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Coder Trainee
Coder Trainee
Apr 19, 2026 · Backend Development

How to Optimize Performance and Deploy a Production‑Ready Blog System

This article walks through a complete performance‑optimization and deployment pipeline for a Spring Boot blog, covering multi‑level caching with Caffeine and Redis, database indexing and cursor pagination, read‑write splitting, asynchronous processing, rate limiting, Docker multi‑stage builds, Nginx reverse‑proxy setup, Actuator monitoring, custom metrics, health checks, alerting, JMeter load testing, and JVM tuning.

CaffeineDockerPerformance Optimization
0 likes · 17 min read
How to Optimize Performance and Deploy a Production‑Ready Blog System
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Apr 9, 2026 · Databases

Master PostgreSQL Full-Text Search: From Basics to Advanced Chinese Tokenization

This article explains PostgreSQL's native full‑text search, its core concepts of tsvector and tsquery, demonstrates how to use built‑in functions and operators, compares built‑in, zhparser, and pg_search extensions for Chinese tokenization, and provides best‑practice tips for indexing, triggers, and performance optimization.

BM25Chinese TokenizationFull‑Text Search
0 likes · 14 min read
Master PostgreSQL Full-Text Search: From Basics to Advanced Chinese Tokenization
Java Companion
Java Companion
Apr 2, 2026 · Backend Development

Why Java 26’s New UUID API Fixes the Biggest UUID Pitfall

The article explains how random UUIDs degrade database write performance, introduces the RFC 9562 UUIDv7 format that embeds a timestamp for natural ordering, shows JDK 26’s new UUID.ofEpochMillis() API, discusses its monotonicity limitation, and compares UUIDv7 with Snowflake and ULID for practical ID generation.

JavaULIDUUIDv7
0 likes · 12 min read
Why Java 26’s New UUID API Fixes the Biggest UUID Pitfall
Architect's Guide
Architect's Guide
Feb 13, 2026 · Fundamentals

Unlocking Fast Disk Indexing: How B‑Trees, B+‑Trees & R‑Trees Work

This article explains why multi‑way search trees such as B‑trees, B+‑trees, B*‑trees and R‑trees are crucial for reducing disk I/O in large‑scale storage systems, covering their hardware background, structural definitions, height analysis, insertion and deletion algorithms, and practical examples with code and diagrams.

B+TreeB-treeData Structures
0 likes · 51 min read
Unlocking Fast Disk Indexing: How B‑Trees, B+‑Trees & R‑Trees Work
Woodpecker Software Testing
Woodpecker Software Testing
Jan 6, 2026 · Backend Development

How ChatGPT Analyzes and Optimizes Application Performance

The article shows how ChatGPT examines JMeter load‑test results, identifies CPU, database and I/O bottlenecks, and then proposes concrete optimizations such as adding indexes, tuning HikariCP pool settings, caching, code refactoring, and monitoring to improve login throughput and latency.

ChatGPTHikariCPJMeter
0 likes · 11 min read
How ChatGPT Analyzes and Optimizes Application Performance
Java Architect Handbook
Java Architect Handbook
Jan 1, 2026 · Databases

COUNT(*), COUNT(1) or COUNT(column) in MySQL? Deep Interview Guide

This article explains the semantic differences, performance characteristics, and optimal usage scenarios of COUNT(*), COUNT(1), and COUNT(column) in MySQL 8.0+, covering engine behavior, indexing effects, common misconceptions, and practical best‑practice recommendations for interview preparation and real‑world development.

SQL_countdatabase indexing
0 likes · 10 min read
COUNT(*), COUNT(1) or COUNT(column) in MySQL? Deep Interview Guide
IT Services Circle
IT Services Circle
Oct 3, 2025 · Databases

Why Does PostgreSQL’s B‑Tree Index Behave Like a B+ Tree?

PostgreSQL’s index system, officially called a B‑Tree, actually implements B+‑tree functionality, storing only TIDs in leaf nodes, using linked leaf pages for efficient range queries, and includes optimizations like deduplication, index‑only scans, and reverse‑key indexes to boost performance.

