In a previous blog post, we presented how Flink’s network stack works from the high-level abstractions to the low-level details. This second post discusses monitoring network-related metrics to identify backpressure or bottlenecks in throughput and latency.
Apache Flink has multiple types of operator state, one of which is called Broadcast State. In this post, we explain what Broadcast State is, and show an example of how it can be applied to an application that evaluates dynamic patterns on an event stream.
Flink’s network stack is one of the core components that make up Apache Flink's runtime module sitting at the core of every Flink job. In this post, which is the first in a series of posts about the network stack, we look at the abstractions exposed to the stream operators and detail their physical implementation and various optimisations in Apache Flink.