What is OLTP? The Ultimate Guide to Online Transaction Processing

Businesses need a database to store and manage their daily transactions. Payments, inventory updates, and customer records are examples of transactional data.

To centralize and process this data, they use OLTP

OLTP (Online Transaction Processing) is a type of database optimized for storing and processing high volumes of simple online transactions in real time. 

OLTP systems can support concurrent users and maintain data integrity while minimizing the response time for any database changes.

In this article, we will explain what OLTP is, how it serves as a foundational system for modern companies, and the key differences between OLTP and OLAP solutions. We will examine common OLTP use cases and list best practices for organizations to implement effective OLTP systems.

What is OLTP?

Online Transaction Processing (OLTP) is a type of database system that is optimized for high-speed data processing and rapid transaction execution in real-time.

OLTP supports transaction-oriented applications used in e-commerce, online banking, bookings, inventory management, and more.

Businesses use OLTP systems to manage a large volume of concurrent database transactions from many users without harming database consistency and accuracy. A database transaction is any change made in a database, like inserting or deleting.

OLTP databases are relational databases. They organize data in tables consisting of rows and columns. Data analysts use SQL queries to execute transactions for data manipulation.

OLTP Architecture

An OLTP system uses a 3-tier architecture: 

• The presentation layer: This layer is the front end or user interface where transactions are generated.
• The logic layer: Also called the business logic or application layer, this layer processes transaction data based on predefined rules.
• The data or data store layer: This is where each transaction and related data are stored and indexed. It includes the database management system (DBMS) and the database server.

Each tier has its own infrastructure and updating intervals. They operate independently so that changes to one tier do not impact the others.

Key characteristics of OLTP

OLTP systems have four critical characteristics:

1. Fast query processing

OLTP systems are used for high-speed query processing. They handle transactions in real-time, meaning that transactions are executed as soon as they are received, with little or no delay.

Data engineers measure the effectiveness of an OLTP system by the total number of database transactions that can be completed per second.

2. High concurrency 

OLTP systems use algorithms that allow many concurrent users to perform transactions simultaneously. Each transaction is executed independently of the others and in the proper order.

Two users cannot change the same data within an OLTP database at the same time, which is crucial for maintaining data integrity.

3. ACID properties

To ensure data integrity, consistency, and reliability, OLTP transactions comply with the ACID (Atomicity, Consistency, Isolation, Durability) properties. These are:

• Atomicity: Transactions in OLTP systems are atomic, meaning they are treated as a single, indivisible unit of work. If any part of a transaction fails, the entire transaction is rolled back, so the database is left in its original state.
• Consistency: OLTP databases are designed to maintain data consistency, despite failures or errors. Every transaction changes tables in predefined and predictable ways, and the database will always be in a valid state.
• Isolation: Transactions in OLTP systems are isolated from each other. This means that when multiple users read and write data simultaneously, they are executed independently.

Isolation ensures that the changes made by one transaction do not interfere with the changes made by others. It keeps the database in a consistent state.

• Durability: When a transaction is successfully executed in an OLTP database, the changes to the data are permanent and will survive any subsequent failures or errors, like system crashes or power outages.

4. Support for simple transactions

OLTP systems support specific applications or business processes like order processing, inventory management, or customer service. They are typically not used for complex queries, data analysis, or reporting tasks.

OLTP transactions are typically short and simple. They include inserting, updating, and deleting data, along with basic data queries.

OLTP vs OLAP

OLTP is used for rapidly executing online database transactions. Data teams use another type of database, called OLAP (Online Analytical Processing), for data analytics and business intelligence. 

OLAP databases enable data scientists to aggregate, manipulate, and analyze data from various perspectives. It allows them to run complex queries on large amounts of data.

OLAP drives data analysis and helps analysts identify patterns, understand trends, and generate reports. They can garner data-driven insights to improve business operations.

OLTP and OLAP are complementary database architectures that serve different purposes.

Typically, OLAP systems integrate data from many sources, including applications and transaction data stored in OLTP databases. They store source data in a centralized repository like a cloud data warehouse and represent this data in different dimensions using an OLAP cube.

This cube enables data engineers to perform analytical operations to aggregate data and gain insights.

