Online transaction processing (OLTP) enables rapid, accurate data processing for most of today’s business transactions, such as through ATMs, online banking, e-commerce, and other types of daily services. With OLTP, the common, defining characteristic of any transaction is its atomicity, or indivisibility. A transaction either succeeds as a whole, fails, or is canceled. It cannot remain in a pending or intermediate state. Non-financial database exchanges, such as text messaging and changes to passwords, are also assisted by OLTP. Organizations can improve their OLTP processes by implementing best practices that make business more efficient for the customer while helping to advance the next generation of this important technology.

Defining OLTP Today

The functionality of OLTP has advanced tremendously since the 1960s, when IBM designed the initial OLTP databases to automate business transactions, such as payroll processing and inventory management. In 1981, IBM also introduced structured English query language (SEQUEL), known today as SQL (structured query language), which provided a standardized language for querying and manipulating data. Examples of the first relational database management systems (RDBMS) include IBM’s System R and Oracle. The rise of web-based applications and cloud computing has significantly impacted OLTP since the early 2000s. Databases such as Amazon RDS and Microsoft Azure SQL have simplified the scalability and management of OLTP workloads. NoSQL, or “not only SQL” databases, have also emerged as alternatives to RDBMS for certain applications. There are several OLTP databases in use today. 

OLTP databases are designed to handle high transaction volumes and are optimized for quick, efficient reads and writes of small amounts of data. While OLTP databases might not be as well suited to manage complex analytical queries or big data workloads, it is critical to many organizations’ data management and operation. 

Identifying Best Practices

Despite the benefits and high rate of utilization, OLTP is not always suitable for all operations. For instance, overhead associated with atomicity, consistency, isolation, and durability (ACID) compliance, a set of transaction properties that attempt to validate related data when errors, power failures, and/or other unexpected events occur, can slow operations, particularly for applications that require frequent database writes. To balance the need for ACID compliance and high performance, OLTP can rely on options to optimize transaction processing, such as reduced locking or isolation levels, using memory-optimized tables, or implementing caching mechanisms. ACID issues can be challenging to address when large volumes of concurrent transactions are present, however, there are solutions available, including microservices architecture with hybrid computing. Microservices architecture is a method of designing software systems as a collection of loosely coupled, independently deployable services. Each microservice can be designed to handle specific parts of the system and can be scaled and updated independently. This can help improve system scalability and resiliency.

Hybrid tactics can also be used to improve performance and scalability. For example, in-memory computing, distributed computing, and other advanced techniques can improve processing speed and reduce latency. By distributing data and processing across multiple nodes, transaction bottlenecks can be avoided. Another database strategy is sharding, which involves partitioning data across multiple nodes to ensure transactions are processed in parallel without interfering with one another. Additionally, data replication can ensure the availability of data when the cluster fails. 

When building an OLTP database in a microservices architecture, entity relationships between primary and child tables can be impacted in a few ways. Each microservice typically manages its own data, which can lead to decentralized data management. This means that the relationships between primary and child tables might need to be managed across multiple microservices, which can be more complex than in a traditional monolithic architecture. This can also be managed by duplicate tables across multiple microservices, or the business can manage the entity relationship in an application layer, which means the data can be read/written independently in the database. In this instance, the application code will handle the entity relationship after reading or writing the data.

That said, planning a database properly is crucial for any business that relies on data. Consider implementing the following best practices as they relate to OLTP:

1. Organize and manage data efficiently. A properly planned database can structure data in a logical and efficient way, making it easier to store, retrieve, and update information.
2. Ensure data accuracy and consistency. Planning database structure and data types can ensure the accuracy and consistency of data, reducing errors and inconsistencies.
3. Support business operations. A well-planned database can provide the necessary data to support operations such as sales, inventory, customer management, and financial reporting.
4. Use appropriate hardware. OLTP databases require fast processing speeds and low-latency access to data. Hardware such as solid-state drives (SSDs) and high-performance processors can ensure an efficient database.
5. Optimize database schema. OLTP databases typically contain many tables. A well-optimized schema can improve performance. This includes using appropriate data types, setting up primary and foreign keys, and avoiding redundant data.
6. Use indexing effectively. Indexing can improve OLTP database performance by reducing time spent searching for data. Businesses should use indexing effectively, including creating indexes on commonly used columns, avoiding too many indexes, and periodically checking and optimizing indexes.
7. Implement data backup and recovery. OLTP databases contain critical business data, and it’s important to have a solid backup and recovery strategy in place. This includes implementing regular data backups, testing the backup and recovery process, and ensuring that backups are stored securely and offsite.
8. Monitor database performance. OLTP databases require continuous monitoring to perform efficiently. Businesses should monitor performance metrics such as response time, throughput, and resource utilization. This involves setting up alerts and notifications to address issues before they become critical.
9. Implement security measures. OLTP databases contain sensitive data. Some of the appropriate security measures include using strong passwords, multiple authentications, restricting database access, and encrypting data in transit and at rest.

The Future of OLTP

As more businesses host OLTP in the cloud, it is important to consider a few factors. Cloud-based OLTP databases may experience higher network latency or there may be a lack of options to achieve the native or better architecture than on-premise architecture, which can impact performance and response times. Also, storing sensitive transactional data in the cloud can raise security concerns, such as breaches or unauthorized access. It’s important to monitor the security controls and certifications of all cloud providers.

Depending on the industry and regulatory environment, compliance issues can arise when transitioning to the cloud. Moving OLTP databases to the cloud can result in vendor lock-in, making it difficult or expensive to switch providers or return to on-premises. While moving to the cloud can provide cost savings, it can also cause unexpected costs due to data transfer fees, storage fees, and database licensing. Cloud-based databases can also face performance limitations due to shared infrastructure, storage, compute, and network resources. 

Next-generation cloud transactional database engines will likely have innovative traits, including the assistance of artificial intelligence and machine learning technologies that can optimize performance and automate routine tasks such as indexing, query optimization, and workload management. These technologies can also help to identify and mitigate security threats and anomalies. Serverless architecture will eliminate the need for customers to manage underlying infrastructures, which can reduce operational costs and simplify deployment while allowing for more granular scaling and resource allocation. Blockchain technology can now provide an immutable, tamper-proof ledger of transactions, which can be particularly useful in applications such as supply chain management, financial transactions, and healthcare data management. Cloud-based engines will also need to support multi-cloud deployments and provide seamless data synchronization with replication across numerous providers. Hybrid cloud models that combine on-premises and cloud resources can provide a flexible, cost-effective approach, but they will need to support hybrid deployment models and seamless data migration between on-premises and cloud environments.