Flink: Difference between revisions
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* [[Distributed_Systems#Distributed_Computation|Distributed Systems]] | * [[Distributed_Systems#Distributed_Computation|Distributed Systems]] | ||
* [[Stream Processing]] | |||
* [[Spark]] | * [[Spark]] | ||
* [[Beam]] | |||
* [[Hadoop]] | |||
* [[Flink on K8s Operator]] | |||
=Overview= | =Overview= | ||
Apache Flink is a data processing framework. It enables distributed data processing at scale. | Apache Flink is a fourth generation data processing framework. It enables distributed data processing at scale. It supports both [[System_Design#Batch_Processing|batch processing]] and [[System_Design#Stream_Processing|stream processing]] and it is designed for stream processing natively. Flink streaming seems to be superior to Spark streaming. It promotes continuous streaming where event computations are triggered as soon as the event is received. That means Flink processes each event in real-time and provides very low latency. | ||
=Organizatorium= | |||
* The concept of a Flink application. Needs to be packaged and deployed. | |||
* Flink operator. | |||
* A Flink operator state machine: https://github.com/lyft/flinkk8soperator/blob/master/docs/state_machine.md | |||
Latest revision as of 01:38, 10 December 2021
External
Internal
Overview
Apache Flink is a fourth generation data processing framework. It enables distributed data processing at scale. It supports both batch processing and stream processing and it is designed for stream processing natively. Flink streaming seems to be superior to Spark streaming. It promotes continuous streaming where event computations are triggered as soon as the event is received. That means Flink processes each event in real-time and provides very low latency.
Organizatorium
- The concept of a Flink application. Needs to be packaged and deployed.
- Flink operator.
- A Flink operator state machine: https://github.com/lyft/flinkk8soperator/blob/master/docs/state_machine.md