This article details the design, scaling challenges, and successive upgrades of JD's VOP message warehouse—from early V1.0 bottlenecks through V2.0 sharding to the current V3.0 MongoDB‑based architecture—highlighting performance improvements, traffic governance, cost reduction, and future outlook for handling billions of daily messages.
This article demonstrates how to create a fully reactive backend using Spring Cloud Finchley, WebFlux, Eureka service discovery, and Reactive MongoDB, covering service registration, inter‑service communication with WebClient, and reactive data access to achieve a complete reactive microservices stack.
This guide walks you through MongoDB sharding fundamentals, step‑by‑step cluster setup, common failure diagnostics, and practical commands, enabling you to build, manage, and optimize a sharded MongoDB deployment for scalable storage and high‑performance applications.
This guide walks through diagnosing a MongoDB outage, explains sharding concepts, shows when to shard, and provides step‑by‑step instructions for configuring config servers, mongos routers, converting replica sets, sharding collections, tracking chunks, and managing the balancer.
This comprehensive guide explains MongoDB cluster architecture, component roles, common use cases, monitoring commands, essential maintenance operations, data migration steps, troubleshooting of typical production issues, and practical optimization recommendations for high‑performance deployments.
This article explains how to query, analyze, and visualize time‑series data stored in MongoDB using the aggregation framework, MongoDB Compass, read‑only views, the BI connector with SQL tools, as well as integrations with Spark and R for advanced analytics.
This article introduces time series data concepts, outlines the challenges of storing and analyzing high‑frequency data, and presents best‑practice guidelines for building MongoDB‑based time‑series applications, covering ingestion, read/write workloads, retention, security, and real‑world use cases.
The article explains how null or empty query values caused MongoDB to ignore the intended index, leading to massive slow queries and service timeouts, and details the step‑by‑step investigation, plan‑cache invalidation, and the corrective addition of a compound index that restored normal performance.
This article provides an in‑depth overview of Percona Backup for MongoDB (PBM), covering its architecture, installation steps, configuration, logical full backups, point‑in‑time incremental backups, restoration procedures, performance testing, selective backup options, known limitations, and useful command‑line examples.
This guide walks through creating a fully reactive microservice architecture using Spring Cloud Finchley, WebFlux, and Spring Data Reactive, including setting up a Eureka service registry, building account and customer services with reactive MongoDB repositories, configuring load‑balanced WebClient calls, and demonstrating load‑balancing across multiple instances.
This tutorial demonstrates how to create a fully reactive microservice architecture using Spring Cloud Finchley, WebFlux, Spring Data Reactive, Eureka for service discovery, and MongoDB (via Docker), covering service registration, inter‑service calls with WebClient, and reactive data access.
This tutorial demonstrates how to create a full‑reactive microservice system using Spring Cloud Finchley, WebFlux, Spring Data Reactive MongoDB, and Eureka for service discovery, covering project setup, service registration, reactive repositories, controllers, inter‑service calls with WebClient, and load‑balanced testing.
This article explains why dual‑write consistency between MongoDB and a relational database is challenging, compares common misconceptions with cache usage, and provides practical patterns—including write ordering, retry mechanisms, and scheduled cleanup—to reliably synchronize core and non‑core data.
This article details the architectural evolution of JD's VOP message warehouse, describing the challenges of massive data volumes, the transition from V1.0 to V3.0+ through database sharding, MongoDB adoption, traffic governance, stability improvements, and cost reduction strategies, while presenting performance metrics and future outlook.
This article provides a detailed comparison of two document‑oriented NoSQL databases, MongoDB and CouchDB, covering their architectures, features, replication models, mobile support, query mechanisms, code examples, and guidance on choosing the appropriate system based on application requirements.
This article explores common misconceptions and practical strategies—including write ordering, delayed double‑delete, retry mechanisms, and scheduled cleanup jobs—to ensure reliable dual‑write consistency between MongoDB and traditional relational databases.
This article details the overall backend architecture, module relationships, RESTful API design, database schemas, authentication flow, activity creation process, JSON schema validation, component platform APIs, and server‑side rendering considerations for a Node.js‑based service supporting editors, components, and end‑user pages.
