Database Optimization Strategies

Database Optimization: A Key to Fast and Responsive Applications

In today's digital age, data is akin to currency. It powers every application, with the database playing a pivotal role in managing and storing this critical information. As systems evolve over the years, one common challenge they face is the rapid growth of data. Whether dealing with legacy systems or modern applications involving human interactions, transactions, or IoT devices, data continues to grow exponentially, and database sizes increase steadily.

In such scenarios, ensuring that a live application’s database remains optimized is crucial for maintaining speed and responsiveness. No one likes slow systems, and databases are key components in determining how fast applications can perform.

To achieve fast applications with responsive databases, several strategies can be implemented:

1. Separation of OLTP and OLAP

Online Transaction Processing (OLTP) involves everyday transactional systems, such as UPI applications, office suites, and other operational systems. On the other hand, Online Analytical Processing (OLAP) is primarily used for reporting, where organizations analyze data to extract patterns and gain insights. OLAP typically deals with larger time spans—such as monthly, quarterly, or yearly data—depending on reporting needs.

One of the most effective ways to optimize databases is by separating OLTP and OLAP. This involves copying live transactional data into a separate schema, database, or server specifically dedicated to reporting. The advantage of this separation is that reporting queries, which often demand more time and resources, do not interfere with transactional operations. By offloading analytical tasks, the transactional area remains fast and responsive for end users.

2. Use of Materialized Views

Building on the idea of separating OLTP and OLAP, materialized views can be a useful semi-operational solution for OLTP systems. A materialized view is essentially a snapshot of table data, but it’s periodically updated using a scheduler to capture changes, known as the “delta.” This means the materialized view remains a near-live copy of the data that can be used for reporting without affecting the main transactional system.

Materialized views offer a practical way to avoid running resource-intensive queries on the live transactional database, thus maintaining its performance while still providing relatively up-to-date data for reporting purposes.

3. Optimized Use of Indexes

Efficient use of indexes is critical to boosting database performance. Indexes help databases quickly retrieve data without scanning entire tables, thus reducing resource consumption. However, it’s important to only create necessary indexes—over-indexing can lead to increased overhead during insert, update, or delete operations.

Queries used for business logic should be designed with the available indexes in mind. In particular, making use of primary keys and other key indexes helps speed up data retrieval and improves application performance.

4. Primary Keys as Business Entities

The design of the database should avoid the use of artificial or unnecessary primary keys. Instead, business entities should be used as primary and foreign keys directly. Many database designers create additional auto-generated primary keys that are not needed, which can complicate data structure and performance.

For example, rather than generating a serial number for each employee's monthly salary, a composite key combining the employee_id and pay_period should be used as the primary key. This has two benefits: it ensures data uniqueness and, when tables are joined, the indexes are automatically applied, speeding up data extraction.

5. Query Optimization

A poorly optimized query can significantly impact the overall performance of a database and, in turn, the application. Therefore, it’s essential to optimize queries before using them in live systems, whether they are part of data entry or reporting functions.

Optimization should involve reviewing the query for:

• Proper use of business conditions

• Efficient use of indexes

• Performance considerations based on the specific database vendor

It’s a good practice to test queries using vendor-supplied clients like SQL Server Management Studio (SSMS), MySQL Workbench, or PGAdmin before applying them to live environments. Avoid third-party tools, as they may not be optimized for specific vendors and can introduce inefficiencies.

6. Additional Optimization Techniques

Optimization techniques can vary depending on the database architecture and use case. Some advanced strategies include:

• Table Clustering: Organizing tables to store related data together on disk, improving access times.

• Table Partitioning: Dividing large tables into smaller, more manageable pieces based on certain criteria (e.g., date range), making data access faster and more efficient.

However, these techniques depend heavily on the unique design and structure of the database in question. The strategies mentioned earlier are proven methods that cover the major aspects of performance optimization for most databases.

Conclusion

A well-optimized database is the backbone of any high-performance application. By implementing strategies such as separating OLTP and OLAP, using materialized views, optimizing indexes, and carefully designing queries, you can ensure that your database supports your application’s speed and responsiveness. Ultimately, this not only enhances the user experience but also instills confidence in the overall system.

Written By: Amey Inamdar

Designation: Project Manager

LinkedIn: https://www.linkedin.com/in/amey-inamdar-43b149120/

Introduction

Relational databases are now part of most IT project as knowledge is stored in them. I am saying knowledge and not data because, now a days, Object Relational Mappings or ORMs are happening in the majority of IT systems and the relation between multiple business objects make it active linked network of information. Such databases are backbone of any system as they closely work with data generated by users. They are no longer standalone systems as backend.

In the systems, databases work as data managers by storing, updating and retrieving the data for the systems ahead to them. In this task, their efficient working decides the response time of entire system you develop.

So coming to the point, time taken for storing and updating data is little bearable for users however time taken to retrieve the data is matter of concern where the database performance is to be observed. If we hustle in designing database and go ahead, the data accumulated in the database becomes structural and creates bottleneck in the performance throughout the lifetime of the database design for particular system. There can be arguments that using indices, one can make them faster however if you want it the ‘fastest’ i.e. inherently fast, you should consider following dos and don’ts of design principles -

Dos

1. Always prepare database based on sample data provided by customer - have a memorandum of understanding with client does not share data with third party but for design purpose, actual data makes lot of sense.

2. Identify ‘entities’ of the business logic and prepare separate master tables for each of them. e.g. Users, Roles, Materials, etc.

3. Where the second layer of master data is getting generated, keep them separate like User_Roles.

4. Try to achieve ‘third normal form’ in the design for entire design to balance the space and performance.

5. In major databases, Primary keys and foreign keys constraints also bear built-in indices. So whenever multiple entities generate a new layer of data like ‘User_Roles’, foreign keys must be used. It is observed that designers are reluctant in implementing foreign keys. Then they get hit by low performance, rework in design and poor validation in every corner of system.

Don’ts:

1. Never design a query without consideration of indices. Indices inherently cover primary keys and foreign keys. So, secret behind fast queries, is use of primary keys and foreign keys in the joining conditions. e.g. instead of joining conditions like follows -
   select A.user_name, B.role_name from users as A inner join roles as B on A.user_name = B.user_name where B.role_name = ‘user’;
   Consider following - select A.user_name, B.role_name from users as A inner join roles as B on A.user_id = B.user_id where B.role_name = ‘user’;

2. Never design a table without keys: it is the converse of Dos sereial 5. However, it is always tempting to create a table quickly for business requirements. We must avoid it and should always try to identify keys before implementing a table. Never design database in a hurry.

3. Never use query without indices for business logic. Whenever queries require conditions that are not related to previously designed keys, use indices. e.g. consider where condition in earlier query - where B.role_name = ‘user’;

4. Here, firstly, instead of role_name, role_id should be used to avoid indexless operation. Secondly, if at all, you want to use the column, implement an index on the database column.

5. Never allow anybody else to design your database. It means that you should design your own database as system requirements and business requirements are clear to you. Always design database after having discussion with end user.

6. Never put business logic in triggers. As far as possible, avoid triggers to implement business logic. It has catastrophic effect on the performance of database. Always use an application layer to clock the business logic. In case application layer is not to be concerned and you find the series of steps can be performed in database without input of further layers, implement the same in stored procedure but not triggers.

   Written By: Amey Inamdar

Designation: Project Manager

LinkedIn: https://www.linkedin.com/in/amey-inamdar-43b149120/