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Jun 29, 2024, 11:57:03 AM6/29/24

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Event streaming is the digital equivalent of the human body's central nervous system. It is the technological foundation for the 'always-on' world where businesses are increasingly software-defined and automated, and where the user of software is more software.

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Technically speaking, event streaming is the practice of capturing data in real-time from event sources like databases, sensors, mobile devices, cloud services, and software applications in the form of streams of events; storing these event streams durably for later retrieval; manipulating, processing, and reacting to the event streams in real-time as well as retrospectively; and routing the event streams to different destination technologies as needed. Event streaming thus ensures a continuous flow and interpretation of data so that the right information is at the right place, at the right time.

And all this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka can be deployed on bare-metal hardware, virtual machines, and containers, and on-premises as well as in the cloud. You can choose between self-managing your Kafka environments and using fully managed services offered by a variety of vendors.

Kafka is a distributed system consisting of servers and clients that communicate via a high-performance TCP network protocol. It can be deployed on bare-metal hardware, virtual machines, and containers in on-premise as well as cloud environments.

Servers: Kafka is run as a cluster of one or more servers that can span multiple datacenters or cloud regions. Some of these servers form the storage layer, called the brokers. Other servers run Kafka Connect to continuously import and export data as event streams to integrate Kafka with your existing systems such as relational databases as well as other Kafka clusters. To let you implement mission-critical use cases, a Kafka cluster is highly scalable and fault-tolerant: if any of its servers fails, the other servers will take over their work to ensure continuous operations without any data loss.

Clients: They allow you to write distributed applications and microservices that read, write, and process streams of events in parallel, at scale, and in a fault-tolerant manner even in the case of network problems or machine failures. Kafka ships with some such clients included, which are augmented by dozens of clients provided by the Kafka community: clients are available for Java and Scala including the higher-level Kafka Streams library, for Go, Python, C/C++, and many other programming languages as well as REST APIs.

An event records the fact that "something happened" in the world or in your business. It is also called record or message in the documentation. When you read or write data to Kafka, you do this in the form of events. Conceptually, an event has a key, value, timestamp, and optional metadata headers. Here's an example event:

Producers are those client applications that publish (write) events to Kafka, and consumers are those that subscribe to (read and process) these events. In Kafka, producers and consumers are fully decoupled and agnostic of each other, which is a key design element to achieve the high scalability that Kafka is known for. For example, producers never need to wait for consumers. Kafka provides various guarantees such as the ability to process events exactly-once.

Topics are partitioned, meaning a topic is spread over a number of "buckets" located on different Kafka brokers. This distributed placement of your data is very important for scalability because it allows client applications to both read and write the data from/to many brokers at the same time. When a new event is published to a topic, it is actually appended to one of the topic's partitions. Events with the same event key (e.g., a customer or vehicle ID) are written to the same partition, and Kafka guarantees that any consumer of a given topic-partition will always read that partition's events in exactly the same order as they were written.

To make your data fault-tolerant and highly-available, every topic can be replicated, even across geo-regions or datacenters, so that there are always multiple brokers that have a copy of the data just in case things go wrong, you want to do maintenance on the brokers, and so on. A common production setting is a replication factor of 3, i.e., there will always be three copies of your data. This replication is performed at the level of topic-partitions.

In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strongdurability guarantees Kafka provides.In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ orRabbitMQ.Website Activity TrackingThe original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds.This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type.These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop oroffline data warehousing systems for offline processing and reporting.Activity tracking is often very high volume as many activity messages are generated for each user page view.MetricsKafka is often used for operational monitoring data.This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.Log AggregationMany people use Kafka as a replacement for a log aggregation solution.Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing.Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages.This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption.In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication,and much lower end-to-end latency.Stream ProcessingMany users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and thenaggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing.For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic;further processing might normalize or deduplicate this content and publish the cleansed article content to a new topic;a final processing stage might attempt to recommend this content to users.Such processing pipelines create graphs of real-time data flows based on the individual topics.Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streamsis available in Apache Kafka to perform such data processing as described above.Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm andApache Samza.Event SourcingEvent sourcing is a style of application design where state changes are logged as atime-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.Commit LogKafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncingmechanism for failed nodes to restore their data.The log compaction feature in Kafka helps support this usage.In this usage Kafka is similar to Apache BookKeeper project. 1.3 Quick Start /*Licensed to the Apache Software Foundation (ASF) under one or morecontributor license agreements. See the NOTICE file distributed withthis work for additional information regarding copyright ownership.The ASF licenses this file to You under the Apache License, Version 2.0(the "License"); you may not use this file except in compliance withthe License. You may obtain a copy of the License at -2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.*/// Define variables for doc templatesvar context= "version": "37", "dotVersion": "3.7", "fullDotVersion": "3.7.0", "scalaVersion": "2.13"; Step 1: Get Kafka Download the latest Kafka release and extract it:

Example events are payment transactions, geolocation updates from mobile phones, shipping orders, sensor measurements from IoT devices or medical equipment, and much more. These events are organized and stored in topics. Very simplified, a topic is similar to a folder in a filesystem, and the events are the files in that folder.

All of Kafka's command line tools have additional options: run the kafka-topics.sh command without any arguments to display usage information. For example, it can also show you details such as the partition count of the new topic:

Because events are durably stored in Kafka, they can be read as many times and by as many consumers as you want. You can easily verify this by opening yet another terminal session and re-running the previous command again.

You probably have lots of data in existing systems like relational databases or traditional messaging systems, along with many applications that already use these systems. Kafka Connect allows you to continuously ingest data from external systems into Kafka, and vice versa. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. It is thus very easy to integrate existing systems with Kafka. To make this process even easier, there are hundreds of such connectors readily available.