This article explains why database selection is critical, outlines the evolution and types of databases, presents a methodology for evaluating requirements, and reviews Volcengine's cloud database offerings to help you pick the most suitable solution for OLTP workloads.
Mongoose 6.7, the asynchronous‑environment MongoDB object‑modeling library for Node.js, introduces driver upgrades, new UUID schema type, $fill pipeline stage, enhanced TypeScript support, aliasing, mergeHooks, and timestamp utilities, while encouraging readers to like and share the article.
This article details a lightweight intelligent monitoring platform built on the open‑source WVP framework, describing its modular architecture, edge‑computing workflow with KubeEdge, SIP registration process, real‑time streaming setup, core features, and technical innovations such as MongoDB adoption and flexible pod scheduling.
This tutorial walks through building a full‑stack backend for a frontend publishing platform using Koa2, @koa/router, and MongoDB, covering project initialization, route modularization, controller and service layers, middleware for response handling, environment‑specific configuration, and complete CRUD operations verified with Postman and Mongo shell.
This article provides a step‑by‑step tutorial on using the MongoDB PHP driver to insert, query, update, and delete documents in the ‘wj’ collection of the ‘test’ database, including full code examples for each CRUD operation.
The September 2023 DB-Engines ranking update shows Oracle’s steep decline, while MySQL and MongoDB gain points, and presents the top ten databases across relational, key‑value, document, time‑series, and graph categories along with the five metrics used to calculate popularity.
The September DB‑Engines ranking shows Oracle’s steep decline while MySQL and MongoDB climb, presents top‑10 lists for relational, key‑value, document, time‑series and graph databases, explains the five metrics behind the scores, and advises professionals to choose databases based on business needs rather than pure popularity.
This guide explains how to install mlaunch and use it to rapidly set up MongoDB single nodes, replica sets, and sharded clusters, providing step‑by‑step command examples, operational commands, and reference links for efficient testing environments.
This article introduces MongoDB as a popular NoSQL database, explains its schema‑free, collection‑oriented design, storage format, and platform support, and provides a complete PHP class that implements common CRUD and aggregation operations for MongoDB.
A comprehensive AWS benchmark reveals that RedisJSON dramatically outperforms MongoDB and ElasticSearch across isolated writes, isolated reads, mixed workloads, ops‑per‑second, and latency, maintaining sub‑millisecond response even under high update rates, while the other databases suffer significant slowdowns.
MongoDB is evolving from a purely operational document store to a hybrid system that embeds native analytics, cloud‑native features, and SQL access, aiming to boost developer productivity, support real‑time insights, and complement rather than replace traditional data warehouses.
The article examines MongoDB’s evolution toward built‑in analytics, detailing new features like native search, time‑series support, change streams, Atlas analytics nodes, and the upcoming Atlas SQL interface, while arguing that these capabilities aim to empower developers rather than replace dedicated data‑warehouse solutions.
This article presents a complete food delivery system project built with Spring Boot and Vue.js, detailing its technology stack, modules, database setup, and step‑by‑step instructions for launching both the admin backend and mobile client, along with resource download links.
An Ansible custom module was developed to back up MongoDB databases within a Docker container, including Dockerfile setup, Python handler code, parameter definitions, and a test playbook, with both successful and failure scenarios demonstrated, highlighting automation benefits for operations.
The article recounts a performance incident on a MongoDB 4.2 sharded cluster where one shard’s CPU spiked to 100% during a mail‑sending test, describes the diagnostic steps using monitoring data, slow‑query logs, and executionStats, and concludes with the root cause and remediation.
This comprehensive guide explores MongoDB’s core features—including schema flexibility, high availability, sharding, storage engine internals, indexing, and performance tuning—while providing practical examples, configuration tips, and best‑practice recommendations for developers and architects seeking to efficiently deploy and operate MongoDB in production environments.
The article describes how a MongoDB test environment on a single VM experienced persistent high CPU load despite low visible QPS, how the root cause was traced to thousands of short‑lived processes spawned by Zabbix monitoring, and how execsnoop was used to identify and eliminate the offending processes.
This roundup explores key frontend topics—including race‑condition handling in React, client‑side field‑level encryption with Node.js and MongoDB, iQIYI’s low‑code engine, leveraging Rust for WebAssembly, performance analysis of JavaScript frameworks using Google CrUX, and the risks of AI‑generated deepfake videos.
