Tag archive

#performance

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69 articles

gRPC vs REST: The Decision-Maker's Guide for Backend Architecture

gRPC vs REST: Which One for Your Microservices? In modern backend architecture, how services talk is as important as what they say. Choosing between REST and gRPC isn't just about syntax; it's about the trade-off between…

Apr 20, 20262 min read

ComparisonBackend Systems Mastery

#grpc#rest#api-design

System DesignBeginner

gRPC vs REST: A Decision-Maker's Guide for Backend Architecture

gRPC vs REST: Which One for Your Microservices? > Prerequisite: Before diving into protocols, ensure you understand the fundamentals of Load Balancing and API Idempotency. Choosing between REST and gRPC is one of the mos…

Apr 20, 20262 min read

ComparisonBackend Systems Mastery

#grpc#rest#api-design

System DesignAdvanced

Pagination at Scale: Keyset vs Offset

Pagination at Scale Pagination looks simple until your table has tens of millions of rows, users sort by multiple fields, and product asks for infinite scroll with strict latency SLOs. Many APIs start with LIMIT ... OFFS…

Apr 20, 20265 min read

Comparison

#api-design#pagination#performance

System DesignIntermediate

API Pagination at Scale: Why OFFSET 100,000 is a Database Killer

API Pagination at Scale: Moving Beyond OFFSET Designing a paginated API seems simple: just use LIMIT 20 OFFSET 100. This works perfectly for the first few pages. However, once your users reach page 5,000, your database p…

Apr 20, 20262 min read

Deep DiveBackend Systems Mastery

#api-design#pagination#sql

System DesignAdvanced

API Rate Limiting at Scale: Redis-Based Strategies

API Rate Limiting at Scale with Redis Rate limiting is essential for protecting your APIs from abuse, ensuring fair usage, and preventing cascading failures. Redis is the ideal store for rate limiting because of its spee…

#performance

69 articles

gRPC vs REST: The Decision-Maker's Guide for Backend Architecture

gRPC vs REST: A Decision-Maker's Guide for Backend Architecture

Pagination at Scale: Keyset vs Offset

API Pagination at Scale: Why OFFSET 100,000 is a Database Killer

API Rate Limiting at Scale: Redis-Based Strategies

Bloom Filters: The Speed Secret of Modern NoSQL Databases

Cassandra Gotchas: Dealing with Tombstones and Wide Partitions

The 'Small Files' Problem in Data Lakes: Why Your Kafka Sink is Slow

Optimistic vs. Pessimistic Locking: Concurrency Control in Practice

Database Sharding Part 1: The Vertical Ceiling

Distributed Caching at Scale: Mitigating the Thundering Herd

Docker for Java Developers: A Production Guide to Containerization

DynamoDB Pitfalls: Throttling, Hot Partitions, and the 400KB Limit

Bypassing the Kernel: User-Space Networking for Sub-Microsecond Performance

Cgroup Awareness in Java: Avoiding OOM Kills

CPU Pipeline Stalls: Identifying Cache Misses in Java

Query Optimization: The Hidden Cost of Cross-Shard Joins

Database Statistics: How Query Planners Build Histograms

Hardware-Aware Programming: Optimizing for Modern CPUs

Hardware-Level False Sharing: Designing High-Speed Java Objects

HyperLogLog at Scale: Billion-Cardinality Estimation

LSM-Tree Compaction Strategies: Leveled vs. Size-Tiered

S3 Express One Zone: When to Use it for Stateful Workloads

Hardware-Level False Sharing: Designing High-Speed Java Objects

HikariCP Tuning: Diagnosing Database Connection Pool Exhaustion

HikariCP Tuning: The Math behind Connection Pools

Java Flight Recorder (JFR): Continuous Profiling with Zero Overhead

Java Heap Dump Analysis: A Step-by-Step Guide to Finding Memory Leaks

The Java Memory Model (JMM): Volatile, Happens-Before, and Visibility

Java Virtual Threads: High-Concurrency without the Complexity

Kafka Consumer Rebalancing: The Senior Engineer's Playbook

Kafka Internals: Zero-Copy, Log Storage, and High Throughput

Kafka Internals: The Storage Layer, Zero-Copy, and High Throughput

Kafka Partition Skew Management

Inside the Linux Page Cache: The Invisible Database Accelerator

LMAX Disruptor: How to Process 6M Transactions/Sec on One Thread

LSM-Tree Compaction Strategies: Leveled vs. Size-Tiered

Modern Java GC: G1 vs. ZGC - Choosing the Right Collector

MongoDB Aggregation Pipeline: Optimization and Performance

MongoDB Anti-Patterns: From Unbounded Arrays to Shard Imbalance

Negative Result Caching: Protecting Databases

PostgreSQL JIT Tuning: Understanding the Cost

PostgreSQL MVCC: How Transactions Work Without Locking

PostgreSQL Vacuum Tuning: Internals of Bloat Control

RabbitMQ Pitfalls: Managing Unacked Messages and Thundering Herds

Advanced RAG Architecture: Beyond Simple Vector Search

Redis in Production: 5 Common Pitfalls and How to Avoid Them

Redis Internals: Event Loop, Data Structures, and Persistence

Redis Lua Scripting: Ensuring Atomicity in Complex Workflows

S3 Express One Zone: When to use it

B-Trees vs. LSM-Trees: The Battle of Storage Engine Internals

System Design: Designing a Local-First Key-Value Store (LevelDB/RocksDB)

The Write-Ahead Log (WAL): The Universal Engine of Data Durability

Zero-Copy in Netty: The Backbone of High-Speed Java Networking

PostgreSQL Locking Playbook: Deadlocks, Blocking Queries, and Timeouts

Cache Invalidation Patterns: TTL, Write-Through, Cache-Aside, and Event-Driven Eviction

Database Connection Pool Tuning: HikariCP, PostgreSQL, and Traffic Spikes

AWS Lambda in Production: Cold Starts, Concurrency, and Cost Optimization

Java Memory Management Deep Dive: Heap, GC, and Production Tuning

PostgreSQL Performance Tuning: From Slow Queries to Sub-Millisecond Reads

Elasticsearch Query Optimization: From Slow to Sub-100ms

Scaling Spring Boot Applications to Handle 10 Million Daily Active Users

Java Profiling and Heap Analysis: Finding Memory Leaks and CPU Bottlenecks

Redis Caching Strategy at Scale: Beyond Simple Key-Value

Thread Pool Exhaustion in Spring Boot: Diagnosis, Prevention, and Recovery

Java Streams API: Advanced Patterns and Performance

Spring Boot Performance Tuning: From 200 to 2000 RPS

Database Indexing Deep Dive: B-Trees, Hash Indexes, and Query Planning

Java GC Tuning: From G1 to ZGC — Eliminating Pause-Time Spikes