B+TreeB-TreePerformance Optimization
0 likes · 7 min read
Why Does PostgreSQL’s B‑Tree Index Behave Like a B+ Tree?
DeWu Technology
DeWu Technology
Sep 15, 2025 · Databases

Why MySQL Single-Table Data Should Stay Under 20 Million Rows

This article explains how InnoDB stores data in 16 KB pages, how B+Tree indexes work, derives the formula for the maximum number of rows a single MySQL table can hold, and why the practical limit of about twenty million rows exists, along with performance implications and optimization tips.

B+TreeInnoDBPage Size
0 likes · 20 min read
Why MySQL Single-Table Data Should Stay Under 20 Million Rows
Java Backend Technology
Java Backend Technology
Sep 10, 2025 · Databases

8 Proven MySQL Tricks to Supercharge Query Performance

This article walks through eight practical MySQL optimization techniques—including smarter LIMIT usage, avoiding implicit type conversion, rewriting UPDATE/DELETE with JOINs, handling mixed sorting, replacing EXISTS with joins, pushing predicates, narrowing result sets early, and leveraging WITH clauses—to dramatically reduce query execution time from seconds to milliseconds.

Query Planningdatabase indexingmysql
0 likes · 13 min read
8 Proven MySQL Tricks to Supercharge Query Performance
macrozheng
macrozheng
Aug 15, 2025 · Backend Development

10 Proven Strategies to Supercharge API Performance in Spring Boot

This article presents a comprehensive guide to optimizing API performance in Spring Boot projects, covering batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction management, code refactoring, pagination, SQL tuning, and lock granularity, complete with code examples and diagrams.

API optimizationBackend PerformanceSpring Boot
0 likes · 10 min read
10 Proven Strategies to Supercharge API Performance in Spring Boot
macrozheng
macrozheng
Jul 16, 2025 · Databases

Why Auto‑Increment Beats UUID in MySQL: Performance Tests and Deep Dive

An in‑depth MySQL study compares auto‑increment, UUID, and random Snowflake keys by building three identical tables, running insert‑performance tests with Spring Boot/JdbcTemplate, and analyzing index structures, revealing why auto‑increment keys outperform UUIDs despite the latter’s uniqueness.

Spring Bootauto_incrementdatabase indexing
0 likes · 11 min read
Why Auto‑Increment Beats UUID in MySQL: Performance Tests and Deep Dive
IT Services Circle
IT Services Circle
Jul 4, 2025 · Databases

Mastering MySQL Deep Pagination: 6 Proven Optimization Techniques

This article examines the performance pitfalls of MySQL deep pagination and presents six practical optimization methods—including cursor‑based pagination, deferred joins, covering indexes, table partitioning, precomputed pages, and Elasticsearch integration—to dramatically speed up large‑scale order queries.

cursor paginationdatabase indexingmysql
0 likes · 8 min read
Mastering MySQL Deep Pagination: 6 Proven Optimization Techniques
Su San Talks Tech
Su San Talks Tech
Jun 30, 2025 · Databases

10 Proven Index Design Rules to Supercharge Database Performance

This article presents ten practical rules for designing database indexes—covering business‑scenario analysis, the left‑most prefix principle, avoiding over‑indexing, using covering indexes, data‑type choices, function traps, prefix indexes, NULL handling, maintenance scripts, and monitoring—each illustrated with SQL examples, diagrams, and real‑world performance gains.

Performance OptimizationQuery TuningSQL
0 likes · 13 min read
10 Proven Index Design Rules to Supercharge Database Performance
Architect's Tech Stack
Architect's Tech Stack
Jun 22, 2025 · Backend Development

Boost API Performance: 12 Proven Backend Optimization Techniques

This article presents a comprehensive set of twelve backend optimization strategies—including batch processing, asynchronous execution, caching, pooling, parallelism, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve overall system efficiency.

API optimizationAsynchronousBackend Performance
0 likes · 9 min read
Boost API Performance: 12 Proven Backend Optimization Techniques
Su San Talks Tech
Su San Talks Tech
May 24, 2025 · Backend Development

12 Proven SpringBoot Performance Hacks to Boost Your API Speed

Discover twelve practical SpringBoot performance optimization techniques—from connection pool tuning and JVM memory settings to caching, async processing, and full‑stack monitoring—each illustrated with code snippets and actionable guidance to prevent full‑table scans, OOM errors, and latency spikes in high‑traffic applications.

