Learn how data engineers can reduce latency and improve performance of Databricks SQL queries running on always-on endpoints used by multiple users. Discover the most effective approach to scale endpoints.
Table of Contents
Question
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.
Which approach can the data engineering team use to improve the latency of the team’s queries?
A. They can increase the cluster size of the SQL endpoint.
B. They can increase the maximum bound of the SQL endpoint’s scaling range.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can turn on the Serverless feature for the SQL endpoint.
Answer
The most effective approach the data engineering team can use to improve the latency of the data analysis team’s queries is:
B. They can increase the maximum bound of the SQL endpoint’s scaling range.
Explanation
The key issue is that many users on the data analysis team are running small queries simultaneously on the same always-on SQL endpoint. This is causing slow performance and high latency.
Increasing the maximum bound of the SQL endpoint’s scaling range is the best solution. SQL endpoints have a Min/Max scale range that controls the number of cluster nodes. Queries are automatically distributed across the nodes.
By increasing the maximum bound, the endpoint can automatically scale up to more nodes when there is high concurrency from multiple users running queries at the same time. More nodes allows the workload to be distributed and parallelized across a larger cluster, improving performance.
The other options are not as effective for this scenario:
A) Increasing cluster size sets a fixed, static cluster size. This doesn’t help automatically scale when load increases.
C) Turning on Auto Stop terminates the cluster when not in use. It doesn’t help with scalability under high load.
D) Turning on Serverless mode decouples compute from storage, but doesn’t necessarily add more nodes to handle increased concurrency.
Therefore, increasing the max scale range is the recommended approach to dynamically scale up the endpoint and improve query performance when multiple users are running concurrent queries.
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