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652 articles · Page 7 of 7
Tencent Architect
Tencent Architect
Apr 12, 2017 · Databases

Tencent File System (TFS): Architecture, 3D Indexing, High‑Performance Key‑Value Store, and Storage Engines

The article details Tencent File System (TFS), describing its platform components, 3D indexing techniques, high‑performance key‑value storage (TSSD) with MHT, dual‑read and smooth scaling mechanisms, hybrid index storage, host‑level FTL, Append‑Only and erasure‑coding storage engines, and how these innovations deliver scalable, low‑cost, high‑performance data storage for massive workloads.

IndexingKey-ValueSSD
0 likes · 12 min read
Tencent File System (TFS): Architecture, 3D Indexing, High‑Performance Key‑Value Store, and Storage Engines
21CTO
21CTO
Feb 15, 2017 · Fundamentals

How Twitter Evolved Its Search Engine: From MySQL to Earlybird and Beyond

This article explains the fundamentals of search engine architecture, covering text collection, indexing, ranking and evaluation, and then traces Twitter's internal search evolution from MySQL full‑text search to the Earlybird index server, Blender aggregation, and smart memory‑SSD strategies.

Big DataIndexingInformation Retrieval
0 likes · 8 min read
How Twitter Evolved Its Search Engine: From MySQL to Earlybird and Beyond
Practical DevOps Architecture
Practical DevOps Architecture
Nov 22, 2016 · Databases

Common MySQL SQL Statements and Table Alteration Cheat Sheet

This article compiles frequently used MySQL SQL commands—including data migration, multi‑table updates, conditional updates, aggregation inserts, and various ALTER TABLE operations such as adding indexes, dropping and adding columns—to serve as a practical reference for database developers.

DDLDMLData Migration
0 likes · 6 min read
Common MySQL SQL Statements and Table Alteration Cheat Sheet
dbaplus Community
dbaplus Community
Nov 3, 2016 · Databases

Taming a Million‑Row Log Table: Real‑World SQL Performance Optimization

A detailed case study describes how a rapidly growing edit‑log feature caused query times to soar to 30 seconds, and walks through the step‑by‑step investigation, identification of a custom function bottleneck, data‑volume analysis, and the eventual implementation of partitioning, mandatory time filters, and composite indexing to restore acceptable performance.

IndexingPL/SQLPartitioning
0 likes · 12 min read
Taming a Million‑Row Log Table: Real‑World SQL Performance Optimization
ITPUB
ITPUB
Nov 1, 2016 · Databases

How to Slash Redo Log Generation and Logical Reads in Oracle UPDATE Statements

A detailed Oracle performance case study shows how adding selective predicates and a supporting index to an UPDATE that runs hourly can reduce redo log generation from over 100 MB to under 1 MB and cut logical reads by more than 95%, with step‑by‑step measurements and code examples.

IndexingLogical ReadsOracle
0 likes · 8 min read
How to Slash Redo Log Generation and Logical Reads in Oracle UPDATE Statements
Java Backend Technology
Java Backend Technology
Oct 15, 2016 · Databases

Boost Query Speed in Million-Row Databases: Proven Optimization Techniques

This article presents a comprehensive set of practical strategies for improving database query performance, covering index design, SQL statement refinements, Java backend considerations, hardware tuning, and storage‑procedure usage to accelerate operations on large‑scale relational databases.

IndexingPerformance TuningQuery Optimization
0 likes · 13 min read
Boost Query Speed in Million-Row Databases: Proven Optimization Techniques
ITPUB
ITPUB
Oct 6, 2016 · Databases

How Adding Indexes Cut a 30‑Second MySQL View Query to 2 Seconds

A .NET MVC warehouse system’s MySQL view was taking over 30 seconds to fetch 70,000 inventory records, but by adding a composite index on the dictionary table, removing a UNION ALL, and adjusting view algorithms, the query time dropped to a few seconds, with a stored‑procedure alternative for flexible use.

