Popular Databases Articles

Database scalability is rarely solved by one optimization. Production systems scale through a combination of correct schemas, bounded queries, selective indexes, short transactions, controlled concurrency, caching, partitioning, replication, observability, and safe operational workflows.

ClickHouse is an open-source, column-oriented database management system designed for online analytical processing ( OLAP ).

PostgreSQL is one of the strongest default database choices for modern backend systems. It combines relational modeling, ACID transactions, rich SQL support, JSONB, extensibility, and a mature ecosystem.

Database sharding divides a large dataset across multiple independent database nodes. Each shard owns only part of the data, allowing storage capacity, write throughput, and query processing to grow beyond the limits of one server.

SQL databases (also known as relational databases) are systems designed to store, organize, and query structured data using a predefined schema. They are built around the relational model, where data is stored in tables and relationships between entities are explicitly defined.

This benchmark compares PostgreSQL and ClickHouse using identical data and queries to evaluate performance in a read-heavy analytical workload.

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 pipelin

NoSQL engines provide different trade-offs in latency, consistency, scalability, and query flexibility . Choosing the right engine requires understanding data access patterns, workload characteristics, and operational complexity , not just database type.

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 prod

Database transactions and locks are both used to protect data correctness, but they solve different problems. Transactions define failure boundaries, while locks define concurrency behavior.

Database transactions are the foundation of correctness in systems that update important shared data, such as balances, inventory, orders, payments, permissions, and user state.

In Scalability for Dummies - Part 1: Clones , we scaled the application by adding more servers.

Choosing between SQL and NoSQL is not a syntax decision. It is a decision about transactions, consistency, query flexibility, data distribution, failure recovery, and the operational complexity the team is prepared to own.

Large database tables rarely fail because the database cannot store another row. They fail because indexes no longer fit efficiently in memory, maintenance operations take too long, queries scan irrelevant data, and routine deployments become operationally dangerous.

A high-performance database schema reduces the amount of work required to answer common queries while preserving data integrity and supporting safe application changes. Performance comes from aligning tables, relationships, constraints, indexes, and data types with actual production access patterns.

Database replication maintains copies of data across multiple database nodes. A primary node usually accepts writes, while one or more replicas copy those changes and may serve read traffic, provide disaster-recovery capacity, or support analytics workloads.

Database performance problems rarely appear because a database suddenly becomes slow. They emerge as data volume, concurrent connections, transaction rates, analytical queries, and background jobs gradually exceed the capacity of the original design.

PostgreSQL is easy to start with, but production PostgreSQL has many behaviors that developers usually learn only after slow queries, lock contention, failed migrations, or painful bulk imports.

Choosing between SQL and NoSQL for an MVP is one of the most common early architectural decisions — and one of the most overthought. The honest answer is still: it depends , but in practice, the decision usually comes down to two things: time to build and total cost .

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

NoSQL databases are designed to handle large-scale, high-velocity, and unstructured data that traditional relational databases struggle with. They often sacrifice strict consistency for scalability, flexibility, and performance , making them ideal for modern distributed systems.