DEV Community: Oleksandr Kashytskyi

Product Maintainability - design principles and practices

Oleksandr Kashytskyi — Sat, 24 May 2025 11:38:37 +0000

Introduction
Main 3 Design Principles
- Operability
- Simplicity
- Evolvability
Additional Maintainability Practices
- Code Readability
- Testing & Automation
- Decoupling Components
- Continuous Refactoring
- Monitoring & Logging
Conclusion

Introduction

Most of the total cost of software development and ownership is tied not to the initial build but to its maintenance. In fact, multiple industry studies, including those by IEEE and Gartner, indicate that 60–80% of a software system’s total lifecycle cost is spent on maintaining and evolving it. These costs encompass fixing bugs, enhancing functionality, adapting to new requirements, updating dependencies, and ensuring security compliance.

A well-maintained software system is easier to operate, understand, and extend over time. This not only boosts productivity but also improves developer morale and reduces turnover. Software maintainability is a key metric in software quality, and ensuring it requires a deliberate and thoughtful approach throughout the development lifecycle—from design to deployment.

So let's check what can increase maintainability of our product!!!

Main 3 Design Principles

Operability

Operability refers to how well the system supports day-to-day operations such as deployment, monitoring, and troubleshooting. A highly operable system:

Integrates with monitoring tools (e.g., Prometheus, Datadog, Sentry)
Has built-in health checks and metrics
Supports automated recovery and graceful failure handling
Enables fast incident diagnosis and resolution

Improving operability leads to lower Mean Time to Recovery (MTTR) and better system uptime, both of which are crucial for business continuity.

Simplicity

Simplicity aims to eliminate unnecessary complexity. Systems that are simpler:

Have clear responsibilities and minimal side effects
Are easier to test, reason about, and modify
Encourage consistent coding styles and patterns

As per software engineering research, complexity is the primary factor behind bugs and delayed development. Tools like static analyzers, linters, and code review checklists can help enforce simplicity at scale.

The best tool for removing complexity is abstraction. By encapsulating intricate logic behind well-defined interfaces, abstraction helps reduce cognitive load, prevent errors, and improve reusability.

Evolvability

Evolvability ensures the system is ready to change and grow with new requirements. Key practices include:

Modular design and domain-driven boundaries
Clear, stable APIs with versioning
Backward compatibility and migration support

NOTE: Except of backward compatibility there is forward compatibility, but it's implementation require very high skills and knowledge of the product.

Additional Maintainability Practices

Code Readability

Readable code is easier to debug and extend. It involves:

Clear naming conventions
Consistent formatting (e.g., via Prettier or Black)
Logical structure and separation of concerns
Inline comments and documentation for complex logic

Surveys show that developers spend over 70% of their time understanding existing code. Readable code is not a luxury—it's a necessity for long-term maintainability.

Testing & Automation

Automated tests form the backbone of a reliable system. Key strategies include:

Unit tests to validate business logic
Integration tests to catch cross-module bugs
End-to-end tests for user-facing flows
CI/CD pipelines for fast, reliable delivery

According to Capers Jones, defect rates drop by 60–90% in systems with strong test coverage and automation.

Decoupling Components

Reducing interdependencies makes systems easier to change. Strategies include:

Applying microservice, service-oriented, or hexagonal architecture
Using message queues or APIs for communication
Defining clear interfaces and contracts

Organizations that adopt modular and decoupled architectures report higher deployment frequencies and lower change failure rates, as seen in the State of DevOps reports.

Continuous Refactoring

Refactoring is essential to maintain code health. Benefits include:

Reduction of technical debt
Improved performance and maintainability
Easier onboarding of new developers

Unchecked technical debt can reduce development speed by 15–20% annually, compounding into major delays over time. Scheduled refactoring sprints or “engineering health” time allocations are vital.

Monitoring & Logging

Visibility into system behavior is critical for proactive maintenance. Good observability includes:

Structured logging with correlation IDs
Metrics collection and dashboards
Distributed tracing
Real-time alerts and anomaly detection

Teams that invest in observability tools report a 30%+ improvement in reliability and resolution times, according to research by Honeycomb and Google SRE practices.

