How do you troubleshoot and resolve SQL performance issues in production environments?

In my experience, we identified a SQL query, which was fetching customer data and expected it to be very quick, in reality it was performing very slow.

Monitor SQL queries, identify slow-performing queries using tools like SQL Profiler, analyze query execution plans, optimize indexes, update statistics, consider query rewriting, and test changes in a staging environment before applying them to production.

Upon digging deep, we found that the query had multiple GROUP BY clause in it. GROUP BY clause in a SQL query can slow down performance, particularly with large datasets. It involves additional computation for grouping rows, indexing is crucial, and complex queries or sorting can contribute to slower execution. Efficient indexing and query optimization are essential for mitigating slowdowns.

Something to think about is the evolution of the database. What is good enough for 2000 rows doesn't always hold true for 2,000,000 rows. Filtered index that matches queries closer, removing unused or too many indexes. Query stores can help with this and sometimes just a recompile of the execution plan if an issue happens right after patching and cold start picking a bad plan.

Optimizing the SQL query involves indexing key columns, filtering data early with WHERE clauses, streamlining JOINs, and carefully selecting and using aggregate functions. Regularly profile queries, maintain the database, and consider partitioning for large tables. Test performance after each optimization step to ensure improvements without regressions.

First, check if the execution plan is correct: means the Estimated rows match the real ones at every step of the execution plan... It doesn't need to match perfectly but the numbers should be as close as possible. if it doesn't you should first try to fix it with stats or hints. If it does it means that the compiler did it best with the code and the dB structure you gave him : Now you have to spot the part of the query who need to be optimized, understand why and that's the part you can rethink the query (avoid a Or, add a With, replace a distinct by an exist, find a way to reduce the line number sooner with subquery group by ...) or the dB structure (missing index/ partitioning/change index order/columnstorage/cluster index...).

Monitor server resources: Ensure adequate CPU, memory, disk, and network capacity. Eg: Resolving a high CPU issue by adding more cores. Identify slow queries: Use tools like SQL Server Profiler and DMVs to pinpoint long-running queries. Eg: Optimizing a slow query by adding a missing index. Examine execution plans to detect inefficiencies. Eg: Fixing a parameter sniffing issue by adding OPTION (RECOMPILE). Update statistics: Stale statistics can lead to suboptimal query plans. Eg: Resolving a performance regression after an upgrade by updating statistics. Implement proper indexing: Missing or inefficient indexes are a common cause of slow queries. Eg: Improving performance by creating a new index on a frequently queried column.

This is less of an issue with today's hardware but, several things are still good to do. Keep the MDF for metadata only and store the table and indexes on NDF number depending on hardware but 8 has been the sweet sport most time. Jumbo packets if the network supports them can help a lot. elliptic curve cryptography (ECC) offers better performance than RSA

Validate SQL query optimization through benchmarking, test data with various sizes and scenarios and ongoing production monitoring to ensure sustained performance improvements.

there is no cookbook for mssql. Every database is different. Event OLTP vs OLAP ( DW ). You need to understand every layer. Even disk subsystem to know where your pain is hidden. So if someone has a very quick answer, he does not fully understand mssql engine.

Here's something I do for the health-checking databases, check the compatibility level, you would be surprised how many times people migrate SQL version but forget to change this one setting. Lookout for overuse of Union when a Join was 80% faster. Undeeded order by operations left in after testing is another common issue. Nested views, can be a big problem not just for troubleshooting but also for performance when a fresh query is 100% better. And if you are using view with WHERE options don't forget to place an index on it.