Kafka and Google Protobuf
One of the coolest things in Apache Kafka is that record values are shapeless, meaning that developers can write any set of bytes they want and it works just fine. This can be quite powerful if they need to share data among different programming languages. However, this characteristic is sometimes bad. It creates coupling problems as producers and consumers need to negotiate a common format that they can rely on to write and read data. Therefore, it is important to use a neutral format — likely something that is accepted within the industry such as Avro, Protobuf, and JSON Schemas.
This post will focus on the problems raised by using Protobuf to share data between Java and Go. Though one could argue that this is exactly what technologies like Protobuf mean to address, the reality is that in the context of Kafka they solve only part of the problem.
There is still a need to come up with a format that bytes are arranged in the record. Recently I had to build a prototype of an application written in Java and Go to share data using Protobuf. I started by creating a producer and consumer in Go to share data and it worked fine. Then, I wrote a consumer in Java to read the data produced by Go and for my surprise I got a heck of an deserialization exception.
After a couple hours investigating the deserializer code The KafkaProtobufDeserializer from Confluent I was able to figure out which format was being expected and refactor the Go code accordingly. Where [Message Index Data] is an array containing the indexes that correspond to the message type serialized, and the first element of this array is always an item representing the size of the array.
I have wrote two examples one in Java and another in Go that shows producers and consumers sharing data using Kafka and Protobuf successfully.
If you ever struggle in executing code that tries to serialize and deserialize data using Kafka and Protobuf always check how the bytes are being arranged by one of the serializers.
How to Use Protobuf With Apache Kafka and Schema Registry
Skip to content. Next Post Next Troubleshooting Protobufs has many pluses that are easily measurable - data size and serialization performance are the most commonly attributed ones among them.
However, I think the most important ones are the quality attributes that a data representation format backed by an IDL specification guarantee. Automatically the data quality in the data plane improves. There are fewer errors due to bad data. Consumers know how to parse the data. All our micro-services talk protobufs over gRPC.
In fact, all the Protobuf message types and gRPC service definitions live in a single repository that is a dependency of all services. So it was a natural progression to extend the same binary protocol to the async messages over Kafka. The producer side of things is easy. All messages on Kafka are binary is a direct match to protobuf. However, there is a challenge on the consumer side.
Unlike JSON which is self describing format, a protobuf message cannot be de-serialized without prior knowledge of the message type. So it is imperative that additional metadata is included by the producer to describe the message. The below snippets outline a strategy to serialize and de-serialize Protobuf messages. The key abstractions are KafkaEventPublisher that takes a Domain Event object that can be converted to a proto message using the toProto method.
The protobuf java type name is included in the header proto. On the receiver side, the same header kafka. The property to set is ConsumerConfig. Protocol Buffers Protobufs has many pluses that are easily measurable - data size and serialization performance are the most commonly attributed ones among them. Kafka Message Headers The producer side of things is easy.Since Confluent Platform version 5.
When applications communicate through a pub-sub system, they exchange messages and those messages need to be understood and agreed upon by all the participants in the communication. Additionally, you would like to detect and prevent changes to the message format that would make messages unreadable for some of the participants. That's where a schema comes in — it represents a contract between the participants in communication, just like an API represents a contract between a service and its consumers.
In addition, together with Schema Registry, schemas prevent a producer from sending poison messages - malformed data that consumers cannot interpret. Schema Registry will detect if breaking changes are about to be introduced by the producer and can be configured to reject such changes. An example of a breaking change would be deleting a mandatory field from the schema.
Similar to Apache AvroProtobuf is a method of serializing structured data. A message format is defined in a. Unlike Avro, Protobuf does not serialize schema with the message. So, in order to deserialize the message, you need the schema in the consumer. In the first line, we define that we're using Protobuf version 3. Each field is assigned a so-called field numberwhich has to be unique in a message type. These numbers identify the fields when the message is serialized to the Protobuf binary format.
Google suggests using numbers 1 through 15 for most frequently used fields because it takes one byte to encode them.Python mingus
Protobuf supports common scalar types like string, int32, int64 longdouble, bool etc. For the full list of all scalar types in Protobuf check the Protobuf documentation.
Besides scalar types, it is possible to use complex data types. Below we see two schemas, Order and Product, where Order can contain zero, one or more Products:. Schema Registry is a service for storing a versioned history of schemas used in Kafka. It also supports the evolution of schemas in a way that doesn't break producers or consumers.
