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Home

Technical Articles

Database

MongoDB

Serverless Functions and MongoDB

When using ServerlessFunctions with MongoDB, the database connection must be reused to avoid performance issues. 1. Cache MongoClient instances in the global scope, use hot-start multiplexing connections to reduce cold start delays; 2. Priority is given to MongoDBAtlas, because it is deeply integrated with the cloud platform, supports automatic scaling and provides free tier; 3. Do not manually close connections, rely on the platform to automatically recover, prevent connection leakage, and set reasonable timeouts; 4. It is recommended to use MongoDBServerlessInstances, bill according to request, automatically manage connections, and reduce cold start delays; 5. Store connection strings through environment variables, combined with IP whitelist

Jul 26, 2025 am 03:44 AM

MongoDB Version 6.0 New Features

Although MongoDB6.0 has not been officially released, its planning functions have been gradually implemented in 5.3 and subsequent versions; 2. Enhanced real-time change flow supports persistent cursors, global logical clocks and metadata monitoring to improve data synchronization reliability; 3. Query observability improvements include execution of statistical APIs, structured slow logs and automatic indexing suggestions to facilitate performance tuning; 4. In terms of security, multi-tenant field encryption, enhanced audit logs, zero trust support and integration of KMSs such as HashicorpVault; 5. New window functions, $unionWithpipeline support, $topN and other operators are added to the aggregation pipeline, and the regular engine is optimized to RE2 to improve security; 6. Rolling patches and dynamic segmentation are implemented in operation and maintenance.

Jul 26, 2025 am 02:45 AM

The MongoDB Aggregation Framework Explained

MongoDB's aggregation framework is the preferred tool for processing large-scale data sets and aggregating, filtering and reshaping. The answer is to use an aggregation pipeline to enable complex data analysis. 1. The aggregation pipeline consists of multiple stages, each stage processes the document and passes the results in sequence; 2. Common stages include $match filtering documents, $group group aggregation, $sort sorting, $project reshaping fields, $lookup implements collection association, and $unwind disassembly array; 3. For example, for counting the total sales of each category, you must first filter and complete the order, then group and sum it by category, and finally arrange it in descending order; 4. $project can calculate new fields such as merged names, which are suitable for API data formatting; 5. $lookup supports cross-sets

Jul 26, 2025 am 01:13 AM

Connecting and a Managing MongoDB Database with Python and PyMongo

Install PyMongo: Use pipinstallpymongo; 2. Connect MongoDB: Connect the local or Atlas database through MongoClient and manage credentials with environment variables; 3. Access database and collections: client['db'] and db['collection'] to create or access resources; 4. Insert data: Use insert_one() or insert_many() to add documents; 5. Query data: Use find_one() and find() to search with conditions; 6. Update and delete: Call update_one() and delete_one() to operate the data, and finally remember to close the connection clie

Jul 26, 2025 am 12:06 AM

mongodb PyMongo

How does the choice of a shard key impact data distribution and query performance in a sharded cluster?

Improper shardkey selection can lead to data skew, hotspot writing, and slow queries. shardkey determines how the data is distributed to each shard. If a monotonic incremental field such as ObjectId is used, the new data will be concentrated in a single shard, causing insertion bottlenecks and uneven loads; while using fields with good discreteness such as user_id or hash can achieve uniform distribution. Query performance also depends on whether the shardkey hits. If the query condition contains shardkey, it can execute efficient targetedquery, otherwise broadcastquery all shards need to increase latency. For example, when user_id is used as shardkey, the order is efficient and efficient.

Jul 25, 2025 am 02:17 AM

Data distribution shard key

What is MongoDB?

MongoDB is a document-oriented NoSQL database. 1. Use flexible BSON documents to store data without predefined fixed table structure; 2. Support dynamic modification of document fields to adapt to data structure changes; 3. Realize horizontal expansion through sharding to improve storage and performance; 4. Provide rich query languages and high-availability replication sets, suitable for real-time applications, content management, e-commerce, Internet of Things and other scenarios. It is especially suitable for modern web development with diverse data and needs to be expanded across servers. It is often used in conjunction with the MERN technology stack, perfectly fits the data model of JavaScript objects, making development more natural and efficient.

