This article explains MySQL storage engines, compares MyISAM and InnoDB, introduces the main index types—B‑Tree, Hash, Full‑text, R‑Tree—details the structures and characteristics of B‑Tree, B‑Tree and B+Tree, and outlines key principles for creating effective indexes.
This article provides a comprehensive overview of MySQL InnoDB's internal architecture, covering its memory and disk structures, buffer pool, change buffer, adaptive hash index, log buffer, various tablespace types, page layout, undo and redo logs, double‑write buffer, transaction isolation levels, and how the engine satisfies ACID properties.
This article explains InfluxDB's storage engine workflow—including WAL, Cache, TSM files, compression components, and file management—then provides hardware sizing guidance based on write/query load, series cardinality, and recommends SSD storage with sample configuration settings.
This article explains how OceanBase’s distributed, shared‑nothing architecture, with dual data replicas, Paxos‑based consistency, online compression, and resource‑isolated compaction, enables it to achieve top TPC‑C performance while addressing storage costs and CPU usage.
The article highlights the critical role of databases in big data ecosystems, surveys existing open‑source and commercial storage and query engines such as Druid, Kylin, Impala, Greenplum, Vertica and Redshift, and introduces Baidu’s open‑source Palo project, questioning its unique features and performance.
This article explains how InnoDB’s 16 KB pages, row size assumptions, and B+‑tree node capacities combine to allow roughly 20 million rows per tree, demonstrates how to calculate tree height from page metadata, and shows why MySQL chooses B+‑trees for primary‑key indexes.
This article provides a comprehensive overview of InnoDB's file structure, covering physical organization from pages to tablespaces, logical structure including segments and indexes, and file management processes.
This comprehensive guide covers MySQL basics, including the definition of MySQL, transaction concepts and ACID properties, isolation levels, common concurrency issues, index structures and usage, storage engine differences, lock mechanisms, and practical techniques for optimizing large tables and improving performance.
This article provides an in‑depth technical overview of Alibaba's X‑Engine storage engine, explaining its LSM‑based architecture, compaction optimizations, transaction pipeline, caching strategies, and how these innovations enable low‑cost, high‑throughput OLTP for large‑scale e‑commerce workloads.
An InnoDB storage engine sits above the file system and below SQL tools, providing transactions with redo/undo logs, row and table locking, MVCC, B+‑tree and adaptive hash indexes, compression, encryption, checkpointing, multi‑threaded buffering, backup and replication mechanisms, and extensive performance‑monitoring commands.
This article explains how InnoDB’s B+‑tree index stores data, calculates the maximum number of rows a single tree can hold (around 20 million), shows how to determine the tree height from the tablespace file, and answers why MySQL uses B+ trees instead of other structures.
This article explains how MySQL storage engines differ, clarifies the structures and search mechanisms of B‑Tree and B+Tree indexes, and outlines practical principles for creating efficient indexes to reduce I/O and speed up query execution.
Pika 3.0, the open‑source C++ Redis‑compatible storage built on RocksDB, introduces a new Blackwidow engine, enhanced binlog, and optimized server layer, delivering higher interface performance, reduced disk usage, extended key length, full ZSET compatibility, and improved overall efficiency.
This article provides an in‑depth technical overview of Alibaba's AnalyticDB, covering the challenges of massive real‑time analytics, the cloud‑native multi‑tenant architecture, data model, import/export capabilities, high‑performance SQL parser, the Xuanwu storage engine, Xihe compute engine, optimizer, GPU acceleration, and elastic scaling features.
This article details the design, implementation, and performance tuning of a Java‑based key‑value storage engine built for a competition, covering the competition requirements, interface definitions, data layout, recovery logic, multi‑threaded read/write strategies, Direct I/O techniques, JVM tuning, and practical lessons learned.
This article explains how Ant Group's financial‑grade distributed relational database OceanBase delivers extreme performance on ordinary servers by optimizing its storage engine, CPU usage, and system scalability, and it highlights the impressive throughput and latency gains demonstrated during the 2018 Double‑11 shopping festival.
The article reviews recent advances in cloud computing hardware‑software integration, including Alibaba's FusionEngine storage engine, AI‑driven reliability and performance challenges, near‑data network acceleration, and the concept of server hardware AI, while also highlighting related research talks and recruitment notices.
An in‑depth comparison of MySQL 8 and PostgreSQL 10 examines feature parity, storage architecture, indexing, memory usage, replication, update overhead, and garbage collection, highlighting how recent improvements narrow the gap and offering guidance on choosing the right database for specific workloads.
This article presents the design, implementation, and evaluation of X‑Engine, a next‑generation LSM‑Tree based storage engine that offloads compaction to FPGA, achieving up to 50% KV‑interface and 40% SQL‑interface performance gains for write‑intensive OLTP workloads.
