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dbaplus Community
dbaplus Community
Jan 15, 2026 · Backend Development

How to Design a High‑Throughput Database Architecture for a Billion‑Row Daily Log System

This guide breaks down a real‑world interview scenario where a backend engineer must design a scalable database solution for a billing‑log or feed system handling 100 million daily inserts and 100 k QPS reads, covering partitioning vs sharding, sharding key selection, shard count, read‑write separation, multi‑level caching, consistency patterns, hot‑key mitigation, and online schema changes.

Backend ArchitectureOnline Schema ChangeRead-Write Separation
0 likes · 10 min read
How to Design a High‑Throughput Database Architecture for a Billion‑Row Daily Log System
Ma Wei Says
Ma Wei Says
Mar 5, 2024 · Backend Development

How to Prevent Cache Penetration Attacks with Bloom Filters and Null Caching

The article explains what cache penetration is, why it can crash databases under malicious traffic, and presents practical mitigation techniques such as Bloom filters, null-value caching, data pre‑warming, and request validation to protect Redis‑backed systems.

Backend PerformanceNull Cachingbloom-filter
0 likes · 6 min read
How to Prevent Cache Penetration Attacks with Bloom Filters and Null Caching
Liangxu Linux
Liangxu Linux
May 28, 2023 · Backend Development

Preventing Redis Cache Penetration, Avalanche, and Thundering Herd

This article explains the causes of Redis cache penetration, avalanche, and thundering herd, and provides practical mitigation strategies such as caching null values, using white‑lists, Bloom filters, pre‑warming hot keys, staggered expirations, multi‑level caching, and lock mechanisms.

BackendCacheDistributed Systems
0 likes · 7 min read
Preventing Redis Cache Penetration, Avalanche, and Thundering Herd
dbaplus Community
dbaplus Community
Sep 15, 2022 · Backend Development

How to Keep Redis Cache and MySQL Consistent: Strategies and Pitfalls

This article explains why Redis is used as a cache, outlines common consistency problems such as cache miss, penetration, and avalanche, compares cache‑aside, read‑through, write‑through and write‑behind patterns, and presents practical solutions like delayed double‑delete, retry mechanisms and binlog‑based asynchronous deletion to maintain eventual consistency between cache and database.

cache-asidecaching strategiesdata-consistency
0 likes · 19 min read
How to Keep Redis Cache and MySQL Consistent: Strategies and Pitfalls
Top Architect
Top Architect
Aug 10, 2022 · Backend Development

Cache Consistency: Challenges and Strategies for Backend Systems

This article examines why cache consistency cannot be fully guaranteed, analyzes common inconsistency scenarios between Redis and MySQL, compares four update strategies, and offers practical recommendations such as updating the database before deleting cache, using short expiration times, delayed double‑delete, MQ or binlog approaches to achieve eventual consistency.

BackendConsistencycaching strategies
0 likes · 19 min read
Cache Consistency: Challenges and Strategies for Backend Systems
Tencent Cloud Developer
Tencent Cloud Developer
Jan 13, 2022 · Backend Development

Strategies for Ensuring Cache Consistency in Distributed Systems

The article reviews major cache‑consistency strategies—Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—detailing their performance and consistency trade‑offs, discusses compensation techniques such as delayed double‑delete and MySQL binlog‑based incremental parsing (DTS) for reliable deletion and HA, and advises selecting the appropriate approach based on specific business requirements.

Cache ConsistencyDatabase OptimizationDistributed Systems
0 likes · 15 min read
Strategies for Ensuring Cache Consistency in Distributed Systems
DeWu Technology
DeWu Technology
Dec 21, 2021 · Backend Development

Performance Optimization: From Understanding to Practice

The article guides readers from grasping hardware, OS, and software layers—including Intel Xeon, virtualization, VPC, and containers—to gathering performance data with tools like JProfiler and perf, then applying data‑driven optimization across business logic, architecture, code, caching, databases, runtime and hardware, illustrated by real‑world case studies and emphasizing continual learning.

Container TechnologiesDatabase OptimizationHardware Understanding
0 likes · 43 min read
Performance Optimization: From Understanding to Practice
vivo Internet Technology
vivo Internet Technology
Nov 3, 2021 · Backend Development

Evolution and Architecture of vivo Mall's Product System

From its 2017 v2.0 upgrade to a service‑oriented design, vivo Mall’s product system was split from the monolithic mall, progressively adding independent activity, flash‑sale, consignment, and inventory services while employing rate limiting, multi‑level caching, circuit breaking, and distributed transactions to ensure stability, high performance, and data consistency.

