That’s why I have chosen Protocol Buffer vs Avro (from Hadoop) for the final comparison. ^ The current default format is binary. Compared to AVRO, JSON might be slower in general, because JSON is a text-based format whereas AVRO is a binary format. The size of data encoded in JSON is generally larger, which impacts network transmission throughput. Apache Avro was has been the defacto Kafka serialization mechanism for a long time. All serializers will implement the following simple interface in the sample project The producer and consumer use those classes and libraries to serialize and deserialize the payload. ^ The "classic" format is plain text, and an XML format is also supported. The test data that was serialized is around 200 bytes and I generated schema for both Avro and Protobuf. In most cases a static approach fits the needs quite well, in that case Thrift lets you benefit from the better performance of generated code. I wrote a JMH benchmark to compare the serialization performance of Avro (1.8.2) & Protobuf (3.5.0) in java 1.8. Kafka with AVRO vs., Kafka with Protobuf vs., Kafka with JSON Schema. Avro versus Protobuf There is an interesting comparison in this post that compares Avro, Protobuf and Thrift of binary messages sizes and how well the protocol supports schema evolution. AVRO might generally be de-/serialized faster than JSON. The libraries also provide compatibility checks between the writer and reader schema. What most other benchmarks do, is create a couple of objects to serialize and deserialize, run those a number of times in a row and calculate the average. a. e. ^ Means that generic tools/libraries know how to encode, decode, and dereference a reference to another piece of … Both protobuf and Apache Avro follow that approach. d. ^ The primary format is binary, but a text format is available. c. ^ Theoretically possible due to abstraction, but no implementation is included. Protocol buffers, also known as Protobuf, is a protocol that Google developed internally to enable serialization and deserialization of structured data between different services.Google’s design goal was to create a better method than XML to make systems communicate with each other over a wire or for the storage of data. Confluent just updated their Kafka streaming platform with additioinal support for serializing data with Protocol buffers (or protobuf) and JSON Schema serialization. b. Avro is being driven largely by Hadoop, afaict. If that is not the case, Avro might be more suitable. Messages are defined in JSON (truly more painful than Protobuf or Thrift). Support and tools for Java and Scala are on a very good level. According to JMH, Protobuf can serialize some data 4.7 million times in a second where as Avro can only do 800k per second. For C++ I used Visual Studio 2017 (not an update version) with Cereal 1.2.2 (which uses rapidjson and rapidxml), protobuf 3.2.0 (static library can be found on the repository) Methodology. Apache Avro – Avro is a newer project designed to accomplish many of the same goals of Protobuf or Thrift but without the static compilation step and greater interop with dynamic languages. The key difference is that protobuf has a language-agnostic schema definition but Avro uses compact JSON. With Protobuf and JSON both being sequential, it is very hard to achieve a 5x performance boost running in the same CPU and the same core. This is independent of Kafka Streams. JSON; Avro; Protobuf; I am going to use simple project which wraps and abstracts different serialization formats through simple interface and we'll use Unit test project to check the speed of the process and size of serialized data.  Also it is worth mentioning that besides Thrift, Protobuf and Avro there are some more solutions on the market, such as Capt'n'proto or BOLT. Thrift — from Facebook, almost the same when it comes to functionalities as Google’s Protocol Buffers, but subjectively Protobuf is easier to use. Protocol Buffers offer several compelling advantages over JSON for sending data over the wire between internal services. Another interesting data transfer protocol is Parquet , which is optimized for column- oriented data. Documentation is very detailed and extensive.
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