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Home

Technical Articles

Database

MongoDB

MongoDB Schema Design Best Practices

Schema is designed according to the query pattern. Data that is checked at high frequency is embedded, and multiple are used for independent updates are updated; 2. Embedding is suitable for a small pair, and references are suitable for big data or frequent updates; 3. Control the document size to avoid expansion and move, and build a collection of growing data separately; 4. Accurate index design, analyze slow queries, and make good use of composite and overlay indexes; 5. Unify the style and type of field naming to avoid chaos in later maintenance. These practices help you leverage MongoDB performance advantages and make the structure efficient and easy to maintain.

Aug 06, 2025 am 11:59 AM

MongoDB for Big Data Applications

MongoDB is suitable for big data due to flexible schema, horizontal scaling, high write throughput, aggregation analysis and ecological integration; 2. It is suitable for multi-source heterogeneous data storage, real-time write and query, and dynamic schema changes; 3. It is not suitable for strong transactions, complex association queries and rebatch scenarios, and should be used as a link of the hierarchical architecture rather than a full-stack solution.

Aug 06, 2025 am 07:36 AM

Securing Your MongoDB Database

Enableauthenticationwithstrongaccesscontrolbyconfiguringauthorization:enabledinmongod.confandcreatinguserswithleast-privilegerolesusingstrongpasswordsandcredentialrotation.2.SecurenetworkexposurebybindingMongoDBtolocalhostorspecificinternalIPs,usingf

Aug 05, 2025 pm 01:18 PM

Best Practices for Connection Pool Management in MongoDB Applications

UnderstandhowconnectionpoolsworkbyrecognizingthatMongoDBdriversreuseconnectionstoreduceoverhead,limitconcurrentoperationsviapoolsize,andrequirepropermanagementtoavoidtimeoutsorresourceexhaustion.2.Tuneconnectionpoolsettingsbasedonworkloadbyconfigurin

Aug 05, 2025 am 08:46 AM

Leveraging MongoDB Atlas Search for Powerful Full-Text Search Capabilities

CreateadeclarativeAtlasSearchindexusingJSONtospecifyfieldslikename,description,andcategorywithdynamic:falseforcontrol.2.Usethe$searchaggregationstageinsteadof$match,enablingtextsearchacrossspecifiedfieldswithrelevancescoring.3.Boostrelevancebyassigni

Aug 05, 2025 am 08:21 AM

High Availability with MongoDB Replication

MongoDB high availability is achieved through replica sets, including primary nodes, secondary nodes and optional arbitrators; 2. Automatic failover is completed within 10-30 seconds to ensure that the service does not interrupt when the primary node is down; 3. Data redundancy avoids single point of failure, and multiple replicas ensure data security; 4. Secondary nodes can share the read load, improve performance, but pay attention to replication delays; 5. Maintenance does not require downtime, and supports rolling upgrades and backups; 6. Best practices include deploying odd members, distribution across fault domains, monitoring replication delays, adjusting timeouts for optimal readouts, using most write attention, and enabling logging; 7. Beware of brain split-brain problems caused by failover rollback, hidden members cannot be upgraded to primary and network partitions, and only by correct configuration can we truly achieve high availability.

Aug 05, 2025 am 02:33 AM

mongodb copy

How does MongoDB's server discovery and monitoring (SDAM) protocol work within drivers?

MongoDB's SDAM protocol drives the identification and monitoring of cluster status through four steps: initial discovery, continuous monitoring, topology updates and event notifications, and impact on connections and routing. 1. In the initial discovery stage, the driver initiates a request to the seed address through the hello command, obtains replica set member or shard cluster information to build the initial topology; 2. In the continuous monitoring stage, the driver asynchronously sends hello commands every 500 milliseconds to check the node's survival status, role changes and feature support; 3. In the topology update stage, the driver maintains the topology description and updates the node's status when a difference is detected, and can trigger event notifications; 4. In terms of the impact on connections and routing, SDAM provides a decision basis, so that the driver can read and write operations according to the current topology.