JVMJavaPerformance Optimization
0 likes · 13 min read
12 Proven SpringBoot Performance Hacks to Boost Your API Speed
StarRocks
StarRocks
Feb 11, 2025 · Databases

How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost

This article explains the principles and practical implementation of vector retrieval in StarRocks, covering approximate nearest‑neighbor algorithms, index design, query planning, performance optimizations, real‑world case studies, and future challenges, showing how query latency dropped from 15 seconds to 2 seconds while cutting costs to a third.

ANNHNSWIVFPQ
0 likes · 25 min read
How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost
DataFunSummit
DataFunSummit
Jan 22, 2025 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

This article presents a comprehensive overview of the RAG2.0 engine design, covering RAG1.0 limitations, effective chunking methods, accurate retrieval techniques, advanced multimodal processing, hybrid search strategies, database indexing choices, and future directions such as agentic RAG and memory‑enhanced models.

Hybrid SearchRAGRetrieval Augmented Generation
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
Top Architect
Top Architect
Dec 5, 2024 · Databases

Why Not Use UUID as Primary Key in MySQL? Performance Analysis and Comparison with Auto‑Increment IDs

This article examines MySQL's recommendation against UUID primary keys by creating three tables with different key strategies, running large‑scale insert tests using Spring JdbcTemplate, and analyzing the resulting performance, index behavior, and trade‑offs of auto‑increment, UUID, and random keys.

auto_incrementdatabase indexingmysql
0 likes · 12 min read
Why Not Use UUID as Primary Key in MySQL? Performance Analysis and Comparison with Auto‑Increment IDs
Efficient Ops
Efficient Ops
Sep 11, 2024 · Backend Development

Why Do MySQL and Java Processes Hit 900% CPU? Proven Diagnosis & Fixes

Learn how to identify and resolve extreme CPU usage spikes—up to 900%—in MySQL and Java applications by using top, show processlist, indexing, caching, thread analysis, and code adjustments, with real‑world examples and step‑by‑step command guides.

CPU optimizationJavadatabase indexing
0 likes · 11 min read
Why Do MySQL and Java Processes Hit 900% CPU? Proven Diagnosis & Fixes
DeWu Technology
DeWu Technology
Jul 24, 2024 · Backend Development

Mastering Unique Identifiers and Distributed Locks: From UUIDs to CAS

This article explores how various unique identifier schemes—random UUIDs, sequential numbers, hierarchical paths, and distributed ID generators—are applied across programming languages, file systems, databases, and networks, and explains when and how to enforce uniqueness using pre‑validation, database constraints, or distributed locks such as Redis and Zookeeper, while also linking these mechanisms to CAS‑based concurrency control.

Backend DevelopmentCASDistributed Systems
0 likes · 23 min read
Mastering Unique Identifiers and Distributed Locks: From UUIDs to CAS
Top Architect
Top Architect
May 9, 2024 · Databases

Effective Indexing Strategies for Database Performance

This article provides comprehensive guidance on effective database indexing, covering when and how to create various index types, practical SQL examples, strategies for equality, range, fuzzy, join, and composite queries, and tips for verifying index usage with EXPLAIN.

SQL Optimizationdatabase indexingperformance tuning
0 likes · 11 min read
Effective Indexing Strategies for Database Performance
Aikesheng Open Source Community
Aikesheng Open Source Community
Apr 28, 2024 · Databases

Database Indexing Algorithms: B‑Tree vs Hash Indexing

This article explains the purpose and inner workings of various database indexing algorithms—including B‑Tree, Hash, Bitmap, and Full‑Text indexes—illustrates their strengths and weaknesses with SQL examples, and provides guidance on when to choose each type for optimal query performance.

B+TreeBitmap IndexFull‑Text Search
0 likes · 12 min read
Database Indexing Algorithms: B‑Tree vs Hash Indexing
Liangxu Linux
Liangxu Linux
Apr 25, 2024 · Databases

How MySQL’s B+ Tree Indexes Supercharge Query Speed and Reliability

MySQL, born in the 1990s as a free open‑source database, became a core component of the LAMP stack and remains widely used; the article explains why indexes are needed, compares binary trees, B‑trees and B+‑trees, and describes how modern cloud‑based MySQL services improve performance and reliability.

