Apache kafka
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Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers due to its better throughput, built-in partitioning for scalability, replication for fault tolerance, and ability to handle large message processing applications. Kafka uses topics to organize streams of messages, partitions to distribute data, and replicas to provide redundancy and prevent data loss. It supports reliable messaging patterns including point-to-point and publish-subscribe.
Apache kafka
- 1. Apache Kafka is a fast, scalable, durable and distributed messaging system.
- 2. We need basic Java programming skills plus access to: Apache Kafka 0.9.0 Apache Maven 3.0 or later Git Step 1: Download Kafka Download the Apache Kafka 0.9.0release and un-tar it. Prerequisites & Installation
- 3. Kafka is designed for distributed high throughput systems. Kafka tends to work very well as a replacement for a more traditional message broker. In comparison to other messaging systems, Kafka has better throughput, built-in partitioning, replication and inherent fault-tolerance, which makes it a good fit for large-scale message processing applications.
- 4. A Messaging System is responsible for transferring data from one application to another, so the applications can focus on data, but not worry about how to share it. Distributed messaging is based on the concept of reliable message queuing. Messages are queued asynchronously between client applications and messaging system. Two types of messaging patterns are available − one is point to point and the other is publish-subscribe (pub-sub) messaging system. Most of the messaging patterns follow pub-sub. What is a Messaging System?
- 5. In a point-to-point system, messages are persisted in a queue. One or more consumers can consume the messages in the queue, but a particular message can be consumed by a maximum of one consumer only. Once a consumer reads a message in the queue, it disappears from that queue. The typical example of this system is an Order Processing System, where each order will be processed by one Order Processor, but Multiple Order Processors can work as well at the same time. The following diagram depicts the structure. Point to Point Messaging System Sender Message Queue Receiver
- 6. In the publish-subscribe system, messages are persisted in a topic. Unlike point-to-point system, consumers can subscribe to one or more topic and consume all the messages in that topic. In the Publish-Subscribe system, message producers are called publishers and message consumers are called subscribers. A real-life example is Dish TV, which publishes different channels like sports, movies, music, etc., and anyone can subscribe to their own set of channels and get them whenever their subscribed channels are available. Publish-Subscribe Messaging System Sender Message Queue Receiver Receiver Receiver
- 7. Following are a few benefits of Kafka − Reliability − Kafka is distributed, partitioned, replicated and fault tolerance. Scalability − Kafka messaging system scales easily without down time.. Durability − Kafka uses Distributed commit log which means messages persists on disk as fast as possible, hence it is durable.. Performance − Kafka has high throughput for both publishing and subscribing messages. It maintains stable performance even many TB of messages are stored. Kafka is very fast and guarantees zero downtime and zero data loss. Benefits
- 8. Kafka is a unified platform for handling all the real-time data feeds. Kafka supports low latency message delivery and gives guarantee for fault tolerance in the presence of machine failures. It has the ability to handle a large number of diverse consumers. Kafka is very fast, performs 2 million writes/sec. Kafka persists all data to the disk, which essentially means that all the writes go to the page cache of the OS (RAM). This makes it very efficient to transfer data from page cache to a network socket. Need for Kafka
- 9. such as topics, brokers, producers and consumers. Topics: A stream of messages belonging to a particular category is called a topic. Data is stored in topics Partition:Topics may have many partitions, so it can handle an arbitrary amount of data. Partition offset: Each partitioned message has a unique sequence id called as offset Replicas of partition: Replicas are nothing but backups of a partition. Replicas are never read or write data. They are used to prevent data loss. Brokers Brokers are simple system responsible for maintaining the pub-lished data. Each broker may have zero or more partitions per topic. Assume, if there are N partitions in a topic and N number of brokers, each broker will have one partition. Kafka main terminologies
- 10. The sample Producer is a classical Java application with a main() method, this application must: Initialize and configure a producer Use the producer to send messages 1- Producer Initialization Create a producer is quite simple, you just need to create an instance of the org.apache.kafka.clients.producer.KafkaProducer class with a set of properties, this looks like: producer = new KafkaProducer(properties); In this example, the configuration is externalized in a property file, with the following entries: Producer
- 11. Once you have a producer instance you can post messages to a topic using the ProducerRecord class. The ProducerRecord class is a key/value pair where: the key is the topic the value is the message As you can guess sending a message to the topic is straight forward: ... producer.send(new ProducerRecord("fast-messages", "This is a dummy message")); ... 2- Message posting
- 12. Once you are done with the producer use the producer.close() method that blocks the process until all the messages are sent to the server. This call is used in a finally block to guarantee that it is called. A Kafka producer can also be used in a try with resources construct. ... } finally { producer.close(); } Producer End
- 13. The Consumer class, like the producer is a simple Java class with a main method. This sample consumer uses the Hdr Histogram library to record and analyze the messages received from the fast-messages topic, and Jackson to parse JSON messages. Consumer
- 14. ZooKeeper is used for managing and coordinating Kafka broker. ZooKeeper service is mainly used to notify producer and consumer about the presence of any new broker in the Kafka system or failure of the broker in the Kafka system. As per the notification received by the Zookeeper regarding presence or failure of the broker then pro-ducer and consumer takes decision and starts coordinating their task with some other broker. Kafka stores basic metadata in Zookeeper such as information about topics, brokers, consumer offsets (queue readers) and so on. ZooKeeper