Jul 25, 2025 am 02:07 AM

nosql mongodb

Using MongoDB with Node.js and Mongoose

Using Mongoose can provide structured schema, data verification and middleware support for MongoDB, making Node.js applications easier to maintain; 2. First install express, mongoose, dotenv and other dependencies, and connect to the database through mongoose.connect; 3. Use Schema to set field types, verification rules and enable timestamps when defining the user model; 4. Create an instance through new in the route and call save data, and use find to obtain all users; 5. Use pre/post middleware to execute the logic before and after saving, add instance methods and query assistants to improve readability; 6. Create users and articles through ref

Jul 25, 2025 am 12:37 AM

node.js mongodb

What are some common data modeling patterns in MongoDB (e.g., embedding vs. referencing, attribute pattern, bucket pattern)?

Data modeling is crucial in MongoDB and directly affects performance, scalability, and query efficiency. 1. Embed and citation: Select the associated data to be stored in the same document (embed) according to the usage scenario to improve reading speed, such as blog comments; or separate the data and link it with ID (references) to maintain consistency, such as orders and product information. 2. Attribute mode: Applicable to dynamic fields, avoid sparse structures through key-value pairing arrays, such as storing different product attributes, but attention should be paid to the complexity of deep nested query. 3. Bucket mode: used for time series or batch data, such as sensor recording, grouping by time period to reduce the number of documents and improve writing efficiency. 4. Other strategies include version control, pre-calculated value optimization query speed and index design to improve performance. Reasonable

Jul 24, 2025 am 02:11 AM

mongodb Data modeling

Getting Started with MongoDB

Install MongoDB: Newbie recommends using the free MongoDBAtlas cloud service, or use mongosh after local installation; 2. Understand the document (Document), collection (Collection), and database (Database) structure, and store data in flexible JSON-like documents; 3. Master the basic CRUD operations: use to create database, insertOne insert, find query, updateOne update, deleteOne delete; 4. Learn to create indexes in the early stage (such as db.users.createIndex({email:1})) to improve query performance; use meaningful field names and Mo

Jul 24, 2025 am 01:43 AM

Working with Arrays in MongoDB

MongoDB array operation requires mastering the three core methods of storage, query and update: 1. Directly assign the array during storage; 2. Query supports $in (match of any element), complete array matching (value and order are the same), $all (including all specified elements), and $elemMatch (single elements in the object array meet multiple conditions); 3. Updates can be added with $push, $each batch addition, $pull deletes elements, and $set modified by location. Accurate understanding of each operational semantics can avoid common pitfalls and improve data modeling efficiency.

Jul 24, 2025 am 12:48 AM

How does MongoDB handle array indexing and querying elements within arrays?

MongoDB efficiently handles query and indexing of array fields through multi-key indexing. When you create an index on a field containing an array, MongoDB creates an independent index entry for each element in the array, called a multi-key index, which supports efficient query of array elements. 1. Use $elemMatch to match a single array element that meets multiple conditions; 2. Pass the array directly to query documents that exactly match the entire array; 3. Use basic queries to find documents containing specific elements. In terms of performance, multi-key indexing is suitable for simple searches, but complex nested object queries should be used with caution; unnecessary large arrays should be avoided, composite indexes should be used reasonably, and optimization limitations should be paid to the combination of multi-key indexes and other indexes. In addition, index intersection limit

Jul 23, 2025 am 01:28 AM

mongodb array index

A Deep Dive into MongoDB Indexes

MongoDBindexesareessentialforfastqueriesbyavoidingfullcollectionscans;1)Usesingle-fieldindexesforsimplequerieslikeemaillookup;2)Applycompoundindexesformulti-fieldfilters,respectingfieldorder;3)Leveragemultikeyindexesforarrayfieldsliketags;4)Employtex

Jul 23, 2025 am 01:19 AM

mongodb Indexes

Understanding MongoDB Documents

MongoDB documents are a single record stored as key-value pairs, and use BSON format to support more data types; its core advantages are 1. Flexible pattern design, each document field can be different; 2. Support nested objects and arrays to reduce association queries; 3. Each document needs to have a unique \_id field, which is convenient for mapping object structures in programming languages, improve development efficiency and avoid complex migrations, but attention should be paid to controlling the document size not exceeding 16MB, avoiding deep nesting and rational use of arrays to ensure query performance, which is especially suitable for modern application development that handles hierarchical data.

Jul 23, 2025 am 12:20 AM

How does the election process work in a MongoDB replica set to determine a new primary?

WhenaMongoDBreplicasetneedstoelectanewprimary,itfollowsastructuredprocessbasedondatafreshness,priority,andvoting.Anelectionistriggeredwhenthecurrentprimarybecomesunreachable,stepsdownvoluntarily,orduringinitialization.Secondariesdetermineeligibilityb

Jul 23, 2025 am 12:06 AM

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