How can you identify and resolve slow queries in MongoDB?

MongoDB security improvement mainly relies on three aspects: authentication, authorization and encryption. 1. Enable the authentication mechanism, configure --auth at startup or set security.authorization:enabled, and create a user with a strong password to prohibit anonymous access. 2. Implement fine-grained authorization, assign minimum necessary permissions based on roles, avoid abuse of root roles, review permissions regularly, and create custom roles. 3. Enable encryption, encrypt communication using TLS/SSL, configure PEM certificates and CA files, and combine storage encryption and application-level encryption to protect data privacy. The production environment should use trusted certificates and update policies regularly to build a complete security line.

$unwinddeconstructsanarrayfieldintomultipledocuments,eachcontainingoneelementofthearray.1.Ittransformsadocumentwithanarrayintomultipledocuments,eachhavingasingleelementfromthearray.2.Touseit,specifythearrayfieldpathwith$unwind,suchas{$unwind:"$t

The main difference between updateOne(), updateMany() and replaceOne() in MongoDB is the update scope and method. ① updateOne() only updates part of the fields of the first matching document, which is suitable for scenes where only one record is modified; ② updateMany() updates part of all matching documents, which is suitable for scenes where multiple records are updated in batches; ③ replaceOne() completely replaces the first matching document, which is suitable for scenes where the overall content of the document is required without retaining the original structure. The three are applicable to different data operation requirements and are selected according to the update range and operation granularity.

MongoDBAtlas' free hierarchy has many limitations in performance, availability, usage restrictions and storage, and is not suitable for production environments. First, the M0 cluster shared CPU resources it provides, with only 512MB of memory and up to 2GB of storage, making it difficult to support real-time performance or data growth; secondly, the lack of high-availability architectures such as multi-node replica sets and automatic failover, which may lead to service interruption during maintenance or failure; further, hourly read and write operations are limited, the number of connections and bandwidth are also limited, and the current limit can be triggered; finally, the backup function is limited, and the storage limit is easily exhausted due to indexing or file storage, so it is only suitable for demonstration or small personal projects.

ShardingshouldbeconsideredforscalingaMongoDBdeploymentwhenperformanceorstoragelimitscannotberesolvedbyhardwareupgradesorqueryoptimization.First,ifthedatasetexceedsRAMcapacityorstoragelimitsofasingleserver—causinglargeindexes,diskI/Obottlenecks,andslo

Use deleteOne() to delete a single document, which is suitable for deleting the first document that matches the criteria; use deleteMany() to delete all matching documents. When you need to remove a specific document, deleteOne() should be used, especially if you determine that there is only one match or you want to delete only one document. To delete multiple documents that meet the criteria, such as cleaning old logs, test data, etc., deleteMany() should be used. Both will permanently delete data (unless there is a backup) and may affect performance, so it should be operated during off-peak hours and ensure that the filtering conditions are accurate to avoid mis-deletion. Additionally, deleting documents does not immediately reduce disk file size, and the index still takes up space until compression.

TTLindexesautomaticallydeleteoutdateddataafterasettime.Theyworkondatefields,usingabackgroundprocesstoremoveexpireddocuments,idealforsessions,logs,andcaches.Tosetoneup,createanindexonatimestampfieldwithexpireAfterSeconds.Limitationsincludeimprecisedel

MongoDBhandlestimeseriesdataeffectivelythroughtimeseriescollectionsintroducedinversion5.0.1.Timeseriescollectionsgrouptimestampeddataintobucketsbasedontimeintervals,reducingindexsizeandimprovingqueryefficiency.2.Theyofferefficientcompressionbystoring