Improve Performance and Decrease Costs of Amazon Redshift Cluster
Question
MindPyramid Limited is a multinational information technology and outsourcing company headquartered in Vizag, India and New Jersey, USA.
Founded in 2003, the company employs approximately 2000 employees.
The company offers consulting services in cloud computing, big data and analytics.
They offer services to major cloud providers including AWS.
The team is working with one of the major clients having their infrastructure build on AWS.
Currently the client is having lot of performance issues and wants to have a detailed understanding and recommendations.
MindPyramid runs Amazon Redshift Advisor to provide recommendations to improve the performance and decrease the operating costs of the Amazon Redshift cluster.
Identify the recommendations.
select 3 options.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E. F. G.Answer : A, B, F.
Option A is correct - rebuild any tables that contain uncompressed columns that are both large and frequently accessed.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationOption B is correct -it enables you to set the size for each cluster for the storage, cost, and performance needs of each workload.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationOption C is incorrect -Consider reducing the configured slot count for queues where the slots have never been fully utilized.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationOption D is incorrect -recommend running ANALYZE whenever a significant number of new data rows are loaded into an existing table with COPY or INSERT commands.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationOption E is incorrect - Modify the WLM configuration to enable SQA.
Amazon Redshift uses a machine learning algorithm to analyze each eligible query.
Predictions improve as SQA learns from your query patterns.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationOption F is correct -Use the column ENCODE parameter when creating any tables that you load using the COPY command or Disable compression altogether by supplying the COMPUPDATE OFF parameter in the COPY command.
https://docs.aws.amazon.com/redshift/latest/dg/advisor-recommendations.html#skip-compression-analysis-recommendationSure, I'd be happy to provide a detailed explanation for the recommendations given in the exam question:
A. Addressing uncompressed storage for a single table is a one-time optimization that requires the table to be rebuilt: This recommendation is suggesting that if there is a table in the Amazon Redshift cluster that is using uncompressed storage, it should be optimized by rebuilding it with compressed storage. This can help reduce the storage size and improve query performance. However, it is important to note that this is a one-time optimization and requires the table to be rebuilt.
B. Consider moving each actively queried database to a separate dedicated cluster. Using a separate cluster can reduce resource contention and improve query performance: This recommendation is suggesting that if there are multiple databases in the Amazon Redshift cluster and some are more actively queried than others, it may be beneficial to move the actively queried databases to a separate dedicated cluster. This can help reduce resource contention and improve query performance for those databases.
C. Increasing the configured slots to match peak workload requirements redistributes underutilized memory to active slots: This recommendation is suggesting that if the Amazon Redshift cluster is experiencing performance issues during peak workloads, it may be beneficial to increase the number of configured slots to match the peak workload requirements. This can help redistribute underutilized memory to active slots and improve query performance.
D. Whenever table content changes significantly, update statistics with VACCUM: This recommendation is suggesting that if there are significant changes in the content of a table in the Amazon Redshift cluster, it may be beneficial to update the statistics using the VACCUM command. This can help ensure that the query optimizer has up-to-date statistics to make optimal query plans and improve query performance.
E. Modify the WLM configuration to disable SQ: This recommendation is suggesting that the Workload Management (WLM) configuration for the Amazon Redshift cluster should be modified to disable the Short Query Acceleration (SQ) feature. SQ is designed to accelerate short-running queries, but it can result in resource contention and negatively impact longer-running queries. Disabling SQ can help improve query performance for longer-running queries.
F. Amazon Redshift uses a machine learning algorithm to analyze each eligible query: This recommendation is providing information about how Amazon Redshift works. Amazon Redshift does use a machine learning algorithm to analyze each eligible query and optimize query plans for improved performance.
G. Skip compression analysis using COPY: This recommendation is suggesting that when using the COPY command to load data into the Amazon Redshift cluster, it may be beneficial to skip the compression analysis step. This can help reduce the time it takes to load data and improve query performance.
Overall, these recommendations are designed to address specific performance issues and improve query performance and operating costs for the Amazon Redshift cluster. However, it is important to note that not all recommendations may be applicable or appropriate for every situation and should be carefully evaluated before implementation.
