Oleksandr Andrushchenko

Oleksandr Andrushchenko

@oleksandr-andrushchenko

Software Engineer & Solution Architect

User info

Buy Me a Coffee: oleksandr.andrushchenko
Address: Missoula, MT 59808, USA
4 Followers
3 Following
28 Posts
0 Comments
Joined: Nov 03, 2025

About

Senior Software Engineer with 15+ years of 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.

Latest Posts

ClickHouse: what is it and what is it for?

ClickHouse is an open-source, column-oriented database management system designed for online analytical processing (OLAP). It is built to process massive datasets quickly and efficiently, making it ideal for analytics rather than transactional workloads. Traditional databases are optimized for trans
Apr 06 1 clickhouse olap database

ClickHouse vs PostgreSQL: Real-World Query Performance at Billion-Row Scale

This benchmark comparesPostgreSQLandClickHouseusing identical data and queries to evaluate performance in a read-heavy analytical workload. Each query was executed once. Both PostgreSQL and ClickHouse were run with default configurations (no custom tuning). The goal of this article is to demonstrate
Apr 05 1 clickhouse postgresql database
ClickHouse vs PostgreSQL: Real-World Query Performance at Billion-Row Scale

OLAP Engines Compared: Pros, Cons, and Use Cases for Modern Data Systems

Online Analytical Processing (OLAP) engines power analytical workloads involving aggregations, scans, and complex queries over large datasets. Selection impacts query latency, cost efficiency, scalability limits, and operational complexity. Different engines optimize for columnar storage, distribute
Mar 30 olap database analytics

OLAP Databases: Pros, Cons, Use Cases, and Architecture Patterns

OLAP databases(Online Analytical Processing) are optimized for complex queries, aggregations, and large-scale analytics workloads. Unlike transactional systems, they focus on read-heavy operations, enabling fast insights across massive datasets, making them essential for BI tools, reporting pipeline
Mar 30 olap database analytics
OLAP Databases: Pros, Cons, Use Cases, and Architecture Patterns

NoSQL Engines Compared: Trade-offs, Performance, and Use Cases

NoSQL engines provide different trade-offs inlatency, consistency, scalability, and query flexibility. Choosing the right engine requires understandingdata access patterns, workload characteristics, and operational complexity, not just database type. Choosing the right NoSQL engine directly impactsl
Mar 23 nosql database
NoSQL Engines Compared: Trade-offs, Performance, and Use Cases

NoSQL Databases: Types, Trade-offs, and Use Cases

NoSQL databases are designed to handlelarge-scale, high-velocity, and unstructured datathat traditional relational databases struggle with. They often sacrifice strict consistency forscalability, flexibility, and performance, making them ideal for modern distributed systems. Choosing the right NoSQL
Mar 22 nosql database
NoSQL Databases: Types, Trade-offs, and Use Cases

RDBMS (SQL) Engines: Pros, Cons, and Use Cases

Relational Database Management Systems (RDBMS)remain foundational infrastructure in modern distributed systems because they provide strong consistency, transactional guarantees, and mature tooling for data integrity. Large-scale systems such as payment platforms, logistics networks, and SaaS product
Mar 07 1 rdbms database sql
RDBMS (SQL) Engines: Pros, Cons, and Use Cases

Communication Protocols: Pros, Cons, and Use Cases

Modern distributed systems rely on communication protocols that define how services exchange data. As a software developer designing APIs, microservices, real-time systems, or event-driven platforms, choosing the correct protocol directly impacts latency, scalability, observability, cost, and develo
Feb 23 1 protocols
Communication Protocols: Pros, Cons, and Use Cases

Building Multi-Tenant Support in a User-Based SaaS App (case study)

Many SaaS applications begin with a very simple idea: one user signs up, creates data, and owns that data. This model works extremely well in the early stages of a product. It is easy to reason about, easy to implement, and easy to secure. However, as the product grows, a common requirement appears:
Feb 14 saas case-study rdbms
Building Multi-Tenant Support in a User-Based SaaS App (case study)