Entity FrameworkIndexingMySQL
0 likes · 10 min read
How Adding Indexes Cut a 30‑Second MySQL View Query to 2 Seconds
ITPUB
ITPUB
Sep 23, 2016 · Databases

Master MySQL Performance: Practical Table, Index, and Connection Pool Optimizations

This article presents practical MySQL optimization techniques covering table schema design, character set choices, primary key strategies, field definitions, index creation, SQL query tuning, pagination tricks, join handling, connection‑pool parameter tuning, and historical data archiving for large‑scale databases.

Connection PoolIndexingMySQL
0 likes · 13 min read
Master MySQL Performance: Practical Table, Index, and Connection Pool Optimizations
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 21, 2016 · Databases

10 Powerful MongoDB Performance Tricks You Need to Know

This guide shares ten practical MongoDB performance tips—including proper index ordering, using explain() and hint(), avoiding costly query operators, leveraging capped collections, upsert, sorting limits, aggregation, drop vs remove, batch inserts, and optimal indexing for arrays and range queries—to help developers boost database efficiency.

AggregationIndexingMongoDB
0 likes · 5 min read
10 Powerful MongoDB Performance Tricks You Need to Know
dbaplus Community
dbaplus Community
Sep 13, 2016 · Databases

How to Speed Up Zabbix Latest Data Display by Adding Dedicated Tables and Indexes

This article explains why Zabbix's original table design slows down data insertion and query in large‑scale deployments, and provides a step‑by‑step code‑level optimization that creates separate "latest" tables with primary‑key indexes, modifies the server source, and updates the front‑end query to dramatically improve performance without affecting historical data ingestion.

IndexingMySQLOracle
0 likes · 11 min read
How to Speed Up Zabbix Latest Data Display by Adding Dedicated Tables and Indexes
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 7, 2016 · Databases

MongoDB Indexing Best Practices: Real‑World Tips & Common Pitfalls

This article shares practical MongoDB indexing guidelines—including compound index rules, naming limits, cardinality considerations, TTL and geospatial indexes, lock behavior, and real‑world case studies—to help DBAs and developers design efficient, safe indexes for production workloads.

Database PerformanceGeospatial IndexIndexing
0 likes · 8 min read
MongoDB Indexing Best Practices: Real‑World Tips & Common Pitfalls
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 1, 2016 · Databases

MongoDB Document Best Practices: Avoid Common Pitfalls with Real‑World Cases

This article outlines essential MongoDB document best‑practice guidelines, including key naming restrictions, collection organization, avoiding custom _id values, pitfalls of array fields, compression of large fields, using MD5 hashes for long strings, case‑sensitivity handling, and index size limits, illustrated with real‑world cases.

Best PracticesDocument DesignIndexing
0 likes · 11 min read
MongoDB Document Best Practices: Avoid Common Pitfalls with Real‑World Cases
dbaplus Community
dbaplus Community
Aug 17, 2016 · Databases

How We Turned a Sluggish Hospital HIS on SQL Server 2008R2 into a Faster System

This article walks through a real‑world case study of a hospital HIS running on SQL Server 2008R2, detailing the system’s hardware specs, performance metrics, three‑phase optimization process—including general tuning, statement‑level fixes, and deep CPU/memory analysis—and the measurable results achieved without adding new hardware.

Hospital HISIndexingParameterization
0 likes · 12 min read
How We Turned a Sluggish Hospital HIS on SQL Server 2008R2 into a Faster System
ITPUB
ITPUB
Jul 25, 2016 · Databases

How BRIN Indexes Use allnulls and hasnulls to Slash PostgreSQL Scans

This article demonstrates how PostgreSQL 9.5's BRIN index leverages the allnulls and hasnulls flags in its regular pages to filter out unnecessary heap page scans, dramatically improving query performance while also discussing the impact of MVCC bloat and maintenance.

