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Distributed Databases

118 articles · Page 2 of 2
37 Interactive Technology Team
37 Interactive Technology Team
Aug 23, 2018 · R&D Management

Technical Research Group Introduction

The article describes a Technical Research Group initiative within a tech center, offering several focused sub‑groups (frontend integration, distributed databases, Python engineering, AI, productization) led by experts, limited to eight members each, running Sep‑Dec 2018 with weekly training, demos, and final deliverables, requiring commitment and registration by Aug 30.

AIDistributed DatabasesTeam Collaboration
0 likes · 6 min read
Technical Research Group Introduction
Youzan Coder
Youzan Coder
Aug 4, 2018 · Databases

Key Takeaways from the 2018 Youzan PaaS Tech Meetup: TiDB, Zankv & Search

The 2018 Youzan PaaS meetup in Hangzhou featured deep dives into TiDB's distributed SQL architecture and performance gains in version 2.1, introduced the open‑source Zankv KV store built on Raft, and shared practical Elasticsearch search‑engine optimizations used at Youzan.

Distributed DatabasesElasticsearchKV store
0 likes · 4 min read
Key Takeaways from the 2018 Youzan PaaS Tech Meetup: TiDB, Zankv & Search
Architecture Digest
Architecture Digest
Jun 22, 2018 · Databases

Distributed Databases for OLAP: MPP, Hadoop Ecosystem, and Like‑Mesa (ClickHouse/Palo) Overview

This article examines the evolution and classification of distributed databases for OLAP workloads, comparing traditional RDBMS, MPP solutions such as Teradata and Greenplum, Hadoop‑based ecosystems, and newer architectures like ClickHouse and Palo, while highlighting their architectural traits, strengths, and limitations.

ClickHouseDistributed DatabasesHadoop
0 likes · 17 min read
Distributed Databases for OLAP: MPP, Hadoop Ecosystem, and Like‑Mesa (ClickHouse/Palo) Overview
Efficient Ops
Efficient Ops
Jun 10, 2018 · Databases

How MongoDB Enables Active‑Active Multi‑Data‑Center Architectures

This article examines the requirements of active‑active applications across multiple data centers, compares distributed database architectures—including two‑phase commit, multi‑master, and sharded designs—and explains how MongoDB’s sharding and write/read concerns enable high availability, consistency, and rapid failover in such environments.

Active-ActiveDistributed DatabasesMongoDB
0 likes · 16 min read
How MongoDB Enables Active‑Active Multi‑Data‑Center Architectures
AntTech
AntTech
May 14, 2018 · Databases

Insights from Ant Financial’s Self‑Developed Database Technology and Architecture

The article recounts Ant Financial’s journey from relying on commercial databases to building its own OceanBase system, detailing the strategic, technical, and architectural challenges of self‑developed databases, distributed middleware, unit‑based design, and multi‑city fault‑tolerant solutions.

Ant FinancialDistributed DatabasesOceanBase
0 likes · 18 min read
Insights from Ant Financial’s Self‑Developed Database Technology and Architecture
dbaplus Community
dbaplus Community
May 6, 2018 · Databases

NewSQL Explained: Storage Engines, Sharding, and Distributed Transactions

This article examines the core technologies behind NewSQL databases for OLTP workloads, contrasting them with NoSQL and traditional relational systems by exploring storage engines such as B‑Tree and LSM‑Tree, sharding strategies, replication models, and distributed transaction mechanisms like two‑phase commit.

Distributed DatabasesNewSQLSharding
0 likes · 20 min read
NewSQL Explained: Storage Engines, Sharding, and Distributed Transactions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 9, 2017 · Databases

How AliSQL X‑Cluster Achieves Strong Consistency and Global Scalability

AliSQL X‑Cluster is Alibaba's MySQL‑compatible distributed database that integrates the X‑Paxos consensus protocol to provide strong consistency, multi‑region deployment, low‑cost replica types, asynchronous transaction commit, hotspot‑update optimizations and superior performance compared with native MySQL and Group Replication, while offering flexible online configuration and robust failover mechanisms.