This article introduces a full‑stack food‑delivery project built with Spring Boot, Vue.js, MySQL and MongoDB, detailing its architecture, module breakdown, technology choices, database setup, and step‑by‑step instructions for launching the backend API, admin portal, and mobile client, along with download links for data and images.
This tutorial demonstrates how to build a Scrapy spider that crawls Zhihu user follow and follower data via Zhihu’s public APIs, handles request headers, parses JSON responses, paginates results, and stores the extracted information into MongoDB using a custom item pipeline.
This article explains microservice fundamentals, their advantages, and how to design data architectures that support independent services, covering database-per-service versus shared databases, polyglot persistence, scaling strategies, and a detailed MongoDB case study with practical guidelines.
This article explains how to design data architecture for microservice systems, covering the advantages of microservices, decoupling principles, lightweight APIs, DevOps integration, database per service versus shared databases, polyglot persistence, and why MongoDB is a suitable choice for scalable, dynamic, and sharded data storage.
This article details iQIYI's design and implementation of a unified OLTP data center that consolidates data across business lines, solves data‑island issues, ensures strong consistency between MongoDB and Elasticsearch, and provides high‑availability, massive‑scale storage for billions of records.
After months of using MongoDB for frontend monitoring logs, the data grew to billions of records causing severe query slowdown, prompting a migration to ClickHouse where columnar storage, partitioning, and OLAP capabilities dramatically improved performance and storage efficiency.
This article explains microservice fundamentals, outlines their advantages, and provides detailed guidance on data design, database selection, and scaling techniques—including decoupling, lightweight APIs, DevOps CI/CD, polyglot persistence, and sharding—to help architects build robust, flexible microservice systems.
The article reviews the 50‑year evolution of databases, explains why modern enterprises need multiple database types, compares PostgreSQL and MongoDB for a typical market‑place application, and highlights how team expertise and architectural constraints should drive the final selection.
This article explains microservice architecture fundamentals, its advantages, and key design principles for data management, including database selection, polyglot persistence versus multi‑model databases, scaling strategies, and why MongoDB is a suitable choice for microservice‑based systems.
This article introduces the mongo2file library for converting MongoDB collections into various table formats such as CSV, Excel, JSON, pickle, feather, and parquet, explains its PyArrow dependency, shows installation and quick‑start code, discusses performance bottlenecks, and provides a full reference API.
This article recounts the author's journey from using MongoDB for front‑end monitoring logs to migrating to ClickHouse, detailing the challenges with large‑scale data, optimization attempts, the fundamental differences between row‑ and column‑oriented databases, and the resulting performance and storage improvements.
The article compares MySQL’s relational tables with MongoDB’s schema‑free documents, detailing equivalent concepts, CRUD syntax, and benchmark results that show MongoDB achieving higher write and query throughput, easier schema evolution, and built‑in sharding, recommending it for new agile projects while suggesting MySQL remain viable for legacy systems.
This article compares Redis, Memcached, and MongoDB, outlining each system's strengths and weaknesses, performance characteristics, storage capabilities, availability, reliability, consistency, data analysis features, and typical application scenarios to help developers choose the appropriate NoSQL solution.
When installing MongoDB 5.0 on a CentOS 7.2 server, the mongo and mongod commands abort with an "Illegal instruction" error because the CPU lacks AVX support, and the article explains how to diagnose this issue, verify CPU capabilities, and resolve it by using AVX‑compatible hardware.
The paper describes iQIYI’s Content Platform Data Center (OLTP), a unified data hub that solves microservice data‑island problems by providing hundred‑billion‑scale storage, high‑QPS reads/writes, field‑level change notifications, final consistency between MongoDB and Elasticsearch, active‑active high availability, and a generic SDK, now serving 26 business lines with thousands of QPS and hundreds of millions of rows.
This article presents a curated, non‑ranked collection of Chinese database community public accounts and resources covering PostgreSQL, MongoDB, MySQL, and miscellaneous IT knowledge, thanking followers and outlining future content plans for professional development.
