Golang kafka tutorial

Download it here. In this tutorial, we will take a look at how Kafka can help us with handling distributed messaging, by using the Event Sourcing pattern that is inherently atomic. The whole application is delivered in Go. The most common argument that calls for microservices is scalability first and foremost. As an application grows, it can be hard to maintain all the code and make changes to it easily.

This is why people turn to microservices. By decomposing a big system and creating various microservices for handling specific functions e.

However, building a microservice can be challenging. One of the challenges is atomicity — a way of dealing with distributed data, inherent to microservice architecture. Querying is also a challenge. It can be quite difficult to do a query like this when a customer and an order are two different services:. The two architectural patterns are that are key for creating a microservice-based solution are Command-Query Responsibility Segregation, and Event Sourcingwhen it makes sense.

Not all systems require event sourcing. Event sourcing is good for a system that needs audit trail and time travel. If the system in question needs only basic decoupling from a larger system, event-driven design is probably a better option. If we compare Kafka to a database, a table in a database is a topic in Kafka.

Each table can have data expressed as a row, while in Kafka, data is simply expressed as a commit logwhich is a string. Each of the commit logs has an index, aka an offset. In Kafka, the order of commit logs is important, so each one of them has an ever-increasing index number used as an offset. However, unlike a table in a SQL database, a topic should normally have more than one partition. As Kafka performance is guaranteed to be constant at O 1each partition can hold thousands, millions, or even more commit logs, and still do a fine job.

Each partition then holds different logs. Partitioning is the the process through which Kafka allows us to do parallel processing. Thanks to partitioning, each consumer in a consumer group can be assigned to a process in an entirely different partition.Kafka is a distributed streaming platform. Kafka can be used for building realtime streaming application that can transform the data streams or deduce some intelligence out of them. This Apache Kafka Tutorial provides details about the design goals and capabilities of Kafka.

It also provides Command Line Interface. So, having a knowledge of Java programming and using command line tools would help to follow this course easily.

Apache Kafka is a platform for real-time distributed streaming. Apache Kafka Architecture is a good choice for scalable real-time distributed streaming. Kafka has been originally developed at LinkedIn and has become a top level Apache project during The main goal of Apache Kafka is to be a unified platform that is scalable for handling real-time data streams.

If you are familiar with working of a messaging system, you may find that Kafka reads and writes data streams just like a messaging system. Apache Kafka could be setup on your machine easily. Please go through one of the following installation steps to setup Apache Kafka on your machine based on the Operating System. A Kafka cluster can have multiple topics. A Kafka Topic could be divided into multiple partitions. Partitions are the ones that realize parallelism and redundancy in Kafka.

Broker is an instance of Kafka that communicate with ZooKeeper. Each broker holds partition s of topic s. Some of those partitions are leaders and others are replicas of leader partitions from other brokers. ZooKeeper is used to automatically select a leader for a partition. In case of any broker shutdown, an election is held, by ZooKeeper, for leader position of partitions that went down with the broker. Also metadata like, in which broker a leader partition is living, etc. Producers that stream data to topics, or Consumers that read stream data from topics, contact ZooKeeper for the nearest or less occupied broker.

Producers are applications that send data streams to topics in Kafka Cluster. A producer can send the stream of records to multiple topics.

Consumers are applications that feed on data streams from topics in Kafka Cluster. A consumer can receive stream of records from multiple topics through subscription.

Stream Processors are applications that transform data streams of topics to other data streams of topics in Kafka Cluster.Mar Nov 17 May 28 Write your response Never miss a post from Snehal Kumarwhen you sign up for Ednsquare. Go Language. Log in. Sign Up.

How to create and consume messages using sarama. Snehal Kumar. What Is Apache Kafka? Apache Kafka is an open-source stream processing software platform which started out at Linkedin. Kafka Architecture. So, rather than just write into one queue like the image above, Kafka can writes into several queue with same topic name.

For each topic, the Kafka cluster maintains a partitioned log that looks like this.

golang kafka tutorial

In a busy day, when a guest of a hotel want to go to upstairs, they will choose one of three elevators which have a few passengers on it. With same analogical logic, when a data arrives, we can tell Kafka to write into specific partition. Kafka Consumer Group. In Kafka you can consume data from specific partition of a topic, or you can consume it from all partition.

