DEV Community: ScalaBrix

⚙️ Scaling to 100K+ Tenants in a Single DB: Isolation, Partitioning, and Indexing Strategies That Actually Work 💡

ScalaBrix — Sat, 02 Aug 2025 09:28:45 +0000

Dive into a 🧱 production-grade blueprint for building high-scale multi-tenant SaaS systems on a shared relational DB (PostgreSQL/MySQL). This guide shows you how to:

🧠 Enforce strict per-tenant isolation using tenant_id + RLS

⚙️ Design tenant-aware indexes (compound, partial, GIN)

🗂️ Scale with hash & time partitioning

🏗️ Implement a flexible Hybrid Tenancy Model (shared → schema → DB)

🔄 Handle safe schema evolution & GDPR-compliant deletes

📉 Avoid noisy neighbor pitfalls with smart indexing & routing

Whether you're scaling to 100K+ tenants or just architecting your first multi-tenant layer, this is your ultimate blueprint for isolation, performance, and compliance.

📖 Read here →

DB Design : Multi-Tenant Data Isolation in a Shared Database | by ScalaBrix | Aug, 2025 | Medium

ScalaBrix ・ Aug 2, 2025 ・
scalabrix.Medium

PostgreSQL #SaaSArchitecture #SystemDesign #MultiTenancy #BackendEngineering #DatabaseDesign #Indexing #DevCommunity

🚀 System Design Interview Vault: 75+ Must-Know Concepts & Scenarios

ScalaBrix — Mon, 28 Jul 2025 03:19:04 +0000

🚀 System Design Interview Vault: Concepts, Frameworks & Problems

🧠 Are you prepping for System Design Interviews or looking to level up your architecture skills?

I've curated a 🔥 hand-picked collection of must-know concepts, real-world problem statements, and deep-dive breakdowns — all from my most-read Medium series!

🧩 What's Inside?

✅ 75+ Core System Design Concepts — from scalability to consistency
🛠️ Architecture Blueprints used in real production systems
🔍 Practical Problems like:
- “System Architecture : Payment Wallet”
- “System architecture : High Throughput Reads & Writes in Databases”
- “Repackaging Microservices into Single-Tenant Monoliths”
🧪 Scenario-based questions with trade-off analysis
📈 Best practices and patterns for high availability, caching, partitioning, and more

📚 Perfect For:

Aspiring and senior software engineers prepping for interviews
Architects and backend engineers solving scale problems
Bloggers, mentors, and tech influencers in the system design space

🔗 Dive into the Collection:

👉 [Read the Full Collection]
https://scalabrix.medium.com/list/system-design-concepts-for-interviews-7b12980141be

🔖 Save it. Share it. Bookmark it. Your System Design Toolbox just got sharper.

💬 Feel free to drop your thoughts, feedback, or follow-up questions in the comments!

#systemdesign #interviewprep #softwarearchitecture #scalability #devto

🔐 Repackaging Microservices into Single-Tenant Monoliths: The Architecture Behind Scalable, Compliant SaaS

ScalaBrix — Mon, 28 Jul 2025 03:07:38 +0000

🧱 Tired of the complexity, latency, and compliance headaches that come with managing a sprawling microservices mesh for your SaaS platform? This deep-dive article explores how leading engineering teams are transforming their architecture by repackaging dozens of services into per-tenant isolated monoliths — all while retaining centralized governance via shared control planes, observability stacks, and policy enforcement layers. We break down how this hybrid model enables 🔒 strong tenant isolation, ⚡ low-latency in-process execution, 🧩 simplified deployments, and 🧠 clean operational boundaries — especially for enterprise and regulated markets.

If you're scaling B2B SaaS, thinking multi-region, or just trying to break free from the operational drag of distributed spaghetti, this article is a must-read.

levelup.gitconnected.com / repackaging-microservices-into-single-tenant-monoliths-a-scalable-architecture-for-enterprise-saas-22fd168040b6

Building a Distributed Prime Number Finder: Scalable Sieve Architecture for 1 Billion Integers

ScalaBrix — Mon, 28 Jul 2025 02:57:46 +0000

🧮 From Math to Distributed Systems: Scaling Prime Number Computation

What if you had to find every prime number up to 1,000,000,000 — but couldn’t use a single machine? This isn’t just a number theory problem anymore. At this scale, it becomes a distributed computing challenge. In this deep-dive, we design a production-grade system that transforms the classic Segmented Sieve of Eratosthenes into a parallel, fault-tolerant, and memory-optimized architecture. 💡 We break down how to slice the number space into stateless computation segments, broadcast base primes efficiently, and distribute work across compute clusters — all while ensuring retry safety, deterministic output, and high throughput.

🖥️ Cloud-Native, Observable, and Battle-Tested

This isn’t just a theoretical model. We show how to bring it to life using real-world distributed principles: REST APIs for job submission 🚀, distributed task queues like Kafka/Redis for dispatch 📬, and stateless compute workers running in Kubernetes or Ray 🧠. The final output aggregation is streaming-friendly, memory-safe, and extensible to S3, HDFS, or parquet stores 📦. Whether you're building on Spark, Ray, or just containers and queues, this article gives you an end-to-end reference implementation with code walkthroughs, database schemas, retry logic, and observability patterns via Prometheus/Grafana 📊. If you love distributed system design and want to see algorithm meets architecture — this post is for you.

levelup.gitconnected.com / distributed-prime-number-finder-scalable-sieve-architecture-for-1-billion-integers-80a06c0939d4

How to Design a 1M RPS API: Scalable, Resilient, and Production-Ready Architecture

ScalaBrix — Mon, 28 Jul 2025 02:46:00 +0000

Designing an API that handles 1 million requests per second (1M RPS) is a significant engineering challenge that pushes the boundaries of system scalability, performance, and reliability. This article presents a layered, distributed system architecture built to meet such extreme demands while maintaining low latency and high availability. It breaks down the system into key functional layers—Global Traffic Management, API Gateway, Stateless Processing, Multi-Tier Caching, Sharded Data Storage, Asynchronous Processing, and Observability—to ensure each component scales independently and operates fault-tolerantly.

We begin with user traffic management via global load balancers and CDN caches to offload redundant requests before they hit the backend. API Gateways enforce security, rate limits, and intelligent routing. Stateless API servers enable horizontal scaling, while caching at various layers drastically reduces load on core systems. For persistence, we implement sharded databases and read replicas to handle concurrent writes and massive read workloads efficiently. Time-consuming tasks are offloaded to a background processing layer using queues and worker pools. The system is equipped with circuit breakers, failover logic, auto-scalers, and monitoring pipelines to ensure resilience and rapid recovery from failures.

The architecture balances eventual vs. strong consistency, optimizes resource usage with elastic scaling, and supports cost-efficient operations at scale. This guide provides a full-stack perspective on how modern high-throughput APIs are designed to serve millions while staying performant, reliable, and observable. Whether you're building the next big platform or scaling an existing service, these patterns and principles are your foundation for internet-scale architecture. 🚀

System Architecture : Deep Dive into 1M RPS API Design | by ScalaBrix | Level Up Coding

Technology-agnostic design for high-throughput systems, ensuring low latency, high availability, and cost efficiency

levelup.gitconnected.com