Oleksandr Andrushchenko

@oleksandr-andrushchenko

Software Engineer & Solution Architect

Website: oleksandr-andrushchenko.com

GitHub: oleksandr-andrushchenko

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Location: The Colony, TX

Joined: Nov 03, 2025

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158 Articles

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4 Followers

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About

Software Engineer with experience building and optimizing scalable applications across healthcare, fintech, e-commerce, media, and logistics. Skilled in Python, JavaScript/TypeScript, PHP, cloud architecture (AWS Certified Solutions Architect & Developer), and database design (RDBMS, NoSQL). Delivered 40+ projects, improving performance, scalability, and reliability. Experienced in architectural decision-making and driving projects from concept to deployment.

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reliability aws aws-lambda amazon-ecs databases serverless protocols dynamodb postgresql nosql kubernetes infrastructure production-systems

Latest Articles

Storage Best Practices for Production Systems

Storage Best Practices for Production Systems

Production storage must remain reliable while data volume grows, hardware fails, workloads change, and background operations compete for capacity. A storage architecture that performs well during normal traffic can still fail when a node disappears, a volume fills, replication falls behind, or recov

Oleksandr Andrushchenko Aug 31

storage-systems reliability production-systems

Data Lifecycle Management

Data Lifecycle Management

Production systems rarely keep every byte of data forever in the same storage tier. Transaction records, application logs, uploaded files, analytics datasets, backups, and temporary artifacts have different values as they age. Keeping all of them on high-performance storage increases cost, operation

Oleksandr Andrushchenko Aug 31

storage-systems data-archiving data-lifecycle-management

Storage Performance Optimization

Storage Performance Optimization

Storage performance is determined by more than disk speed. Production latency depends on the entire I/O path: application access patterns, filesystem behavior, operating-system caches, storage queues, network latency, replication, device characteristics, and background workloads.

Oleksandr Andrushchenko Aug 31

storage-systems performance-optimization scalability

Replication, Snapshots, and Backup Strategies

Replication, Snapshots, and Backup Strategies

Replication, snapshots, and backups all protect data, but they protect against different failure modes . Treating them as interchangeable creates one of the most dangerous storage architecture mistakes: a system can have several replicas and frequent snapshots while still having no reliable recovery

Oleksandr Andrushchenko Aug 31

storage-systems data-replication data-backup-and-recovery

Designing Reliable File Storage Systems

Designing Reliable File Storage Systems

A reliable file storage system must do more than persist files. It must preserve data integrity, namespace consistency, availability, predictable latency, and recoverability while clients concurrently create, read, modify, rename, and delete files.

Oleksandr Andrushchenko Aug 31

storage-systems distributed-systems reliability

Object Storage vs File Storage vs Block Storage

Object Storage vs File Storage vs Block Storage

Choosing between object, file, and block storage is an architectural decision about access patterns, latency, consistency, scalability, and operational semantics . All three ultimately persist bytes, but they expose those bytes differently and therefore behave very differently under production workl

Oleksandr Andrushchenko Aug 31

storage-systems system-design cloud-storage

Storage Systems Explained: Block, File, and Object Storage

Storage Systems Explained: Block, File, and Object Storage

Storage architecture affects far more than where bytes are persisted. The storage model influences latency, throughput, consistency, failure recovery, scalability, operational complexity, and cost . A poor storage choice can become an architectural bottleneck long before CPU or network capacity beco

Oleksandr Andrushchenko Aug 31

storage-systems system-design distributed-systems

Networking Best Practices for Production Systems

Networking Best Practices for Production Systems

Production networking is not only about making services reachable. A reliable network architecture must continue operating when instances fail, traffic spikes, dependencies become slow, DNS changes, connections accumulate, certificates rotate, or entire failure domains become unavailable.

Oleksandr Andrushchenko Aug 30

networking production-best-practices reliability

TLS, HTTPS, and Secure Communication

TLS, HTTPS, and Secure Communication

Network traffic often crosses infrastructure that is not controlled by the application: Wi-Fi networks, ISPs, corporate proxies, cloud networks, load balancers, and internet routing infrastructure. Without transport security, attackers on the network path may be able to read traffic, modify requests

Oleksandr Andrushchenko Aug 30

tls https network-security

DNS, Load Balancers, and Reverse Proxies

DNS, Load Balancers, and Reverse Proxies

Production traffic rarely travels directly from a client to an application server. A request typically passes through several network layers responsible for finding the service, selecting healthy capacity, terminating connections, and routing the request to the correct backend .

