PostgreSQL vs MySQL: Which Database Is Right for Your Business?

From the point of view of a software-development company, the computing infrastructure needed for developing and supporting software can be of two types: Servers owned by the development company The application developer physically hosts the server equipment directly in their office or data center, maintains it themselves, and ensures data security. This approach has become less popular over the years. Developers prefer using a provider that offers computing power on its own equipment on a paid basis. Advantages: Full physical control over the servers High data security and confidentiality Disadvantages: Financial costs for server hardware Financial costs for maintenance (specialists, equipment) Need for experience and expertise to support data centers Servers managed by another organization The application developer chooses a provider from whom they remotely rent computing resources. Today, this is the most optimal option for most companies. Advantages: No need for own equipment The amount of computing resources used is flexibly adjustable No need for experience or expertise in network communications Disadvantages: The provider is responsible for data security and confidentiality No physical control over the servers What Are Cloud Technologies? Cloud technologies are technical solutions that provide users with computing power through a flexibly configurable online service. The cloud belongs to the provider of cloud resources, who owns the physical servers. Although clouds from different providers have their own unique features, they generally include the following basic components: servers storage databases network infrastructure software testing and analytics tools Physical and Logical Layers The main feature of the cloud is that, unlike the traditional server-rental model, it separates the physical and logical layers. This means that data processing and network interactions exist on top of the physical base—the server machines located in data centers (DCs). This is why cloud allows the flexible configuration of the resources a specific application requires. This architecture can be represented hierarchically: Logical layer: operating systems, virtualization systems, containers, server applications, application-level network protocols, business logic, client applications Physical layer: server machines, motherboards, ASIC controllers, low-level network protocols Cloud Models There are several types of clouds that differ in the degree of abstraction of the software (logical) level. IaaS (Infrastructure as a Service) Only computing power is provided, with just the operating system and sometimes additional software. Examples: Amazon Web Services Microsoft Azure Google Cloud Hostman Cloud Servers PaaS (Platform as a Service) Along with computing power, a set of tools for development and administration is provided. Examples: Red Hat OpenShift AWS Elastic Beanstalk Google App Engine Hostman App Platform SaaS (Software as a Service) A client application with ready-to-use functionality that does not require specialized knowledge. Examples: Google Drive Salesforce Dropbox Types of Clouds Clouds differ in their degree of isolation, from fully public to fully private. There are also mixed implementations. Public Computing resources are located in the provider’s data center. All cloud provider resources are distributed among users depending on the chosen pricing plans. Private The cloud is completely isolated (sometimes even disconnected from the global Internet) and is physically located on the user's server equipment. There are also cases when the cloud provider offers isolated server resources for a specific client, while other users use the public part of the cloud. Hybrid A combination of a private cloud and a public one. Usually, business logic containing sensitive data is placed in the private cloud, while “peripheral” computing is performed in the provider’s public cloud. Advantages of Cloud Technologies Simplified Development Using any of the existing cloud models clearly simplifies the development process. The closer a chosen model is to SaaS, the easier it is to interact with the cloud, but the more standardized and typical the solution becomes. The closer a chosen model is to IaaS, the more precisely the cloud can be configured, but the more complex it becomes to maintain. Therefore, the cloud model is selected individually for every case. A standardized solution may not fit someone if the product being developed requires a more unique technical implementation. For example, using cloud databases (DBaaS) provided by Hostman removes many responsibilities from the developer regarding managing their own storage. While deploying a database cluster can be done fairly quickly and easily, using