Here are scenario-based interview questions related to Azure SQL Database query tuning and system performance tuning, along with guidance on how to answer:
1. Scenario: High CPU Usage
Question: You notice that CPU utilization on your Azure SQL Database is consistently above 90%. How would you identify the root cause and resolve it?
Answer:
- Identify High-CPU Queries:
- Use Query Performance Insight in the Azure portal or query
sys.dm_exec_query_stats
to find the top CPU-consuming queries.
SELECT TOP 10 qs.total_worker_time / qs.execution_count AS AvgCPUTime, SUBSTRING(qt.text, qs.statement_start_offset / 2, (qs.statement_end_offset - qs.statement_start_offset) / 2) AS QueryText FROM sys.dm_exec_query_stats qs CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt ORDER BY AvgCPUTime DESC;
- Use Query Performance Insight in the Azure portal or query
- Resolve Issues:
- Optimize high-CPU queries: Add missing indexes, rewrite complex joins, or reduce table scans.
- Update statistics: Ensure the query optimizer has up-to-date information.
- Scale up to a higher service tier if optimization does not resolve the issue.
- Monitor Resource Usage:
- Use Azure Monitor to set alerts for CPU usage thresholds.
- Review Query Store to check for regressed queries.
2. Scenario: Slow Query Performance
Question: A query that used to run in 10 seconds now takes over 2 minutes to execute. How would you troubleshoot and fix it?
Answer:
- Analyze Execution Plan:
- Review the query’s current execution plan using SSMS or Query Store to identify bottlenecks like table scans or missing indexes.
- Check Query Changes:
- Confirm if the query logic or parameters were changed recently, causing a suboptimal execution plan.
- Investigate Parameter Sniffing:
- If parameter sniffing is suspected, use
OPTION (RECOMPILE)
orOPTIMIZE FOR UNKNOWN
to generate a new plan.
SELECT * FROM Orders WHERE CustomerID = @CustomerID OPTION (RECOMPILE);
- If parameter sniffing is suspected, use
- Update Statistics and Indexes:
- Ensure table statistics are up-to-date.
UPDATE STATISTICS TableName;
- Rebuild fragmented indexes.
ALTER INDEX ALL ON TableName REBUILD;
- Test Alternative Query Logic:
- Rewrite or simplify the query to improve performance.
3. Scenario: Deadlocks
Question: Users report that their queries intermittently fail due to deadlocks. How would you handle and prevent deadlocks?
Answer:
- Capture Deadlock Details:
- Enable Deadlock Graph in Azure SQL Database.
- Query
sys.event_log
to identify deadlocks.
SELECT * FROM sys.event_log WHERE event_type = 'deadlock';
- Analyze Root Cause:
- Identify conflicting queries and the resources they are locking.
- Resolve Issues:
- Optimize queries to acquire locks in the same order.
- Use
READ COMMITTED SNAPSHOT
isolation level to reduce locking.
ALTER DATABASE CURRENT SET READ_COMMITTED_SNAPSHOT ON;
- Implement Retry Logic:
- Add retry logic in the application for transient deadlock errors.
4. Scenario: Spool Issues
Question: A user reports spool errors while running a query on a large table. How would you address this?
Answer:
- Investigate Query:
- Review the query to identify potential inefficiencies like missing WHERE clauses or excessive joins.
- Optimize Indexes:
- Add indexes to reduce temporary storage requirements (spool operations).
- Use the
sys.dm_db_missing_index_details
DMV to identify missing indexes.
- Partition Large Tables:
- Partition the table to split data into smaller, more manageable segments.
- Monitor TempDB:
- Check if TempDB is being exhausted and scale the database to add more TempDB resources.
5. Scenario: Query Store Regression
Question: A query’s performance has regressed significantly after a recent database update. How would you fix this?
Answer:
- Check Query Store:
- Use Query Store to identify the regressed query and its historical execution plans.
- Force a Good Plan:
- If a previous execution plan performed better, force it using Query Store.
EXEC sp_query_store_force_plan @query_id = 123, @plan_id = 456;
- Analyze Execution Plan Changes:
- Identify why the new plan is suboptimal (e.g., statistics, parameter sniffing).
- Fix Underlying Issues:
- Update statistics, add indexes, or refactor the query.
- Validate Changes:
- Test query performance with the changes applied.
6. Scenario: Missing Indexes
Question: Users complain about slow query performance, and you suspect missing indexes. How would you proceed?
Answer:
- Identify Missing Indexes:
- Use the
sys.dm_db_missing_index_details
DMV.
SELECT * FROM sys.dm_db_missing_index_details;
- Use the
- Create Missing Indexes:
- Add the recommended indexes after reviewing the impact on writes.
CREATE NONCLUSTERED INDEX IX_Table_Column ON TableName (ColumnName);
- Monitor Index Usage:
- Use
sys.dm_db_index_usage_stats
to ensure the indexes are being used.
- Use
- Review Index Maintenance:
- Regularly rebuild or reorganize indexes to avoid fragmentation.
7. Scenario: Resource Bottlenecks
Question: Your Azure SQL Database is experiencing slow performance due to resource bottlenecks (CPU, memory, or IO). How would you address it?
Answer:
- Identify Bottlenecks:
- Use Query Performance Insight or query
sys.resource_stats
to analyze resource usage.
SELECT * FROM sys.resource_stats WHERE database_name = 'MyDatabase';
- Use Query Performance Insight or query
- Optimize Queries:
- Identify top resource-consuming queries and optimize them.
- Scale Resources:
- Scale up the database to a higher tier or vCore model.
- Review Application Logic:
- Minimize unnecessary queries and batch operations.
8. Scenario: TempDB Usage
Question: Your application frequently runs into TempDB contention issues. How would you resolve this?
Answer:
- Monitor TempDB Usage:
- Use
sys.dm_db_resource_stats
to check TempDB usage.
- Use
- Optimize Queries:
- Reduce operations that depend on TempDB (e.g., large sort operations or hash joins).
- Scale Up:
- Increase the service tier to provide more TempDB resources.
- Partition Tables:
- Partition large tables to reduce TempDB dependency during queries.