If you worked with Avro and Kafka before, this section will not contain any surprises. The job of this serializer is to convert the Java object to a Protobuf binary format before the producer writes the message to Kafka.
The additional job of the serialiser is to check whether the Protobuf schema exists in the Schema Registry. If not, it will write the schema to Schema Registry and it will write the schema id to the message at the beginning of the message.
Then, when the Kafka record reaches the consumer, the consumer will use KafkaProtobufDeserializer to fetch the schema from the Schema Registry based on the schema id from the message.
Once the schema is fetched, the KafkaProtobufDeserializer will use it to deserialize the message. This way the consumer doesn't need to know the schema in advance to be able to consume messages from Kafka. Ok, now we know how a Protobuf schema looks and we know how it ends up in Schema Registry.
Let's see now how we use Protobuf schemas from Java. The first thing that you need is a protobuf-java library. In these examples, I'm using maven, so let's add the maven dependency:. The next thing you want to do is use the protoc compiler to generate Java code from.
But we're not going to invite the compiler manually, we'll use a maven plugin called protoc-jar-maven-plugin :.In this tutorial we will see how data stored in Apache Kafka with Google Protobuf can be visualised and processed.
Once the data is lifted in Lenses, data masking and stream processing with SQL can be unleashed. The code can be found on Github. I want do view my Google Protobuf data in Kafka, protect the sensitive information and be able to process it. The Google Protobuf schema is this:. Lenses exposes a light library to allow to plugin Google Protobuf payloads in Kafka. What is needed is to implement the Serde interface:. At the moment, the serializer Properties properties is not required it is not used by Lenses.
First thing is to provide the schema for your Kafka payload. Next step is to translate the raw bytes, storing the card details using Google Protobuf, to a GenericRecord. Step one is to lift the raw bytes into an instance of CreditCard class:.
Going from bytes to CreditCard to bytes to GenericRecord can be short-circuited to avoid the intermediary bytes.
This is where this implementation comes into play at the expense of more code. Lifting the raw bytes into a CreditCard remains the same. The next, and last step, requires requires the creation of an instance of GenericRecord and populate with a value from the CreditCard class instance.
Quite often, the payloads sent over Kafka using Google Protobuf contain nested data. The implementation for getSchema stays the same, the only thing which changed here is to extract the Avro schema for the b field. Next, the deserializer code needs to create and populate the GenericRecordincluding the nested one for field b :.
If you get the source code, run the following command in the folder containing the pom. Follow the docs to make these two artifacts available to Lenses:. In this tutorial you learned how to enable data stored in Kafka with Google Protobuf. Once the plugin is provided to Lenses, the topic containing the data will be associated with it. As a result the data can be queried using Lenses SQL. Alongside it, the data policies will also apply to the data and you can make sure the sensitive information is not available to the users accessing the data.
Last but not least, you can process the topic data as a stream using the SQL processor. The simplest example is to convert the data to Avro:. Kafka and Google Protobuf. The code can be found on Github Requirements I want do view my Google Protobuf data in Kafka, protect the sensitive information and be able to process it.Confluent's Apache Kafka Client for.
NET ships with a serializer and deserializer for the Avro serialization format, seamlessly integrated with Confluent Schema Registry. Avro is well matched to scenarios where you would like your schemas to be centrally managed, and as i'll explain in an upcoming blog post, this is often very desirable - especially in more complex scenarios - because it decreases coupling of producer and consumer applications. In this post, I'll talk a little bit about another popular serialization format - protobuf.
Unlike Avro, protobuf serialized data can be deserialized without the writer schema present. It's therefore possible to use protobuf without any system in place for schema management. NET Kafka client doesn't ship with protobuf support out of the box, but it's straightforward to implement this yourself. Here's a suitable serializer:. That's all! Default implementations are all you need for the Configure and Dispose methods and to Serializeyou just need to call the ToByteArray method on your protobuf IMessage instance.
This serializer can be used with any type generated using the protoc tool.Tabla salarial ecopetrol
Check out the official tutorial for more information on using this tool and protobuf with C in general. Note that I'm using the new produce API available in the 1.
Kafka nuget package which I happen to be unreasonably excited about. It's worth noting that the type used by the consumer does not need to exactly match the type of the serialized data - it just needs to be compatible. The protobuf compatibility rules are fairly straightforward - you can read more about them in the official docs.
That's all for now! Protobuf is a widely used serialization format and as demonstrated in this article, it's super simple to use with Kafka and. Matt Howlett every blog should have a tagline.Sabritas usa
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