A greater number of people are plugged into the digital world than ever before, which is leading to an explosion of big data. To keep up with this growing activity, systems have needed to find new solutions that offer greater performance, reliability, and scalability. With all the solutions that have emerged in recent years, high throughput databases stand out as a powerhouse that's gaining serious traction.
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High throughput databases are becoming a necessary addition for many applications. These databases have been a game-changer for businesses and organizations. By handling and processing enormous data volumes efficiently, they've allowed applications to fully utilize the power of big data to deliver personalized and real-time features. In this article, we're diving into the world of high throughput databases. We'll explore their inner workings, reflecting on their significance in today's world of big data, and discuss the role they may play in our data-driven future. 
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To truly grasp the importance of high throughput databases and the reason behind their growing popularity, we first need to rewind and take a look at the technological landscape that led up to their creation.

The Birth of High Throughput Databases

In the early days of computing, data volumes were much smaller than they are today. Traditional relational databases were more than enough to handle their load and required levels of processing. As the internet grew in popularity, social media, streaming, and other platforms gained millions of users. And along with this growth came an enormous amount of data. The problem wasn't just with the sheer amount of data being generated and processed, but challenges also stemmed from their rising complexity. Off the back of this growing problem, databases that could handle unstructured and semi-structured data such as images, video, and audio were created.
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As computing power improved, applications and platforms grew in complexity and began to offer extensive new features such as real-time processing and personalization. Businesses saw the potential of harnessing big data to provide real-time value to their end users. Traditional databases struggled to meet these requirements and provide acceptable levels of performance. They lacked the features and scalability needed to handle mass amounts of concurrent transactions and maintain optimal speeds during periods of high traffic. High throughput databases emerged as a viable solution to this problem. These databases are optimized to process large volumes of data very quickly, and they have been designed to allow for multiple simultaneous operations. The creation of high throughput databases was made possible thanks to technological trends such as distributed architecture and advancements in hardware such as solid-state drives (SSDs) and high-speed networks.
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The Mechanisms Behind High Throughput Databases

High throughput databases have been carefully designed to ensure big data and large amounts of transactions can be processed at high speeds. A distributed architecture is used to boost scalability and enable the simultaneous processing of transactions. A distributed design uses multiple servers to spread load and data storage while allowing each individual server to scale independently during periods of high traffic. Sharding is used to divide the database into smaller and more manageable parts; referred to as a shard. Each shard is allocated a dedicated server which allows for parallel processing and speeds up transactions as operations are done on a smaller subset of data.
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Simultaneous processing is a major benefit of high throughput databases. Advanced concurrency control mechanisms are used to make it possible for multiple simultaneous transactions to occur on a high throughput database, without causing inconsistencies or conflicts. There are many ways to implement concurrency control. The strategies used, and the extent of each strategy, differ for each high throughput database.
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Locking is a popular strategy due to its simplicity. It involves locking data so that other transactions cannot modify it while a transaction is running. Locks can be shared or exclusive. With a shared lock, concurrent reading is allowed but no modifications can be done during that time. Whereas an exclusive lock only allows a single transaction to either read or write the data. Multi-Version Concurrency Control (MVCC) is an alternative that allows multiple transactions to run on the same data. When data is modified, MVCC creates a version of that data which other transactions can access. By providing a snapshot of the database at any given time, transactions can be done concurrently while minimizing the risk of conflict. Optimistic Concurrency Control (OCC) takes a far less defensive approach to transaction conflicts. It allows transactions to run without restrictions and instead, conflicts are checked before a transaction is committed. If a conflict is detected the transaction is aborted. Finally, timestamp ordering is a simple technique that executes transactions in the order they were created.
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In-memory processing is another technological advancement that high throughout databases have utilized to deliver their benefits. Prior to in-memory databases, traditional databases were disk-based which is considerably slower than memory access. Disk-based databases are still a very viable solution for many use cases. However, for use cases that require the real-time processing of big data, reading and writing from disk can become a significant bottleneck. High throughput databases combine in-memory processing with disk storage to achieve high performance.
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Data compression is an important component in high throughput databases that greatly contributes to their overall performance. In high throughput databases, data is encoded to lessen the amount of storage space used and speed up data transfer. For systems that process and store very large amounts of data, this would mean significant cost savings in data storage. However, the process of compressing and decompressing data can also add computing costs. High throughput databases use many different algorithms and techniques to compress their data, each with its own set of trade-offs and levels of compression. The compression techniques used in each high throughput database vary depending on the data types and workloads they're looking to support.
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The Application of High Throughput Databases in Today’s Digital Landscape

High throughput databases have played a significant role in the evolution of modern-day computing. Nowadays, applications are processing unprecedented amounts of data at record speed.

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Social media platforms are among the most used applications with millions of users that produce billions of data every day. These platforms process large-scale data to deliver a personalized experience across their entire user base. High throughput databases help ensure that the system behind these platforms stays performant and scales effectively when needed.
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Online gaming platforms are another example that caters to millions of users and processes billions of data points. High performance is integral to the success of any online gaming platform. Frequent lag, constant buffering, and high latency will eventually cause a large portion of the customer base to abandon the platform. This is why high throughput databases are commonly used within these systems. Their ability to handle large levels of concurrent operations is a perfect match for the levels of concurrent users and activity gaming platforms are faced with. High throughput databases also help make real-time data processing possible while still delivering a smooth gaming experience without sacrificing quality.
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The IoT (Internet of Things) landscape has taken up a significant place in the digital world and its wide adoption will only continue to grow. There are more devices connected to the network than ever before, all producing and processing very large amounts of data. Real-time analytics and insights are often a requirement for these IoT devices, which makes them a key use case for high throughput databases. IoT devices are usually met with challenges regarding latency and data consistency due to their distributed nature. High throughput databases help overcome these challenges thanks to their highly performant and scalable capabilities.

The rising complexity of modern-day applications paired with an increasing number of users becoming highly active in the digital world has meant that there is a growing need for high throughput databases. Use cases extend far beyond the three that are mentioned above. Some more notable mentions are telecom companies, FinTech platforms, and e-commerce platforms due to their exponential growth in users and activity.

The Future of High Throughput Databases

High throughput databases such as HarperDB are one of many innovations that have emerged during the past few years of technological innovation and growth. As the digital landscape evolves, there will be significant improvements in the implementation and design of high throughput databases to power even more complex and robust systems than we see today. With the current wave of innovation in AI and machine learning, we can expect some form of integration between AI and high throughout databases in the future. This step would introduce an exciting set of features such as AI-powered automation, predictive analysis, real-time insights, and anomaly detection. Moreover, the growing adoption of blockchain technology will open up opportunities for decentralized high throughput databases.
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The computational power of IoT devices is rapidly growing, and with that is the need for secure, performant, and scalable data management storage. As we progress into the future of the digital age, IoT companies will continue to adopt high throughput databases to meet the needs of real-time analytics and insights.
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High throughput databases are sure to evolve as technology progresses and new trends emerge. These databases will continue to be a powerful solution for the storage and management of big data, giving them a pivotal role in our digital future.