Avro vs Protobuf: What are the differences?

Introduction

Avro and Protobuf are both data serialization frameworks that are used to efficiently exchange data between different systems. While they have some similarities, there are key differences between the two.

1. Schema Evolution: One of the key differences between Avro and Protobuf is how they handle schema evolution. Avro allows for forward and backward compatibility, meaning that new fields can be added or existing fields can be removed without breaking compatibility with older versions. On the other hand, Protobuf requires explicit versioning and any changes to the schema require bumping up the version number. This makes Avro more flexible and easier to work with when it comes to evolving schemas.

2. Wire Format: Avro and Protobuf also differ in their wire format. Avro uses a compact binary format that is self-describing, meaning that the schema is included with the serialized data. This makes it easier to work with dynamically typed languages and allows for schema evolution as mentioned earlier. Protobuf, on the other hand, uses a binary format that is smaller and faster to serialize and deserialize, but it requires the schema to be shared between the producer and consumer separately. This can be a drawback when dealing with dynamically typed languages or when schema evolution is needed.

3. Schema Definition: Avro and Protobuf also have different ways of defining schemas. Avro uses a JSON-like format called Avro IDL (Interface Definition Language) to define schemas, which is more human-readable and can be easily understood by developers. Protobuf, on the other hand, uses a language-specific IDL that is then compiled into the corresponding language. This gives Protobuf more type safety and allows for generation of code that is specific to the target language.

4. Language Support: Another difference between Avro and Protobuf is their language support. Avro has support for multiple programming languages including Java, C, C++, C#, Python, and Ruby, among others. Protobuf also has support for multiple languages, but it provides more extensive support for languages like C++, Java, and Python. The availability of language support can be a deciding factor depending on the specific use case and the programming language being used.

5. Community and Ecosystem: Both Avro and Protobuf have active communities and ecosystems, but they differ in their focus. Avro is more widely used in the Apache Hadoop ecosystem and has integration with other Apache projects like Kafka and Hive. Protobuf, on the other hand, has a wider adoption in the Google ecosystem and is commonly used in Google services like Protocol Buffers and gRPC. Depending on the specific use case and the ecosystem being used, the community and ecosystem support can play a significant role in the decision-making process.

6. Encoding Efficiency: Another key difference between Avro and Protobuf is their encoding efficiency. Protobuf is known for its compact binary format, which results in a smaller serialized size compared to Avro. This makes Protobuf more efficient in terms of network bandwidth and storage space. However, Avro's self-describing format with included schema can provide advantages in terms of ease of use and flexibility.

In summary, Avro and Protobuf have key differences in schema evolution, wire format, schema definition, language support, community and ecosystem, and encoding efficiency, which makes them suitable for different use cases depending on specific requirements.