Stream processing is used to do things like: This, in a rather crude nutshell, is stream processing. Maybe that stream we'll use for reporting, or driving another application that needs to respond to only red widgets events: We want to filter that stream based on a characteristic of the 'widget', and if it's red route it to another stream. Let's imagine we want to take this unbounded stream of events, perhaps its manufacturing events from a factory about 'widgets' being manufactured. An unbounded stream of events could be temperature readings from a sensor, network data from a router, order from an e-commerce system, and so on. Taking that unbounded stream of events, we often want to do something with it.

Stream Processing is based on the fundamental concept of unbounded streams of events (in contrast to static sets of bounded data as we typically find in relational databases). What we want to achieve is to add artificial delay between window and sink operators to postpone sink emition. It is located in one region (with a read replica in a different region)īecause we are using event time characteristics with 1 minute tumbling window all regions' sink emit their records nearly at the same time.It is hosted in AWS via RDS (currently it is a PostgreSQL).The exact same code is running in each region.It is hosted in AWS via Kinesis Data Analytics (KDA).The application emits the data into a Postgres sink.So, for each session we will have 1 computed record.The windowing is specified by a reduce and a process functions.The application has windowing with 1 minute tumbling window.

The application shards ( keyBy) events based on the sessionId field.

The application uses event time characteristics.

We have an Apache Flink application which processes events

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