Session windows provide the ability to aggregate data for logical sessions in a data stream. Learn how session windows can be used to aggregate information on specific keys in the stream.
- [Narrator] In this video, I will show you … the use of session windows on a partitioned data stream. … We continue with the windowing operations class … as in the previous video. … In this example, we will partition the stream by user. … We will create session windows with a gap … of five seconds for each user. … This means that if a given user … does not have an event within five seconds, … the current session ends … and the next one will start. … We start with a map function to create a tuple … of the user, a count of one … and then minimum and maximum time stamps. … We also specify the return types. … Then we use a keyBy function … to specify that this stream needs … to be partitioned by the user column. … To create a session window, … we will use the window function … and specify that we will use a … processing time-based session window … with a gap of five seconds. … Processing time means that the timestamp … on the Flink processor, will be used … as a timestamp for windowing. …
- Streaming with Apache Flink
- Using the DataStream API for basic stream processing
- Working with process functions
- Windowing and joins
- Setting up event-time processing
- State management in Flink
Skill Level Advanced
1. Apache Flink
2. DataStream API
4. Event Time Processing
5. State Management
6. Use Case Project
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.