In this tutorial, you are going to create advanced Kafka Consumers.
The prerequisites to this tutorial are
Welcome to the first article on Advanced Kafka Consumers.
In this article, we are going to set up an advanced Kafka Consumer.
This comprehensive Kafka tutorial covers Kafka architecture and design. The Kafka tutorial has example Java Kafka producers and Kafka consumers. The Kafka tutorial also covers Avro and Schema Registry.
This Kafka course teaches the basics of the Apache Kafka distributed streaming platform. The Apache Kafka distributed streaming platform is one of the most powerful and widely used reliable streaming platforms. Kafka is a fault tolerant, highly scalable and used for log aggregation, stream processing, event sources and commit logs. Kafka is used by LinkedIn, Yahoo, Twitter, Square, Uber, Box, PayPal, Etsy and more to enable stream processing, online messaging, facilitate in-memory computing by providing a distributed commit log, data collection for big data and so much more.
In this tutorial, you are going to create advanced Kafka Producers.
The prerequisites to this tutorial are
This tutorial picks up right where Kafka Tutorial Part 11: Writing a Kafka Producer example in Java and Kafka Tutorial Part 12: Writing a Kafka Consumer example in Java left off. In the last two tutorial, we created simple Java example that creates a Kafka producer and a consumer.
In this tutorial, you are going to create advanced Kafka Consumers.
UNDER CONSTRUCTION.
The prerequisites to this tutorial are
This tutorial picks up right where Kafka Tutorial Part 11: Writing a Kafka Producer example in Java left off. In the last tutorial, we created advanced Java producers, now we will do the same with Consumers.
This post really picks off from our series on Kafka architecture which includes Kafka topics architecture, Kafka producer architecture, Kafka consumer architecture and Kafka ecosystem architecture.
This article is heavily inspired by the Kafka section on design around log compaction. You can think of it as the cliff notes about Kafka design around log compaction.
Kafka can delete older records based on time or size of a log. Kafka also supports log compaction for record key compaction. Log compaction means that Kafka will keep the latest version of a record and delete the older versions during a log compaction.
If you are not sure what Kafka is, see What is Kafka?.
This post really picks off from our series on Kafka architecture which includes Kafka topics architecture, Kafka producer architecture, Kafka consumer architecture and Kafka ecosystem architecture.
This article is heavily inspired by the Kafka section on design. You can think of it as the cliff notes.
LinkedIn engineering built Kafka to support real-time analytics. Kafka was designed to feed analytics system that did real-time processing of streams. LinkedIn developed Kafka as a unified platform for real-time handling of streaming data feeds. The goal behind Kafka, build a high-throughput streaming data platform that supports high-volume event streams like log aggregation, user activity, etc.
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 this tutorial, we are going to create simple Java example that creates a Kafka producer.
You create a new replicated Kafka topic called my-example-topic, then you create a Kafka
producer that uses this topic to send records. You will send records with the
Kafka producer. You will send records synchronously. Later, you will send records asynchronously.
Prerequisites to this tutorial are Kafka from the command line and Kafka clustering and failover basics.
If you are not sure what Kafka is, start here “What is Kafka?”.
This Kafka tutorial picks up right where the first Kafka tutorial from the command line left off. The first tutorial has instructions on how to run ZooKeeper and use Kafka utils.
In this tutorial, we are going to run many Kafka Nodes on our development laptop so that you will need at least 16 GB of RAM for local dev machine. You can run just two servers if you have less memory than 16 GB. We are going to create a replicated topic. We then demonstrate consumer failover and broker failover. We also demonstrate load balancing Kafka consumers. We show how, with many groups, Kafka acts like a Publish/Subscribe. But, when we put all of our consumers in the same group, Kafka will load share the messages to the consumers in the same group (more like a queue than a topic in a traditional MOM sense).
If you are not sure what Kafka is, start here “What is Kafka?”.
Let’s show a simple example using producers and consumers from the Kafka command line.
Download Kafka 0.10.2.x from the Kafka download page. Later versions will likely work, but this was example was done with 0.10.2.x.
We assume that you have Java SDK 1.8.x installed.
We unzipped the Kafka download and put it in ~/kafka-training/, and
then renamed the Kafka install folder to kafka. Please do the same.
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