This article provides a comprehensive, step‑by‑step overview of MySQL's InnoDB storage engine, covering database vs instance definitions, internal processes, tablespace layout, file formats, record layouts, B‑tree indexes, lock types, and transaction isolation levels for developers seeking a solid foundation.
This article walks through the internal structures of MySQL, Oracle, and PostgreSQL, detailing their logical and storage layers, key modules, process interactions, memory components, and how these elements cooperate to handle client requests and ensure data integrity.
The article explores Twitter’s Manhattan storage system, detailing its architecture, CAP trade‑offs across various database types, the design of its modular storage engines, high‑performance operations, and the DevOps practices that enable reliable, low‑latency handling of billions of requests in a massive distributed environment.
This article examines Ceph's Bluestore storage engine, describing its raw‑device management, metadata structures, write and read I/O processing, allocation strategies, and clone handling, highlighting how it reduces write amplification and optimizes for SSDs compared to the older Filestore.
An in‑depth comparison of MySQL’s default storage engines—MyISAM and InnoDB—covers their evolution, core features, performance benchmarks, locking mechanisms, transaction support, file structures, and practical guidance on choosing the right engine for different workloads.
This article presents the motivation, architecture, functional and interface layers, storage design, and performance evaluation of CFL, a MySQL plug‑in storage engine created by Ctrip to provide a high‑throughput, SQL‑based time‑series database while preserving existing MySQL operational practices.
This article presents the design, architecture, and performance evaluation of CFL, a MySQL time‑series storage engine developed by Ctrip, detailing its functional and interface layers, storage strategy, and benchmark results compared with InnoDB, MyISAM, and other engines.
This article explains how MariaDB, a community‑driven fork of MySQL, maintains full compatibility while adding new storage engines, advanced replication, performance‑boosting features like XtraDB and thread pools, and why many organizations consider it a superior replacement for Oracle‑owned MySQL.
MyISAM, the original MySQL storage engine used for over two decades, is now limited and discouraged in MySQL 8.0 due to its lack of transactions, table‑level locking, and crash recovery, with InnoDB taking over its former advantages.
This comprehensive guide walks you through MySQL's features, table size limits, Linux installation methods, source compilation, post‑install security hardening, character‑set management, engine selection, essential commands, and password recovery techniques for robust database administration.
The article explains how MySQL’s ease of use, open‑source nature, early replication support, flexible storage‑engine architecture, and strong community contributions have driven its widespread adoption and sustained popularity in modern high‑concurrency, high‑availability applications.
This article explains the theoretical foundations of relational databases, outlines the essential storage, engine, and UI layers, and walks through a concrete minimal implementation using fixed‑length tables, B+‑tree indexes, simple SQL parsing with regular expressions, and a TCP‑based client interface.
This article provides a step-by-step guide on creating and managing MySQL indexes (including INDEX and UNIQUE), defining primary keys with AUTO_INCREMENT, establishing foreign key relationships with cascade updates/deletes, and configuring the default storage engine, illustrated with practical SQL commands and examples.
This article explains MySQL's overall logical architecture, details each component such as connectors, connection management, parser, optimizer, caches, and the pluggable storage engine framework, and provides examples of the storage engine API functions for creating, opening, scanning, indexing, and handling transactions, plus guidance on building a custom engine.
This article details Ctrip's motivation, design, implementation, performance evaluation, and future plans for a custom MySQL storage engine called CFL that efficiently stores time‑series data by leveraging MySQL's replication and a sequential write‑optimized file format.
This article explains how MongoDB's MMAPv1 storage engine organizes databases, namespaces, data files, extents, and records, detailing the structures, write, delete, update, and query processes, and how space reclamation and fragmentation are handled.
This article explains MySQL 5.7 InnoDB's traditional compressed storage format, system page structures, external storage pages, encrypted and R‑TREE pages, the IO subsystem including asynchronous AIO handling and read‑ahead strategies, as well as buffer pool initialization, management, and concurrency control.
The article reviews MongoDB's storage engine developments—including MMAP, WiredTiger, RocksDB, and Memory engines—highlights replication protocol improvements, automatic sharding changes, and introduces new features such as batch and partial indexes, document validation, and join capabilities, while providing performance test results and a Q&A section.
The article outlines the step‑by‑step lifecycle of a database server—from system initialization and buffer allocation, through connection and thread management, request parsing, optimization, storage‑engine interaction, to final response delivery and cleanup—illustrated with diagrams of each stage.
This article explains MySQL’s logical architecture, detailing its three main layers, the responsibilities of each layer, connection management, authentication, query parsing, optimization, caching, and the key modules that together enable MySQL’s pluggable storage engine design.
The article examines the impact of Oracle's acquisition of MySQL, introduces Percona XtraDB and Percona Server, outlines MariaDB's origins and compatibility, compares their storage engines, performance, and version histories, and provides resources for further comparison of these MySQL alternatives.