So, what is a consumer group? Consumer group is like a label of a group of consumer. For each consumer under the same label, you will consume different messages. From now on, you and your other 2 friends are recognized as one entity rather than 3 entity. Kafka is usually compared to a queuing system such as RabbitMQ. In that way, messages stay in Kafka longer, and they can be replayed. Setting Up Kafka.

This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data. Step 1: Download the code. Download the 2. Step 2: Start the server. Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don't already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.

Now start the Kafka server:. Step 3: Create a topic. Let's create a topic named "test" with a single partition and only one replica:.Nowadays it seems as though more and more companies are using event-based architectures to provide communication between services across various domains.

Confluent maintain a huge list of companies actively using Apache Kafkaa high performance messaging system and the subject of this post. Kafka has been so heavily adopted in part due to its high performance and the large number of client libraries available in a multitude of languages. One service might handle sending me a welcome email, whereas another will attempt to identify other users I may want to connect with. These are parallel event streams that allow multiple consumers to process events from the same topic.

Imagine all events for a topic partition are stored as an array, the offset would be the index where a particular event is located in time. This allows consumers to specify a starting point from which to consume events, granting the ability to avoid duplication of events processed, or the consumption of events produced earlier in time. This is especially useful when running replicated services and can increase event throughput.

This tutorial focuses on sarama-clustera balanced consumer implementation built on top the existing sarama client library by Shopify. The library has a concise API that makes getting started fairly simple. The first step is to define our consumer configuration. We can use the NewConfig method which creates a default configuration with some sensible starting values.

TLS struct field:. In a Dockerfile based on alpine this looks like:. SASL struct field like so:. The sarama-cluster library allows you to specify a consumer mode within the config. This can be modified via the config. Mode struct field and has two options. These are:. When using ConsumerModeMultiplexall messages come from a single channel exposed via the Messages method.

Reading these messages looks like this:. This provides you the individual partitions via the consumer. Partitions method. This exposes an underlying channel that partitions are written to when the consumer group rebalances. You can then use each partition to read messages and errors:.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. We rely on both Go and Kafka a lot at Segment. Unfortunately, the state of the Go client libraries for Kafka at the time of this writing was not ideal. The available options were:. It is poorly documented, the API exposes low level concepts of the Kafka protocol, and it doesn't support recent Go features like contexts.

It also passes all values as pointers which causes large numbers of dynamic memory allocations, more frequent garbage collections, and higher memory usage. It has much better documentation than sarama but still lacks support for Go contexts.

It provides abstractions for using Kafka as a message passing bus between services rather than an ordered log of events, but this is not the typical use case of Kafka for us at Segment. The package also depends on sarama for all interactions with Kafka. This is where kafka-go comes into play. It provides both low and high level APIs for interacting with Kafka, mirroring concepts and implementing interfaces of the Go standard library to make it easy to use and integrate with existing software.

While latest versions will be working, some features available from the Kafka API may not be implemented yet. To use with older versions of golang use release v0. The Conn type is the core of the kafka-go package. It wraps around a raw network connection to expose a low-level API to a Kafka server.

Because it is low level, the Conn type turns out to be a great building block for higher level abstractions, like the Reader for example. A Reader is another concept exposed by the kafka-go package, which intends to make it simpler to implement the typical use case of consuming from a single topic-partition pair.

A Reader also automatically handles reconnections and offset management, and exposes an API that supports asynchronous cancellations and timeouts using Go contexts. By default, CommitMessages will synchronously commit offsets to Kafka.

For improved performance, you can instead periodically commit offsets to Kafka by setting CommitInterval on the ReaderConfig. To produce messages to Kafka, a program may use the low-level Conn API, but the package also provides a higher level Writer type which is more appropriate to use in most cases as it provides additional features:.

Note: Even though kafka.

golang kafka tutorial

They are intended for read use only. Hash balancer. Hash routes messages to the same partitions that Sarama's default partitioner would route to. Use the kafka.

Apache Kafka Tutorial - What is Apache Kafka? - Kafka Tutorial for Beginners - Edureka

Murmur2Balancer balancer to get the same behaviour as the canonical Java client's default partitioner. Note: the Java class allows you to directly specify the partition which is not permitted.

Compression can be enabled on the Writer by configuring the CompressionCodec :. The Reader will by determine if the consumed messages are compressed by examining the message attributes. However, the package s for all expected codecs must be imported so that they get loaded correctly.

For example, if you are going to be receiving messages compressed with Snappy, add the following import:.

