Data is a goldmine for every organization, no matter the industry. But to make the most of it, businesses need technology to maintain and manage transactional data like payments, inventory updates, and customer records. This is where OLTP databases come in.
Online Transaction Processing (OLTP) databases are used to store and process large numbers of simple online transactions in real time. One of the key benefits of such systems is their ability to serve multiple concurrent users while maintaining data integrity.
But what exactly is an OLTP database and how does it work? Keep reading this article to learn more about this fundamental technology for modern businesses and get some best practices for building efficient OLTP systems.
Table of contents
- What is OLTP?
- OLTP key features and benefits
- OLTP vs. OLAP: What’s the difference?
- Challenges of OLTP systems
- Understanding OLTP database design
- OLTP database systems
- 6 popular OLTP databases
- Real-world use cases of OLTP
- Best practices for implementing OLTP
- Management of transactions and concurrencies
- Schema design
- The future of OLTP
- Wrap up
What is OLTP?
Online Transactional Processing (OLTP) systems enable the real-time execution of a huge number of transactions by a massive volume of people. Companies use such systems to power business applications that require accuracy, speed, and scalability.
To understand what an OLTP database is, we need to differentiate between operational and transactional data:
- Operational data – data businesses use in their daily operations, for example, individual records that reflect particular events, such as a sale, purchase, or customer interaction. This data is updated regularly and accurately reflects the current business situation.
- Transactional data – a specific type of operational data that is captured from transactions. Examples include payments for products and services or a client connection with a business channel.
OLTP databases deal with transactional data. As you can imagine, OLTP finds lots of use cases in banking. You’ll find them in systems that process deposits, withdrawals, transfers, and balance inquiries in banking, as well as online banking systems, credit, and debit card authorization systems, or wire transfer systems. But this is just the tip of the iceberg when it comes to OLTP implementations.
Teams often use a relational database management system (DBMS) to administer OLTP because they can manage a high volume of queries and changes while maintaining quick response times.
OLTP key features and benefits
Real-time data processing
OLTP systems capture, store, and process data from transactions in real time. They can process a huge number of very basic operations, such as data insertions, updates, and removals, as well as simple data queries (for example, a balance check at an ATM).
High transaction volume and multi-user accessibility
OLTP is synonymous with a high number of users simultaneously accessing the same data. Imagine an online store that has just released the new version of the iPhone everyone wants. You can be sure that the store will be flooded with customers once the product drops. OLTP is here to deal with such a scenario while maintaining data integrity.,
High concurrency and scalability
OLTP systems use concurrency algorithms to guarantee that no two users can change the same data at the same time and that all transactions are completed in the correct sequence. This stops consumers who use online reservation systems from reserving the same accommodation twice and protects joint bank account holders from unexpected overdrafts.
A mobile money transfer application is a great example of high concurrency since thousands of users may make transactions on the platform at any time of day.
ACID (Atomicity, Consistency, Isolation, Durability) properties
OLTP relies on databases capable of quickly storing and retrieving data. They ensure that data is maintained accurately in line with ACID:
- Atomicity – treating every statement in a transaction as a separate unit (to read, write, update, or delete data). Either the full statement or nothing of it gets performed. This attribute prevents data loss and corruption if your streaming data source breaks in the middle of a stream.
- Consistency – it guarantees that transactions only make prescribed, predictable changes to tables. Transactional consistency ensures that data corruption or mistakes do not have unexpected effects on your table’s integrity.
- Isolation – when a large number of users can read and write from the same table simultaneously, transaction isolation becomes extremely important. It guarantees that these concurrent transactions don’t interact with or impact one another in any way. Each request might appear as if it were happening one at a time, even when it’s happening all at once.
- Durability – it guarantees that changes made to your data by properly conducted transactions are saved even if the system fails.
OLTP vs. OLAP: What’s the difference?
People often confuse online transaction processing (OLTP) and online analytical processing (OLAP). The acronyms are quite similar, and both refer to online data processing platforms.
So, what’s the difference between OLTP and OLAP?
OLTP is designed to handle online database transactions used by frontline personnel or consumer self-service applications like online banking or travel reservations.
OLAP is designed to perform complicated data analysis. Its primary users include data scientists and business analysts. OLAP supports data mining, business intelligence (BI), and various decision-support applications.
While, OLTP systems involve a relational database that can support a high number of concurrent and frequent, OLAP systems usually employ multidimensional databases that allow for complicated searches using various data facts from current and historical data.
Another difference lies in queries. OLTP queries tend to be pretty basic and include only a few database records tops. OLAP queries, on the other hand, are sophisticated searches that may involve a huge number of records.
Speed is another key differentiator. Response times for OLTP transactions and queries are very fast, while OLAP takes its time to respond.
Note that OLAP systems never edit data and their workloads are write-intensive. In contrast, OLTP systems often alter data and have read-intensive workloads.
OLTP systems are frequently used as a source of data for OLAP systems. In many cases, the purpose of OLAP analytics is to enhance company strategy and optimize business processes, which can become the foundation for making improvements to the OLTP system.
