Introduction

The foundation of fast, scalable, and high-performance applications relies on database performance. Whether it is about handling financial transactions, processing customer queries, or supporting business intelligence, databases need to be optimized for high-speed queries, minimize resource consumption, and work seamlessly under heavy workloads.

The responsiveness of the database is essential in high-traffic environments where databases are constantly bombarded with requests. This is because quick processing and response to queries means enhanced user experience, which is what the users and businesses need.

Implementing proactive optimization techniques is essential and it starts with identifying bottlenecks, that slow down the queries and increase the server load. Then, a combination of query tuning, indexing strategies, caching mechanisms, partitioning, and hardware optimization techniques can enhance the overall performance.

Identifying Bottlenecks

Detecting performance bottlenecks is the first step in database optimization. Below are few key methods which help developers and database administrators monitor, analyze, and troubleshoot the performance issues:

Database Profiler

There are many DB profilers, which can help monitor database performance in real time. Tools like SQL Server Profiler and Database Engine Tuning Advisor help track query execution slowness, locking issues, and missing indexes. There are third-party tools, like Datadog, Redgate, and SQL Sentry, which provide deeper analytics and performance insights.

Activity Monitor

Activity Monitor is a built-in tool in SQL Server Management Studio (SSMS) which displays information about the SQL Server processes and how these processes affect the current instance of SQL Server. It provides information on running processes, resource waits, data file I/O, recent expensive queries, and active expensive queries. It also provides insights about slow queries and recommendations to optimize them.

Query Store

Another way to gauge database performance is by enabling the query store feature. This feature introduced in SQL Server 2016, provides insights about query plan choice and performance.

It stores the query execution history, which helps in tracking performance over time, and it provides execution statistics, query wait times, and plan changes. It separates data by time windows to see database usage patterns and understand when query plan changes happen on the server.

The Query Store can be configured using the ALTER DATABASE SET option.

Here’s the syntax:

Here is a good resource for Best Practices for monitoring workloads with Query store.

Execution Plan Analysis

The actual execution plan shows how the database engine processes a query. It shows the actual sequence of operations (like filtering, scanning, joining etc.,) which the database will perform to retrieve the query results. It will also identify inefficient operations like missing indexes and other potential issues which would affect query performance. Understanding and analyzing execution plans can help in identifying the bottlenecks and optimize them for better efficiency.

Performance Optimization Techniques

Once the root cause for the issue has been found, below are few tried and tested performance optimization techniques which an application developer could do to boost database performance.

Caching

One of the best ways to improve performance is to avoid database calls as much as possible. It’s always good to have a caching layer in front of database and use it to retrieve data, as reading from memory is always faster than reading from a hard disk.

Connection Pooling

Connection pooling is a technique that reuses a set of pre-established database connections instead of creating a new connection for every request. Without connection pooling, the query execution becomes slow and resource-intensive. However, with connection pooling, the queries use a shared pool of open connections, reducing latency and overhead.

BoneCP, HikariCP, and Apache Commons DBCP are some of the popular Java based connection pooling libraries. While using connection pooling, it is essential to fine-tune the settings like Max Connections, Connection Timeout, Minimum Idle, Idle Timeout etc.,

Avoid SELECT * and Return Only the Data you Need

SELECT * retrieves all columns from the table and, if you need only a few, mention only those columns in the SELECT query. Using SELECT * increases query execution time, network load, and memory usage. Hence, it is best to specify and return only the columns you need, to improve efficiency.

Use ORM Properly

Object-relational mapping tools, like Hibernate, myBatis, Entity Framework, SQLAlchemy etc., simplify the database interactions but can generate inefficient SQL queries, if not used properly. It is important that application developers understand the queries being executed rather than treating ORM methods as simple Java or C# function calls.

Use Proper Indexes

Indexes speed up query execution by allowing faster lookups of data. Without an index, the database performs a full table scan, which is slow for large tables. Indexes should be used for columns used in WHERE, ORDER BY, GROUP BY, JOINs, primary and foreign keys.

