Resolving Slow Performance Issues with Redshift Cluster Queries
Question
A company has a Redshift cluster defined in AWS.
Different departments currently have tables defined in the cluster.
Some of the users are complaining on slow performance for queries fired against the tables in the cluster.
After careful investigation it seems that some long running queries are consuming resources are not allowing other queries to run efficiently.
Which of the following would have the LEAST impact and also ensure that the issue can be resolved?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - B.
The AWS Documentation mentions the following.
When you have multiple sessions or users running queries at the same time, some queries might consume cluster resources for long periods of time and affect the performance of other queries.
For example, suppose one group of users submits occasional complex, long-running queries that select and sort rows from several large tables.
Another group frequently submits short queries that select only a few rows from one or two tables and run in a few seconds.
In this situation, the short-running queries might have to wait in a queue for a long-running query to complete.
You can improve system performance and your users' experience by modifying your WLM configuration to create separate queues for the long-running queries and the short-running queries.
At run time, you can route queries to these queues according to user groups or query groups.
Option A is invalid since this would require a completely new setup.
Option C is invalid since this is not the underlying reason for the issue.
Option D is invalid since this is used for querying nested data in Parquet, ORC, JSON, and Ion file formats.
For more information on implementing work load management, please refer to the below URL.
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.htmlThe correct answer to the question is B. Modify the WLM configuration for the cluster.
Amazon Redshift is a powerful data warehousing solution that allows businesses to store and analyze large amounts of data efficiently. In Redshift, a workload management (WLM) configuration controls the way queries are executed and resources are allocated. Each query that is executed in Redshift is assigned to a queue and the WLM configuration determines how much memory and CPU resources are allocated to each queue.
In this scenario, the issue seems to be that some long-running queries are consuming resources and not allowing other queries to run efficiently. This is a common problem in Redshift and can be resolved by modifying the WLM configuration for the cluster.
Option A, creating a new cluster for slow running queries, is not the best solution because it will require additional resources and could lead to further fragmentation of data. It would also not necessarily solve the problem of long-running queries consuming resources.
Option C, disabling any cross-region snapshots for the cluster, is not a relevant solution to the problem at hand. Cross-region snapshots are used for data backup and recovery purposes, and disabling them would not address the issue of long-running queries.
Option D, querying data using Amazon Redshift Spectrum, is not the best solution for this problem because it does not address the issue of long-running queries. Redshift Spectrum allows users to query data stored in Amazon S3 using SQL syntax, but it does not directly address the performance issue caused by long-running queries in the Redshift cluster.
Therefore, the best solution is to modify the WLM configuration for the cluster to allocate more resources to short-running queries and limit the resources available to long-running queries. This will ensure that all queries run efficiently and the resources are utilized effectively.