BRINIndexingMVCC
0 likes · 10 min read
How BRIN Indexes Use allnulls and hasnulls to Slash PostgreSQL Scans
ITPUB
ITPUB
Jul 16, 2016 · Databases

BRIN vs BTREE in PostgreSQL: Massive Space Savings and Performance Trade‑offs

This article experimentally compares PostgreSQL 9.5's BRIN and BTREE indexes across seven data distributions, revealing that BRIN uses up to 6,848 times less storage while BTREE consistently outperforms BRIN in query speed, with the gap narrowing as data repetition increases.

BRINBTreeIndexing
0 likes · 12 min read
BRIN vs BTREE in PostgreSQL: Massive Space Savings and Performance Trade‑offs
Java High-Performance Architecture
Java High-Performance Architecture
Jun 20, 2016 · Databases

Boost MySQL Query Performance with Generated Virtual Columns

MySQL 5.7 introduces generated (virtual) columns, allowing you to store computed values such as day‑of‑week without extra storage, enabling indexed queries that bypass function‑based limitations, improving read performance while avoiding the write‑time overhead and redundancy of manual columns.

Generated ColumnsIndexingMySQL
0 likes · 4 min read
Boost MySQL Query Performance with Generated Virtual Columns
21CTO
21CTO
Jun 15, 2016 · Databases

Why Adding Indexes to Every Column Won’t Speed Up MySQL Queries

This article explains MySQL’s indexing fundamentals, the left‑most prefix rule, B+‑tree structure, disk I/O considerations, and provides step‑by‑step methods and real‑world cases for diagnosing and optimizing slow queries in production systems.

B+TreeDatabase PerformanceIndexing
0 likes · 25 min read
Why Adding Indexes to Every Column Won’t Speed Up MySQL Queries
Baidu Maps Tech Team
Baidu Maps Tech Team
Jun 2, 2016 · Backend Development

How Baidu Maps Re‑engineered Its Indexing Unit for Scalable Data Storage

This article details Baidu Maps' technical team’s refactoring of the indexing (build) unit, outlining existing bottlenecks, design challenges, and a new decoupled architecture that separates storage, incremental updates, and full‑index construction using distributed table storage and message‑driven pipelines to improve scalability and reliability.

Baidu MapsDistributed storageIndexing
0 likes · 9 min read
How Baidu Maps Re‑engineered Its Indexing Unit for Scalable Data Storage
Qunar Tech Salon
Qunar Tech Salon
Apr 30, 2016 · Big Data

Designing and Optimizing Log Storage and Query in HBase

This article analyzes the characteristics of log data, explains why HBase is chosen for log storage, discusses the shortcomings of self‑built indexes, and presents optimization strategies such as rowKey design, filter usage, coprocessor integration, and third‑party indexing to improve query performance.

HBaseIndexingQuery Optimization
0 likes · 12 min read
Designing and Optimizing Log Storage and Query in HBase
dbaplus Community
dbaplus Community
Apr 28, 2016 · Databases

How Uber Built Schemaless: A Scalable MySQL‑Based No‑Schema Datastore

This article explains how Uber engineered Schemaless, a highly available, horizontally scalable datastore built on MySQL that stores immutable JSON cells without a fixed schema, detailing its design goals, architecture, data model, trigger system, indexing strategy, and fault‑tolerant read/write mechanisms.

IndexingMySQLNoSQL
0 likes · 32 min read
How Uber Built Schemaless: A Scalable MySQL‑Based No‑Schema Datastore
Tencent Music Tech Team
Tencent Music Tech Team
Apr 28, 2016 · Mobile Development

Understanding and Using EventBus 3 in Android: Setup, Core Architecture, Index Acceleration, and Best Practices

EventBus 3 provides a lightweight publish/subscribe framework for Android that simplifies inter‑module communication by allowing any object to be posted and received via @Subscribe methods, with optional index acceleration for faster registration, customizable thread modes, and guidance on setup, ProGuard rules, pitfalls, and alternatives.

