ElephpantDB is an experimental SQL database implemented in a single PHP file, supporting CREATE TABLE, SELECT, UPDATE, DELETE, indexes, prepared statements, an append-only heap with crash recovery, hash indexes, and an optional HTTP JSON endpoint, running on PHP 8.3+.
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.
Introducing Hudi’s Bucket Index, a hash‑based indexing module that replaces Bloom Filter and HBase indexes, dramatically reducing file reads and writes, improving upsert performance, enabling efficient bucket pruning and joins, and offering practical guidance on design, implementation, and future extensions.
This article explains the background, design, and practical benefits of Hudi's Bucket Index—a hash‑based indexing mechanism that reduces unnecessary file reads and writes, improves upsert speed on terabyte‑scale datasets, and enables query optimizations such as bucket pruning and bucket join.
This article introduces MySQL indexes, explains why they are needed, compares B+Tree and hash index structures, describes how indexes improve query performance, outlines their limitations, shows how to create single‑ and multi‑column indexes, and discusses the trade‑offs of using them.
This article explains the fundamentals and structures of database indexes—including binary trees, red‑black trees, B‑Tree, B+Tree, hash indexes—and details how MySQL’s InnoDB and MyISAM engines implement clustered and non‑clustered indexes, covering their characteristics, storage files, and query behavior.
This article explains MySQL indexing fundamentals, covering index types, underlying data structures such as B‑Tree and Hash, the differences between clustered and non‑clustered indexes, practical performance tips, and advanced features like adaptive hash indexes and index condition pushdown.
This article explains how ordered data indexes improve query efficiency, compares B+Tree and hash indexes, discusses table partitioning versus sharding, outlines MVCC read types, examines row‑level lock pros and cons, and provides practical MySQL optimization tips including key vs index differences and engine choices.
This article explains how MySQL implements hash indexes, illustrates their data distribution on memory tables, and evaluates their suitable scenarios and limitations compared with B‑tree indexes through detailed SQL examples and performance analysis.
This article explains MySQL's hash index by introducing fundamental data structures such as arrays, dictionaries, linked lists, and hash tables, discussing their advantages and disadvantages, and showing how they are used in MySQL with practical code examples.
This article explains the fundamental concepts and practical design of MySQL indexes, covering implementation-level index types, application-level index categories, the differences between clustered and non‑clustered indexes, B‑Tree and hash structures, covering indexes, and best‑practice strategies for optimal query performance.
This article explains what database indexes are, outlines their advantages and disadvantages, advises when to create or avoid them, compares common index data structures such as B+Tree, Hash, and FullText, and details MySQL's MyISAM and InnoDB index implementations.
This article explains the fundamentals of MySQL indexes, covering their conceptual purpose, logical classifications, underlying data structures such as hash, B‑Tree, and B+Tree, physical storage differences between clustered and non‑clustered indexes, and practical tips for effective index optimization.
This article explains what MySQL indexes are, the different categories such as ordinary, unique, composite, clustered and non‑clustered, how B‑Tree, B+Tree and hash indexes are implemented in InnoDB and MyISAM, and why auto‑increment primary keys are recommended for optimal performance.
This article recounts a detailed MySQL interview, explaining index fundamentals, B‑Tree versus hash structures, clustered and covering indexes, composite indexes with left‑most prefix rules, and advanced optimizations like index condition pushdown, offering practical insights for database professionals.
This article explains the principles, structures, and operations of MySQL indexes, covering B+ tree indexes, their search, insertion, and deletion mechanisms, as well as hash indexes, adaptive hash indexing, and full‑text indexes with inverted indexing, cache handling, and practical limitations.
This article explains why auto‑increment primary keys are preferred, compares B+‑tree and hash indexes, discusses MySQL partitioning, isolation levels, MVCC, and provides practical tips for designing efficient database tables and indexes.
This article explains the Bitcask key‑value storage model, covering its log‑structured file design, in‑memory hash index, handling of deletions and updates, periodic merge operations, and the use of hint files to speed up hash index reconstruction.
This article explains MySQL's indexing mechanisms—including BTREE and hash structures, page layout, index types, and practical query patterns—while showing how to interpret EXPLAIN output and avoid common pitfalls for efficient database performance.
This article explains the characteristics and usage scenarios of B-Tree and Hash indexes in MySQL, including supported operators, example queries, when indexes are applied or ignored, and performance considerations for different storage engines such as MEMORY.
