The Hazelcast Jet DAG API plays a crucial role in modeling and executing data processing jobs within the Hazelcast Jet platform.
Let me break it down for you:
Pipeline Modeling :
* The Pipeline API in Hazelcast Jet represents a data processing job as a pipeline. This pipeline consists of interconnected stages.
* Each stage accepts events from the upstream stages, processes them, and passes the results downstream.
* The pipeline expresses the computation steps in a clear and structured manner.
Directed Acyclic Graph (DAG) :
* Hazelcast models your pipeline code into a directed acyclic graph (DAG).
* The DAG consists of stages, where each stage corresponds to a specific computation step.
* The stages are connected in a cascade, forming the overall data processing flow.
Transformation and Parallelization :
* To run a job, Hazelcast transforms the pipeline DAG into the core DAG.
* The top-level component responsible for this transformation is called the Planner.
* The Planner sets up several concurrent tasks, each receiving data from the previous task and emitting results to the next one.
* Lambda functions specified in the pipeline are applied as plug-ins to these tasks.
* The computation becomes amenable to auto-parallelization, allowing Hazelcast Jet to start multiple parallel tasks for a given step.
Example: Word Count Task :
* Let’s consider the Word Count problem, where we analyze input text lines and derive a histogram of word frequencies.
* The pipeline expression for Word Count involves several steps: reading from a text source, flat-mapping lines into words, filtering out empty words, grouping by word, aggregating by counting, and writing to a sink.
* Hazelcast Jet transforms this pipeline into a DAG, enabling parallel execution of these steps.