AndroidAnnotation ProcessingEventBus
0 likes · 22 min read
Understanding and Using EventBus 3 in Android: Setup, Core Architecture, Index Acceleration, and Best Practices
ITPUB
ITPUB
Apr 6, 2016 · Databases

Boost MySQL Performance: Engine Choice, Indexing, and EXPLAIN Tips

This guide explains practical MySQL optimization techniques—including selecting InnoDB vs. MyISAM, avoiding COUNT(*), using EXPLAIN, and adding selective indexes—to dramatically reduce query time and prevent full‑table scans.

EXPLAINIndexingMySQL
0 likes · 7 min read
Boost MySQL Performance: Engine Choice, Indexing, and EXPLAIN Tips
Architect
Architect
Mar 22, 2016 · Backend Development

Youzan Search Engine Practice – Engineering Part: Architecture, Indexing, and Performance Optimization

This article describes the practical architecture of Youzan's commercial e‑commerce search engine, covering data source integration, distributed real‑time indexing with Elasticsearch, Hadoop and Kafka, advanced search modules, and several performance‑tuning techniques for large‑scale deployments.

ElasticsearchIndexingKafka
0 likes · 13 min read
Youzan Search Engine Practice – Engineering Part: Architecture, Indexing, and Performance Optimization
21CTO
21CTO
Mar 22, 2016 · Databases

How Facebook’s Dragon Engine Accelerates Graph Queries at Scale

Facebook’s Dragon distributed graph query engine optimizes high‑volume single‑hop and multi‑hop queries by introducing specialized indexing, socially aware inverted indices, and functional primitives, dramatically reducing latency, storage reads, and CPU usage while handling massive social‑graph data.

DragonFacebookIndexing
0 likes · 11 min read
How Facebook’s Dragon Engine Accelerates Graph Queries at Scale
Baidu Maps Tech Team
Baidu Maps Tech Team
Mar 9, 2016 · Cloud Computing

How Baidu’s LBS Cloud Storage Revolutionizes Massive Geospatial Data Retrieval

Baidu’s LBS Cloud Storage and Retrieval platform offers developers a fully managed, high‑performance solution for massive geospatial data, featuring free large‑scale storage, GeoHash‑based spatial queries, real‑time updates, strong isolation, and a series of architectural optimizations that dramatically improve latency, availability, and scalability.

IndexingLBScloud storage
0 likes · 9 min read
How Baidu’s LBS Cloud Storage Revolutionizes Massive Geospatial Data Retrieval
Architect
Architect
Mar 3, 2016 · Databases

Understanding InnoDB Adaptive Hash Index (AHI) Mechanism

This article explains how InnoDB's Adaptive Hash Index works, covering its initialization, information gathering, index and block statistics updates, page‑level hash construction, usage conditions, maintenance procedures, shortcut query mode, and monitoring via performance metrics.

Adaptive Hash IndexIndexingInnoDB
0 likes · 15 min read
Understanding InnoDB Adaptive Hash Index (AHI) Mechanism
Architect
Architect
Mar 2, 2016 · Databases

InnoDB Full-Text Index Architecture and Operations in MySQL 5.7

This article explains MySQL InnoDB full-text indexing, covering supported modes, auxiliary tables, creation, DML handling, query processing, transaction management, cache synchronization, optimization, background threads, monitoring, stopwords, plugins, and the built‑in n‑gram parser, with code references from MySQL 5.7.

Full-Text SearchIndexingInnoDB
0 likes · 17 min read
InnoDB Full-Text Index Architecture and Operations in MySQL 5.7
High Availability Architecture
High Availability Architecture
Jan 20, 2016 · Databases

MongoDB Storage Engine Evolution, Replication Enhancements, and New Features

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.

IndexingMongoDBStorage Engine
0 likes · 14 min read
MongoDB Storage Engine Evolution, Replication Enhancements, and New Features
ITPUB
ITPUB
Dec 30, 2015 · Databases

Essential MySQL DBA Interview Guide: Key Topics & Evaluation Checklist

This guide outlines a structured MySQL DBA interview process, covering self‑introduction, resume deep‑dive, career‑development questions, fundamental knowledge such as index types, core technical skills like backup, replication and performance troubleshooting, and broader potential areas including Linux and programming language proficiency.

