Application performance can be affected by a variety of factors, ranging from poorly-written code to networking issues. As companies continue to collect and manage huge volumes of data, storage engine challenges and data bottlenecks in the deepest parts of the underlying software architecture lead to ever-increasing performance issues.  

Application development has undergone fundamental changes over the past decade with the emergence of new architectures that support faster development and deployment cycles. Distributed, cloud-native architectures such as microservices are now the standard for modern application development, offering unprecedented flexibility, agility and scalability.

In a microservices architecture, applications are constructed as a collection of loosely coupled services that can be independently developed, deployed and managed. Each service is a unit of functionality that can communicate with other services via APIs, and the same service can be reused in multiple business processes and systems. This concept represents an evolution from earlier generations of software systems that were based on a monolithic architecture. However, the emergence of microservices has not made monolithic systems obsolete as they still provide key advantages and are still widely used across business processes.

A monolithic architecture is made up of only three layers – a client-side user interface, a server-side application and a database. All the application components reside on the same server, share the same memory and CPU resources as well as the same file system and database. As monolithic architectures have much fewer moving parts compared to microservices, they are easier to develop, deploy, debug and manage.  

In a monolithic architecture, the application communicates directly with the hardware layer through system calls. Hence, a monolithic system can offer better performance compared with microservices architectures where each operation may involve multiple API calls to multiple microservices.

On the other hand, a monolithic architecture has significant weaknesses, most notably rigidity and the tight coupling of application components. Any modification to a monolithic system requires developers to rebuild and redeploy the server-side application, and rather than scaling individual services or components independently,  you can only scale the application as a whole. Due to its structure, a monolithic architecture is also not as resilient as a microservices-based application because the failure of a single application will result in a complete system crash.

‍

Why Performance Must be Handled at the Root

These shortcomings have led to the emergence of new application architectures such as service-oriented architectures (SOA) – and later, microservices. However, moving to a distributed architecture is not a panacea for all problems. Perhaps most notably, a distributed architecture where multiple microservices interact with each other requires a networking infrastructure for exchanging information. This makes data transfer slower than in a monolithic system with centralized code and memory, resulting in performance hits.

The performance advantages of a monolithic architecture are gradually dissipating as the volume of data continues to grow exponentially. One of the main factors that impact the performance of a system is the speed of I/O operations. Every application, database or any other software piece uses a storage engine(aka data engine) to handle the basic operations of storage management, e.g., create, read, update and delete data. As the volume of data continues to grow, existing storage engines struggle to keep up as they were not designed to support the scales of modern datasets.

Many times, organizations that are faced with performance issues along these lines and are reluctant to move to a microservices architecture, address the problem by deploying powerful monolithic systems equipped with a large amount of CPU and memory resources. While this approach can help handle extreme application workloads, it is not an optimal solution in terms of cost-effectiveness.

Due to the abovementioned inherent limitations of existing architectures, performance issues are common to both microservices and monolithic systems. Eventually, beneath all the layers of abstraction, data must be laid out on the physical hardware. Hence, it is becoming apparent that application performance must be addressed at the very core of the underlying data architecture.

‍

The Road to Architectural Freedom

Whether using monolithic or microservices-based architectures, boosting data read and write speeds at the storage engine level is key for optimizing application performance. In a microservices environment, faster data operations can compensate for the inherent performance impact caused by the need to move large amounts of data over the network, reducing latency, I/O hangs, etc.

In addition, removing storage engine bottlenecks could help extend the lifespan of legacy, monolithic systems. There are many use cases, for example, where implementing a scale-out architecture is not possible or beneficial – particularly if there is a high cost and complexity involved, or concerns over the impact on critical processes, or edge computing deployments in which scaling out options are limited.

Speedb was founded based on the realization that application performance must be addressed through fundamental changes in the data architecture. One of our main design goals was to create a brand-new data engine that boosts performance at the deepest level of the software architecture, as close as possible to the underlying physical hardware. By doing so, we aim to offer developers more freedom to choose the software architecture that is most suitable to their needs. With Speedb, developers can either scale up with existing hardware while freeing up system resources or significantly reduce the cost and complexity involved in supporting the growing scale-out needs of modern, microservices-based applications.

‍