it properly “in the long run” requires certain expertise. Thus, to stay focused on development, it makes sense to entrust this function to an experienced provider with established methodologies, expertise, and the necessary technical solutions. Cost Optimization In the cloud, user expenses match the amount of resources used in their work tasks. This payment model, "pay-as-you-go", is fundamental to cloud computing: you pay only for the resources you use. If, at some point, more resources are needed, they are allocated quickly and increase costs. Conversely, extra resources can be “returned” to the provider, reducing expenses. In other words, the user’s financial expenses correlate with the number of resources consumed. Allocation and release of resources happen instantly. Additionally, delegating cloud-infrastructure management to the provider eliminates the developer’s costs associated with maintaining their own data center: Hiring specialists and administrators Purchasing server equipment Renting premises Faster Deployment Instant access to cloud resources and the ability to configure them flexibly significantly reduces the time: From the start of MVP development of a new product to its deployment in a production environment From the start of developing new features to adding them to the finished product For example, the development process of a new service might look like this: Managers formalize requirements for the future product. Developers determine the technology stack. The development company rents the necessary computing resources from a cloud provider, including cloud servers, Kubernetes clusters, and object storage. Developers write code, which is automatically deployed in the provider’s cloud environment. The new product is tested, and user feedback is collected. Based on the collected data, managers decide whether further support is justified. If the product does not meet expectations, the allocated resources are returned to the provider. If successful, the computing resources can be expanded. In some cases, a developer may use multiple clouds from completely different providers, forming what is called a multi-cloud. 1. Cloud Server Cloud servers differ from dedicated servers in that the “physical” configuration is adjusted within a certain range. For example, Hostman cloud servers can be parameterized using a configurator. In this case, the rental cost corresponds to the capacities selected in the account panel. Accordingly, when workloads change, a cloud server can be scaled: CPU cores, RAM, disk space, or connection speed can be increased. 2. Cloud Databases In addition to the computing power provided by the cloud server, most projects require a specialized database. Hostman provides most popular database engines as a separate cloud service: MySQL PostgreSQL Redis and others As with servers, computing resources for a database can be configured based on several parameters: Number of cores RAM NVMe disk size Rental period When ordering a database, you don’t need to configure hardware, track software updates, or administer the database: the infrastructure is managed by Hostman. 3. Kubernetes A crucial component of cloud development is automation of building, deploying, and testing using CI/CD pipelines. CI/CD is the basis of the DevOps methodology, whose goal is to automate changes to software. Cloud services provide the needed capabilities for this. For example, GitHub CI/CD and GitLab CI/CD infrastructures have special cloud tools that simplify creating CI/CD pipelines, including test environments, storage, notification functions, and databases. Modern applications consist of microservices placed in containers. Kubernetes, the standard for container orchestration, is used for management and monitoring. Kubernetes clusters from Hostman can be a suitable solution. In this case, the developer avoids the costs associated with maintaining their own infrastructure as the execution of Kubernetes applications is handled by the provider. Conclusion In this article, we reviewed what efficient development using cloud services looks like, and also mentioned the most popular developer tools: Cloud servers Databases Monitoring tools (CI/CD, Kubernetes) While creating your own data centers requires time, expertise, and significant financial investment, the cloud is provided by a provider in a refined and secure form. A ready-made cloud helps developers immediately focus on the business logic of the application, thus speeding up development and integration of innovations. Today, public cloud services are available even in areas that were previously predominantly non-cloud. Clouds are being implemented even in enterprises unrelated to IT. Therefore, cloud technologies not only accelerate development but also accelerate digital transformation as a whole.

