After checking the monitor tab in the Azure Synapse Studio environment, you realize that you can improve the performance of the run.
Now, you decide to use bucketed tables to improve the performance.
Which of the following are the recommended practices to consider while using bucketed tables? (Select all options that are applicable)
Click on the arrows to vote for the correct answer
A. B. C. D. E. F.Correct Answers: A, D, E and F
While using bucketed tables, you need to deal with Merge join.
A correctly pre-sorted and pre-partitioned dataset will skip the costly sort phase from a SortMerge join.
The order of joins does matter, especially in more complex queries.
Start with the most selective joins.
You should also consider moving the joins that increase the number of rows after aggregations, whenever possible.
Option A is correct.
While using bucketed tables, you should avoid the use of SortMerge join whenever possible.
Option B is incorrect.
You should avoid using expensive SortMerge join while using bucketed tables.
Option C is incorrect.
Instead of prefering the use of SortMerge join as much as you can, you should start with the most selective joins.
Option D is correct.
You should start with the most selective joins to improve the performance.
Option E is correct.
To increase the performance using bucketed tables, you should move joins that increase the number of rows after aggregations whenever possible.
Option F is correct.
The order of various types of joins matters when it comes to the resource consumption.
To know more about Apache Spark Performance, please visit the below-given performance:
Bucketing is a technique that can be used to improve the performance of queries in Azure Synapse Analytics by reducing the amount of data that needs to be scanned. Bucketed tables partition data into smaller, more manageable subsets based on the values of one or more columns, which makes it easier to perform operations such as joins and aggregations.
When using bucketed tables in Azure Synapse Analytics, there are several best practices to consider:
A. Avoid the use of SortMerge join whenever possible: SortMerge join can be expensive in terms of memory and CPU usage. It is preferable to use a Broadcast join, which involves broadcasting the smaller table to all nodes and then joining it with the larger table.
B. Prefer the use of SortMerge join as much as you can: This option is not correct. It is generally recommended to avoid using SortMerge join whenever possible.
C. Never consider the most selective joins: This option is not correct. Selective joins can reduce the amount of data that needs to be scanned, which can improve query performance.
D. Start with the most selective joins: This option is correct. Selective joins should be performed first to reduce the amount of data that needs to be scanned.
E. Move joins that increase the number of rows after aggregations whenever possible: This option is correct. It is generally recommended to perform joins before aggregations to reduce the amount of data that needs to be scanned.
F. The order of various types of joins matters when it comes to the resource consumption: This option is correct. The order in which joins are performed can affect the amount of memory and CPU resources required for the query.
In summary, when using bucketed tables in Azure Synapse Analytics, it is recommended to start with the most selective joins, prefer Broadcast joins over SortMerge joins, perform joins before aggregations, and consider the order in which joins are performed to minimize resource consumption.