Utilizing Maximum Resources for Increasing Throughput in Azure Stream Analytics
Question
You have an Azure stream analytics environment.
You are working on tuning the stream analytics query to increase throughput for streaming analytics jobs.
You need to design the solution following the guide to scale your job to growing loads and utilize maximum resources.
Your queries are inherently fully parallelizable across input partitions.
What will be your first step in this design process?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: A.
There is a main point we should notice first.
The queries are inherently fully parallelizable across input partitions.
Based on this, we have to decide on the partition.
So, in this case, we need to use PARTITION BY keyword for authoring your query to be embarrassingly parallel first.
Option A is correct: It's the right solution.
Option B is incorrect: This works if your query is not embarrassingly parallel.
Option C is incorrect: This works if you are running lots of independent queries in a job.
Option D is incorrect: It is completely removing the partitioning which is not correct.
To know more, please refer to the docs below:
When working with Azure Stream Analytics, tuning the query is a critical aspect of increasing throughput for streaming analytics jobs. If the queries are inherently fully parallelizable across input partitions, then the first step in designing the solution to scale the job to growing loads and utilize maximum resources is to use the PARTITION BY keyword for authoring the query to be embarrassingly parallel.
Embarrassingly parallel refers to a type of parallel computing in which the input data is easily separable into independent subsets that can be processed in parallel. In Azure Stream Analytics, the PARTITION BY keyword enables you to partition the input data into independent subsets based on a specific field. Each partition can then be processed independently and in parallel, which can significantly increase throughput.
Therefore, using the PARTITION BY keyword in the query allows the data to be processed in parallel across multiple partitions. This can help to optimize performance and increase throughput for streaming analytics jobs, especially as the load on the system grows.
Option A is the correct answer as it suggests using the PARTITION BY keyword to partition the input data into independent subsets. Option B may be a good initial step to avoid partitioning complexity, but it does not make use of the full potential of parallel processing. Option C and D are incorrect as they suggest not using the PARTITION BY keyword and not partitioning the input data, respectively.