Every developer strives to speed up product development while maintaining enough flexibility and confident control over the process. Microservices application architecture helps solve these tasks and, over the past 10 years, has begun actively competing with the traditional monolithic approach. To begin, let’s look at the difference between them. Microservices Architecture vs. Monolith The difference between these two software development approaches is easiest to illustrate with an example. Let’s imagine two online stores: one implemented as a monolith and the other as microservices. A monolithic online store is a single, indivisible structure that combines all components: databases (catalog, customer data), shopping cart, order and payment forms. All of these elements are tightly interconnected and located on the same server. In a microservices system, each component is an independent module that developers can work on separately. And naturally, nothing requires hosting these modules on a single server. Thus, microservices architecture is a kind of constructor that allows you to add new elements painlessly as you scale the application. A monolith, on the other hand, can be compared to a solid wall: scaling here is only possible by adding another identical monolith. It’s worth adding that microservices are sometimes mistakenly perceived as a set of very small services. This is not the case: for example, the database of a large online store may contain millions of records and occupy tens of gigabytes, yet still be just one of the modules within the microservices architecture of the whole application. Comparing Microservices and Monoliths by Key Criteria Now let’s look at the main characteristics of microservices technology in comparison with a monolith and see how both approaches solve the same developer tasks. Release Cycles Development speed and frequency of updates increase with microservices thanks to modularity: changes are made not to the entire codebase but to individual modules. With a monolith, however, the whole platform must be updated first, which increases testing and debugging time. As a result, development slows down and updates are released less frequently. Technology Stack The microservices model offers significantly greater flexibility because each service can be written in its own programming language and may use different libraries and data-storage technologies. With a monolith, the situation is different: changing the technology stack is nearly impossible. Developers are forced to stick to the initial tools. Developer Onboarding Each module in a microservices architecture is self-contained, making it possible to bring in programmers who are familiar with the functionality of a specific service. This substantially lowers the onboarding threshold. With a monolith, new developers must dive into the code of the entire application, understand the functions of every block, and only then begin productive work. Thus, maintaining a monolith is more dependent on specific team members. Optimization Characteristics The modularity of microservices architecture also positively affects optimization, since developers can optimize each service separately. Optimizing a monolithic structure is more difficult because the team must account for links between indivisible blocks, and updating any one of them inevitably affects the entire application. Application Scalability The distributed structure of microservices and their ability to run on separate servers make scaling fast and easy. In monoliths, scaling one component inevitably requires scaling the entire application as a whole. Fault Tolerance Because services are hosted on different servers and have a modular structure, microservices architecture achieves independence of each module. This significantly increases system resilience: a failure in one service does not cause the entire application to fail. With a monolith, the situation is different: all components are tightly interconnected, so the failure of one module can make the entire application inoperable. Do I Need to Switch to Microservices Right Now? As we’ve seen, microservices have advantages in many key areas. But does this mean you need to abandon monoliths as an outdated approach and immediately switch to microservices? The answer depends on the current state of your project. And let’s say right away: rushing to adopt microservices is not always the right choice. Distributed architecture also has its drawbacks. First, microservices require ensuring network connectivity between modules. If a network connection is unstable, this leads to delays and data inconsistencies, which create potential problems in application behavior. Second, each module of a microservices system requires separate testing and health monitoring. Additionally, you will need to allocate cloud resources for each module, which may increase costs. Third, with the microservices approach, teams responsible for different modules may encounter interaction issues. This means you may need a connecting link in the form of DevOps specialists who can streamline collaboration and speed up development. All the factors listed above allow us to conclude that the transition to microservices must be timely. Usually, during the early stages of a project, this is unnecessary, especially if developers have limited human or financial resources. Switching to a microservices architecture makes sense when there is a clear need for significant scaling, and scaling a monolith has already become difficult. Microservices may be right for you if: You have a large team. In this case, it makes sense to divide the team into separate, independent groups, each responsible for its own service; You have a complex, branched application. In this case, it’s far more convenient to update and maintain modules separately than to rebalance the entire system each time; Your application traffic is highly variable. For example, you see sharp spikes in load during certain periods. Microservices' distributed structure allows quick scaling during peak loads, after which you can easily return to normal capacity levels; Your application is frequently updated. Working with separate modules in this scenario is much simpler, and new releases will be significantly faster. If your project meets at least one of these criteria, this is a reason to consider breaking it into independent elements. However, if your application is relatively small and does not require frequent updates, it is reasonable not to rush away from monolithic architecture. Useful Tools for Organizing Microservices A modern development approach requires a containerization platform. In most cases, developers use Docker for these purposes. Docker tools allow them to isolate the application from the infrastructure, meaning they can work with it equally well locally or in the cloud, which is very convenient for development. Once containers become numerous, an orchestrator becomes essential for managing and organizing groups of containers. Kubernetes is most commonly used as an orchestrator due to its strong compatibility with Docker. Another necessary tool is a load balancer, which ensures even distribution of network traffic across all cloud resources. This significantly increases the application’s fault tolerance.