Oleksandr Andrushchenko Aug 30

dns load-balancing reverse-proxy

Designing Highly Available Network Architectures

Designing Highly Available Network Architectures

Highly available network architecture is designed so that the failure of a single host, network device, availability zone, gateway, or traffic path does not make an application unreachable.

Oleksandr Andrushchenko Aug 30

networking high-availability reliability

HTTP/1.1 vs HTTP/2 vs HTTP/3

HTTP/1.1 vs HTTP/2 vs HTTP/3

HTTP/1.1, HTTP/2, and HTTP/3 expose largely the same application-level request and response model, but they move those requests across the network very differently. The differences become important when applications make many concurrent requests, operate over high-latency networks, or experience pac

Oleksandr Andrushchenko Aug 30

http networking web-protocols

TCP vs UDP: Choosing the Right Protocol

TCP vs UDP: Choosing the Right Protocol

TCP and UDP are transport protocols that move data between applications, but they make very different trade-offs. TCP provides ordered, reliable delivery through a connection-oriented model, while UDP sends independent datagrams with much less protocol overhead and no built-in guarantee that data wi

Oleksandr Andrushchenko Aug 30

networking tcp udp

Computer Networking Explained for Backend Engineers

Computer Networking Explained for Backend Engineers

Backend applications rarely operate in isolation. A typical request may pass through DNS, a load balancer, a reverse proxy, several application services, a cache, a database, and an external API before a response reaches the client.

Oleksandr Andrushchenko Aug 30

networking distributed-systems backend-engineering

Kubernetes Best Practices for Production

Kubernetes Best Practices for Production

Kubernetes provides primitives for scheduling, service discovery, health checks, scaling, configuration, and workload recovery, but production reliability depends on how those primitives are combined. A cluster can be technically healthy while applications suffer from poor scheduling, weak failure i

5.0/5 (1)

Oleksandr Andrushchenko Aug 28 1

kubernetes production-best-practices reliability

Managing Configuration and Secrets

Managing Configuration and Secrets

Kubernetes applications need configuration that changes independently from application code: database endpoints, feature flags, queue names, service URLs, credentials, API tokens, certificates, and environment-specific settings. Embedding these values directly into container images couples deploymen

Oleksandr Andrushchenko Aug 28

kubernetes configuration-management secrets-management

Autoscaling Kubernetes Workloads

Autoscaling Kubernetes Workloads

Kubernetes autoscaling adjusts application or infrastructure capacity as workload demand changes. Instead of permanently provisioning enough resources for peak traffic, a cluster can add application replicas, increase pod resource allocations, or expand the underlying node pool when additional capac

Oleksandr Andrushchenko Aug 28

kubernetes autoscaling scalability

Designing Highly Available Kubernetes Applications

Designing Highly Available Kubernetes Applications

Kubernetes can restart containers, replace failed pods, and reschedule workloads after node failures, but those mechanisms do not automatically make an application highly available. A workload can have ten replicas and still fail completely if they share the same node, availability zone, overloaded

Oleksandr Andrushchenko Aug 28

kubernetes high-availability reliability

Services, Ingress, and Networking

Services, Ingress, and Networking

Kubernetes pods are dynamic. They are created during scaling, replaced after failures, moved between nodes, and recreated during deployments. Each replacement can receive a different IP address. Production applications therefore cannot safely depend on discovering and calling individual pod addresse

Oleksandr Andrushchenko Aug 28

kubernetes networking ingress

Deployments, ReplicaSets, and StatefulSets

Deployments, ReplicaSets, and StatefulSets

Production Kubernetes applications rarely run as manually created pods. Pods are disposable runtime units: they can disappear during node failures, deployments, scaling events, evictions, and infrastructure maintenance. Something must continuously ensure that the required number and type of pods exi

Oleksandr Andrushchenko Aug 28

kubernetes deployments statefulsets

Kubernetes Explained: Pods, Nodes, and Clusters

Kubernetes Explained: Pods, Nodes, and Clusters

Kubernetes turns a pool of compute resources into a platform where applications can be scheduled, restarted, replicated, and moved without tying application architecture to individual servers. The core abstraction is not a virtual machine or even a container. It is a hierarchy of clusters, nodes, an

Oleksandr Andrushchenko Aug 28

kubernetes container-orchestration containers

Cloud Architecture Best Practices

Cloud Architecture Best Practices

Good cloud architecture is not defined by how many managed services an application uses. It is defined by whether the system can scale predictably, survive failures, protect data, remain observable, control cost, and evolve without unnecessary operational complexity .