Here’s a simple table to help you understand the differences between OLTP and OLAP:

Database systems for OLTP

OLTP uses two main types of databases to process workloads:

1. Relational databases

Relational databases are the most widely used database systems for OLTP applications. They are designed to store data in tables, with each table representing a specific entity or relationship. 

Relational databases provide robust support for transaction processing and data consistency.

Since the modern data stack requires scalable, flexible, and cost-effective systems, businesses are turning to cloud-based OLTP solutions like a cloud data warehouse.

2. NoSQL databases

NoSQL databases are used to manage large volumes of unstructured or semi-structured data. They are often used in high-velocity data processing environments, where the volume and complexity of data are constantly increasing.

NoSQL databases do not use a fixed schema. Instead, they use a flexible data model that can adapt to changing data structures.

NoSQL databases offer high scalability, flexibility, and performance, making them a viable option for modern data-driven applications. However, they may require more complex data modeling and query languages than standard relational databases.

Popular OLTP databases

OLTP requires a relational database management system (RDBMS) to store and modify data. Here are five popular RDBMS tools that are used by data teams.

1. MySQL

MySQL is a well-known RDBMS that is used to manage structured data. It is known for its performance, scalability, availability, and ease of use.

It supports many transactional features, such as locking and row-level concurrency control, that are critical for ensuring data consistency and reliability in OLTP applications.

MySQL provides several features for managing OLTP workloads, including:

• Query optimization
• Replication
• Automatic failover
• Monitoring and management

MySQL has both open-source and commercial versions. Data engineers can integrate third-party applications and databases with MySQL using connectors and APIs.

2. PostgreSQL

PostgreSQL is a highly flexible and customizable database system well-suited for OLTP workloads. It is used to manage structured data in high-volume transactional environments.

Key features of PostgreSQL for OLTP workloads include:

• MVCC (Multi-Version Concurrency Control)
• Locking mechanisms
• Replication
• High availability
• Indexing
• Partitioning

MVCC is an important feature that allows multiple transactions to access the same data at the same time without blocking each other. This improves performance in OLTP environments.

PostgreSQL integrates with many third-party platforms.

3. Microsoft SQL Server

Microsoft SQL Server is used to store, manage, and retrieve structured data. It is a comprehensive database platform that supports OLTP, data warehousing, and company intelligence workloads.

It is a popular choice for OLTP systems because of features like:

• Query optimization via tools like Query Optimizer and Database Engine Tuning Advisor
• Support for in-memory OLTP
• Transact-SQL (T-SQL)
• Always On Availability Groups
• Disaster recovery
• Indexing
• Cloud integration

Microsoft SQL Server is commonly used in enterprise-level applications, such as business intelligence, e-commerce, and financial services.

4. Oracle Database

Oracle Database is a robust and reliable RDBMS used for OLTP workloads. Notable features include:

• Horizontal and vertical scaling
• Indexing
• Query optimization via tools like the Cost-Based Optimizer
• Real Application Clusters (RAC)
• In-memory database

Oracle Database is a comprehensive platform that can support various use cases, from OLTP to advanced analytics.

5. MongoDB 

MongoDB is an open-source NoSQL database designed to handle unstructured and semi-structured data.

Its document-oriented model allows it to handle transactions effectively, making it a good choice for OLTP solutions. It provides features such as: 

• Indexing
• Replication
• Transactions
• Automatic failover
• Aggregation and data processing
• Schema flexibility

Many modern, cloud-based applications use MongoDB because of its flexibility, scalability, and availability.

OLTP use cases

Online transaction processing systems are used in applications where the primary goal is to manage and process many transactions in real time. Some standard use cases include:

E-commerce systems

E-commerce applications use OLTP to manage customer orders, payments, and inventory in real time. This allows them to provide exceptional customer service, boost customer loyalty, and drive growth.

For example, an OLTP database can help maintain up-to-date and accurate inventory data, allowing e-commerce companies to fulfill orders promptly.

Banking and financial services

Banks and other financial services use OLTP to process financial transactions in real-time, manage customer data, and enable customers to make deposits, withdraw money, transfer funds, and access other services quickly.

OLTP solutions for financial transaction systems, like online banking, must have secure and reliable data management practices, multi-currency support, and custom reporting options.

