OLTP is a transaction-centric data processing that follows a three-tier architecture. 

Every day, businesses worldwide perform millions of financial transactions. This fact brings to mind client-facing personnel such as bank tellers and supermarket cashiers tapping away on keyboards and at cash registers, and with good reason. According to ACI Worldwide, a payment systems company, there was a 42.2% growth in global real-time transaction volumes in 2023, amounting to 266.2 billion transactions. 

However, businesses outside the financial and retail sectors also generate plenty of transactions daily. And each of these transactions requires safe storage for future review, usage, and other applications. 

Online Transaction Processing (OLTP) is designed to store these large volumes of transactional data in a data warehouse, and this blog discusses it in detail. Let’s begin. 

OLTP is distinguished by the following two factors: 

• Its dedicated design, which supports the execution and recording of hundreds (or more) of concurrent transactions. 
• The ‘online’ component in its name, which refers to how it leverages the internet to handle transactions in real-time. 

Examples of OLTP Systems 

1. ATMs and online banking processes 
2. Ticketing, booking, and reservation platforms 
3. Credit card payment processing systems 
4. Order entry 
5. Record-keeping 

OLTP vs. OLAP

OLTP and Online Analytical Processing (OLAP) are two different types of data processing systems that businesses use in conjunction with each other. 

While OLTP is primarily designed for recording transactions, OLAP databases are designed to help data teams query and analyze them. 

Here’s a comparison between the two: 

 Characteristics of OLTP Systems 

• Quick Responses: OLTP systems boast rapid responses to facilitate real-time operations. Response time is typically measured in milliseconds. 
• Indexed Datasets: These systems feature indexed datasets to help users search for, query, and retrieve data quickly. 
• High Availability: Given an OLTP system’s importance in real-time applications, ensuring high availability minimizes downtime. 
• Simpler Operations: Common operations include insertions, deletions, and data updates. These are all simpler operations since these systems don’t cater to complex analytical requirements. 

Which Databases are Used for OLTP Workloads? 

OLTP workloads need relational database management systems (RDBMS) for data storage and modification, as relational databases are purpose-built for transaction applications.  

Here are a few examples: 

1. PostgreSQL/Postgres

PostgreSQL’s customizability and flexibility make it ideal for OLTP.  

In particular, Postgres’ Multi-Version Concurrency Control (MVCC) enables simultaneous data access for multiple transactions while preventing one transaction from blocking another. This improves performance and efficiency. 

2. Oracle Database

Oracle Database is a comprehensive RDBMS that supports different use cases. Besides OLTP, Oracle Database is used for data warehousing and mixed (OLTP and data warehousing) applications. 

3. Microsoft SQL Server

Much like Oracle Database, Microsoft SQL Server also supports data warehousing and business intelligence-related tasks in addition to OLTP workloads. 

4. MySQL

MySQL is widely used as both an open-source and commercial RDBMS. Its scalability, ease of use, availability, and transactional capabilities make it a great option for OLTP workloads. 

5. MongoDB

MongoDB is flexible, scalable, and suitable for enterprises looking for a NoSQL option to process their OLTP workloads. Payment systems are among MongoDB’s common use cases, and it has a flexible document-centric model that works well for OLTP tasks. 

The Benefits and Challenges of Working with OLTP Workloads 

OLTP systems have both their benefits and their challenges, highlighted below: 

Benefits: 

The two biggest benefits of an OLTP system are concurrency and ACID (atomicity, consistency, isolation, and durability) compliance. 

Concurrency allows an OLTP system to effectively and simultaneously cater to multiple users. Two or more users can freely make transactions in a system because its concurrency will keep these transactions from conflicting or overlapping with each other. 

ACID compliance comprises the following components: 

Atomicity: breaks database transactions down into smaller components to ensure the integrity of the transaction as a whole. 

Consistency: database transactions are subject to certain data validation rules and only the data that matches these rules is written into the database. 

Isolation: database transactions are concurrently processed without any conflicts. 

Durability: ensures that after a transaction is complete, the resulting data changes are written into the database. This maintains data changes despite technical faults. 

ACID compliance enables OLTP databases to manage transactions from start to finish. It ensures the functionality of a transaction by breaking down each transaction into smaller components, applying data validity rules, processing them concurrently, and then saving the data. 