The Vivo comment middleware achieves high‑availability, cross‑service comment publishing by using Dubbo tag‑routing for traffic isolation, Sentinel hotspot‑parameter flow control with dynamic per‑tenant limits, and both physical and logical MongoDB isolation via a multi‑factory selector and runtime collection naming, preventing data leakage across business lines.
This guide introduces MongoDB fundamentals and demonstrates how to integrate it with Spring WebFlux to create a fully reactive CRUD service, covering dependencies, configuration, entity definitions, repository interfaces, service logic, and controller endpoints.
The article presents a comprehensive performance benchmark of RedisJSON (with RediSearch) against MongoDB and ElasticSearch, showing RedisJSON’s superior write, read, and mixed‑workload throughput and latency across isolated and mixed scenarios, supported by detailed YCSB test results and analysis.
This article explores microservice data design, covering the definition and benefits of microservices, principles for decoupling, lightweight APIs, DevOps integration, decentralized data governance, database per service versus shared databases, polyglot persistence, multi‑model databases, and why MongoDB’s dynamic schema, change streams, and sharding make it a strong fit for scalable microservice architectures.
This article explains microservice fundamentals, outlines their advantages and technical traits, and provides practical guidance on data design, database selection, polyglot persistence, scaling strategies, and why MongoDB often fits microservice architectures best.
Amid the rise of the industrial internet, NoSQL databases such as MongoDB, Redis, and Elasticsearch have surged in popularity, with strong SaaS offerings and impressive financial results that illustrate their growing market share, cloud integration, and the broader shift toward open‑source, cloud‑native data solutions.
A comprehensive benchmark shows RedisJSON outperforms MongoDB and ElasticSearch by up to 12.7× on isolated reads, 5.4× on isolated writes, and delivers far lower latency and higher throughput across mixed workloads, thanks to its real‑time indexing and efficient query engine.
Recent benchmark tests show RedisJSON (with RediSearch) dramatically outperforms MongoDB and ElasticSearch, delivering up to 5.4‑times faster isolated writes, over 200‑times faster than ElasticSearch, 12.7‑times faster reads, and significantly lower latency and higher throughput across mixed workloads.
The report presents extensive YCSB‑based performance benchmarks showing that RedisJSON (with RediSearch) outperforms MongoDB and ElasticSearch by large margins in isolated writes, isolated reads, mixed workloads, and latency across a variety of operation mixes on identical AWS hardware.
This article explains microservice fundamentals, outlines their advantages, and dives deep into data‑design considerations such as one‑database‑per‑service, polyglot persistence versus multi‑model databases, and why MongoDB’s dynamic schema, change streams, and sharding make it a strong fit for scalable microservice architectures.
This article explains microservice fundamentals, their advantages, technical characteristics, and detailed data‑design considerations, comparing shared versus per‑service databases, polyglot persistence versus multi‑model databases, and demonstrates why MongoDB is a strong fit for scalable, resilient microservice architectures.
This article presents a detailed performance benchmark of RedisJSON (RediSearch) against MongoDB and ElasticSearch, showing RedisJSON’s superior throughput and latency across isolated writes, reads, and mixed workloads, supported by YCSB test results and extensive latency analysis.
This comprehensive guide covers MongoDB's core concepts—including schema‑free design, high availability, sharding, storage engine internals, indexing, and performance tuning—while providing practical code examples, configuration steps, and best‑practice tips for developers and architects looking to master the database in production environments.
The article details how Tencent Cloud’s K‑Song feed service, serving over 150 million daily users, was engineered with a read‑expansion model, cached results, auxiliary index tables, hashed sharding, write‑concern tuning, disabled chain replication, and WiredTiger and backup optimizations, achieving sub‑10 ms write latency and significantly lower CPU and slow‑query rates.
This article describes the design and implementation of a cloud‑native automated web‑page recording system for Douyu advertising, covering the problem background, technology stack, end‑to‑end workflow, Chrome extension tricks, containerization with Docker, database handling, and deployment on Kubernetes, enabling scalable, hands‑free video capture and delivery.
This guide introduces MangoDB, an open‑source MongoDB‑compatible database built on PostgreSQL, and walks you through a three‑step Docker‑Compose setup, service startup, and connection using mongosh, enabling developers to quickly try a true open‑source MongoDB replacement.
