Which solution will meet these requirements with the MOST operational efficiency?
Log in to the database by using the PostgreSQL administration tool. Issue a SELECT * command against each table in the database.
Create a new parameter group and set the max_connections parameter to 100. Assign the parameter group to the new database. Apply the changes immediately.
Edit the default parameter group for the matching PostgreSQL engine. Set the max_connections parameter to 100. Reboot the new database to pick up the changes to the parameter group.
Login to the database by using the PostgreSQL administration tool. Issue the VACUUM (ANALYZE, DISABLE_PAGE_SKIPPING) command.
Explanations:
Issuing aSELECT *command against each table is not an efficient way to diagnose or resolve read latency issues. This approach can actually exacerbate the problem by adding additional load to the database without providing meaningful insights into the performance issue.
Creating a new parameter group to set themax_connectionsparameter may not address the underlying cause of the read latency. Additionally, settingmax_connectionsto a higher value could lead to increased contention for resources if the application does not actually require that many connections. This solution does not provide operational efficiency as it involves additional configuration steps without ensuring immediate performance benefits.
Editing the default parameter group to change themax_connectionsparameter and rebooting the database could help in specific scenarios but may not directly resolve the latency issue. Also, modifying the default parameter group is not best practice since it affects all DB instances using that parameter group. This approach is less operationally efficient compared to creating a custom parameter group for better management.
Issuing theVACUUM (ANALYZE, DISABLE_PAGE_SKIPPING)command is an effective way to reclaim storage and update statistics for the query planner, which can significantly improve read performance by optimizing the database’s physical storage and execution plans. This approach directly targets potential causes of latency, providing a targeted and efficient solution.