How to Use Protobuf With Apache Kafka and Schema Registry

Since Confluent Platform version 5.5, Avro is no longer the only schema in town. Protobuf and JSON schemas are now supported as first-class citizens in Confluent universe. But before I go on explaining how to use Protobuf with Kafka, let’s answer one often-asked question:

Why Do We Need Schemas?

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. And just as REST APIs can be described using OpenAPI (Swagger) so the messages in Kafka can be described using Avro, Protobuf or Avro schemas.

Schemas describe the structure of the data by:

  • specifying which fields are in the message
  • specifying the data type for each field and whether the field is mandatory or not

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.

Introduction to Protobuf

Similar to Apache Avro, Protobuf is a method of serializing structured data. A message format is defined in a .proto file and you can generate code from it in many languages including Java, Python, C++, C#, Go and Ruby. Unlike Avro, Protobuf does not serialize schema with the message. So, in order to deserialize the message, you need the schema in the consumer.

Here’s an example of a Protobuf schema containing one message type:

#integration #apache kafka #schema #apache kafka tutorial #protobuf

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How to Use Protobuf With Apache Kafka and Schema Registry

How to Use Protobuf With Apache Kafka and Schema Registry

Since Confluent Platform version 5.5, Avro is no longer the only schema in town. Protobuf and JSON schemas are now supported as first-class citizens in Confluent universe. But before I go on explaining how to use Protobuf with Kafka, let’s answer one often-asked question:

Why Do We Need Schemas?

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. And just as REST APIs can be described using OpenAPI (Swagger) so the messages in Kafka can be described using Avro, Protobuf or Avro schemas.

Schemas describe the structure of the data by:

  • specifying which fields are in the message
  • specifying the data type for each field and whether the field is mandatory or not

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.

Introduction to Protobuf

Similar to Apache Avro, Protobuf is a method of serializing structured data. A message format is defined in a .proto file and you can generate code from it in many languages including Java, Python, C++, C#, Go and Ruby. Unlike Avro, Protobuf does not serialize schema with the message. So, in order to deserialize the message, you need the schema in the consumer.

Here’s an example of a Protobuf schema containing one message type:

#integration #apache kafka #schema #apache kafka tutorial #protobuf

Brain  Crist

Brain Crist

1600347600

SCHEMAS in SQL Server -MS SQL Server – Zero to Hero Query Master

Introduction

This is part 3 of “MS SQL Server- Zero to Hero” and in this article, we will be discussing about the SCHEMAS in SQL SERVER. Before getting into this article, please consider to visit previous articles in this series from below,

A glimpse of previous articles
Part 1

In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.

Part 2
  • We learned to create a database and maintain it using SQL statements.
  • Best practice methods were also mentioned.

#sql server #benefits of schemas #create schema in sql #database schemas #how to create schema in sql server #schemas #schemas in sql server #sql server schemas #what is schema in sql server

Sierra  Grimes

Sierra Grimes

1594476840

How to Use Protobuf with Apache Kafka and Schema Registry

Full guide on working with Protobuf in Apache Kafka

Since Confluent Platform version 5.5 Avro is no longer the only schema in town. Protobuf and JSON schemas are now supported as the first-class citizens in Confluent universe. But before I go on explaining how to use Protobuf with Kafka, let’s answer one often asked question…

Why do we need schemas

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. And just as REST APIs can be described using OpenAPI (Swagger) so the messages in Kafka can be described using Avro, Protobuf or Avro schemas.

Schemas describe the structure of the data by:

  • specifying which fields are in the message
  • specifying the data type for each field and whether the field is mandatory or not

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.

Introduction to Protobuf

Similar to Apache Avro, Protobuf is a method of serializing structured data. A message format is defined in a .proto file and you can generate code from it in many languages including Java, Python, C++, C#, Go and Ruby. Unlike Avro, Protobuf does not serialize schema with the message. So, in order to deserialize the message, you need the schema in the consumer.

Here’s an example of a Protobuf schema containing one message type:

syntax = "proto3";

package com.codingharbour.protobuf;
message SimpleMessage {
 string content = 1;
 string date_time = 2;
}

In the first line, we define that we’re using protobuf version 3. Our message type called SimpleMessage defines two string fields: content and date_time. Each field is assigned a so-called field number, which 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.

Protobuf supports common scalar types like string, int32, int64 (long), double, 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:

message Order {
 int64 order_id = 1;
 int64 date_time = 2;
 Product product = 3;
}
message Product {
 int32 product_id = 1;
 string name = 2;
 string description = 3;
}

Now, let’s see how these schemas end up in the Schema Registry.

#tutorial #protobuf #apache

Carmen  Grimes

Carmen Grimes

1613612825

Using Apache Kafka with .NET

Diogo Souza explains using Apache Kafka with .NET including setting it up and creating apps to test sending messages asynchronously.

Have you ever used async processing for your applications? Whether for a web-based or a cloud-driven approach, asynchronous code seems inevitable when dealing with tasks that do not need to process immediately. Apache Kafka is one of the most used and robust open-source event streaming platforms out there. Many companies and developers take advantage of its power to create high-performance async processes along with streaming for analytics purposes, data integration for microservices, and great monitoring tools for app health metrics. This article explains the details of using Kafka with .NET applications. It also shows the installation and usage on a Windows OS and its configuration for an ASP.NET API.

How It Works

The world produces data constantly and exponentially. To embrace such an ever-growing amount of data, tools like Kafka come into existence, providing robust and impressive architecture.

But how does Kafka work behind the scenes?

Kafka works as a middleman exchanging information from producers to consumers. They are the two main actors in each edge of this linear process.

Apache Kafka

Figure 1. Producers and consumers in Kafka

Kafka can also be configured to work in a cluster of one or more servers. Those servers are called Kafka brokers. You can benefit from multiple features such as data replication, fault tolerance, and high availability with brokers.

Kafka clusters

Figure 2. Kafka clusters

These brokers are managed by another tool called Zookeeper. In summary, it is a service that aims to keep configuration-like data synchronized and organized in distributed systems.

#dotnet #kafka #apache #apache-kafka #developer

Myrl  Prosacco

Myrl Prosacco

1594533600

Using Apache Flink for Kinesis to Kafka Connect

In this blog, we are going to use kinesis as a source and kafka as a consumer.

Let’s get started.

Step 1:

Apache Flink provides the kinesis and kafka connector dependencies. Let’s add them in our build.sbt:

name := "flink-demo"

version := "0.1"

scalaVersion := "2.12.8"

libraryDependencies ++= Seq(
  "org.apache.flink" %% "flink-scala" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kinesis" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kafka" % "1.10.0",
  "org.apache.flink" %% "flink-streaming-scala" % "1.10.0"
)

Step 2:

The next step is to create a pointer to the environment on which this program runs.

val env = StreamExecutionEnvironment.getExecutionEnvironment

Step 3:

Setting parallelism of x here will cause all operators (such as join, map, reduce) to run with x parallel instance.

I am using 1 as it is a demo application.

env.setParallelism(1)

Step 4:

Disabling the aws cbor, as we are testing locally.

System.setProperty("com.amazonaws.sdk.disableCbor", "true")
System.setProperty("org.apache.flink.kinesis.shaded.com.amazonaws.sdk.disableCbor", "true")

Step 5:

Defining Kinesis consumer properties.

  • Region
  • Stream Position – TRIM_HORIZON to read all the records available in the stream
  • Aws keys
  • Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

#apache flink #flink #scala ##apache-flink ##kinesis #apache #flink streaming #kafka #scala