A fast, scalable, fault-tolerant messaging system
Apache™ Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system. Kafka is often used in place of traditional message brokers like JMS and AMQP because of its higher throughput, reliability and replication.
Kafka works in combination with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data. Kafka can message geospatial data from a fleet of long-haul trucks or sensor data from heating and cooling equipment in office buildings. Whatever the industry or use case, Kafka brokers massive message streams for low-latency analysis in Enterprise Apache Hadoop.
Apache Kafka supports a wide range of use cases as a general-purpose messaging system for scenarios where high throughput, reliable delivery, and horizontal scalability are important. Apache Storm and Apache HBase both work very well in combination with Kafka. Common use cases include:
Some of the important characteristics that make Kafka such an attractive option for these use cases include the following:
Kafka’s system design can be thought of as that of a distributed commit log, where incoming data is written sequentially to disk. There are four main components involved in moving data in and out of Kafka:
In Kafka, a Topic is a user-defined category to which messages are published. Kafka Producers publish messages to one or more topics and Consumers subscribe to topics and process the published messages. Finally, a Kafka cluster consists of one or more servers, called Brokers that manage the persistence and replication of message data (i.e. the commit log).
One of the keys to Kafka’s high performance is the simplicity of the brokers’ responsibilities. In Kafka, topics consist of one or more Partitions that are ordered, immutable sequences of messages. Since writes to a partition are sequential, this design greatly reduces the number of hard disk seeks (with their resulting latency).
Another factor contributing to Kafka’s performance and scalability is the fact that Kafka brokers are not responsible for keeping track of what messages have been consumed – that responsibility falls on the consumer. In traditional messaging systems such as JMS, the broker bore this responsibility, severely limiting the system’s ability to scale as the number of consumers increased.
For Kafka consumers, keeping track of which messages have been consumed (processed) is simply a matter of keeping track of an Offset, which is a sequential id number that uniquely identifies a message within a partition. Because Kafka retains all messages on disk (for a configurable amount of time), consumers can rewind or skip to any point in a partition simply by supplying an offset value. Finally, this design eliminates the potential for back-pressure when consumers process messages at different rates.
Hortonworks is working to make Kafka easier for enterprises to use . New focus areas include creation of a Kafka Admin Panel to create/delete topics and manage user permissions, easier and safer distribution of security tokens and support for multiple ways of publishing/consuming data via a Kafka REST server/API.
|Apache Kafka Version||Enhancements||HDP Version||HDF Version|
Introduction Hadoop has always been associated with BigData, yet the perception is it’s only suitable for high latency, high throughput queries. With the contribution of the community, you can use Hadoop interactively for data exploration and visualization. In this tutorial you’ll learn how to analyze large datasets using Apache Hive LLAP on Amazon Web Services […]
A very common request from many customers is to be able to index text in image files; for example, text in scanned PNG files. In this tutorial we are going to walkthrough how to do this with SOLR. Prerequisites Download the Hortonworks Sandbox Complete the Learning the Ropes of the HDP Sandbox tutorial. Step-by-step guide […]
Introduction In this tutorial, you will learn about the different features available in the HDF sandbox. HDF stands for Hortonworks DataFlow. HDF was built to make processing data-in-motion an easier task while also directing the data from source to the destination. You will learn about quick links to access these tools that way when you […]
Introduction JReport is a embedded BI reporting tool can easily extract and visualize data from the Hortonworks Data Platform 2.3 using the Apache Hive JDBC driver. You can then create reports, dashboards, and data analysis, which can be embedded into your own applications. In this tutorial we are going to walkthrough the folllowing steps to […]
The Hortonworks Sandbox is delivered as a Dockerized container with the most common ports already opened and forwarded for you. If you would like to open even more ports, check out this tutorial.
Introduction R is a popular tool for statistics and data analysis. It has rich visualization capabilities and a large collection of libraries that have been developed and maintained by the R developer community. One drawback to R is that it’s designed to run on in-memory data, which makes it unsuitable for large datasets. Spark is […]
Apache Zeppelin on HDP 2.4.2 Author: Vinay Shukla In March 2016 we delivered the second technical preview of Apache Zeppelin, on HDP 2.4. Meanwhile we and the Zeppelin community have continued to add new features to Zeppelin. These features are now available in the final technical preview of Apache Zeppelin. This technical preview works with […]
Welcome to the Hortonworks Sandbox! Look at the attached sections for sandbox documentation.
Apache, Hadoop, Falcon, Atlas, Tez, Sqoop, Flume, Kafka, Pig, Hive, HBase, Accumulo, Storm, Solr, Spark, Ranger, Knox, Ambari, ZooKeeper, Oozie, Phoenix, NiFi, Nifi Registry, HAWQ, Zeppelin, Slider, Mahout, MapReduce, HDFS, YARN, Metron and the Hadoop elephant and Apache project logos are either registered trademarks or trademarks of the Apache Software Foundation in the United States or other countries.