Besides concurrency and ACID compliance, other advantages of an OLTP system include: 

• Normalized Data: OLTP systems generally use normalized data structures. Doing so minimizes redundancy, enables optimized storage, and prevents data anomalies. 
• Transaction Rollback: An OLTP system’s transaction rollback capability lets users reverse transactions or other changes in case of a hardware issue or another type of error. 
• Data Integrity: Maintaining data integrity and consistency is easy as ACID compliance and normalization increase the transactions’ reliability and keep the database accurate. 

Challenges 

Common challenges associated with using OLTP databases include: 

• Querying Historical Data: They are built for handling current transactions and real-time data. Consequently, querying historical data using an OLTP system is difficult, especially compared to OLAP.
• ACID Overheads: ACID compliance, while beneficial, can lead to some overhead. Better data integrity can affect the system’s performance, as ACID transactions require additional processing power overhead.
• Frequent Maintenance: Maintaining complex OLTP systems can be somewhat resource-intensive in a dynamic and expansive environment. As noted above, these systems need frequent backups, maintenance, and optimization, which require dedicated, ongoing efforts to maintain high availability. 

How to Select the Right Database for OLTP Workloads 

Keeping the following factors in mind can help data teams select the right database: 

Understanding Performance Requirements 

Knowing the estimated transaction volume, concurrency requirements, and ideal response times is essential. The selected database should effectively handle an organization’s transactional load. 

Support for ACID Compliance 

ACID compliance is vital for ensuring data integrity in an OLTP system, so a database with comprehensive ACID guarantees is the right fit for an OLTP workload. 

Concurrency Control 

Simultaneous transactions are another key feature; managing them effectively requires a database to have sufficient concurrency control mechanisms, such as: 

• Locking 
• Optimistic Concurrency Control (OCC) 
• Timestamp Ordering 
• Snapshot Isolation 
• Multi-Version Concurrency Control (MVCC)
• Serializable Snapshot Isolation (SSI) 

Scalability 

Scalability can have varying importance depending on the business model and application.  

A database that can scale horizontally (by integrating more servers) as well as vertically (by increasing server resources) can keep up with a range of workloads. 

Data Model 

Generally, relational databases work well with structured data, and NoSQL databases are more suitable for semi-structured and unstructured data. Database selection for OLTP workloads should match the application’s schema requirements.  

Durability and Fault Tolerance 

Mechanisms such as replication, automatic failover, and backups assist with data durability and fault tolerance, making them necessary for preventing data loss. 

Consistency Models 

A database can feature one of the following consistency models: 

• Strong Consistency: This model provides immediate and consistent visibility of updates across all nodes. This is the highest consistency level but may increase latency and decrease availability. 
• Eventual Consistency: In this model, synchronous updates eventually bring all replicas into the same state. Since it prioritizes low latency and high availability, it’s a better choice for distributed systems. 
• Causal Consistency: Causally related operations are consistently ordered across nodes in this model. It preserves the causal relationship between events and balances consistency and performance. 

Since each model has a trade-off between consistency, availability, and partition tolerance, data personnel can select one based on the application requirements. 

Security 

Security capabilities may vary between databases, but features such as authorization, encrypting, authentication, and auditing can boost compliance with organizational security policies and regulations. 

Community and Support Infrastructure 

A database’s support services and the size of its community are also important factors to consider. A large, active community can be a useful resource for learning best practices and acquiring insights, and a support ecosystem can help troubleshoot issues quickly. 

Cost 

A database’s total cost of ownership (TCO) — which includes licensing, infrastructure costs, maintenance, and support expenditure — can impact its viability for a business. Ideally, a database should provide good value for money and meet organizational requirements. 

Conclusion 

Having been around since the 1970s, online transaction processing systems support transactional applications across a wide range of sectors. There has been progress towards combining OLTP and OLAP systems on a unified platform (such as MySQL Heatwave by Oracle). However, OLTP systems largely maintain their distinct functionality, which organizations can fully leverage by using the right database. 

Astera features built-in connectors for different databases — such as MySQL, PostgreSQL, MongoDB, and Oracle — for running OLTP workloads. Its hassle-free, no-code platform lets users design and deploy OLTP data models easily. 

Reach out to our team to learn more.