mxmxm

Not quite. All of these are open-source projects but they are very different in many aspects:

* Apache Flink

Sophisticated stream processing framework with focus on robustness (managed memory) and correctness (exactly-once semantics)

* Apache Flume

Tailored towards log data.

* Apache Storm

First stream processing framework. Legacy.

* Apache Samza

Only used at LinkedIn. Tight to Hadoop's YARN.

* Apache Spark

Only great in batch processing.

* Apache Apex

Dead project. Tight to Hadoop's YARN.

* Apache Kafka

A distributed message queue with simple stream processing built on top via the Confluent Platform.

Counting views/impressions in combination with Apache Kafka sounds like the ideal use case for a stream processor like Apache Flink. It supports very large state which can be managed off-hand. This should enable you to count the exact number of unique views in real time with exactly once semantics. Here is a blog post on large scale counting with more details. It also includes a comparison with other streaming technologies like Sanza and Spark: https://data-artisans.com/blog/counting-in-streams-a-hierarc...

Also check out this blog post by a Twitter engineer on counting ad impressions: https://data-artisans.com/blog/extending-the-yahoo-streaming...