Serverless is one of the popular directions in IT and continues to grow. Cloud providers are ready to provide all the necessary infrastructure and maintain it, and the user pays only for the execution time of a function—idle time is not billed. Serverless Ecosystem Serverless architecture is a way of building applications in which the developer writes the application logic, while the cloud provider is responsible for launching that logic, monitoring, scaling, maintaining the execution environment, and managing load planning. This is not always an external provider; such infrastructure can also be implemented inside a company using its own computing resources. The simplification of working with infrastructure traces back to the appearance of the first IaaS (Infrastructure as a Service), where the provider simply offered virtual infrastructure for rent, and then extended to widespread BaaS (Backend as a Service), where a developer’s microservice or a database is simply several containers running in the provider’s environment, and in the case of a database, with preconfigured replication. Another product is FaaS (Function as a Service), where the execution unit is a single function. In part, this resembles a microservices approach. Your function should not rely on state between calls (it must be stateless), because each call is launched by the provider as a separate instance in a container. There are also some limitations imposed by the cloud platform: not all programming languages can be used to write function code (although the list is usually wide), and there are limits on execution time and memory usage. The exact limits differ by platform and may depend on your pricing plan. Many providers offer trial periods from three months to a year with limits on the number of requests. The foundation of Serverless is FaaS and the provider’s ecosystem of products. Typically, there are Databases, Authorization Services, API Gateways, Message Brokers (such as Kafka), all provided as services (BaaS) that your function can integrate with. Architecture Each provider may offer a unique set of services, but applications built on FaaS generally have similar architectures. As an example of integration between provider products, you can consider a TODO application (the example is taken from the AWS website). The application logic is packaged in lambda functions, which interact with the DynamoDB database, provided by AWS as a separate service. In front of the functions is the API Gateway, which handles incoming requests and routes them appropriately. The architecture also includes the Amplify Console for managing the web interface of the application and the Amazon Cognito authentication service. Advantages and Disadvantages The main positive aspects of Serverless typically include: Flexible scalability Helps handle peak loads and rapid product growth. The provider will automatically scale the function, and when demand decreases, resource consumption will shrink. Pricing You pay only for the execution time of the function. For example, on AWS, the price includes the cost of function invocations and the amount of memory (GB/s) allocated during execution. Even with 24/7 availability (with some nuances), you do not pay for periods of inactivity. Automation CI/CD, integration with other services, monitoring, log management, and maintaining the execution environment—the provider handles all of this, allowing for faster product deployment. Compared to BaaS infrastructure or full server control (VPS), deploying an application into cloud functions has its downsides: Resource limits Cloud functions have memory and execution time limits, as well as restrictions on data sizes sent or received. Here are examples of such limits in AWS. Cold starts The provider will not keep a container with your function running at all times. If the function has been idle for some time, its next invocation will require time to start up. Startup time depends on the programming language, code size, and the provider’s internal processes, and can reach up to 10 seconds. Here’s a small article with examples on the topic, and a GitHub repo with code and research results. Best Practices Although FaaS-based systems and service ecosystems resemble microservice architecture and functional programming, they have their own best practices beyond monitoring, logging, request tracing, and mocking service responses. Warm Start If your application requires consistently fast responses, cold starts can be a major issue. The solution is to constantly “warm up” the function, for example, by periodically sending requests that create some load so the provider keeps the function active. The load itself does not matter; what matters is triggering the function. If necessary, the provider will scale by starting additional instances on the fly. Some providers offer built-in mechanisms for this (e.g., AWS), allowing you to keep a certain number of instances always running. Fan-Out Pattern A pattern that allows you to bypass memory, data size, and execution time limits by splitting a large task into smaller chunks. Downloading and processing data in chunks, sending emails in small batches—cloud functions will execute tasks in parallel across