Apache Flink: An Overview of End-to-End Exactly-Once …
Flink’s support for end-to-end exactly-once semantics is not limited to Kafka and you can use it with any source / sink that provides the necessary coordination mechanism. For example, Pravega, an open-source streaming storage system from Dell/EMC, also supports end-to-end exactly-once semantics with Flink via the TwoPhaseCommitSinkFunction.
Flink’s support for end-to-end exactly-once semantics is not limited to Kafka and you can use it with any source / sink that provides the necessary coordination mechanism. For example, Pravega, an open-source streaming storage system from Dell/EMC, also supports end-to-end exactly-once semantics with Flink via the ` TwoPhaseCommitSinkFunction`.
Exactly once is one of the core features of Flink, spark and other stream processing systems. This semantics will ensure that every message is processed only once by the stream processing system. ” “Precise once” semantics is an important feature introduced in Flink version 1.4.0. Moreover, Flink claims to support “end-to-end precise once” …
Real-time Exactly-once ETL with Apache Flink. Apache Flink is another popular big data processing framework, which differs from Apache Spark in that Flink uses stream processing to mimic batch processing and provides sub-second latency along with exactly-once semantics. One of its use cases is to build a real-time data pipeline, move and transform …
Flink generates checkpoints on a regular, configurable interval. When a checkpoint is restored, Flink rolls back state to the position in the input stream that was last check-pointed (not necessarily the same as last processed/consumed). There are different ways to ensure exactly-once semantics.
Apache Spark, Apache Kafka, Apache Storm, Apache Hadoop, Apache Tomcat