Challenges of OLTP systems
OLTP comes with a few drawbacks that surface if the system isn’t properly built and managed:
- Data overload – it can be challenging for businesses to analyze significant business insights in a backend OLAP database or data warehouse.
- Information silos – since each application has its own allocated database, data can quickly become siloed and impossible to transfer between apps.
- Limited analytics – OLTP applications handle short, uncomplicated database transactions and aren’t a good pick for in-depth research.
- Hardware issues – if something goes wrong in the hardware section (like physical damage to the server holding the relational database), the customer-facing operations will suffer.
Understanding OLTP database design
OLTP systems are often developed on a three-tiered design that includes:
- The presentation layer – this is the frontend/user interface where transactions are produced.
- The data processing layer – this layer is called business logic or application. This layer processes transaction data according to specified criteria.
- The data layer – it stores and transaction manages data, including the database management system (DBMS) and the database server.
Each of the three layers is self-contained, with its own infrastructure, development, and update cycles that have no bearing on the others.
How exactly do all those layers work together? Here’s a quick overview:
- The application serves as the user’s interface to the database. It transforms user input into commands the database can carry out.
- A database often takes the form of a server computer that stores massive quantities of data and conducts complicated queries to extract insights from it.
- In an OLTP system, the database can be tuned to handle transactions rapidly and effectively.
- You also have a file system working as a secondary data storage medium. It’s like a repository for backup copies of the database and other relevant files. You can also use it to hold interim results of analytical processes or log files documenting OLTP system activities.
- Between all these different components of OLTP, you’ll find a network acting as a link and enabling communication between them.
OLTP database systems
To process workloads, OLTP uses two types of databases: relational database systems and NoSQL database systems.
Relational database systems
Relational databases are the most frequently used OLTP database systems. They’re designed to store data in tables. Each of the tables represents a different entity or connection. All in all, they provide strong transaction processing and data consistency support.
Many businesses use cloud-based OLTP solutions like cloud data warehouses to be more scalable, versatile, and cost-effective.
NoSQL database systems
NoSQL databases are a great pick for managing large amounts of unstructured or semi-structured data. Teams often used them in high-speed data processing where data volume and complexity rise.
Note that you don’t use a fixed schema in NoSQL databases. Instead, you need an adaptable data model that can adjust to the evolving data architectures.
NoSQL databases are a good solution for current data challenges because of their scalability, flexibility, and performance. However, you might need to do some advanced data modeling and query languages than ordinary relational databases.
6 popular OLTP databases
To store and change data, OLTP systems need a database management system (RDBMS). Here are a few popular options.
The PostgreSQL database system is known as highly adaptable and customizable, a great match for OLTP workloads. Engineers use it to manage structured data in high-volume transactional scenarios.
Key features relevant to OLTP workloads:
- Locking techniques for MVCC (Multi-Version Concurrency Control)
- High accessibility
A well-known RDBMS for structured data, MySQL offers performance, scalability, availability, and user-friendliness. It also has a bunch of transactional capabilities that are crucial for assuring data integrity and dependability in OLTP applications, including locking and row-level concurrency management.
MySQL capabilities for OLTP:
- Query enhancement
- Failover occurring automatically
- Management and supervision
This NoSQL database is open-source and a good choice for managing unstructured and semi-structured data. MongoDB has a document-oriented approach, which makes it efficient in the transactions universe.
Key MongoDB features that make it a great candidate for OLTP systems:
- Indexing replication transactions
- Failover occurs automatically.
- Data aggregation and processing
- Schema adaptability
4. Oracle Database
This reliable RDBMS for OLTP workloads is in fact a complete platform capable of supporting a wide range of use cases, from OLTP to sophisticated analytics.
Oracle Database comes with a few features helpful for OLTP scenarios:
- Vertical and horizontal scaling
- Query optimization using cost-based optimization techniques such as the Cost-Based Optimizer
- In-memory database Real Application Clusters (RAC)
5. Microsoft SQL Server
A database management system teams use to store, manage, and retrieve structured data. Microsoft SQL Server is a platform that can handle many scenarios: OLTP, data warehousing, and business intelligence workloads – especially for enterprise-scale operations.
It is a common choice for OLTP systems because of the following features:
- Query optimization with the use of tools such as Query Optimizer and Database Engine Tuning Advisor
- In-memory OLTP Transact-SQL (T-SQL) support
- Groups with Constant Availability
- Indexing for disaster recovery Cloud integration
CockroachDB is a cloud-native, distributed SQL database created to help teams build, scale, and manage data-intensive applications. The database scales horizontally, survives failures with minimal latency disruption and provides a familiar SQL API.
Why are teams using CockroachDB for OLTP? Here are a few reasons:
- The database supports strongly-consistent ACID transactions,
- It achieves high OLTP performance of more than 128,000 tpmC on a TPC-C dataset over 2 terabytes in size. This performance is over 10x more TPC-C throughput than Amazon Aurora, in a 3x replicated deployment with single-digit seconds recovery time and zero-downtime migrations and upgrades.