Cross-Region DeploymentDistributed DatabasesHigh Availability
0 likes · 28 min read
How AliSQL X‑Cluster Achieves Strong Consistency and Global Scalability
Qunar Tech Salon
Qunar Tech Salon
Jul 31, 2017 · Databases

Core Functions of Relational Database Middleware and an In‑Depth Look at Sharding‑JDBC Architecture

This article explains why relational database middleware is essential for scaling internet‑level workloads, describes the principles of horizontal sharding and distributed primary‑key generation, and provides a comprehensive overview of Sharding‑JDBC’s architecture, core modules, performance benchmarks, and future roadmap.

Database MiddlewareDistributed DatabasesSQL
0 likes · 18 min read
Core Functions of Relational Database Middleware and an In‑Depth Look at Sharding‑JDBC Architecture
21CTO
21CTO
Jun 11, 2017 · Databases

Can Pilosa Handle Dense Relational Data? A Deep Dive with NYC Taxi Dataset

Pilosa, originally built for sparse high‑cardinality user attributes, is evaluated on a dense, low‑cardinality NYC taxi dataset to see if it can serve as a general‑purpose index, with performance comparisons against Spark, PostgreSQL, Elasticsearch, and kdb+ across multiple query scenarios.

Bitmap IndexDistributed DatabasesNYC Taxi Data
0 likes · 8 min read
Can Pilosa Handle Dense Relational Data? A Deep Dive with NYC Taxi Dataset
Architects' Tech Alliance
Architects' Tech Alliance
May 31, 2017 · Cloud Computing

What I Know About Cloud Computing

This article provides a comprehensive overview of cloud computing evolution, covering virtualization, x86 servers, cloud management platforms, container technologies, orchestration tools, resource scheduling, microservices, and distributed databases, while also noting a conference discount promotion.

Distributed DatabasesVirtualization
0 likes · 21 min read
What I Know About Cloud Computing
WeChat Backend Team
WeChat Backend Team
Sep 8, 2016 · Databases

Why PhxSQL Rejects Multi-Write, Sharding, and Serializability: Design Trade‑offs

This article explains how PhxSQL prioritizes strong linearizable consistency, high availability, serializable isolation, and full MySQL compatibility, and why it deliberately forgoes features such as multi‑write, sharding, and strict serializable isolation due to the high cost of distributed transactions and protocol complexity.

Distributed DatabasesMySQL CompatibilityPhxSQL
0 likes · 17 min read
Why PhxSQL Rejects Multi-Write, Sharding, and Serializability: Design Trade‑offs
Architecture Digest
Architecture Digest
Mar 9, 2016 · Databases

Overview of Distributed Database Systems: Concepts, Classification, Features, and Data Sharding

This article provides a comprehensive overview of distributed database systems, covering their evolution from centralized architectures, classifications, key characteristics, advantages and disadvantages, data sharding methods, allocation strategies, architectural models, and management considerations, highlighting the benefits and challenges of distributed data management.

DDBMSDatabase ArchitectureDistributed Databases
0 likes · 9 min read
Overview of Distributed Database Systems: Concepts, Classification, Features, and Data Sharding
MaGe Linux Operations
MaGe Linux Operations
Dec 17, 2015 · Backend Development

How Do Mega‑Sites Scale? Inside the Architecture of High‑Traffic Web Platforms

This article examines the technical challenges of massive web sites—such as billions of users, extreme concurrency, and petabyte‑scale data—and explains how architectural evolution, including service separation, caching, clustering, load balancing, CDN, distributed storage, NoSQL, and micro‑service decomposition, enables scalable, highly available, and secure operations.

CDNDistributed Databasesscalability
0 likes · 13 min read
How Do Mega‑Sites Scale? Inside the Architecture of High‑Traffic Web Platforms
21CTO
21CTO
Sep 29, 2015 · Databases

Boost Application Performance with Apache Ignite’s In‑Memory Caching

Apache Ignite lets developers store hot data in memory, offering partitioned or replicated caching, seamless integration with any backend, write‑through/read‑through and write‑behind modes, automatic schema mapping, and full SQL support, enabling scalable, high‑speed data access for TB‑scale workloads.

Apache IgniteDistributed DatabasesSQL query
0 likes · 5 min read
Boost Application Performance with Apache Ignite’s In‑Memory Caching