To ensure that your Azure Stream Analytics (ASA) jobs are running at peak performance, it's important to have a deep understanding of your job's execution plan and closely monitor the allocation of computing resources in the streaming nodes. In this blog post, we'll explore some techniques using the physical job diagram to enhance your job and take your stream analytic skills to the next level.
Understanding the Physical Job Diagram
The physical job diagram is a feature in the ASA portal that provides a visual representation of your Stream Analytics job's running topology. It displays the relationships between all the streaming nodes and gives you an idea of how the computing resources (or streaming units) are utilized. The diagram is a useful tool for identifying optimization opportunities and improving the overall performance of your job.
Importance of a Parallel Job
A parallel job is the most scalable scenario in Azure Stream Analytics. This approach ensures that the computing resources in the streaming nodes are fully utilized and enables timely processing of your data. For a job to be parallel, partition keys need to be aligned between all inputs, all query logic steps and all outputs.
Comparing Job Topology: Parallel vs. Non-parallel Job
- Parallel job
- Non-parallel job
How to use the Physical Job Diagram?
Learn to optimize your Azure Stream Analytics job, examine parallelism, and gain valuable insights at the streaming node level. Watch the video below and take your stream analytics skills to the next level!
For more information about the physical job diagram and query parallelization, please visit: