Pros & Cons
Pros 5 Simple 4 Widely supported	No community feedback yet
Integrations
MongoDB PostgreSQL MySQL JavaScript JSON Server JSONlite	Java PHP Python Ruby C++ C#

What are some alternatives to JSON, Avro?

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.

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.

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!

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.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

Schema Definition: Avro requires a schema to be defined before serializing the data. The schema is used to describe the structure of the data, including field names, types, and optional attributes. JSON, on the other hand, does not have a predefined schema. Each JSON document can have a different structure, and the schema is implied based on the data itself.
Data Size: Avro typically produces more compact data compared to JSON. Avro uses a compact binary format and performs schema-based data encoding, which reduces the overall data size. JSON, on the other hand, uses a text-based format that includes field names and values as human-readable strings, resulting in larger data sizes.
Data Typing: Avro supports a rich set of primitive data types, such as integers, floats, strings, booleans, and complex types like arrays and maps. It also allows for defining custom data types through its schema definition. JSON, on the other hand, has a limited set of primitive data types, including strings, numbers, booleans, null, arrays, and objects. JSON does not have built-in support for custom data types.
Compatibility: Avro provides built-in support for schema evolution, which allows for data compatibility across different versions of schemas. It supports forward and backward compatibility, meaning that new or old data can be read using a different version of the schema without loss of information. JSON, however, does not have built-in support for schema evolution. Changes in the structure of JSON data may require manual handling or explicit transformations to ensure compatibility.
Schema Evolution: Avro allows for schema evolution by adding, removing, or modifying fields in a schema without breaking compatibility. It uses a concept called "resolution rules" to handle schema evolution. JSON, on the other hand, does not have a standardized way of handling schema evolution. Changes in the structure of JSON data may require manual adjustments and coordination between producers and consumers of the data.

Avro vs JSON