This article explains how a user‑behavior data dashboard originally built on a daily T+1 batch process was redesigned to achieve real‑time T+0 updates by introducing message‑queue notifications, a dedicated aggregation service, and MongoDB storage, improving data freshness and user experience.
This article explains how Tencent Cloud MongoDB improves the maxTimeMS server-side timeout feature to prevent request backlog and service avalanche, covering native MongoDB limitations, optimizations in mongos write command support, and default configuration implementation.
The September 2021 DB‑Engines ranking shows Oracle, MySQL and SQL Server plummeting while MongoDB, Snowflake and ClickHouse surge, highlighting a shift toward niche and cloud‑native databases, and the article also recommends ten essential database books for professionals.
The September 2021 DB‑Engines ranking shows longtime leaders Oracle, MySQL and Microsoft SQL Server losing hundreds of points, while MongoDB, Snowflake and ClickHouse surge in popularity, Redis and InfluxDB dominate their niches, and the report stresses that selecting a database should prioritize business needs over mere ranking.
This article explains MongoDB's evolution from Master‑Slave to Replica Set and Sharding architectures, detailing how each model provides high availability, data reliability, and scalability, and offers practical configuration tips to ensure strong consistency and minimal downtime in production deployments.
This guide explains what a CustomResourceDefinition (CRD) is, shows how to create a CRD and corresponding custom resources with YAML files, and walks through deploying the MongoDB Kubernetes Operator, including namespace setup, PersistentVolume and PersistentVolumeClaim configuration, and troubleshooting steps.
The Shenzhen Elasticsearch technical event, co‑hosted by the Elastic Chinese community and Tencent Cloud, presented practical sessions on optimizing the Elastic Stack for search, real‑time analytics, logging, security and APM, featuring compression encoding, MongoDB fusion, ByteDance extensions, cost‑effective log storage, Lucene indexing, cross‑cluster replication, vector engine integration, and large‑scale case studies from Tencent, Tiptop Data and vivo.
The August 2023 DB‑Engines popularity ranking shows Microsoft SQL Server suffering the steepest score decline, while PostgreSQL and MongoDB post significant year‑over‑year gains, and the top three databases also fell, with the report detailing score trends, category rankings, and the five metrics used to calculate popularity.
This comprehensive guide covers everything from MongoDB's core concepts, data model, and architecture to practical commands for creating databases, collections, indexes, sharding, replication, aggregation, and troubleshooting, providing clear answers to over fifty frequently asked questions.
This article provides a comprehensive overview of MongoDB, covering its definition, document‑oriented storage, key features, differences from relational databases, sharding and replication mechanisms, common commands, data types, indexing, aggregation, and practical usage scenarios for modern applications.
This tutorial walks you through installing pymongo, connecting to MongoDB with and without authentication, performing insert, update, delete, query, and index operations, and demonstrates each step with clear Python code snippets and illustrative screenshots.
Tencent’s MongoDB team solved the severe performance degradation caused by millions of collections by redesigning WiredTiger to use a shared table‑space with prefix‑mapped keys, eliminating data‑handle explosion, cutting memory use and lock contention, reducing startup from hours to minutes, and boosting read/write throughput by up to two orders of magnitude while preserving full MongoDB compatibility.
The Tencent CMongo team analyzed severe memory consumption, slow queries, and hour‑long startup caused by millions of MongoDB collections, identified bottlenecks in WiredTiger’s data‑handle management and schema‑lock handling, and introduced a shared‑table‑space architecture that reduces WT tables to a handful, achieving 1‑2 order‑of‑magnitude speedups and eliminating OOM failures.
This comprehensive guide walks you through MongoDB fundamentals, connection strings, creating databases and collections, inserting, updating, deleting, querying documents with AND/OR logic, advanced range and regex queries, and powerful aggregation pipelines including sorting and grouping.
This article demonstrates a step‑by‑step point‑in‑time recovery of a MongoDB sharded cluster by restoring shard instances, replaying oplog entries to a specific timestamp, updating metadata, and finally rebuilding the config server and mongos to achieve a consistent snapshot with max(id)=15.
This article introduces the Flash-Waimai project—a full‑stack food delivery system built with Spring Boot, Vue.js, MySQL and MongoDB—detailing its architecture, module breakdown, database setup, quick‑start commands, and step‑by‑step instructions for launching the backend, admin portal, and mobile client.
