The biggest difference between the two systems with respect to distributed coordination is that Flink has a dedicated master node for coordination, while the Streams API relies on the Kafka broker for distributed coordination and fault tolerance, via the Kafka’s consumer group protocol.
Latency – No doubt Flink is much faster due to it’s architecture and cluster deployment mechanism, Flink throughput in the order of tens of millions of events per second in moderate clusters, sub-second latency that can be as low as few 10s of milliseconds.
Flink is a distributed processing engine and a scalable data analytics framework. You can use Flink to process data streams at a large scale and to deliver real-time analytical insights about your processed data with your streaming application.
When comparing the streaming capability of both, Flink is much better as it deals with streams of data, whereas Spark handles it in terms of micro-batches. Through this article, the basics of data processing were covered, and a description of Apache Flink and Apache Spark was also provided.
Apache Kafka Connector. Flink provides an Apache Kafka connector for reading data from and writing data to Kafka topics with exactly-once guarantees.
Flink does support stateful and stateless operations on the DataStream. … Flink does provide transparent state management for it’s users. It has quite robust stateful stream processing capabilities. It does provide ease of use, high efficiency and high reliability for the state management.
Why Kafka is better than RabbitMQ?
Kafka offers much higher performance than message brokers like RabbitMQ. It uses sequential disk I/O to boost performance, making it a suitable option for implementing queues. It can achieve high throughput (millions of messages per second) with limited resources, a necessity for big data use cases.
Is Pulsar better than Kafka?
Pulsar is much faster and supports geo-replication
Pulsar is much faster than Kafka, thanks to its capability to deliver higher throughput with more consistent, significantly lower latency. However, the thing that really separates Pulsar from Kafka is one of its top-class features – geo-replication.
What is KSQL?
Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka®. It provides an easy-to-use, yet powerful interactive SQL interface for stream processing on Kafka, without the need to write code in a programming language such as Java or Python.
1. Facilitate simultaneous streaming and batch processing. As creators Fabian Hueske and Aljoscha Krettek explain in a DZone post, Flink is built around the idea of “streaming first, with batch as a special case of streaming.” This, in turn, reduces the complexity of data infrastructure.
Apache Flink is an excellent choice to develop and run many different types of applications due to its extensive features set. Flink’s features include support for stream and batch processing, sophisticated state management, event-time processing semantics, and exactly-once consistency guarantees for state.
Companies Currently Using Apache Flink
|Company Name||Website||Sub Level Industry|
|Apple||apple.com||General Interconnection Products & Services|
|Citigroup||citigroup.com||General Financial Services & Insights|
|Tableau Software||tableau.com||Software Manufacturers|
Try to avoid using Flink and go for other options when: You need a more matured framework compared to other competitors in the same space. You need more API support apart from the Java and Scala languages.