By Nishant Garg
Message publishing is a mechanism of connecting heterogeneous purposes including messages which are routed among them, for instance through the use of a message dealer like Apache Kafka. Such strategies care for real-time volumes of knowledge and course it to a number of shoppers with no letting details manufacturers comprehend who the ultimate shoppers are.
Apache Kafka is a realistic, hands-on consultant giving you a sequence of step by step sensible implementations, on the way to assist you make the most of the true energy at the back of Kafka, and provides you a powerful grounding for utilizing it on your publisher-subscriber dependent architectures.
Apache Kafka takes you thru a few transparent, useful implementations to help you to exploit the ability of Apache Kafka, quick and painlessly. you'll research every little thing you must be aware of for constructing Kafka clusters. This ebook explains how Kafka simple blocks like manufacturers, agents, and shoppers truly paintings and healthy jointly. you are going to then discover extra settings and configuration alterations to accomplish ever extra complicated objectives. ultimately you'll find out how Kafka works with different instruments like Hadoop, typhoon, and so on.
You will research every thing you must be aware of to paintings with Apache Kafka within the correct structure, in addition to find out how to leverage its strength of dealing with 1000s of megabytes of messages according to moment from a number of consumers.
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Extra resources for Apache Kafka
When a new process is started with the existing consumer group name, Kafka triggers rebalance between the new and existing process threads for the consumer group. Post rebalance, some of the messages that are intended for a new process may go to an old process, causing unexpected results. To avoid this ambiguous behavior, any existing consumers should be shut down before starting new consumers for an existing consumer group name. KafkaStream class are returned by the ConsumerConnector implementation.
Kafka does not have any concept of a master and treats all the brokers as peers. This approach facilitates addition and removal of a Kafka broker at any point, as the metadata of brokers are maintained in ZooKeeper and shared with producers and consumers. x, ZooKeeper-based load balancing allows producers to discover the broker dynamically. A producer maintains a pool of broker connections, and constantly updates it using ZooKeeper watcher callbacks. x, load balancing is achieved through Kafka metadata API and ZooKeeper can only be used to identify the list of available brokers.
In Kafka, message partitioning strategy is used at the Kafka broker end. The decision about how the message is partitioned is taken by the producer, and the broker stores the messages in the same order as they arrive. The number of partitions can be configured for each topic within the Kafka broker. 8. Though Kafka is highly scalable, for better durability of messages and high availability of Kafka clusters, replication guarantees that the message will be published and consumed even in case of broker failure, which may be caused by any reason.