100 Million members in over 190 countries leads to more than 1 Trillion events and 3 PB of data flowing through Netflix’s real-time data infrastructure each day. We’ve built a data pipeline in the cloud that reliably collects and routes these events to a variety of sinks. The data in these events are are used in several ways; from personalizing the customer experience to business intelligence.

The windowing capabilities offered by most stream processing engines are limited to aligned windows of a fixed duration. However, many real-world event processing use cases don’t fit this rigid structure, resulting in awkward processing pipelines. There haven’t been good alternatives, until recently that is. Apache Flink* offers a rich Window API that supports implementing unaligned windows of varying duration. In this talk, Matt Zimmer will discuss using this API to aggregate events into windows customized along varying definitions of a session. He will talk about implementation details such as:

• Handling out-of-order events
• Limiting state build-up while aggregating a subset of events from an event stream
• Periodically emitting early results
• Creating windows bounded by a type of event

Attendees will leave this talk with practical techniques and knowledge to implement their own custom windows in Apache Flink.

* Apache Flink (https://flink.apache.org/) is an open-source stream processing framework for distributed, high-performing, always-available, and accurate data streaming applications.