B+TreeData Structuresdatabase indexing
0 likes · 9 min read
How MySQL’s B+ Tree Indexes Supercharge Query Speed and Reliability
Deepin Linux
Deepin Linux
Apr 7, 2024 · Backend Development

High‑Performance Development: Core Techniques from I/O Optimization to Distributed Systems

This comprehensive guide covers high‑performance development techniques—including I/O optimization, zero‑copy, multiplexing, concurrency, thread‑pool design, lock‑free programming, inter‑process communication, RPC, serialization, database indexing, caching strategies, Bloom filters, full‑text search, and load balancing—to help developers build fast, scalable, and reliable systems.

I/O optimizationbloom-filtercaching
0 likes · 62 min read
High‑Performance Development: Core Techniques from I/O Optimization to Distributed Systems
政采云技术
政采云技术
Jan 17, 2024 · Databases

Using MySQL 5.7 ngram Full-Text Search for Simple Text Retrieval

This article explains how to leverage MySQL 5.7's built‑in ngram full‑text parser to implement lightweight Chinese full‑text search, covering configuration, index creation, query modes, operator usage, and performance considerations.

Boolean ModeFull‑Text SearchNGram
0 likes · 12 min read
Using MySQL 5.7 ngram Full-Text Search for Simple Text Retrieval
政采云技术
政采云技术
Jan 2, 2024 · Databases

Understanding B‑Tree and B+Tree Indexes in MySQL

This article explains the fundamentals of B‑Tree and B+Tree data structures, their search algorithms, and how MySQL's MyISAM and InnoDB storage engines implement these indexes to achieve efficient disk‑based query performance.

AlgorithmsB+TreeB-Tree
0 likes · 19 min read
Understanding B‑Tree and B+Tree Indexes in MySQL
Liangxu Linux
Liangxu Linux
Jul 26, 2023 · Databases

Boost SQL Server Queries with Column Store Indexes: Architecture & Benefits

This article explains how column store indexes in SQL Server store each column separately, dramatically improve query performance through batch processing and compression, outlines their physical structure, encoding methods, creation syntax, maintenance steps, and space usage considerations.

Column StoreSQL Serverdata compression
0 likes · 11 min read
Boost SQL Server Queries with Column Store Indexes: Architecture & Benefits
MaGe Linux Operations
MaGe Linux Operations
May 14, 2023 · Backend Development

10 Proven Strategies to Supercharge API Performance in Legacy Projects

Discover a comprehensive set of ten practical techniques—including batch processing, asynchronous execution, caching, pooling, parallelization, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve efficiency in legacy backend systems, illustrated with real-world examples and code snippets.

API performanceCode Optimizationasynchronous processing
0 likes · 8 min read
10 Proven Strategies to Supercharge API Performance in Legacy Projects
Su San Talks Tech
Su San Talks Tech
Apr 4, 2023 · Databases

9 Proven MySQL Query Optimizations to Slash Execution Time

This article walks through nine common MySQL performance pitfalls—from inefficient LIMIT clauses and implicit type conversions to sub‑query updates, mixed sorting, EXISTS usage, condition pushdown, early range reduction, and intermediate result pushdown—showing how to rewrite each pattern for dramatically faster execution.

LIMIT clausedatabase indexingmysql
0 likes · 15 min read
9 Proven MySQL Query Optimizations to Slash Execution Time
JD Cloud Developers
JD Cloud Developers
Mar 7, 2023 · Backend Development

10 Proven Strategies to Supercharge API Performance

This article presents a comprehensive guide to optimizing API latency by covering batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, code refactoring, pagination, SQL tuning, and lock granularity, each illustrated with practical code examples and diagrams.

API performanceasynchronous processingbackend optimization
0 likes · 10 min read
10 Proven Strategies to Supercharge API Performance
dbaplus Community
dbaplus Community
Feb 19, 2023 · Databases

Essential MySQL Optimization Checklist: 46 Proven Practices to Boost Performance

This comprehensive guide outlines 46 practical MySQL optimization rules—from understanding the query execution flow and naming conventions to index design, join strategies, pagination techniques, and safe use of data types—providing concrete examples and code snippets that help developers dramatically improve database performance and reliability.