This article details a real‑world case of a hundred‑billion‑record MongoDB deployment that retains only seven days of data, describing resource assessment, sharding strategy, container specs, and multiple rounds of storage‑engine and TTL‑deletion tuning that reduced latency from hundreds of milliseconds to 0.5‑2 ms without adding nodes.
The article details a MongoDB 3.4 sharded cluster incident where a sudden connection storm overwhelmed the primary, leading to replication lag, automatic failover, and how monitoring, log analysis with mtools, and a custom log‑rotation script were used to diagnose and resolve the issue.
This article recounts a real‑world MongoDB sharding incident on a core cluster with billions of records, detailing the root causes, step‑by‑step remediation using removeShard and movePrimary, and the lessons learned to prevent similar outages.
The article explains how lack of native transactions in MongoDB 3.0 can cause data inconsistency during order processing, compares it with SQL transaction mechanisms, and proposes optimization and compensation strategies such as retry queues, asynchronous tasks, and refund handling to ensure eventual consistency.
A latency‑sensitive MongoDB cluster experienced severe jitter and connection exhaustion when multiple background indexes were added sequentially, revealing how index builds overload replica nodes, trigger alerts, and can be mitigated with proper index‑addition strategies and the noIndexBuildRetry option.
This tutorial explains how to use Zabbix Agent 2 to monitor MongoDB databases and clusters, covering the required read‑only user setup, relevant Zabbix templates, key metrics such as jumbo chunks, connection pool stats, server status, collection and replSet information, and practical configuration examples.
This article explains MongoDB TTL indexes, covering their basic concept as single‑field auto‑deletion indexes, how the background process works, alternative creation methods using an expireAt field, practical limitations, and recommendations for designing efficient data expiration strategies.
A large‑scale IoT service migrated 40 billion rows from MySQL to MongoDB, solving capacity, cost, and data‑skew issues while achieving higher performance, lower storage consumption, and a flexible, highly available architecture, with detailed resource evaluation, migration steps, optimization tactics, and cost‑benefit analysis.
This article provides a complete tutorial for building a food‑delivery system with a mobile app, backend management, and API, detailing the technology stack, module structure, database setup, code snippets, and step‑by‑step instructions to launch both the admin and mobile front‑ends.
This article presents a detailed case study of moving a 2‑petabyte smart‑product data workload from Elasticsearch to MongoDB, covering business background, source cluster architecture, MongoDB design and resource planning, step‑by‑step performance optimizations, latency and cost comparisons, and guidance on when each database is appropriate.
The article reproduces a MongoDB issue where queries using a hashed shard key return no results and a compound unique index silently accepts duplicate documents, explains that the failIndexKeyTooLong parameter allows oversized index entries to be stored without indexing, and shows how to reproduce and fix the problem.
This article presents a complete food‑delivery system with mobile, admin, and API modules, detailing its Spring Boot and Vue.js technology stack, module structure, database setup, quick‑start commands, and how to run both the management console and mobile client for learning purposes.
This article explains how Kubernetes Headless Services and DNS can be used to discover and connect to the IP addresses of all Pods behind a Service, with a step‑by‑step example using a MongoDB replica set.
This comprehensive tutorial walks you through installing MongoDB on Windows, configuring data and log directories, setting environment variables, creating and managing databases, collections, indexes, aggregation pipelines, backup and restore procedures, monitoring tools, advanced query operators, user management, and using a visual tool like Navicat for MongoDB.
This article explains MongoDB's document-oriented data model, shows how to create an admin user, configure the mongod service on Linux, and provides detailed command‑line examples for backing up and restoring databases and collections using mongodump, mongorestore, mongoexport, and mongoimport.
This article shares an interview experience that compares MongoDB and MySQL, explains the advantages and disadvantages of document‑oriented and relational databases, introduces other NoSQL solutions such as Elasticsearch, and offers guidance on choosing the right storage technology based on workload characteristics.
This comprehensive guide explains MongoDB cluster components, common use cases, monitoring commands, basic operational tasks, data migration procedures, troubleshooting of production issues, and optimization recommendations to achieve high performance and scalability.
