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Apache Avro VS Protobuf

Compare Apache Avro VS Protobuf and see what are their differences

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Apache Avro logo Apache Avro

Apache Avro is a comprehensive data serialization system and acting as a source of data exchanger service for Apache Hadoop.

Protobuf logo Protobuf

Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.
  • Apache Avro Landing page
    Landing page //
    2022-10-21
  • Protobuf Landing page
    Landing page //
    2023-08-29

Apache Avro features and specs

  • Schema Evolution
    Avro supports seamless schema evolution, allowing you to add fields and change data types without impacting existing data. This flexibility is advantageous in environments where data structures frequently change.
  • Compact Binary Format
    Avro uses a compact binary format for data serialization, leading to efficient storage and faster data transmission compared to text-based formats like JSON or XML.
  • Language Agnostic
    Avro is designed to be language agnostic, with support for multiple programming languages, including Java, Python, C++, and more. This makes it easier to integrate with various systems.
  • No Code Generation Required
    Unlike other serialization frameworks such as Protocol Buffers and Thrift, Avro does not require generating code from the schema, simplifying the development process.
  • Self Describing
    Each Avro data file contains its schema, making the data self-describing. This helps maintain consistency between data producers and consumers.

Possible disadvantages of Apache Avro

  • Lack of Human Readability
    Avro's binary format is not human-readable, making it challenging to debug or inspect data without specialized tools.
  • Schema Management Overhead
    While Avro supports schema evolution, managing and maintaining these schemas across multiple services can become complex and require additional coordination.
  • Limited Support for Complex Data Types
    Avro has limitations when it comes to the representation of certain complex data types, which might necessitate workarounds or transformations that add complexity.
  • Learning Curve
    Users who are new to Apache Avro may face a learning curve to understand schema creation, evolution, and integration within their data pipelines.
  • Dependency on Schema Registry
    Using Avro effectively often requires integrating with a schema registry, adding an extra layer of infrastructure and potential points of failure.

Protobuf features and specs

  • Efficient Serialization
    Protobuf is known for its high efficiency in serializing structured data. It is faster and produces smaller size messages compared to JSON or XML, making it ideal for bandwidth-limited and resource-constrained environments.
  • Language Support
    Protobuf supports multiple programming languages including Java, C++, Python, Ruby, and Go. This makes it versatile and useful in heterogeneous environments.
  • Versioning Support
    It natively supports schema evolution without breaking existing implementations. Fields can be added or removed over time, ensuring backward and forward compatibility.
  • Type Safety
    Being a strongly typed data format, Protobuf ensures that data is correctly typed across different systems, preventing serialization and deserialization errors common with loosely typed formats.

Possible disadvantages of Protobuf

  • Learning Curve
    Protobuf requires learning and understanding its schema definitions and compiler usage, which might be a challenge for new developers.
  • Lack of Human Readability
    Serialized Protobuf data is in a binary format, making it less readable and debuggable compared to JSON or XML without specialized tools.
  • Limited Built-in Support for Complex Data Types
    By default, Protobuf does not provide comprehensive support for handling complex data types like maps or unions compared to some other data serialization formats, requiring workarounds.
  • Tooling Requirement
    Using Protobuf necessitates a compilation step where `.proto` files are converted into code, requiring additional tooling and build system integration.

Apache Avro videos

+ Add

CCA 175 : Apache Avro Introduction

More videos:

  • Review - End to end Data Governance with Apache Avro and Atlas

Protobuf videos

+ Add

StreamBerry, part 2 : introduction to Google ProtoBuf

Category Popularity

0-100% (relative to Apache Avro and Protobuf)

Apache Avro

Protobuf

Development
100 100%
Development
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Configuration Management
0 0%
Configuration Management
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Mobile Apps
0 0%
Mobile Apps
100% 100

User comments

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Social recommendations and mentions

Based on our record, Protobuf should be more popular than Apache Avro. It has been mentiond 83 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Avro mentions (14)

  • Pulumi Gestalt 0.0.1 released
    A schema.json converter for easier ingestion (likely supporting Avro and Protobuf). - Source: dev.to / about 2 months ago
  • Why Data Security is Broken and How to Fix it?
    Security Aware Data Metadata Data schema formats such as Avro and Json currently lack built-in support for data sensitivity or security-aware metadata. Additionally, common formats like Parquet and Iceberg, while efficient for storing large datasets, don’t natively include security-aware metadata. At Jarrid, we are exploring various metadata formats to incorporate data sensitivity and security-aware attributes... - Source: dev.to / 7 months ago
  • Open Table Formats Such as Apache Iceberg Are Inevitable for Analytical Data
    Apache AVRO [1] is one but it has been largely replaced by Parquet [2] which is a hybrid row/columnar format [1] https://avro.apache.org/. - Source: Hacker News / over 1 year ago
  • Generating Avro Schemas from Go types
    The most common format for describing schema in this scenario is Apache Avro. - Source: dev.to / over 1 year ago
  • gRPC on the client side
    Other serialization alternatives have a schema validation option: e.g., Avro, Kryo and Protocol Buffers. Interestingly enough, gRPC uses Protobuf to offer RPC across distributed components:. - Source: dev.to / about 2 years ago
View more

Protobuf mentions (83)

  • JSON vs Protocol Buffers vs FlatBuffers: A Deep Dive
    Protocol Buffers, developed by Google, is a compact and efficient binary serialization format designed for high-performance data exchange. - Source: dev.to / about 2 months ago
  • Developing games on and for Mac and Linux
    Protocol Buffers: https://developers.google.com/protocol-buffers. - Source: dev.to / about 2 years ago
  • Adding Codable conformance to Union with Metaprogramming
    ProtocolBuffers’ OneOf message addresses the case of having a message with many fields where at most one field will be set at the same time. - Source: dev.to / over 2 years ago
  • Logcat is awful. What would you improve?
    That's definitely the bigger thing. I think something like Protocol Buffers (Protobuf) is what you're looking for there. Output the data and consume it by something that can handle the analysis. Source: over 2 years ago
  • Bitcoin is the "narrow waist" of internet-based value
    These protocols prevent an O(N x M) explosion of code that have to solve for many cases. For example, since JSON is an almost ubiquitous format for wire transfer (although other things do exist like protobufs), if I had N data formats that I want to serialize, I only need to write N serializers/deserializers (SerDes). If there was no such narrow waist and there were M alternatives to JSON in wide usage, I would... Source: over 2 years ago
View more

What are some alternatives?

When comparing Apache Avro and Protobuf, you can also consider the following products

Apache Ambari - Ambari is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Hadoop clusters.

gRPC - Application and Data, Languages & Frameworks, Remote Procedure Call (RPC), and Service Discovery

Apache HBase - Apache HBase – Apache HBase™ Home

Messagepack - An efficient binary serialization format.

Apache Pig - Pig is a high-level platform for creating MapReduce programs used with Hadoop.

Apache Thrift - An interface definition language and communication protocol for creating cross-language services.

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