SQL Optimizationbest practicesdatabase indexing
0 likes · 26 min read
Essential MySQL Optimization Checklist: 46 Proven Practices to Boost Performance
Java Architect Essentials
Java Architect Essentials
Nov 21, 2022 · Databases

MySQL Query Optimization Best Practices: Avoid SELECT *, Use Specific Fields, Index Strategies, Joins, and Other Performance Tips

This article presents a comprehensive set of MySQL optimization guidelines, covering the avoidance of SELECT *, proper use of specific columns, eliminating OR and != in WHERE clauses, preferring numeric types, choosing VARCHAR over CHAR, using TRUNCATE, batch operations, index design, join selection, GROUP BY efficiency, and other practical tips to improve query performance.

SQL Optimizationdatabase indexingmysql
0 likes · 18 min read
MySQL Query Optimization Best Practices: Avoid SELECT *, Use Specific Fields, Index Strategies, Joins, and Other Performance Tips
Architecture Digest
Architecture Digest
Nov 18, 2022 · Backend Development

Design and Implementation of a Short URL Service

This article explains the value of short URLs, the HTTP redirection principle behind them, and provides detailed design and implementation strategies—including hash‑based and distributed‑ID approaches, base‑62 encoding, database storage, caching, indexing, and security measures—to build a scalable short‑link service.

HTTP redirectcachingdatabase indexing
0 likes · 12 min read
Design and Implementation of a Short URL Service
Programmer DD
Programmer DD
Jul 15, 2022 · Databases

Boost MySQL Performance: 8 Common SQL Pitfalls and How to Fix Them

This article examines frequent MySQL performance problems—such as inefficient LIMIT usage, implicit type conversion, suboptimal joins, mixed ordering, EXISTS subqueries, condition pushdown limitations, early range reduction, and intermediate result handling—and provides concrete rewrites and execution‑plan analyses that dramatically improve query speed.

Query RewritingSQL Optimizationdatabase indexing
0 likes · 16 min read
Boost MySQL Performance: 8 Common SQL Pitfalls and How to Fix Them
Java Baker
Java Baker
Jun 4, 2022 · Databases

Boost Multi-Dimensional Queries with Elasticsearch: From Basics to Advanced Integration

This article explains common multi‑dimensional query scenarios, compares basic indexing tricks and data heterogeneity approaches, then details how to integrate Elasticsearch with MySQL—including architecture, data import, Java client usage, code examples, and best‑practice recommendations—for scalable, near‑real‑time search.

ElasticsearchJava ClientMulti-dimensional query
0 likes · 15 min read
Boost Multi-Dimensional Queries with Elasticsearch: From Basics to Advanced Integration
Big Data Technology & Architecture
Big Data Technology & Architecture
May 12, 2022 · Databases

Understanding B+ Trees and Log‑Structured Merge (LSM) Trees and Their Use in HBase

This article explains the fundamentals of B+ trees, introduces log‑structured merge (LSM) trees as a modern alternative for write‑intensive workloads, and demonstrates how HBase leverages LSM trees—including MemStore, HFile, compaction, and Bloom filters—to achieve efficient storage and retrieval in NoSQL environments.

B+TreeHBaseLSM‑Tree
0 likes · 7 min read
Understanding B+ Trees and Log‑Structured Merge (LSM) Trees and Their Use in HBase
ITPUB
ITPUB
Jun 20, 2021 · Databases

Unveiling MySQL’s Query Execution: From Architecture to Optimization Strategies

This article explains MySQL’s logical architecture, the client‑server communication protocol, how queries are parsed, optimized and executed, the role of the query cache, and provides concrete performance‑tuning advice on schema design, indexing, B+Tree mechanics, and common pitfalls.

B+Treedatabase indexingmysql
0 likes · 35 min read
Unveiling MySQL’s Query Execution: From Architecture to Optimization Strategies
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Jun 5, 2021 · Databases

How Many Rows Can a MySQL InnoDB B+Tree Store?

This article explains InnoDB's storage hierarchy (sector, block, page), calculates how many rows fit in a 16KB page, shows how B+‑tree height and pointer counts determine total record capacity, and demonstrates the I/O cost of primary and secondary index lookups using practical MySQL commands.