Golang: Implementing kafka consumers using sarama-cluster

Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.The Go client, called confluent-kafka-go, is distributed via GitHub and gopkg.

The Go client uses librdkafka, the C client, internally and exposes it as Go library using cgo. Starting with confluent-kafka-go v1. For other platforms the following instructions still apply: To install the Go client, first install the C client including its development package as well as a C build toolchain including pkg-config.

On Red Hat-based Linux distributions install the following packages in addition to librdkafka:. On macOS using Homebrewinstall the following:. Next, use go get to install the library:. Your Go code can now import and use the client. You can also build and run a small command line utility, go-kafkacatto ensure the installation was successful:.

See the clients documentation for code examples showing how to use the library. All examples include a producer and consumer that can connect to any Kafka cluster running on-premises or in Confluent Cloud. The Go client uses a ConfigMap object to pass configuration to the producer:. In Go, you initiate a send by calling the Produce method, passing a Message object and an optional chan Event that can be used to listen for the result of the send. This producer example shows how to invoke some code after the write has completed, you can use the delivery report channel passed to Produce to wait for the result of the message send:.

Making writes synchronous is typically a bad idea since it kills throughput. Nevertheless, you can make writes synchronous by receiving from the delivery channel passed to the Produce method call:. Or, to wait for all messages to be acknowledge, use the Flush method:. If Flush is called and no goroutine is processing the delivery channel, its buffer may fill up and cause the flush to timeout. The Go client uses a ConfigMap object to pass configuration to the consumer:.

The Go client uses librdkafka internally so it follows a multi-threaded approach to Kafka consumption. The API returns only a single message or event at a time:.

golang kafka tutorial

The consumer API is centered around the Consumer. Poll method, which is used to retrieve records from the brokers. The Consumer. SubscribeTopics method controls which topics will be fetched in poll.

This consumer example shows typical usage,which involves an initial call to Consumer. SubscribeTopics to setup the topics of interest and then a loop which calls Consumer. Poll until the application is shut down. If no records are received, then Consumer. Poll will return an empty record set. Note that you should always call Consumer. Close after you are finished using the consumer.

Doing so will ensure that active sockets are closed and internal state is cleaned up. It will also trigger a group rebalance immediately which ensures that any partitions owned by the consumer are re-assigned to another member in the group.

If not closed properly, the broker will trigger the rebalance only after the session timeout has expired. Latch is added to this example to ensure that the consumer has time to finish closing before finishing shut down.In this tutorial, you are going to create simple Kafka Consumer. This consumer consumes messages from the Kafka Producer you wrote in the last tutorial. This tutorial demonstrates how to process records from a Kafka topic with a Kafka Consumer.

This tutorial describes how Kafka Consumers in the same group divide up and share partitions while each consumer group appears to get its own copy of the same data.

In the last tutorial, we created simple Java example that creates a Kafka producer. We also created replicated Kafka topic called my-example-topicthen you used the Kafka producer to send records synchronously and asynchronously.

golang kafka tutorial

Now, the consumer you create will consume those messages. Just like we did with the producer, you need to specify bootstrap servers. You also need to define a group. Then you need to designate a Kafka record key deserializer and a record value deserializer. Then you need to subscribe the consumer to the topic you created in the producer tutorial.

Next, you import the Kafka packages and define a constant for the topic and a constant to set the list of bootstrap servers that the consumer will connect. Notice that KafkaConsumerExample imports LongDeserializer which gets configured as the Kafka record key deserializer, and imports StringDeserializer which gets set up as the record value deserializer. Go ahead and make sure all three Kafka servers are running.

To create a Kafka consumer, you use java. Properties and define certain properties that we pass to the constructor of a KafkaConsumer. Above KafkaConsumerExample. Just like the producer, the consumer uses of all servers in the cluster no matter which ones we list here. Notice that we set this to LongDeserializer as the message ids in our example are longs.

Apache Kafka Tutorial

Notice that we set this to StringDeserializer as the message body in our example are strings. Important notice that you need to subscribe the consumer to the topic consumer. The subscribe method takes a list of topics to subscribe to, and this list will replace the current subscriptions if any.

Notice you use ConsumerRecords which is a group of records from a Kafka topic partition. The ConsumerRecords class is a container that holds a list of ConsumerRecord s per partition for a particular topic.

There is one ConsumerRecord list for every topic partition returned by a the consumer.