Discover the crucial metric to monitor for optimizing Azure Synapse Analytics performance and troubleshooting slow query execution. Learn how to effectively utilize resources and ensure optimal data warehouse performance.
Table of Contents
Question
You manage an enterprise data warehouse in Azure Synapse Analytics.
Users report slow performance when they run commonly used queries. Users do not report performance changes for infrequently used queries.
You need to monitor resource utilization to determine the source of the performance issues.
Which metric should you monitor?
A. DWU percentage
B. Cache hit percentage
C. Data Warehouse Units (DWU) used
D. Data IO percentage
Answer
B. Cache hit percentage
Explanation
When users report slow performance for commonly used queries but not for infrequently used queries, monitoring the cache hit percentage is the most effective way to determine the source of the performance issues.
In Azure Synapse Analytics, the cache hit percentage metric indicates the percentage of data reads served from the cache rather than from storage. A higher cache hit percentage generally leads to better query performance, as reading data from the cache is significantly faster than reading from storage.
If the cache hit percentage is low for frequently used queries, it suggests that the data is not being effectively cached, resulting in slower query execution. This could be due to insufficient cache capacity, suboptimal query design, or data changes that invalidate the cached data.
By monitoring the cache hit percentage, you can identify whether the performance issues are related to caching. If the percentage is low, you can take steps to optimize query design, increase cache capacity, or implement more efficient caching strategies to improve performance for commonly used queries.
Other metrics like DWU percentage, Data Warehouse Units (DWU) used, and Data IO percentage, while important for overall resource utilization and performance monitoring, do not specifically address the scenario of slow performance for frequently used queries compared to infrequently used queries.
Therefore, monitoring the cache hit percentage is the most appropriate metric to determine the source of the performance issues in this scenario.
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