#mongodb

Building a Shopping Cart In Nodejs

Bài viết hướng dẫn chi tiết cách xây dựng nền tảng thương mại điện tử sử dụng Node.js làm backend và các framework frontend như Angular, React, Vue.js. Nó bao gồm thiết lập backend, cài đặt packages, cấu hình server, tạo models và routes với MongoDB, kiểm thử routes, cùng tích hợp tính năng giỏ hàng.

Lập trình viên Node.js nên đọc bài này để hiểu cách triển khai tính năng cart (giỏ hàng) từ cơ sở hạ tầng backend đến giao diện người dùng với các framework frontend phổ biến.

10 Years of MongoDB Atlas: Built for What’s Next

MongoDB Atlas celebrates its 10th anniversary, tracing its evolution from a managed cloud database launched in 2016 to an AI-ready data platform. Key milestones include multi-cloud cluster support across AWS, Azure, and Google Cloud, ACID transactions, Atlas Search, Vector Search, and the acquisition of Voyage AI for improved embedding and reranking. Today Atlas serves over 250,000 new builders monthly, processes more than three trillion queries per day, and hosts over 726,000 vector indexes across nearly 70,000 customers. The post highlights how Atlas is now positioned as the data layer for agentic AI applications, enabling semantic search, RAG, and real-time context retrieval without requiring separate infrastructure.

Aggregation Optimization in MongoDB: Unnecessary Unwinds (Part 2)

A MongoDB expert walks through the first optimization step for a slow aggregation pipeline in a video streaming service use case. The pipeline was originally designed with an RDBMS mindset, using two unnecessary $unwind stages after $lookup joins. The key insight: when $lookup's localField points to a field inside array elements, MongoDB automatically runs the join for each element — making the preceding $unwind redundant. The second $unwind, used to filter out empty arrays, was replaced with a simpler $match stage. Removing both $unwinds also eliminated the need for a subsequent $group stage, reducing the pipeline from 10 to 8 stages. The result was a 60% performance improvement, dropping average query time from 11.8 seconds to 4.7 seconds, though still short of the 1-second SLA target.

MGLRU Improvement Yielding Nice Gains On Linux 7.2: MongoDB 30~100% Higher Throughput

The Linux 7.2 kernel's memory management (MM) subsystem has received several notable improvements now merged to Git. The headline change is an improvement to MGLRU's reclaim loop and dirty writeback handling, yielding up to 30% throughput gains for MongoDB with NVMe storage and up to 100% gains with slower I/O. Other improvements include faster batch freeing of order-0 pages (addressing vmalloc regressions), DAMON enhancements for pausing/resuming without state loss and monitoring general data attributes, KASAN speedups by removing ineffective stack/page-table tagging, and tightened mmap_miss hit accounting that reduces synchronous mmap readahead I/O for sparse random and strided memory access workloads.

Build Trust in Agentic AI: From POC to Production

A four-step framework for moving agentic AI systems from proof-of-concept to production-grade enterprise deployments. The framework covers: (1) a foundation layer using RAG, operational data, and agent memory to reduce hallucinations; (2) a verification layer with Agent Confidence Score (ACS) and Business Risk Score (BRS); (3) a governance layer using an Agent Decision Score formula (ADS = ACS × (1 - BRS)) with a traffic-light routing system for human-in-the-loop escalation; and (4) an outcomes layer with executive dashboards tracking ROI, cost-per-task, and systemic risk. A customer refund workflow is used throughout as a concrete example. MongoDB is positioned as the unified data platform for agent memory, vector search, and execution traces.

DumboDB Log Filters: A New Way to Query Your Data's History

DumboDB v0.2.2 introduces Log Filters for the dumboLog command, enabling expressive querying of a document database's commit history. Three filter types are supported: collection-level filtering (analogous to Git file filtering), _id-based filtering to track specific document changes, and $match-based filtering that applies MongoDB's query operators against commit diffs. A new DumboDB-specific $changed operator lets you find commits where a field changed regardless of value. The release also adds an all argument to walk all branch HEADs and pagination support via next/from arguments. Examples use a simulated e-commerce dataset with products, customers, and orders collections.

Aggregation Optimization in MongoDB: A Case Study From the Field (Part 1)

A MongoDB expert walks through a real-world design review where a team modeled their data in an RDBMS-like fashion, resulting in 11.8-second query times. The post details a video streaming service use case with profiles, devices, and mappings collections, explaining each stage of a 10-stage aggregation pipeline ($match, $lookup, $unwind, $group, $sort, $skip, $limit). The initial pipeline suffered from poor performance despite proper indexing. Through data model and pipeline redesign, query time was reduced from 11.8 seconds to 14 milliseconds — an 800x improvement. Part 1 focuses on understanding the initial pipeline design before optimizations are introduced in subsequent parts.