JSON vs Avro Comparison | StackShare

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.

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
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