B+TreeInnoDBPage Size
0 likes · 8 min read
How Many Rows Can a MySQL InnoDB B+Tree Store?
Top Architect
Top Architect
Apr 21, 2021 · Databases

How Many Rows Can a Single InnoDB B+ Tree Store?

This article explains the storage units of InnoDB, calculates how many rows a B+‑tree leaf page can hold, derives the total record capacity for trees of different heights, and shows how to determine the actual B+‑tree height in a MySQL table using page metadata.

B+TreeInnoDBSQL
0 likes · 9 min read
How Many Rows Can a Single InnoDB B+ Tree Store?
Aikesheng Open Source Community
Aikesheng Open Source Community
Mar 10, 2021 · Databases

Unsuitable Scenarios for MySQL Composite Indexes and Refactoring Strategies

This article reviews MySQL composite index syntax, explains the mandatory condition that the leftmost column must appear in the query filter, analyzes seven representative SQL statements that cannot benefit from a composite index, and provides practical refactoring suggestions such as adding single‑column indexes or creating more appropriate composite indexes.

Composite Indexdatabase indexingindex merge
0 likes · 11 min read
Unsuitable Scenarios for MySQL Composite Indexes and Refactoring Strategies
Liangxu Linux
Liangxu Linux
Dec 30, 2020 · Backend Development

Master High-Performance Backend Development: 10 Essential Techniques

This guide walks developers through a step‑by‑step progression of performance‑boosting techniques—including zero‑copy I/O, epoll, thread pools, lock‑free programming, IPC, RPC, database indexing, caching, Bloom filters, full‑text search, and load balancing—to help build faster, more scalable backend services.

Backend PerformanceI/O optimizationRPC
0 likes · 22 min read
Master High-Performance Backend Development: 10 Essential Techniques
ITPUB
ITPUB
Oct 19, 2020 · Databases

Why MySQL InnoDB Chooses B+ Trees: A Deep Dive into Index Design

This article explains why MySQL's default InnoDB storage engine uses B+ trees instead of B trees or hash indexes, covering storage engine basics, query performance, I/O considerations, and the structural advantages that make B+ trees ideal for OLTP workloads.

B+TreeInnoDBOLTP
0 likes · 16 min read
Why MySQL InnoDB Chooses B+ Trees: A Deep Dive into Index Design
ITPUB
ITPUB
Sep 7, 2020 · Databases

Why a Missing Index Parameter Crashed Our Production MySQL and How We Fixed It

A production MySQL server was overwhelmed by a full‑table‑scan query that omitted the leftmost index column, causing CPU saturation and read‑only alerts; the article explains the root cause, temporary mitigation, the leftmost‑match principle, code‑level bug, and key lessons learned.

Java ValidationSQL Performancebug fix
0 likes · 9 min read
Why a Missing Index Parameter Crashed Our Production MySQL and How We Fixed It
Java Backend Technology
Java Backend Technology
Aug 11, 2020 · Databases

How I Turned a Half‑Hour SSRS Report into a Sub‑Second Query with a Stored Procedure

The author describes how they transformed a sluggish SSRS report that took over thirty minutes to run into a fast, sub‑second query by analyzing the original SQL, adding missing indexes, avoiding full table scans, and rewriting the logic as a flexible stored procedure, complete with code examples and performance tips.

MESSQL OptimizationSSRS
0 likes · 24 min read
How I Turned a Half‑Hour SSRS Report into a Sub‑Second Query with a Stored Procedure
Architecture Digest
Architecture Digest
Jun 19, 2020 · Databases

How Many Rows Can an InnoDB B+ Tree Store?

This article explains the storage units of InnoDB, how B+‑tree indexes are organized on pages, calculates the maximum number of rows a B+ tree can hold based on page size and pointer count, and shows how to determine the tree height from the tablespace file.

B+TreeInnoDBPage Size
0 likes · 9 min read
How Many Rows Can an InnoDB B+ Tree Store?
Programmer DD
Programmer DD
Apr 9, 2020 · Databases

How Many Rows Can a Single InnoDB B+ Tree Store? A Deep Dive

This article explains how InnoDB organizes data with B+ trees, calculates the maximum number of rows a single tree can hold (around 20 million), and shows how page size, pointer count, and tree height affect MySQL index performance.

