Fault Tolerance and Error Handling Techniques in Apache Kafka

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform; it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application.