ATMs are the most common example of an OLTP system used in the financial industry.

Reservation systems

OLTP drives online reservation systems in the travel and hospitality industry. It is used in applications that manage bookings, flights, payments, and related services.

An online transaction processing system in the travel and hospitality industry must integrate with external systems, like airline reservation or car rental applications, and have multi-language and multi-currency support.

OLTP also enables efficient customer data management and helps provide personalized recommendations that create seamless experiences for travelers.

Customer relationship management (CRM)

Customer relationship management (CRM) platforms use OLTP to manage customer data, interactions, and transactions. An OLTP system can centralize customer data and record interactions across multiple channels, like phone calls, emails, and chat messages.

OLTP helps CRM applications automate many sales and marketing processes, including lead generation and campaign management, so companies can focus on nurturing customer relationships and increasing sales.

Designing an effective OLTP system

There are three key factors to consider when building an OLTP solution:

Best practices for schema design

A schema outlines how data is organized in a relational database. In OLTP, the database schema is designed to process high data volumes. Here are some best practices when creating an OLTP schema:

• Normalization: Normalization involves breaking down a large table into smaller, more manageable tables to minimize data duplication. This ensures data consistency and helps maintain data integrity.
• Choose appropriate indexes: Indexing is a method used to provide quick access to database files. Indexed data can speed up query processing and improve performance. This involves identifying the most frequently used queries and creating indexes on the relevant columns.
• Use the correct keys: Keys are used to uniquely identify the rows in a table. They establish relationships between different tables and ensure that there are no duplicate rows in the database. There are three types of keys:
• Primary key
• Composite key
• Foreign key

Data engineers can use composite primary keys to enforce uniqueness, while foreign keys can enforce referential integrity and ensure that there are no orphaned records in the OLTP database.

• Monitor and optimize: Regularly monitor and optimize the schema to ensure it meets performance metrics and can handle the required transaction volumes.

Scalability consideration

Scalability determines the OLTP system’s ability to handle increasing transaction volumes as the business grows. Factors that improve scalability are:

• Sharding (horizontal scaling): Horizontal scaling involves adding more servers or nodes to the database cluster to distribute the workload and improve performance. This can be achieved through database sharding.

Sharding involves partitioning the data and distributing it across multiple servers, where each server in the cluster operates independently.

• Vertical scaling: Vertical scaling involves increasing the capacity of the hardware or infrastructure that the database is running on. This can include adding more memory, CPUs, or storage to the server.
• Cloud-based solutions: Cloud-based solutions, such as Amazon RDS, Azure SQL Database, and Google Cloud SQL, are scalable and highly available OLTP databases that don’t need infrastructure maintenance.

Data replication, caching, and load balancing are also used to scale OLTP database systems.

Transaction and concurrency management

Transaction and concurrency management are critical components of OLTP solutions. They ensure database consistency and prevent conflicts between multiple users accessing and modifying data.

ACID compliance is the first consideration for transaction management in OLTP. Here are three other crucial mechanisms:

• Locking: Locking is a technique that ensures that multiple transactions do not access the same data simultaneously. Locks can be implemented at the database or table level. They can be either shared or exclusive.

Shared locks allow multiple transactions to read the same data at the same time, while exclusive locks prevent other transactions from accessing the data until the lock is released.

• Isolation level: An isolation level defines how transactions interact and how they see changes made by other transactions. Most OLTP systems offer four different isolation levels - Read Uncommitted, Read Committed, Repeatable Read, and Serializable.

Implementing the correct isolation level depends on the application’s requirements and balancing data consistency and performance.

• Deadlock handling: Deadlocks occur when two or more transactions are waiting for each other to release locked resources and cannot proceed. Deadlock prevention techniques, like timeout mechanisms and prioritizing transactions, are used to detect and resolve deadlocks before they occur. 

Conclusion

OLTP drives the daily operations of most modern businesses.  

An efficient, highly available, and scalable OLTP system enables the real-time and accurate processing of transactions. This is crucial for increasing efficiency and boosting customer satisfaction.

Data engineers can combine the power of OLTP and OLAP systems to create a cohesive data ecosystem. OLTP facilitates the rapid execution of online database transactions, while OLAP systems enable business analysts to perform complex data analytics and data mining. 

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