- CockroachDB doesn’t sacrifice isolation for performance.
Real-world use cases of OLTP
You can find OLTP systems in almost every sector or vertical market, as well as in many consumer-facing systems such as:
- ATMs and internet banking apps,
- Credit card acceptance,
- Order processing,
- Online reservations (ticketing, reservation systems, and so on),
- Patient health records,
- Inventory control and production scheduling,
- Customer relationship management (CRM) platforms,
- Claims processing and customer service ticketing.
Let’s zoom in on a few categories to understand OLTP use cases.
Banking and financial services
OLTP is used by banks and other financial institutions to process financial transactions in real time, maintain client data, and allow consumers to swiftly make deposits, withdraw money, transfer payments, and access other services.
OLTP solutions for financial transaction systems, such as online banking, must enable multi-currency transactions and provide unique reporting choices. The most typical example of an OLTP system utilized in the financial industry is ATMs.
OLTP is used in e-commerce systems to manage client orders, payments, and inventories in real time. This enables them to deliver excellent customer service, increase client loyalty, and propel growth.
An OLTP database, for example, can assist in maintaining up-to-date and correct inventory data, allowing e-commerce enterprises to fulfill orders quickly.
Online reservation systems in the travel and hospitality industries are powered by OLTP – think apps that handle reservations, flights, payments, and other services.
Such an online transaction processing system must interact with external systems, such as airline reservations or car rental apps, and handle several languages and currencies.
Best practices for implementing OLTP
Management of transactions and concurrencies
This one is a no-brainer. Since OLTP systems rely on transaction and concurrency control, while maintaining data integrity, ACID compliance is critical.
But here are several other mechanisms you should keep in mind:
An isolation level decides how transactions can interact with one another and how they perceive changes made by other transactions. Most OLTP systems give you four degrees of isolation: read uncommitted, read committed, repeatable read, and serializable.
Building the right isolation level into your OLTP system depends on the application’s needs and the requirement to balance data consistency and performance.
This mechanism ensures that several transactions can’t access the same data simultaneously. You can put locks at the database or table level. These locks can be shared or exclusive. The former allows several transactions to read the same data at the same time, the latter doesn’t and prevents transactions from accessing the data until the lock is released.
Dealing with deadlocks
What is a deadlock in the context of OLTP? Deadlocks occur when two or more transactions are waiting for each other to release locked resources and can’t proceed. Teams use deadlock prevention methods like timeout and transaction prioritization to detect and resolve them before issues start happening.
A schema describes how data is arranged in a relational database. In the context of OLTP, the structure needs to be able to handle massive amounts of data.
To achieve that, engineers use the following best practices:
Indexing is a method for facilitating speedy access to database files since indexed data can accelerate query processing and performance. Identifying the most frequently used queries and generating indexes on the right columns are part of this process.
Normalization is all about dividing a huge table into smaller, more manageable tables in order to reduce data duplication. This provides data consistency and contributes to data integrity.
Using appropriate keys
Keys are here to help identify rows in a table in a unique way. They build links between tables and guarantee that no duplicate rows exist in the database. Keys are classified into three types: primary, composite, and foreign.
Data engineers can guarantee uniqueness using composite primary keys, while foreign keys can enforce referential integrity and ensure that there are no orphaned records in the OLTP database.
Monitor and adjust the schema on a regular basis to ensure it fulfills performance criteria and can manage the appropriate transaction volumes.
Scalability is the ability of an OLTP system to manage increased transaction volumes as the business expands. You can enhance it via scaling:
This is about adding extra servers or nodes to a database cluster in order to disperse the burden and increase performance. Database sharding makes that possible. Sharding is the process of splitting data and spreading it over multiple servers in a cluster, with each server operating independently.
When you scale vertically, you expand the capacity of the hardware or infrastructure on which the database is executing: the server’s RAM, CPUs, or storage.
Scaling OLTP database systems also involves the use of data replication, caching, and load balancing.
The above just scratches the surface since there are so many other things data practitioners need to take care of, starting with integration testing.
The future of OLTP
Companies in highly regulated industries, such as cybersecurity and financial services, have traditionally dedicated substantial resources to controlling internal record-keeping systems in order to prevent legal and regulatory difficulties.
However, we’re seeing an increase in the deployment of service-based OLTP database products driven by a growing awareness that outdated databases are a barrier to building cloud-native applications, especially regarding agility and speed of innovation.
There is an abundance of accessible cloud relational database-as-a-service solutions, including serverless options, that teams can use to achieve scalability with consumption-based invoicing.
To implement solid data solutions, companies are combining OLAP’s deeper processing skills with OLTP’s capacity to examine data quickly.
While they resort to more complex data technologies like predictive analytics or probabilistic databases, OLTP systems will continue to thrive in many transactional applications for the foreseeable future.
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