B+TreeInnoDBStorage Engine
0 likes · 11 min read
How Many Rows Can a Single InnoDB B+ Tree Store? A Deep Dive
Java Backend Technology
Java Backend Technology
Nov 2, 2019 · Databases

How Indexes Can Slash MySQL Query Time from Hours to Milliseconds

This article walks through a MySQL 5.6 scenario where a nested subquery to find students scoring 100 in Chinese runs for over 30,000 seconds, then demonstrates how adding single‑column and multi‑column indexes, analyzing execution plans, and applying join optimizations can reduce the runtime to milliseconds, while also covering best practices for index design and query tuning.

Query PlanSQL Performancedatabase indexing
0 likes · 9 min read
How Indexes Can Slash MySQL Query Time from Hours to Milliseconds
Architect's Tech Stack
Architect's Tech Stack
Sep 3, 2019 · Databases

Practical MySQL Slow Query Optimization: Case Studies and Techniques

This article presents a step‑by‑step analysis of several real‑world MySQL slow‑query problems, demonstrates how to reproduce the issues, examines execution plans, applies concrete optimizations such as removing function calls, adjusting date comparisons, adding bigint indexes, and explains the impact of different EXPLAIN types on performance.

SQLdatabase indexingexplain
0 likes · 12 min read
Practical MySQL Slow Query Optimization: Case Studies and Techniques
Efficient Ops
Efficient Ops
Jun 18, 2019 · Databases

How to Diagnose and Fix MySQL Slow Queries Caused by Deadlocks

This article walks through a real‑world case where a single MySQL statement took over 1000 ms, showing how to use Druid, JMC, and process‑list analysis to uncover deadlocks, add missing indexes, kill blocking threads, and restore normal performance.

database indexingdeadlockmysql
0 likes · 6 min read
How to Diagnose and Fix MySQL Slow Queries Caused by Deadlocks
ITPUB
ITPUB
Oct 31, 2017 · Databases

How Database Indexing Works: A Deep Dive into Performance Gains

This article explains why database indexes are essential, describes how they are structured and stored, walks through concrete calculations for a 5‑million‑row MyISAM table, compares linear and binary search costs, and outlines when and how to use indexes effectively.

B+Treedatabase indexingdisk storage
0 likes · 10 min read
How Database Indexing Works: A Deep Dive into Performance Gains
21CTO
21CTO
Jun 14, 2016 · Databases

Why Your SSRS Report Is Slowing Down: Real-World SQL Optimization Lessons

An in‑depth case study of a sluggish SSRS report for Siemens China's MES system reveals how missing indexes, full‑table scans on billion‑row tables, and misguided partitioning cripple performance, and demonstrates practical SQL refactoring, stored‑procedure redesign, and indexing strategies that cut query time from hours to seconds.

PartitioningSQLStored Procedures
0 likes · 10 min read
Why Your SSRS Report Is Slowing Down: Real-World SQL Optimization Lessons
WeChat Client Technology Team
WeChat Client Technology Team
Aug 24, 2015 · Databases

How We Boosted SQLite Chat Performance: Fragment Caching, Table Splitting, and Index Refactoring

This article details a multi‑stage performance overhaul of a large‑scale chat application's SQLite database, covering trace‑based I/O analysis, view caching with Fragments, table‑splitting experiments, index size reduction, and the development of a PageTracer tool to quantify page‑level gains.

AndroidFragmentPageTracer
0 likes · 21 min read
How We Boosted SQLite Chat Performance: Fragment Caching, Table Splitting, and Index Refactoring
Java High-Performance Architecture
Java High-Performance Architecture
Aug 3, 2015 · Databases

Boost MySQL Performance: When InnoDB Beats MyISAM and How to Avoid Common Pitfalls

While InnoDB’s row‑level locking generally outperforms MyISAM’s table‑level locking under high concurrency, misuse can negate its advantages; this guide explains why indexing, precise lock scopes, minimal range queries, smaller transactions, and lower isolation levels are essential for optimal InnoDB performance.

InnoDBPerformance OptimizationRow-Level Locking
0 likes · 2 min read
Boost MySQL Performance: When InnoDB Beats MyISAM and How to Avoid Common Pitfalls