Conclusions

Building maintainable software is not a one-time effort but a continuous commitment. By adhering to foundational design principles — operability, simplicity, and evolvability — and reinforcing them with proven best practices like code readability, testing, decoupling, refactoring, and monitoring, teams can ensure that their systems remain robust, adaptable, and cost-effective.

Ultimately, maintainability is a force multiplier — it enhances productivity, reduces risk, and positions software to evolve in harmony with business needs. Prioritizing it from day one is one of the most impactful investments in long-term software success.

Scalability in Data-Intensive applications - Fan-Out, Throughput, Twitter problem, Percentile

Oleksandr Kashytskyi — Tue, 25 Feb 2025 10:18:43 +0000

Introduction
Identifying Bottlenecks
The Twitter Problem
Measuring Response Time Effectively
Conclusion

Introduction

As applications grow, they need to handle more users, more data, and more requests efficiently. Scalability is a term used to describe a system's ability to cope with an increasing load. But how do we ensure that a system scales well? Let's explore some key concepts.

Identifying Bottlenecks

To scale a system effectively, it's essential to analyze its load parameters. Different systems have different constraints, and finding bottlenecks helps in optimizing performance. Here are several key factors to consider:

Fan-out: It's a term which describes the number of requests a service or endpoint makes to other services in order to serve a single incoming call. A high fan-out can lead to increased latency and system overload.
Throughput: In batch processing systems like Hadoop, the focus is on records processed per second rather than individual response times.
Response Time Distribution: Measuring response time is not just about average values but understanding the distribution of values.

The Twitter Problem

A classic example of scalability challenges is Twitter's timeline system. There are two endpoints, one to create a new post and another to fetch newest 20 posts.

A naive approach would be to query the database every time a user requests their home timeline. This results in expensive read operations and high latency.

Instead, Twitter solves this problem by maintaining a cache for each user's homepage. As cache memory is very expensive, there can't be stored all data needed for response, but it's possible to easily store IDs of last 20 posts in the timeline. This approach increases write complexity (It will be necessary to maintain both posts in a database and cache storage), but significantly improves GET request performance.

Measuring Response Time Effectively

Response time can vary significantly depending on system load. One of the best ways to analyze it is through percentiles, rather than averages.

99.9th percentile is often used to track performance (Some companies, like AWS AWS for example use 99.99th percentile). The reasoning behind this is that the top 0.1% of users are usually the most valuable customers, often transferring the most data or making the most critical requests.

Example: Sentry Monitoring: In observability tools like Sentry, response time percentiles help identify slowest transactions affecting real users, allowing engineers to optimize performance accordingly.

Conclusion

Scaling a system is not just about handling more traffic but ensuring efficiency, and optimal resource allocation.

Scalability is essential for handling growing demands in data-intensive applications. Identifying bottlenecks like fan-out, throughput, and response time distribution helps optimize performance. Finally, using percentiles instead of averages ensures a more accurate measure of system performance, helping engineers focus on critical optimizations.

🚀 Ever wondered why some systems never fail while others crash at the worst moments?

Oleksandr Kashytskyi — Sun, 16 Feb 2025 16:23:38 +0000

Reliability in Data-Intensive Applications

Oleksandr Kashytskyi ・ Feb 16

#bigdata #software #computing #fault

[Boost]

Oleksandr Kashytskyi — Sun, 16 Feb 2025 16:21:53 +0000

Reliability in Data-Intensive Applications

Oleksandr Kashytskyi ・ Feb 16

#bigdata #software #computing #fault

Reliability in Data-Intensive Applications

Oleksandr Kashytskyi — Sun, 16 Feb 2025 16:21:12 +0000

Introduction
What is Reliability?
Types of Faults in Data-Intensive Systems
- Hardware Faults
- Software Errors
- Human Errors
Visualizing Reliability in Systems
- Fault Isolation
- Observability Framework
Conclusion

Introduction

Data-intensive applications differ from compute-intensive ones by relying heavily on data storage, processing, and retrieval rather than raw computational power. These applications are typically built from standard building blocks, such as databases, caches, messaging systems, and distributed storage.

