How to Speed Up Development with Cloud Technologies

PostgreSQL and MySQL are among the most popular relational databases. In this article, we will examine the functional differences between them and compare their performance so that you can choose the database that is suitable for your business. PostgreSQL vs MySQL Despite the increasing similarity in features between PostgreSQL and MySQL, important differences remain. For example, PostgreSQL is better suited for managing large and complex databases, while MySQL is optimal for website and online-application databases because it is oriented toward speed. This follows from the internal structure of these relational database systems, which we will examine. Data Storage in PostgreSQL and MySQL Like any other relational databases, these systems store data in tables. However, MySQL uses several storage engines for this, while PostgreSQL uses only a single storage engine. On one hand, this makes PostgreSQL more convenient, because MySQL’s engines read and write data to disk differently. On the other hand, MySQL offers greater flexibility in choosing a data engine. However, PostgreSQL has an advantage: its storage engine implements table inheritance, where tables are represented as objects. As a result, operations are performed using object-oriented functions. Support The SQL standard is over 35 years old, and only the developers of PostgreSQL aim to bring their product into full compliance with the standard. The developers of MySQL use a different approach: if a certain feature simplifies working with the system, it will be implemented even if it does not fully conform to the standard. This makes MySQL more user-friendly compared to PostgreSQL. In terms of community support, the number of MySQL developers still exceeds those working with PostgreSQL, but you can receive qualified help in both communities. In addition, many free guides and even books have been written about PostgreSQL, containing answers to most questions. It is also worth noting that both platforms are free, but MySQL has several commercial editions, which can sometimes lead to additional expenses. Programming Languages Both systems support a wide range of programming languages. Among the popular ones are C++, Java, Python, lua, and PHP. Therefore, a company’s development team will not face difficulties implementing features in either system. Operating Systems MySQL is a more universal system that runs on Windows, Linux, macOS, and several other operating systems. PostgreSQL was originally designed for Linux, but with the REST API interface, it becomes an equally universal solution that operates on any OS. Data Processing PostgreSQL provides more capabilities for data processing. For example, a cursor is used for moving through table data, and responses are written to the memory of the database server rather than the client, as in MySQL. PostgreSQL also allows building indexes simultaneously for several columns. It supports different index types, allowing work with multiple data types. This database also supports regular expressions in queries. However, new fields in PostgreSQL can only be added at the end of a table. Parallel data processing is better organized in PostgreSQL because the platform has a built-in implementation of MVCC (multiversion concurrency control). MVCC can also be supported in MySQL, but only if InnoDB is used. Concerning replication, PostgreSQL supports logical, streaming, and bidirectional replication, while MySQL supports circular replication as well as master-master and master-standby. Replication refers to copying data between databases located on different servers. PostgreSQL and MySQL: Performance Comparison Testing is fair only when comparing two clean, “out-of-the-box” systems. Indexed testing provides the following results: Insertion: PostgreSQL is more than 2.7× faster, processing a 400,000-record database in 5.5 seconds versus 15 seconds for MySQL. Inner join: PostgreSQL processes 400,000 records in 1.1 seconds, MySQL in 2.8 seconds: a gain of more than 2.5×. Indexed sorting: PostgreSQL processes the same number of records in 0.9 seconds, MySQL in 1.5 seconds. Grouping: For the same 400,000-record database, PostgreSQL achieves 0.35 seconds, MySQL 0.52 seconds. Indexed selection: PostgreSQL is 2× faster: 0.6 seconds vs. 1.2 seconds. When it comes to updating data, PostgreSQL’s update time increases gradually as the number of records grows, while MySQL processes them in roughly the same time, starting from 100,000 records. This is due to different data-storage implementations. Nevertheless, PostgreSQL holds a significant advantage over MySQL even with large data volumes: 3.5 seconds versus 9.5 seconds for 400,000 records—more than 2.7× faster. Without indexes, PostgreSQL also shows surprisingly high performance, processing a 400,000-record database in 1.3, 0.7, and 2.2 seconds for inner join, selection, and update operations, respectively. Thus, PostgreSQL delivers an average performance advantage of about 2× (2.06). Although MySQL was originally positioned as a high-performance platform, constant optimization by the PostgreSQL development team has resulted in greater efficiency. Advantages for Developers Here we consider only the unique features characteristic of each platform. Therefore, we will not discuss support for MVCC or ACID, as these features are present in both systems. From a developer’s perspective, MySQL is advantageous because it: Provides increased flexibility and is easily scalable, with more than ten storage engines based on different data-storage algorithms. Handles small read-oriented databases more efficiently (i.e., without frequent writes). Is easier to manage and maintain, because it requires less configuration and fewer preparatory steps before starting work. From a developer’s perspective, PostgreSQL is advantageous because it: Offers an object-oriented approach to data, enabling inheritance and allowing the creation of more complex table structures that do not fit the traditional relational model. Handles write-oriented databases better, including validation of written data. Supports object-oriented programming features, enabling work with NoSQL-style data, including XML and JSON formats. Can support databases without limitations on data volume. Some companies use PostgreSQL to run databases as large as several petabytes. PostgreSQL and MySQL Comparison For clarity, the main features of both systems can be presented in a table:   PostgreSQL MySQL Supported OS Solaris, Windows, Linux, OS X, Unix, HP-UX Solaris, Windows, Linux, OS X, FreeBSD Use cases Large databases with complex queries (e.g., Big Data) Lighter databases (e.g., websites and applications) Data types Supports advanced data types, including arrays and hstore Supports standard SQL data types Table inheritance Yes No Triggers Supports triggers for a wide range of commands Limited trigger support Storage engines Single (Storage Engine) Multiple As we can see, several features are implemented only in PostgreSQL. Both systems support ODBC, JDBC, CTE (common table expressions), declarative partitioning, GIS, SRS, window functions, and many other features. Conclusion Each system has its strengths. MySQL handles horizontal scaling well and is easier to configure and manage. However, if you expect database expansion or plan to work with different data types, it is better to consider implementing PostgreSQL in advance. Moreover, PostgreSQL is a fully free solution, so companies with limited budgets can use it without fear of unnecessary costs.

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.