How to Optimize Queries on New Data in Redshift
Question
A company has an existing Redshift table which contains all the order information for a product for historical analysis.
Currently the timestamp on the table is being used as the sort key.
More batches of data were uploaded on the table, but the performance of the queries on the new batches of data are not up to the mark as the prior queries.
What needs to be done to ensure that the queries on the new data is optimized?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - D.
The AWS Documentation mentions the following.
For tables with a sort key, the VACUUM command ensures that new data in tables is fully sorted on disk.
When data is initially loaded into a table that has a sort key, the data is sorted according to the SORTKEY specification in the CREATE TABLE statement.
However, when you update the table, using COPY, INSERT, or UPDATE statements, new rows are stored in a separate unsorted region on disk, then sorted on demand for queries as required.
If large numbers of rows remain unsorted on disk, query performance might be degraded for operations that rely on sorted data, such as range-restricted scans or merge joins.
The VACUUM command merges new rows with existing sorted rows, so range-restricted scans are more efficient and the execution engine doesn't need to sort rows on demand during query execution.
Since this is clearly mentioned in the AWS Documentation , all other options are incorrect.
For more information on reclaiming storage please refer to the below URL.
https://docs.aws.amazon.com/redshift/latest/dg/t_Reclaiming_storage_space202.htmlBased on the problem statement, the current performance of queries on new batches of data is not up to the mark as prior queries. This suggests that the Redshift table may be experiencing performance degradation due to the addition of new data.
In such cases, there are a few steps that can be taken to optimize the queries on the new data:
A. Run the Query optimizer: The query optimizer is an automated tool that analyzes the query and the data being queried to determine the most efficient execution plan. Running the query optimizer can help optimize the queries on new data by ensuring that the queries are executed in the most efficient manner.
B. Use the Analyze Compression command: The Analyze Compression command analyzes the data in a table and recommends the most efficient compression encoding for each column based on the data's characteristics. Using the Analyze Compression command can help optimize the queries on new data by ensuring that the data is compressed in the most efficient way possible, reducing storage costs and improving query performance.
C. Change the sort key for the table: The sort key determines how the data is sorted and distributed across the Redshift nodes. Changing the sort key for the table can help optimize the queries on new data by ensuring that the data is sorted and distributed in a way that is optimized for the queries being run.
D. Use the VACUUM command: The VACUUM command reclaims space and resorts rows in a table to improve query performance. Using the VACUUM command can help optimize the queries on new data by reclaiming space and re-sorting the rows in the table.
Out of these options, changing the sort key for the table (option C) is the most appropriate solution. This is because changing the sort key can optimize the distribution and sorting of the data in the table, which can significantly improve query performance. However, it is recommended to consider other options as well based on the specific scenario and requirements.