Avoid unnecessary Indexes

Using indexes can speed up query execution, however, just like excess of anything can be harmful, unused/unnecessary indexes can slow down write (INSERT, UPDATE, DELETE) operations.

Avoid unnecessary Joins

Joins can be resource-intensive, especially when dealing with large datasets. So, it’s always good to ensure that only relevant tables are joined in the query and there is no join against unnecessary tables.

Avoid subqueries in favor of Joins

Deeply nested subqueries can lead to multiple evaluations, increasing execution time. Instead, simplify the logic with JOINS to enhance performance by processing the data in a single execution plan, minimizing computation overhead. For example, MySQL query optimizer handles joins more efficiently than subqueries.

Consider Denormalization for Read-Heavy Workloads

Normalization is a recommended traditional design practice that prioritizes data integrity. However, for read-heavy workloads, this strategy might not work as intended and instead may lead to slower query execution and performance. Normalization will lead to increased complexity because of numerous tables and connections, and computation of queries with several joins is expensive.

Hence, in such cases, a denormalization strategy is recommended, which can improve performance. Denormalization refers to the controlled introduction of redundancy, like copying specific data elements from the related table to the primary table for quick retrieval.

Denormalization is ideal for OLAP (Online Analytical Processing) applications, where read performance and quick retrieval of information from a voluminous dataset is required.

Use Pagination for Large Datasets

Pagination is a technique that is used when querying a dataset that has more than a few hundred records. This technique splits the large dataset into smaller chunks, which makes fetching easier. It reduces the load on the database and solves performance issues in both the client and server side.

Covering Indexes

A covering index is an optimized index that includes all the columns required for a query. With a covering index, the database just needs to access the index and doesn’t need to access the underlying table. Here is the syntax to create a covering index:

This technique enhances query performance by reducing disk I/O and optimizing lookups.

For example, let’s say we have an Employee table with the following columns

Employee ID, Name, DOB, Department, Salary

and if there is a query like this

select Name, Department from Employee where Salary > 100000.

With a normal index on Salary alone, the query still has to go to the underlying index to retrieve Name and Department. However, with a covering index like below

this query can retrieve all the necessary fields from the index itself and doesn’t have to go to the underlying table, which enhances the query performance.

Materialized Views

Materialized views are a database object that stores the result set of a query like a physical table. However, they are virtual and derive the data from an underlying table. It can be incrementally updated as per demand or on schedule. By storing the precomputed results, the need for complex aggregations and reporting queries is reduced, improving response times significantly. This is mainly used in OLAP (Online Analytical Processing) systems.

Use Batching – Reduce Database Roundtrips

Executing multiple small queries instead of a single batch can lead to more round-trips. Using batching techniques, such as bulk inserts or batch updates, reduces the number of database calls and improves efficiency.

Bulk Data Upload – BULK INSERT

Using BULK INSERT for large dataset uploads is far more efficient than inserting rows one by one. This method reduces transaction overhead and speeds up data loading significantly.

The FIELDTERMINATOR and ROWTERMINATOR help with customization based on your file format. The BATCHSIZE controls the number of rows processed at once. This technique is particularly useful for importing data from external sources efficiently.

Remove Indexes/Constraints Before Big Deletes (Maintenance/Archival)

Deleting large amounts of data can slow down operations due to indexing overhead. Temporarily removing indexes and constraints before performing bulk deletions and then reapplying them afterward can optimize performance.

The Bottom Line

The primary goal of database performance optimization is to minimize the response time of the queries. While response time is one of the key metrics to assess the database performance, there are other metrics that one should consider, like throughput, scalability, resource utilization, network latency etc. as well.

In this blog, we looked at database performance optimization techniques which an application developer could do to improve performance. However, database performance, is not limited just to query tuning; it extends to server resource management (memory, CPU, and disk allocation) and also architecture-level optimizations (scaling strategies, sharding, and partitioning). Achieving high-performance database requires a holistic approach, balancing efficient query execution, hardware optimization, and proper database design.

These techniques, covered in next blog, discuss how Database Administrators can improvise the database to remain responsive, scalable, and efficient, even as workloads grow.