Beyond databases, maintaining a data-intensive system requires a suite of other tools to ensure reliability, performance, and fault tolerance.

What is Reliability?

A system is considered reliable if it:

Performs its intended function correctly as expected by the user.
Can tolerate user mistakes without severe failures.
Maintains good enough performance for the required use case.
Prevents unauthorized access to sensitive data.

Reliability is closely related to fault tolerance — the system’s ability to continue functioning despite faults.

Fault ≠ Failure

A fault occurs when a component stops working (e.g., a database node crashes).
A failure happens when the system as a whole can no longer function correctly.

Types of Faults in Data-Intensive Systems

Hardware Faults

Hardware failures include disk crashes, memory corruption, and power outages.

Modern distributed systems can tolerate hardware faults through redundancy and failover mechanisms (RAID for storage, replication for databases e.t.c.).

Software Errors

Software errors are trickier to handle than hardware faults. They can be caused by:

Crashes due to bad input or unhandled edge cases.
A runaway process consuming all system resources.
Failures in external services that the system depends on.
Cascading failures, where a small failure triggers larger system-wide outages.

To mitigate software errors:

Implement robust error handling and graceful degradation.
Use circuit breakers and retry mechanisms.
Employ canary releases and feature flags to minimize blast radius.

Human Errors

Studies show that only 10-25% of outages are due to server or network faults, meaning human errors are a major contributor to system failures.

Strategies to reduce human-induced faults:

Design for resilience – Make critical operations harder to break.
Decouple risky operations – Separate places where humans interact most.
Thorough testing – Include unit, integration, and system-level tests.
Quick and easy recovery – Provide rollback mechanisms and automated recovery.
Detailed monitoring and alerting – Detect anomalies early.
Training and process improvement – Foster good management practices and continuous learning.

Visualizing Reliability in Systems

Fault Isolation

A well-architected system uses fault isolation to prevent one failing component from bringing down the entire system.

Load balancer ensures traffic is distributed evenly.
Circuit breakers prevent overload from failed services.
Caching layers reduce direct dependencies on databases.

Observability Framework

A good monitoring and alerting system is essential:

Logs, metrics, and tracing should be unified for quick debugging.
Real-time dashboards help detect anomalies.
Automated alerts ensure rapid response to incidents.

Conclusion

Reliability is a key aspect of data-intensive applications. Achieving it requires:

Understanding and mitigating different types of faults (hardware, software, and human errors).
Designing systems with resilience in mind (e.g., fault isolation, circuit breakers, failover strategies).
Implementing strong observability tools (Sentry, AWS CloudWatch) to detect and resolve issues quickly.

By following these principles, data-intensive applications can achieve high availability, fault tolerance, and consistent performance, ensuring a smooth experience for users.

DEV Community: Oleksandr Kashytskyi

Product Maintainability - design principles and practices

Table Of Contents

Introduction

Main 3 Design Principles

Operability

Simplicity

Evolvability

Additional Maintainability Practices

Code Readability

Testing & Automation

Decoupling Components

Continuous Refactoring

Monitoring & Logging

Conclusions

Scalability in Data-Intensive applications - Fan-Out, Throughput, Twitter problem, Percentile

Table Of Contents

Introduction

Identifying Bottlenecks

The Twitter Problem

Measuring Response Time Effectively

Conclusion

🚀 Ever wondered why some systems never fail while others crash at the worst moments?

Reliability in Data-Intensive Applications

Oleksandr Kashytskyi ・ Feb 16

[Boost]

Reliability in Data-Intensive Applications

Oleksandr Kashytskyi ・ Feb 16

Reliability in Data-Intensive Applications

Table Of Contents

Introduction

What is Reliability?

Types of Faults in Data-Intensive Systems

Hardware Faults

Software Errors

Human Errors

Visualizing Reliability in Systems

Fault Isolation

Observability Framework

Conclusion