Why Percona Backup for MongoDB Is the Right Choice for Production Backups

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.

DumboDB Indexes: They Work Now!

DumboDB 0.3.0 fixes numerous index bugs that made the MongoDB-compatible, version-controlled database largely unusable. On the MongoDB parity side, deleteMany and updateMany failed to remove index keys, the query planner always chose full column scans over index scans, and .hint() with $natural caused panics. On the version-control side, merging branches caused index corruption with duplicate unique entries, no structural sharing occurred during merges, and conflict resolution returned all null values. The fixes involved writing 77 parity tests run against both MongoDB and DumboDB, porting Dolt's structural sharing logic, and introducing a new dumboConflicts response that provides actionable conflict details including conflict type, human-readable messages, and full three-way merge documents. Query performance improvements are planned for a future release.

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.

MongoDB embeds reranking into Atlas as enterprises look to simplify AI stacks for scale

MongoDB has introduced Native Reranking as a public preview feature in Atlas, powered by Voyage AI. It runs directly within the MongoDB aggregation pipeline and can improve retrieval quality by up to 30%. Analysts highlight that embedding reranking natively into the database reduces developer overhead by eliminating separate orchestration layers, retry logic, and failure handling. For CIOs, it simplifies AI stack governance and can lower inference costs by trimming irrelevant context before it reaches expensive LLMs. However, trade-offs include potential vendor lock-in and limited value for enterprises whose data spans multiple repositories. The move reflects a broader industry trend of database and data warehouse vendors consolidating AI, transactional, and analytical capabilities to reduce the integration complexity from multi-vendor AI stacks.

Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas — Part 3: Stateful Workflows and Human-in-the-Loop

Part 3 of a series on building an AI-powered operations assistant using Spring AI and MongoDB Atlas. This installment introduces stateful workflow checkpointing to handle multi-step incident investigations that span session boundaries. Key additions include: a checkpoint document stored in MongoDB tracking workflow state, current step, and status (RUNNING, WAITING_APPROVAL, COMPLETED, FAILED); a ServiceStatusTool annotated with @Tool that lets the model fetch real-time service metrics; ThreadLocal-based context propagation for passing conversation IDs into tool methods; ToolExecution audit documents for full traceability; and pause/resume API endpoints that rehydrate investigation state. MongoDB serves as a unified persistence layer for runbooks, conversation memory, long-term knowledge, and workflow checkpoints. The architecture allows horizontal scaling since workflow state lives outside any single instance.

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.

How Zernio scaled to 6M daily posts on a 7-person team with Tinybird

Zernio, API mạng xã hội hợp nhất cho 15 nền tảng, đã thay thế MongoDB bằng Tinybird để xử lý 6 triệu bài đăng mỗi ngày. Nhờ Tinybird, đội 7 người triển khai thành công trong vòng chưa đầy một tuần, đạt 700.000 yêu cầu API/ngày với độ trễ 250ms (p95) và ingest 16,7 triệu dòng dữ liệu mỗi ngày, đồng thời đẩy nhanh tốc độ phát triển tính năng mới từ tuần xuống giờ.

Lập trình viên cần đọc bài này để hiểu cách chuyển đổi từ MongoDB sang Tinybird giúp tối ưu hóa quy trình phát triển, giảm thời gian triển khai tính năng mới từ hàng tuần xuống chỉ vài giờ trong một đội nhỏ chỉ 7 người.