Kafka + Spark Streaming

Kunal Khamar, et al, show how to integrate Apache Kafka with Spark’s structured streaming:

Kafka is a distributed pub-sub messaging system that is popular for ingesting real-time data streams and making them available to downstream consumers in a parallel and fault-tolerant manner. This renders Kafka suitable for building real-time streaming data pipelines that reliably move data between heterogeneous processing systems. Before we dive into the details of Structured Streaming’s Kafka support, let’s recap some basic concepts and terms.

Data in Kafka is organized into topics that are split into partitions for parallelism. Each partition is an ordered, immutable sequence of records, and can be thought of as a structured commit log. Producers append records to the tail of these logs and consumers read the logs at their own pace. Multiple consumers can subscribe to a topic and receive incoming records as they arrive. As new records arrive to a partition in a Kafka topic, they are assigned a sequential id number called the offset. A Kafka cluster retains all published records—whether or not they have been consumed—for a configurable retention period, after which they are marked for deletion.

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