国产,欧美,日韩中文字幕,а√资源新版在线天堂

Article Tags

All
web3.0
- All
- NoSQL database
- Memcached
- cloudera
- memcache
Backend Development
- All
- PHP Tutorial
- Python Tutorial
- Golang
- XML/RSS Tutorial
- C#.Net Tutorial
- C++
- NoSQL database
- Memcached
- cloudera
- memcache
Web Front-end
Database
- All
- Mysql Tutorial
- navicat
- SQL
- Redis
- phpMyAdmin
- Oracle
- MongoDB
- NoSQL database
- Memcached
- cloudera
- memcache
Operation and Maintenance
- All
- Mac OS
- Linux Operation and Maintenance
- Apache
- Nginx
- CentOS
- Docker
- NoSQL database
- Memcached
- cloudera
- memcache
Development Tools
- All
- VSCode
- sublime
- notepad
- git
- composer
- NoSQL database
- Memcached
- cloudera
- memcache
PHP Framework
- All
- Laravel
- YII
- NoSQL database
- Memcached
- cloudera
- memcache
Common Problem
- All
- NoSQL database
- Memcached
- cloudera
- memcache
Other
- All
- IIS
- SEO
- Premiere
- NoSQL database
- Memcached
- cloudera
- memcache
Tech
- All
- AI
- It Industry
- NoSQL database
- Memcached
- cloudera
- memcache
CMS Tutorial
- All
- WordPress
- NoSQL database
- Memcached
- cloudera
- memcache
Java
- All
- javaTutorial
- NoSQL database
- Memcached
- cloudera
- memcache
System Tutorial
- All
- Windows Series
- LINUX
- MAC
- NoSQL database
- Memcached
- cloudera
- memcache
Computer Tutorials
Hardware Tutorial
Mobile Tutorial
- All
- Mobile News
- iPhone
- Android Phone
- NoSQL database
- Memcached
- cloudera
- memcache
Software Tutorial
Mobile Game Tutorial

Home

Technical Articles

Database

MongoDB

MongoDB Performance Tuning Tips

Reasonably design indexes, use explain() to analyze execution plans, avoid full table scanning and over-index; 2. Optimize query statements, avoid inefficient operations, use projections, limit result sets, and weigh embeddings and references; 3. Ensure sufficient memory and reasonably configure WiredTiger cache, and use SSD to improve I/O performance; 4. Realize horizontal scaling through sharding, select appropriate shard keys, and enable read preferences to achieve read and write separation; 5. Regularly rebuild collections and indexes, enable slow query logs, combine monitoring tools to continuously observe performance indicators, and continuously tune to ensure efficient and stable operation of the system.

Jul 30, 2025 am 12:02 AM

ACID Transactions in MongoDB

Yes, MongoDB supports ACID transactions. 1. It supports single-document ACID operations from version 4.0, and multi-document transactions from 4.2, including replica sets and shard clusters. 2. Multi-document transactions need to be started through a session. All operations must be passed in session. Try-catch handles commits or rollbacks, and finally call endSession to release resources. 3. The transaction has a 60-second default timeout limit, which is large in performance overhead. Especially cross-sanded transactions require mongos coordination, and long-term or high-frequency use should be avoided; 4. Eval, mapReduce and other commands are disabled in transactions, and read and write attention and read preferences must be set at the transaction level; 5. It is recommended to use single-document atomic operations first.

Jul 30, 2025 am 12:02 AM

mongodb ACID事務(wù)

Best Practices for Securing Your MongoDB Atlas Cluster

UsestrongauthenticationbyintegratingMongoDBCloudIAMwithidentityproviders,applyingleastprivilegewithminimalpermissionroles,androtatingcredentialsregularly.2.RestrictnetworkaccessbywhitelistingtrustedIPs,avoiding0.0.0.0/0,usingVPCPeeringorPrivateEndpoi

Jul 29, 2025 am 01:51 AM

What are the key advantages of using a BSON data format in MongoDB?

BSONenhancesMongoDB’sperformanceandflexibilitybyofferingefficientdatastorage,richerdatatypes,improvedquerying,andseamlessdriverintegration.1.BSONusesbinaryencodingtoreducestorageoverhead,resultinginfasterparsingandsmallersizesfornumericvaluescompared

Jul 29, 2025 am 01:44 AM

mongodb BSON

Implementing Robust Schema Validation in MongoDB

MongoDB's flexibility does not mean abandoning data integrity. By combining built-in document verification, reasonable verification behavior control, progressive mode evolution, application-level verification and automated monitoring, a robust architecture that takes into account flexibility and data quality can be built. 1. Use collection-level document verification ($jsonSchema) to ensure compliance with required fields, data types, value ranges and enumeration values; 2. Control the execution of the verification through validationAction ("error" or "warn") and validationLevel ("strict" or "moderate")

