Kafka Tutorial: Writing a Kafka Consumer in Java 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.
Kafka Tutorial: Writing a Kafka Producer in Java 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. Before you start 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?”. Getting started with Kafka cluster tutorial Understanding Kafka Failover 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.
If you are not sure what Kafka is, start here “What is Kafka?”. Getting started with Kafka tutorial 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.
Kafka Consumer Architecture - Consumer Groups and subscriptions This article covers some lower level details of Kafka consumer architecture. It is a continuation of the Kafka Architecture, Kafka Topic Architecture, and Kafka Producer Architecture articles. This article covers Kafka Consumer Architecture with a discussion consumer groups and how record processing is shared among a consumer group as well as failover for Kafka consumers. Cloudurable provides Kafka training, Kafka consulting, Kafka support and helps setting up Kafka clusters in AWS.
Kafka Producer Architecture - Picking the partition of records This article covers some lower level details of Kafka producer architecture. It is a continuation of the Kafka Architecture and Kafka Topic Architecture articles. This article covers Kafka Producer Architecture with a discussion of how a partition is chosen, producer cadence, and partitioning strategies. Kafka Producers Kafka producers send records to topics. The records are sometimes referred to as messages.
Kafka Topic Architecture - Replication, Failover and Parallel Processing This article covers some lower level details of Kafka topic architecture. It is a continuation of the Kafka Architecture article. This article covers Kafka Topic’s Architecture with a discussion of how partitions are used for fail-over and parallel processing. Kafka Topics, Logs, Partitions Recall that a Kafka topic is a named stream of records. Kafka stores topics in logs. A topic log is broken up into partitions.
Kafka vs JMS, SQS, RabbitMQ Messaging Is Kafka a queue or a publish and subscribe system? Yes. It can be both. Kafka is like a queue for consumer groups, which we cover later. Basically, Kafka is a queue system per consumer group so it can do load balancing like JMS, RabbitMQ, etc. Kafka is like topics in JMS, RabbitMQ, and other MOM systems for multiple consumer groups. Kafka has topics and producers publish to the topics and the subscribers (Consumer Groups) read from the topics.
If you are not sure what Kafka is, see What is Kafka?. Kafka Architecture Kafka consists of Records, Topics, Consumers, Producers, Brokers, Logs, Partitions, and Clusters. Records can have key (optional), value and timestamp. Kafka Records are immutable. A Kafka Topic is a stream of records ("/orders", "/user-signups"). You can think of a Topic as a feed name. A topic has a Log which is the topic’s storage on disk.
The Kafka Ecosystem - Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry
The Kafka Ecosystem - Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry The core of Kafka is the brokers, topics, logs, partitions, and cluster. The core also consists of related tools like MirrorMaker. The aforementioned is Kafka as it exists in Apache. The Kafka ecosystem consists of Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry. Most of the additional pieces of the Kafka ecosystem comes from Confluent and is not part of Apache.
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