SQL Server Performance Optimization as a Database Designer

SQL Server performance optimization is one of the most debated topics in database management. When performance issues arise, the first question teams ask is:

 Who is responsible for SQL Server performance — the DBA, the database designer, or the developer? and the general obvious answer is a DBA. But a little reverse engineering will make it clear that a poor DB design and a poorly written code will never make a Database efficient even if a DBA gives it best.

Hence the short answer is, everyone shares responsibility, but at different stages and in different ways.

To understand the responsibilities as a Database Designer, we must follow below listed tasks while designing a Database in the beginning itself.

Performance Optimization as a Database Designer (Structure Level)


1. Correct Data Modeling

What it means:
Design tables correctly for the workload.

Why it matters:
Bad design = complex queries = slow performance.

Example:

  • OLTP → normalized tables
  • Reporting → some denormalization

2. Appropriate Data Types

What it means:
Choose smallest data type that fits.

Why it matters:
Smaller data = fewer reads = faster queries.

Bad:

Name NVARCHAR(1000)

Good:

Name VARCHAR(100)


3. Primary Key Design

What it means:
Primary key uniquely identifies a row.

Why it matters:
Clustered index is built on it.

Example:

OrderID INT IDENTITY PRIMARY KEY

Better than:

OrderID UNIQUEIDENTIFIER


4. Index Strategy Design

What it means:
Indexes should support queries.

Why it matters:
Wrong indexes = table scans.

Example:

CREATE INDEX IX_Orders_CustomerID

ON Orders (CustomerID)

INCLUDE (OrderDate, TotalAmount);


5. Clustered Index Selection

What it means:
Defines physical order of table.

Why it matters:
Poor choice increases page splits.

Good Choices:

  • Identity column
  • Date-based column

6. Partitioning Strategy

What it means:
Split large tables into smaller chunks.

Why it matters:
Improves manageability and performance.

Example:

  • Orders table partitioned by year

7. Avoid Unnecessary Objects

What it means:
Every index and trigger adds overhead.

Why it matters:
INSERT/UPDATE becomes slower.

Example:
Remove unused indexes found via:

sys.dm_db_index_usage_stats


8. Referential Integrity with Performance in Mind

What it means:
Foreign keys ensure data consistency.

Why it matters:
Unindexed foreign keys slow deletes/updates.

Example:

CREATE INDEX IX_Orders_CustomerID ON Orders(CustomerID);


9. Schema for Concurrency

What it means:
Design to allow many users at once.

Why it matters:
Wide rows cause lock escalation.

Example:
Split large text columns into separate tables.


10. Scalability Planning

What it means:
Design for future growth.

Why it matters:
Redesigning later is expensive.

Example:
100K rows today → 100M rows in 3 years.

Let me know your thoughts in comments. 


About the author:

I am an Vivekanand Jha. I specialize in database management and optimization, sharing my journey and technical insights here on SQLwithVJ.

Connect with me on LinkedIn →

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