Jul 29, 2025 am 01:20 AM

mongodb

Indexing Strategies for MongoDB

Choosetheappropriateindextypebasedonusecase,suchassinglefield,compound,multikey,text,geospatial,orTTLindexes.2.ApplytheESRrulewhencreatingcompoundindexesbyorderingfieldsasequality,sort,thenrange.3.Designindexestosupportcoveredqueriesbyincludingallque

Jul 29, 2025 am 01:05 AM

java programming

Optimizing Memory Usage in a Large-Scale MongoDB Instance

SetwiredTigerCacheSizeGBto50–70%ofavailableRAM,allocating60–70%ofthattoWiredTigerwhilereservingmemoryfortheOSfilecachetopreventswapping.2.MonitortheworkingsetsizeusingmongostatandindexStats,shardifnecessary,createonlyessentialindexes,andusecoveredque

Jul 28, 2025 am 02:11 AM

Developing High-Performance Java Applications with Spring Boot and MongoDB

Use SpringBoot to automatically configure and connectivity pool to optimize startup efficiency and database performance; 2. Improve MongoDB read and write efficiency through embedded design, reasonable indexing and aggregation queries; 3. Make good use of ReactiveMongoRepository and @Aggregation to reduce memory overhead; 4. Combine Micrometer monitoring and Redis cache to achieve observability and hot data acceleration, thereby building high-concurrency and low-latency Java applications, and ultimately change the system from "usable" to "flying".

Jul 28, 2025 am 01:33 AM

How to Monitor a Production MongoDB Cluster Effectively

Monitoring MongoDB requires attention to database-level indicators rather than system resources, such as operand mutation, connections approaching the upper limit, lock waiting queue high, replication set delay exceeds several seconds, memory usage is close to physical limit; 2. Use Atlas/OpsManager or Prometheus Grafana Exporter combination to achieve indicator aggregation and threshold alarms; 3. Turn on the slow query log (such as >100ms), and regularly analyze statements in the system.profile that have been executed for more than 500ms and contain full table scanning or index filtering; 4. Set key alarms: replication delay >30 seconds, connection pool usage rate >80%, lock waiting time accounts for >10%, disk remaining

Jul 28, 2025 am 12:26 AM

Full-Text Search in MongoDB

MongoDB's full-text search function can be implemented by creating text indexes. First, you need to create a text index for the field. 1. Use db.collection.createIndex({field:"text"}) to create text indexes on a single or multiple fields. Note that a collection can only have one text index; 2. Use the $text operator to perform searches, such as db.collection.find({$text:{$search:"keyword"}}) to support OR query, double quote precise phrase matching and minus sign exclusion keywords; 3. Use {score:{$meta:"

Jul 28, 2025 am 12:19 AM

Full-Stack Development with Python, FastAPI, and MongoDB

Python FastAPI MongoDB was chosen because Python's syntax is concise and rich in ecology, which is suitable for rapid development; FastAPI has extremely fast performance and supports asynchronous and automatic generation of API documents; MongoDB is a NoSQL document database with flexible data structures, easy to expand, and is naturally compatible with JSON. This combination is particularly suitable for small and medium-sized projects, MVP products and API services that require rapid iteration. 2. When using FastAPI to build RESTAPI, you can quickly build routing, define data models and connect to MongoDB through sample code, and achieve efficient data operations with motor asynchronous drivers. After startup, you can directly test the interface through SwaggerUI. 3. Front-end

Jul 27, 2025 am 03:36 AM

MongoDB for IoT Data

MongoDB is suitable for IoT data management because it has a flexible data model, high write throughput, horizontal scaling capabilities, strong query support and good integration; 1. Use document models to store diversified device data without fixed schema; 2. Support high concurrent writes based on the WiredTiger engine; 3. Realize horizontal scaling through sharding; 4. Support time, geography, and nested field queries; 5. Native JSON is compatible with mainstream development languages; Design recommendations: 1. Create composite indexes for device_id and timestamp to optimize queries; 2. Automatically clean out expiration data with TTL index (such as 7 days later); 3. Shard the device_id or timestamp to distribute the load;

Jul 27, 2025 am 03:06 AM

Advanced Indexing Strategies for High-Performance MongoDB Queries

The order of composite index fields is crucial, and it is necessary to follow the prefix matching rules and prioritize the placement of equivalent query fields; 2. Overwriting query can avoid document reading and improve speed, and ensure that both query and projected fields are in the index; 3. Some indexes only indexes index only necessary data, save space and accelerate query, and are suitable for fixed subset access mode; 4. TTL indexes automatically clean out expiration data, reduce redundancy and improve performance; 5. Index intersections are available but not optimal, and a single composite index should be used for critical paths.

Jul 27, 2025 am 02:33 AM

Best Practices for Backing Up and Restoring a MongoDB Database

Usemongodumpandmongorestoreforlogicalbackups,preferablyonsecondarynodeswithcompressionandproperoptionsforrolesandcollections.2.Forlargeorproductionsystems,usefilesystemsnapshotslikeLVMorAWSEBSbystoppingthesecondarynodetoensureconsistencyandcapturedat

Jul 27, 2025 am 12:33 AM