11/13/2023 0 Comments Job simulator update![]() ![]() ![]() You need to use specified partition key for the output. Upgrade CompatibilityLevel on JobConfig.json file. Input, output and each query step must use the same partition key.Īll inputs must have the same number of partitions.Īll inputs must use the same partition key. ![]() Input and output must have the same number of partitions. Here are the explanations for Enhancements: TypeĬhange input ‘xxx’ partition key to ‘xxx’. This feature helps you locate the query steps doing the aggregating. To map the processor with the query step, double select on the diagram. provide information on whether the Input and Output processors are aligned in parallel.find out what the Time shift is for each computing processor.observe how the input partitions are allocated and being processed at each streaming node.The processor-level diagram allows you to: Once you've adjusted the streaming units to simulate the topology of your job, you can expand any of the streaming nodes to observe how your data is being processed at the processor level. Adjust the streaming units to see how many SUs you needed for handling the workload.Select Enhancements to view the suggestions for improving the query parallelism.It simulates the job running topology based on your query and predefined streaming units. Go to your query editor in Azure portal and select Job simulation on the bottom pane.It gives you an idea of how many SUs you need to handle your workload. You can also adjust Streaming Units to stimulate how streaming nodes are allocated with different SUs. Then you can use Refresh simulation to get the new diagram. However, if you are using aggregate function among all partitions, having a parallel query might not be applicable for your scenarios.įor this example, you add the PartitionId to line#22 and save your change. These are edit suggestions for improving your query parallelism. You must include live input and output in the query.Configure live input and live output for your Stream Analytics job.If you haven't installed it yet, follow this guide to install. As an example, we're using a nonparallel job that takes the input data from an event hub and sends the results to another event hub. In this tutorial, you learn to improve query performance based on edit suggestions and make it executed in parallel. The Job simulation feature simulates how the job would be running topology in Azure. If you want to learn more about query parallelization, see Leverage query parallelization in Azure Stream Analytics. The query logic partitioning is determined by the keys used for aggregations (GROUP BY). It greatly reduces overall execution time of the query and hence less streaming hours are needed.įor a job to be parallel, all inputs, outputs and query steps must be aligned and use the same partition keys. Query parallelism divides the workload of a query by creating multiple processes (or streaming nodes) and executes it in parallel. You learn to visualize a query execution with different number of streaming units and improve query parallelism based on the edit suggestions. This article demonstrates how to use the Job Simulation in the Azure portal and Visual Studio Code (VS Code) to evaluate the query parallelism for a Stream Analytics job. One way to improve the performance of an Azure Stream Analytics (ASA) job is to apply parallelism in query. ![]()
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