A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream
Analytics cloud job to analyze the data. The cloud job is configured to use 120 Streaming Units (SU).
You need to optimize performance for the Azure Stream Analytics job.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
To optimize performance for an Azure Stream Analytics job, you should focus on enhancing parallel processing capabilities through partitioning. Implementing query parallelization by partitioning the data input allows the job to process multiple data partitions concurrently, improving throughput and efficient utilization of streaming units. Additionally, implementing query parallelization by partitioning the data output can distribute the processing load evenly across multiple nodes, reducing bottlenecks during data writing and further optimizing performance.
Partition input and output. REF: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
Agree. And partitioning Input and output with same number of partitions gives the best performance optimization..
No event consumer was mentioned. Therefore, partitioning output is not relevant. Answer is correct
Stream analytics ALWAYS has at least one output. There is no need to mention that. So correct answer is input and output
The stream analytics job is the consumer.
I would say DF is correct. Despite C being a correct option to optimize performance, we have no information about the output. If the output is Power BI, it does not support partition. Therefore we cannot state output partition without more information. Therefore best option will be SU
the question is about actions should you perform, in case of power bi output , we cannot partition the stream analytics output.SO D & F
Chatgpt say DF : The question in the image relates to optimizing the performance of an Azure Stream Analytics job. The correct actions would typically involve scaling the Streaming Units (SUs) appropriately based on the throughput needs and implementing query parallelization. In this context: - Scaling up the SU count (option D) would improve performance if the current SU allocation is insufficient. - Implementing query parallelization by partitioning the data input (option F) could also optimize performance as it would allow the job to process multiple data partitions concurrently.
An embarrassingly parallel job allows the highest degree of parallelization. Looking at how the max number of stream units is calculated, it would not be useful to scale them up if you keep a bottleneck at the output. Unsure what a good reference value would be for the number of SUs, but 120 does not seem very low to me.
Partitioning lets you divide data into subsets based on a partition key. If your input (for example Event Hubs) is partitioned by a key, it's highly recommended to specify this partition key when adding input to your Stream Analytics job. Scaling a Stream Analytics job takes advantage of partitions in the input and output. A Stream Analytics job can consume and write different partitions in parallel, which increases throughput. Ref: https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
As there is no indication of any query parallelization currently, we have to choose to parallelize for both input and output as the first/correct answers.
Scale the SU count for the job up - (ChatGPT) This will not necessarily improve the performance of your job, unless your query is CPU-bound or memory-bound. Scaling up the SU count will increase the amount of resources available for your job, but it will also increase the cost. You should first try to optimize your query by using parallelization and repartitioning techniques, and then scale up the SU count only if needed1
C. Implement query parallelization by partitioning the data output: "Partitioning lets you divide data into subsets based on a partition key. If your input (for example Event Hubs) is partitioned by a key, it's highly recommended to specify this partition key when adding input to your Stream Analytics job. Scaling a Stream Analytics job takes advantage of partitions in the input and output. A Stream Analytics job can consume and write different partitions in parallel, which increases throughput." D. Scale the SU count for the job up: "The total number of streaming units that can be used by a Stream Analytics job depends on the number of steps in the query defined for the job and the number of partitions for each step... All non-partitioned steps together can scale up to one streaming unit (SU V2s) for a Stream Analytics job. In addition, you can add 1 SU V2 for each partition in a partitioned step."
C and D
D. Scale the SU count for the job up: Increasing the number of Streaming Units (SUs) can improve the performance of the Stream Analytics job by providing more processing power to handle the incoming data stream. C. Implement query parallelization by partitioning the data output: Partitioning the data output can help distribute the processing load across multiple partitions, allowing for parallel execution of queries and enhancing performance.
Answer is D & F
It says optimize performance, does not say that it is bad so adding SU may be unneccesary cost increase. Parallelization and embarrassingly parallel job is correct
C and F > same partitions > embarrassingly parallel processing
ChatGPT 4o C. Implement query parallelization by partitioning the data output: Output Partitioning: By partitioning the data output, you can ensure that the processing load is distributed evenly across multiple nodes, which can significantly improve performance by reducing bottlenecks in data writing. F. Implement query parallelization by partitioning the data input: Input Partitioning: Partitioning the data input allows the Stream Analytics job to process different partitions in parallel, leading to better utilization of the available streaming units and improved throughput.
Partitioning on both input and output can help, but we don't know if the output is a service that doesn't support partitioning like Power BI. Scaling up will always assign more resources at least.