Oleksandr Andrushchenko Aug 27

cloud-architecture scalability reliability

Scaling Stateless Applications

Scaling Stateless Applications

Horizontal scaling becomes significantly easier when application instances are interchangeable. A request can reach any healthy instance, failed instances can disappear without losing business state, and new capacity can join the fleet without synchronizing local files or sessions.

Oleksandr Andrushchenko Aug 27

stateless-architecture horizontal-scaling autoscaling

Cloud Storage Patterns and Trade-Offs

Cloud Storage Patterns and Trade-Offs

Storage architecture determines more than where bytes are kept. The storage model affects latency, throughput, durability, scalability, consistency, access patterns, recovery behavior, and cost . A storage system optimized for virtual-machine disks behaves very differently from one designed for bill

Oleksandr Andrushchenko Aug 27

cloud-storage object-storage storage-architecture

Multi-Region Architecture and Disaster Recovery

Multi-Region Architecture and Disaster Recovery

Multi-region architecture addresses a failure boundary that multi-zone systems cannot: the loss or severe degradation of an entire cloud region . Regional outages are uncommon, but when they occur they can affect compute, networking, managed databases, queues, control planes, and other services simu

Oleksandr Andrushchenko Aug 27

disaster-recovery high-availability multi-region-architecture

Designing Highly Available Cloud Systems

Designing Highly Available Cloud Systems

High availability is not achieved by running more servers. A system becomes highly available when individual component failures do not become application-wide outages and recovery happens automatically within an acceptable time.

Oleksandr Andrushchenko Aug 27

high-availability cloud-architecture fault-tolerance

Virtual Machines vs Containers vs Serverless

Virtual Machines vs Containers vs Serverless

Choosing a cloud compute model determines far more than where application code executes. It affects deployment speed, isolation, scaling behavior, startup latency, infrastructure control, operational workload, and cost .

Oleksandr Andrushchenko Aug 27

virtual-machines containers serverless

Cloud Architecture Explained: Building Modern Applications

Cloud Architecture Explained: Building Modern Applications

Modern cloud architecture is less about moving servers into a data center owned by somebody else and more about designing applications around failure, elasticity, automation, and independently scalable components . Compute instances disappear, networks become unreliable, traffic changes quickly, and

Oleksandr Andrushchenko Aug 27

cloud-architecture distributed-systems scalability

API Security Best Practices for Production Systems

API Security Best Practices for Production Systems

Production API security is the result of many small architectural decisions working together. Authentication may be correct while authorization is incomplete. Input validation may be strict while downstream dependencies remain vulnerable to resource exhaustion. Secrets may be protected while logs ac

Oleksandr Andrushchenko Aug 24

security api-security production-security

Secrets Management in Cloud Applications

Secrets Management in Cloud Applications

Cloud applications depend on credentials that cannot safely live in source code or ordinary configuration: database passwords, API keys, signing keys, OAuth client secrets, webhook credentials, encryption material, and third-party service tokens. The difficult problem is not simply storing these val

5.0/5 (1)

Oleksandr Andrushchenko Aug 24 1

security secrets-management cloud-security

Protecting APIs Against Common Attacks

Protecting APIs Against Common Attacks

Production APIs are exposed to more than malformed requests. Attackers can manipulate identifiers, inject untrusted input into downstream systems, abuse authentication flows, force expensive application paths, exploit unsafe URL fetching, or overwhelm shared resources with requests that are technica

Oleksandr Andrushchenko Aug 24

security api-security web-application-security

Designing Secure API Architectures

Designing Secure API Architectures

API security is not a middleware feature added after an application is designed. In production systems, security boundaries influence the entire request path: where traffic enters the network, how identities are established, how permissions are enforced, which services can communicate, where secrets

5.0/5 (1)

Oleksandr Andrushchenko Aug 24 1

security api-architecture api-security

OAuth 2.0 vs JWT vs API Keys

OAuth 2.0 vs JWT vs API Keys

OAuth 2.0, JWT, and API keys are frequently compared as if they were interchangeable authentication mechanisms. They are not. OAuth 2.0 is an authorization framework, JWT is a token format, and an API key is a credential . A production system may use one of them, combine several of them, or use comp

Oleksandr Andrushchenko Aug 24

security oauth-2 api-authentication