Aspire     Pythonista to Python Master     Python Master Track 5: Resource Optimization with Python

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Faust streams support a wide range of operations. In this course, you'll learn how to perform several of these. You'll also work with Faust tables - which store state in the form of key-value pairs and allow for the recovery of failed processing, making Faust fault-tolerant.
You'll start off by using the group by operation to designate a key used to repartition an input stream and create a new topic in Kafka. You'll then use the items() operation to access the key and message value and take() operation to buffer multiple elements in a stream.
Next, you'll work with tables to conduct stateful stream processing, illustrating how table data is stored in an embedded RocksDB database.
When you've finished this course, you'll be able to apply a wide range of operations on input streams and perform stateful stream processing using tables.

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Objectives

Faust: Performing Operations & Maintaining State Using Tables discover the key concepts covered in this course perform group-by operations on streams perform group-by and items operations on streams access raw events and buffer events before performing operations enumerate entities in a stream with a single agent and with multiple agents forward messages to destination topics filter stream records perform word count using tables save table state to an embedded RocksDB database perform grouping operations and understand table sharding compute aggregations on streaming data perform multiple grouping operations with the right agent structure process streaming elements using multiple worker instances recall the different kinds of sinks that can be used with a Faust agent summarize the key concepts covered in this course