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10 months ago Use Cases
[ Messaging ] Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc). In comparison to most messaging systems Kafka has better throuhput, build-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications. In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong durability guarantees Kafka provides. In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or RabbitMQ
[ Website Activity Tracking ] The 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 or offline data warehousing systems for offline processing and reporting. Activity tracking is often very high volums as many acvitivy messages are generated for each user page view.
[ Metrics ] Kafka is often used for operational monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of perational data.
[ Log Aggregation ] Many people use Kafka as a replacement for a log aggregation solution. Log aggregation typically collects physical log files off servers and puts then in a central place (a file server of 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-laytency 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-toend latency.
[ Stream Processing ] Many users of Kafka process data in procesisng pipelines consisting of multiple stages, whrer raw input data is consumed from Kafka topics and then aggregated, 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 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streams is available in Apache Kafka to perfrm such data processing as described above. Apart from Kafka Streams, alternative open source stream processing tools include Apache Strom and Apache Samza.
[ Event Sourcing ] Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application build in this style.
Event Sourcing ensures that all changes to application state are stored as a sequence of events. .. The fundamental idea of Event Sourcing is that of ensuring every chante to the state of events... fore more. see https://martinfowler.com/eaaDev/EventSourcing.html
[ Commit Log ] Kafka 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-syncing mechanism fro failed nodes to restore their data. The log campaction feature in Kafka helps support this usage. In this usage Kafka is simliar to Apache BookKeepr project
Apache Kafka document
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