Amazon DynamoDB: Pros, Cons, and Use Cases

Amazon DynamoDB is a fully managedNoSQLkey–value and document database service provided by AWS. It is designed to deliver single-digit millisecond performance at virtually any scale, without requiring developers to manage servers, storage, or replication. This article covers what DynamoDB is, how it
Jan 24 dynamodb aws nosql
Amazon DynamoDB: Pros, Cons, and Use Cases

Blog platform DynamoDB single-table design (case study)

This case study describes a production blog platform backed entirely byAmazon DynamoDB. The system serves typical blog workloads such as “latest posts”, “most popular posts”, “posts by author”, and “posts by tag”, while also supporting administrative queries like user moderation and contact messages
Jan 19 3 dynamodb case-study database
Blog platform DynamoDB single-table design (case study)

Availability patterns

Availability patternsdescribe architectural techniques that keep systems operational despite failures, overload, or partial outages. High availability matters most in systems that support continuous business processes, such as payment authorization or customer-facing APIs, where downtime directly tr
Jan 05 availability fault-tolerance high-availability
Availability patterns

Consistency patterns

Consistency patternsdescribe how systems behave when multiple actors read and write shared data, especially under concurrency, failure, or distribution. Database transactions are one of the strongest tools for enforcing consistency, but they coexist with weaker models that trade correctness guarante
Dec 29, 2025 1 consistency
Consistency patterns

Domain Name System (DNS)

Domain Name System (DNS)is the distributed naming system that translates human-readable domain names into IP addresses that machines can route. In practical system design, DNS often becomes a hidden dependency whose behavior directly impacts availability, latency, and security.An e-commerce checkout
Dec 22, 2025 1 dns distributed-systems infrastructure
Domain Name System (DNS)

Database Locks and Transactions

Many production data bugs originate not from missing transactions, but from misunderstanding what transactions actually guarantee. Systems often wrap logic inBEGINandCOMMITand assume correctness follows automatically. Under concurrent load, these systems still oversell inventory, double-charge users
Dec 15, 2025 database atomicity consistency
Database Locks and Transactions

Database Transactions

Database transactions are the foundation of correctness in almost every non-trivial system. Any system handling money transfers, inventory updates, or user state changes relies on transactions to prevent partial writes and inconsistent reads. A payment service that debits an account but fails to cre
Dec 15, 2025 database acid
Database Transactions

Scalability for Dummies - Part 4: Asynchronism

This 4th part of the series builds on the previous discussion inScalability for Dummies - Part 3: Cache. Imagine trying to book a popular concert ticket online. A user selects a seat, but instead of an immediate confirmation, the website asks to return in two hours when the order is processed. This
Dec 08, 2025 scalability asynchronism
Scalability for Dummies - Part 4: Asynchronism

Scalability for Dummies - Part 3: Cache

After followingScalability for Dummies - Part 2: Database, the database can now handle massive volumes of data and high concurrency. Despite this, users may still experience slow page loads when queries fetch large amounts of data. The bottleneck is no longer the database itself but the repeated ret
Nov 30, 2025 scalability cache

Scalability for Dummies - Part 2: Database

After implementing horizontal scaling on servers and handling thousands of concurrent requests inScalability for Dummies — Part 1: Clones, performance may still degrade over time. Applications often slow down and eventually fail due to database limitations. Even with a perfectly scaled front end, th
Nov 30, 2025 scalability database

Scalability for Dummies - Part 1: Clones

Simple, practical guide to the first and often-most-used approach to scaling: making copies of what works. No PhD required — just common sense, a few engineering patterns, and awareness of the pitfalls.By Your Friendly Engineer · Part 1 in a short series on practicalscalability. Acloneis simply anot
Nov 24, 2025 scalability clones
Scalability for Dummies - Part 1: Clones
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