Data ConsistencySystem Architecturebackend-development
0 likes · 9 min read
Evolution and Architecture of vivo Mall's Product System
IT Architects Alliance
IT Architects Alliance
Aug 3, 2021 · Industry Insights

How to Split Complex Systems and Evolve Architecture for Scalable Micro‑services

The article examines why growing business complexity and throughput demands necessitate system decomposition and architectural evolution, detailing horizontal scaling, vertical and business splitting, database partitioning strategies, cache and index integration, remote service data sourcing, and asynchronous messaging to achieve scalable, robust micro‑service‑based solutions.

MicroservicesSystem Architectureasynchronous messaging
0 likes · 9 min read
How to Split Complex Systems and Evolve Architecture for Scalable Micro‑services
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Jan 9, 2021 · Backend Development

Preventing Redis Cache Avalanche: Interview Dialogue and Practical Solutions

This article presents an interview scenario where a candidate is asked about Redis cache avalanche, explains the phenomenon, and offers four practical mitigation strategies—including cache pre‑warming, mutex locking, staggered expiration, and high‑availability designs—to keep backend databases from being overwhelmed.

Backendcache-avalanchecaching strategies
0 likes · 4 min read
Preventing Redis Cache Avalanche: Interview Dialogue and Practical Solutions
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Dec 11, 2020 · Backend Development

Common Cache Read/Write Strategies for Production Environments

This article reviews five widely used cache read/write strategies—including simple write‑DB‑then‑cache, Cache‑Aside, Write/Read‑Through, Write‑Back, and LRU eviction—explaining their workflows, advantages, disadvantages, and suitability for different production scenarios.

Cachebackend-developmentcaching strategies
0 likes · 7 min read
Common Cache Read/Write Strategies for Production Environments
Architect's Tech Stack
Architect's Tech Stack
Nov 19, 2019 · Backend Development

Understanding Cache Avalanche, Cache Breakdown, Cache Penetration and Common Caching Patterns

This article explains the concepts of cache avalanche, cache breakdown, and cache penetration, outlines their potential impact on database performance, and presents practical mitigation techniques such as mutex locking, data pre‑warming, multi‑level caches, and popular caching patterns like Cache‑Aside, Read/Write‑Through, and Write‑Behind.

Backendcaching strategies
0 likes · 8 min read
Understanding Cache Avalanche, Cache Breakdown, Cache Penetration and Common Caching Patterns
Architecture Digest
Architecture Digest
Aug 13, 2018 · Backend Development

Cache Consistency, Concurrency, Penetration, Avalanche, and Bottomless Pit Issues and Mitigation Strategies

The article explains various cache problems—including consistency, concurrency, penetration, avalanche, and the bottomless‑pit phenomenon—and presents practical mitigation techniques such as active updates, locking, empty‑object caching, request filtering, consistent hashing, and multi‑level caching to ensure reliable high‑performance systems.

CacheConsistencyavalanche
0 likes · 8 min read
Cache Consistency, Concurrency, Penetration, Avalanche, and Bottomless Pit Issues and Mitigation Strategies
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Aug 7, 2018 · Backend Development

Understanding Distributed Caching: Use Cases, Memcached vs Redis Comparison, and Common Challenges

This article explains why distributed caching is essential for high‑concurrency systems, outlines typical use cases, compares Memcached and Redis across features and performance, and discusses common problems such as cache avalanche, penetration, warm‑up, update strategies, and degradation.

Backend PerformanceMemcachedcaching strategies
0 likes · 8 min read
Understanding Distributed Caching: Use Cases, Memcached vs Redis Comparison, and Common Challenges
ITPUB
ITPUB
May 18, 2017 · Backend Development

How to Prevent Cache Breakdown, Expiration, and Hot Key Issues in Distributed Systems

This article explains common problems of distributed caching such as cache breakdown, cache expiration, and hot‑key bottlenecks, and provides practical mitigation techniques including default null values, staggered expiration times, distributed locking, client‑side caching, and key sharding to maintain high‑concurrency performance.

Backend PerformanceHot Keycache expiration
0 likes · 5 min read
How to Prevent Cache Breakdown, Expiration, and Hot Key Issues in Distributed Systems
MaGe Linux Operations
MaGe Linux Operations
Jul 9, 2015 · Backend Development

How Meipai Scaled to 100M Users in 9 Months: Key Lessons from QCon

An in‑depth recap of Hong Xiaojun’s QCon presentation reveals how Meipai’s architecture evolved from a minimalist design to a highly scalable, high‑availability system, tackling MySQL bottlenecks, cache challenges, monitoring, CDN resilience, and technology migrations to support rapid growth to over a hundred million users.

MySQL optimizationSystem Designcaching strategies
0 likes · 8 min read
How Meipai Scaled to 100M Users in 9 Months: Key Lessons from QCon