4 Pulsar Functions

In our previous chapter, we started to see how you can work with Pulsar using some of the various client libraries. In this chapter we will look at a stream native processing engine known as Pulsar Functions that makes the development of Pulsar-based applications much simpler. This lightweight processing framework automatically handles a lot of the boilerplate coding required to setup Pulsar consumers and producers, allowing you to focus on the processing logic itself rather than the consumption and processing of the messages.

4.1      Stream Native Processing

Included with Apache Pulsar is a lightweight computing engine named “Pulsar Functions” which allows developers to deploy a simple function implementation in Java or Python. This feature allows users to enjoy the benefits of serverless computing, similar to AWS Lambda computing within an open-source messaging platform without being tied to a proprietary cloud providers API.

4.1.1      Traditional Batching

4.1.2      Micro-Batching

4.1.3      Stream Processing

4.2      What are Pulsar Functions?

4.2.1      Programming Model

4.3      Developing Pulsar Functions

4.3.1      Native Functions

4.3.2      The Pulsar SDK

4.3.3      Stateful Functions

4.4      Testing Pulsar Functions

4.4.1      Unit Testing

4.4.2      Integration Testing

4.5      Deploying Pulsar Functions

4.5.1      Generating a Deployment Artifact

4.5.2      Configuration and Deployment

4.5.3      The Function Deployment Lifecycle

4.5.4      Deployment Modes

4.5.5      Pulsar Function Data Flow

4.6      Summary