Window Functions

Flink: Implementing the Count Window

September 10, 2020September 10, 2020Apache Flink, Big Data and Fast Data, Flink, Functional Programming, Java#apache flink, Big Data, Big Data Analytics, DataStream API, Flink Streaming, Window Functions

Reading Time: 3 minutes In the blog, we learned about Tumbling and Sliding windows which is based on time. In this blog, we are going to learn to define Flink’s windows on other properties i.e Count window. As the name suggests, count window is evaluated when the number of records received, hits the threshold. Count window set the window size based on how many entities exist within that window. For example, if we fixed the count Continue Reading

Flink: Time Windows based on Processing Time

September 9, 2020October 22, 2020Apache Flink, Big Data and Fast Data, Flink, Java, ML, AI and Data Engineering#apache flink, #apache-flink, #flink, DataStream API, Flink, Flink Streaming, real time streaming data, Stream Processing, Window Functions

Reading Time: 4 minutes In the previous blog, we talked about Flink’s windows operator, a heart of processing infinite streams. Generally in Flink, after specifying that the stream is keyed or non keyed, the next step is to define a window assigner. The window assigner defines how elements are assigned to windows. Flink provides some useful predefined window assigners like Tumbling windows, Sliding windows, Session windows, Count windows, and Continue Reading