DBAIndexingMySQL
0 likes · 8 min read
Essential MySQL DBA Interview Guide: Key Topics & Evaluation Checklist
ITPUB
ITPUB
Dec 21, 2015 · Databases

Master MySQL Indexes: BTREE, Hash, and Query Optimization Secrets

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.

BTreeHash IndexIndexing
0 likes · 19 min read
Master MySQL Indexes: BTREE, Hash, and Query Optimization Secrets
Efficient Ops
Efficient Ops
Dec 7, 2015 · Backend Development

Mastering the ELK Stack: From Lucene Indexing to ElasticSearch Queries

This article walks through the fundamentals of search engine architecture, explains Lucene's role as an indexing library, details ElasticSearch's distributed design, clustering, sharding, and plugins, and demonstrates practical RESTful API usage and query DSL techniques for effective log analysis.

IndexingLuceneSearch
0 likes · 23 min read
Mastering the ELK Stack: From Lucene Indexing to ElasticSearch Queries
Architect
Architect
Oct 31, 2015 · Databases

MySQL High‑Performance Architecture and Optimization Techniques

This article provides a comprehensive guide to MySQL performance optimization, covering isolation levels, MVCC, schema design, index strategies, query execution, partitioning, replication, scaling, hardware considerations, and backup/recovery techniques for building high‑performance, reliable database systems.

IndexingIsolation LevelsMySQL
0 likes · 22 min read
MySQL High‑Performance Architecture and Optimization Techniques
Qunar Tech Salon
Qunar Tech Salon
Oct 10, 2015 · Fundamentals

Overview of Search Engine Architecture and Core Technologies

This article provides a comprehensive overview of search engine evolution, core technologies such as crawling, indexing, retrieval and link analysis, platform foundations including cloud storage and computing, and techniques for improving search results through anti‑spam, user‑intent analysis, deduplication and caching.

Cloud ComputingIndexingInformation Retrieval
0 likes · 15 min read
Overview of Search Engine Architecture and Core Technologies
Architect
Architect
Jul 9, 2015 · Databases

Building a Flexible, Searchable, Low-Latency Product Catalog with MongoDB

This article explains how retailers can replace costly ETL‑driven vendor systems with a centralized MongoDB‑based service architecture, detailing data models for products, variants, store‑specific pricing, and multi‑facet search, and showing query and indexing techniques to achieve fast, reliable catalog access.

IndexingMongoDBProduct Catalog
0 likes · 11 min read
Building a Flexible, Searchable, Low-Latency Product Catalog with MongoDB
MaGe Linux Operations
MaGe Linux Operations
Apr 14, 2015 · Databases

Unlocking MySQL 5.7 JSON: Native Support, Indexing, and Virtual Columns

This article explains how MySQL 5.7 introduced native JSON support with validity checks, performance improvements, and virtual column indexing, compares it to MariaDB's dynamic column and PostgreSQL, and provides step‑by‑step SQL examples for creating, inserting, querying, and indexing JSON data.

IndexingMySQLVirtual Columns
0 likes · 5 min read
Unlocking MySQL 5.7 JSON: Native Support, Indexing, and Virtual Columns
Baidu Tech Salon
Baidu Tech Salon
Jan 12, 2015 · Artificial Intelligence

Boolean Algebra and Search Engine Technology

The article outlines how search engines combine the Tao of underlying principles—crawling, binary‑based Boolean indexing, PageRank matrix calculations, and TF‑IDF weighting—with specific Shu implementations to efficiently retrieve, rank, and present relevant web pages using Boolean logic, link analysis, and term relevance metrics.

IndexingPageRankTF-IDF
0 likes · 7 min read
Boolean Algebra and Search Engine Technology