different function instances. The trade-off is more complex application code and paying for more function invocations. Event Driven Pattern Cloud FaaS functions can be triggered not only via API Gateway or direct invocation. A trigger can be an event from any integrated service, such as a message from Kafka. Event-driven code organization works extremely well for FaaS. Cost Optimization Unlike a fixed-price service (e.g., renting a VPS for a year), Serverless costs are composed of several different services, each with its own billing model. Continuous monitoring of costs and resource utilization, along with load planning, is key to reducing infrastructure expenses. Some providers have special offerings, such as AWS Lambda Reserved Concurrency, which allows reserving Lambda instances and keeping them active on a cheaper pricing tier. Tools for Working with Serverless Serverless Framework An open-source tool for simplifying the development, deployment, and management of serverless applications. It allows developers to focus on writing code without worrying about infrastructure and provider configuration. It supports AWS, Azure, Google Cloud, IBM Cloud, Oracle Cloud. It enables local function testing, integrates with CI/CD systems like Jenkins or Travis, has a large plugin ecosystem, and allows developers to create their own plugins. Terraform A HashiCorp tool representing the "infrastructure as code" approach. It allows defining infrastructure using configuration files in HCL (HashiCorp Configuration Language), which describe the desired infrastructure state rather than the steps to achieve it. Like Serverless Framework, Terraform supports many major cloud providers and allows defining modules, i.e., reusable sets of configuration files for creating and managing infrastructure components. AWS SAM The Serverless Application Model is an open framework created by AWS for developing, testing, and deploying serverless applications on AWS. SAM provides a simplified way to build serverless apps based on CloudFormation, AWS’s main tool for infrastructure as code. Kubeless Unlike the previous tools that focus on cloud providers, Kubeless is a serverless framework for Kubernetes. It allows creating and managing serverless functions directly inside your Kubernetes cluster. It works with standard Kubernetes objects like Deployments, Services, Ingress, etc. It provides scaling and fault tolerance for functions, has a CLI, and integrates with the Kubernetes ecosystem. OpenFaaS An open-source serverless framework. It allows creating functions in many programming languages, including Python, Node.js, Go, Ruby, Java, and others. It supports scaling and ensures fault tolerance. OpenFaaS integrates with Kubernetes and Docker Swarm. Areas of Application Today, Serverless applications cover a wide range of tasks, from notification and mailing services to analytics platforms, data stream processing, webhooks, game servers, file and multimedia processing, IoT applications, adapters for external services, monitoring and logging systems, business process automation, content rendering servers, customer support chatbots, and testing or deployment servers. Many examples exist within the Serverless Framework ecosystem. Major IT companies are migrating some processes to Serverless. For example: Netflix uses FaaS for encoding and transcoding video when preparing video streams for different devices. Airbnb uses Serverless in its StreamAlert system for real-time data analysis. Using Serverless is not only a way to reduce infrastructure costs, but also a tool for increasing development efficiency and flexibility. Serverless allows for rapid development and deployment of new functions and applications, as well as scaling them based on business needs. Additionally, Serverless technologies support creating more reliable and fault-tolerant applications because cloud providers ensure high availability and automatic scaling. This helps companies avoid downtime or service failures that could harm their reputation and customer experience. Development of Serverless Serverless architecture represents a significant step in the evolution of cloud computing, allowing companies to focus on application development without worrying about managing infrastructure. This approach brings flexibility, scalability, and efficiency to development processes while reducing maintenance and operational costs. In the future, we can expect development in several areas: Greater integration and compatibility between cloud providers, allowing users to choose the best solution without vendor lock-in. New services and tools tailored for Serverless architectures, such as advanced tools for monitoring, performance optimization, security, and expanded development and deployment capabilities. Growing adoption among companies, increasing the market’s growth and expanding opportunities for innovations and new business models. Serverless technologies are poised to continue reshaping the landscape of cloud computing, enabling companies to focus on building applications rather than managing infrastructure. With more integrations, new tools, and broader adoption, Serverless will increasingly become a key element in the modern developer’s toolkit.