Software Development

How can we identify which missing indexes will improve SQL performance?

AR Asked by Arthur Pendleton · 08-10-2025
0 upvotes 15,330 views 0 comments
The question

Our database engine is running slow, and we suspect that missing indexes are the root cause. However, we want to avoid blindly creating indexes that might hurt our system later. What tools or system views can we analyze to see exactly how indexing improve SQL performance for our specific workload? How do we interpret execution plans to identify where a new index would yield the highest performance return?

3 answers

0
PA
Answered on 12-12-2025

To accurately identify missing indexes, you should leverage Dynamic Management Views (DMVs) such as sys.dm_db_missing_index_details and sys.dm_db_missing_index_groups. These system views continuously monitor query execution and log instances where the optimizer wanted an index but couldn't find one. Additionally, analyzing your heaviest queries via execution plans will reveal "Index Scan" or "Table Scan" warnings highlighted in red. These visual alerts explicitly show where the database engine is wasting time scanning millions of records, giving you precise roadmaps for index creation.

0
LO
Answered on 05-01-2026

I have seen SQL Server Management Studio give explicit "Missing Index" suggestions in green text above execution plans. Should we trust those automated recommendations completely, or do they have hidden flaws?

SE 09-01-2026

Never trust them blindly. Those automated suggestions are isolated to that specific query text. They do not take into account your entire workload or how that new index might overlap with existing composite indexes, potentially causing duplicate overhead for your system.

0
DO
Answered on 02-02-2026

Analyzing execution plans allows you to replace heavy table scans with rapid index seeks. System DMVs track exactly which queries are suffering due to missing index paths.

AR 06-02-2025

That is a great strategy. Relying on DMVs keeps you from guessing. It provides concrete empirical data on user query patterns, allowing you to design targeted indexes that offer maximum performance gains with minimal write side-effects.

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