Aug 04, 2025 am 07:21 AM

mongodb SDAM

A Guide to the MongoDB Java Driver

Addthemongodb-driver-syncdependencyviaMavenorGradleforsynchronousoperations.2.ConnectusingMongoClientwithaconnectionstring,optionallyincludingcredentials.3.AccessdatabaseandcollectionobjectstoperformCRUD:insertOne/Manyforinsertion,find()withFiltersfo

Aug 04, 2025 am 05:58 AM

Understanding MongoDB Storage Engines: WiredTiger Deep Dive

WiredTigerisMongoDB’sdefaultstorageenginesinceversion3.2,providinghighperformance,scalability,andmodernfeatures.1.Itusesdocument-levellockingandMVCCforhighconcurrency,allowingreadsandwritestoproceedwithoutblockingeachother.2.DataisstoredusingB-trees,

Aug 04, 2025 am 05:49 AM

mongodb

Optimizing MongoDB Aggregation Pipelines for Large Datasets

Place$matchstagesearlytoreducedocumentvolumeandensurefilteredfieldsareindexed.2.Use$projector$unsetearlytominimizedataflowbyeliminatingunnecessaryfields.3.Optimize$lookupbyindexingforeignfieldsandfilteringwithinthepipeline,andhandle$groupcautiouslywi

Aug 04, 2025 am 12:07 AM

Common MongoDB Use Cases

MongoDB is suitable for content management and directory storage, because its document structure naturally supports JSON format hierarchical data and flexibly expands fields without predefined table structure; 2. Suitable for real-time analysis and log processing, and can efficiently process high-throughput data and generate real-time insights with time series collections and aggregation pipelines; 3. Good at user data management and personalized recommendations, and supports heterogeneous user documents, geospatial indexes and change flows to achieve cross-device synchronization; 4. Suitable for mobile and gaming applications, offline priority and low-latency data synchronization is achieved through Realm built in MongoDBAtlas to meet the needs of fast iteration and expansion - in short, MongoDB is an ideal choice when data is semi-structured, frequently changed or horizontally expands.

Aug 03, 2025 am 06:52 AM

A Guide to MongoDB Collections

MongoDB collections are schema-free document groups, equivalent to tables in relational databases; 2. They can be implicitly created by inserting documents or explicitly created using db.createCollection (supports special options such as fixed size or verification rules); 3. Naming should use lowercase plural forms, avoid special characters, and reasonably group relevant data to improve performance and readability; 4. Avoid common traps such as data inconsistency caused by no verification, spelling errors, and excessive nesting that affect query efficiency; 5. Determine single or multi-collection design based on document structure similarity, access mode and write volume to ensure that query efficiency and maintenance are taken into account, thereby achieving a high-performance and easy-to-scaling application architecture.

Aug 03, 2025 am 06:46 AM

Building a Scalable E-commerce Product Catalog with MongoDB

Useaflexibledocumentschemawithembeddedattributesandvariantstoaccommodatediverseproducttypeswithoutjoins.2.Createtargetedindexessuchascompound,text,anduniqueindexesonfieldslikecategory,brand,name,SKU,andinStocktoenablefastqueries.3.Scalehorizontallyus

Aug 03, 2025 am 06:11 AM

mongodb E-commerce

Solving Complex Joins with the $lookup Operator in MongoDB

Use $lookup to process one-to-many relationships, and implement orders and customer information association by adding customerInfo arrays; 2. Use let to define variables and pipeline filtering conditions to achieve conditional connections; 3. Many-to-many relationships need to be divided into two steps with the help of an intermediate table and deduplicated with $group; 4. Performance optimization requires indexing of the associated fields, matching filtering as soon as possible, avoiding large arrays, and considering embedding of static data. Mastering $lookup can efficiently handle complex association queries in MongoDB and give full play to the advantages of aggregation pipeline.

Aug 03, 2025 am 02:46 AM