---
title: "MongoDB"
description: "42 posts about MongoDB, summarised, each linking to the original."
---

# MongoDB
> 42 posts about MongoDB, summarised, each linking to the original.

## Articles

### [How to Encrypt LangGraph Checkpoints With MongoDB CSFLE](https://yomu.fyi/post/how-to-encrypt-langgraph-checkpoints-with-mongodb-csfle.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 14, 2026
- Type: Tutorial

LangGraph checkpoints persist agent state, including user messages and tool results, as plaintext BSON in MongoDB by default, leaving sensitive data accessible to anyone who can read the database. MongoDB Client-Side Field Level Encryption (CSFLE) encrypts data on the client before transmission, ensuring the database engine never sees plaintext values and protecting against privileged database users or data breaches. This post demonstrates implementing a custom CipherProtocol for LangGraph’s EncryptedSerializer that uses explicit CSFLE with randomized encryption, configured with AWS KMS and a data encryption key stored in a key vault collection for envelope encryption. The encrypted payload replaces the default Base64 encoding, while only thread IDs, checkpoint IDs, and namespaces remain unencrypted to support runtime lookups without exposing sensitive content. This approach aligns with enterprise security and compliance requirements for customer-facing AI agents, preserving resumable sessions and human-in-the-loop workflows while preventing database administrators or attackers from reading the state.


### [Enriching Product Catalogs For Agentic Commerce Discovery with MongoDB](https://yomu.fyi/post/enriching-product-catalogs-for-agentic-commerce-discovery-with-mongodb.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 12, 2026
- Type: Problem & solution

AI agents are transforming retail discovery, requiring product catalogs to be machine-readable and context-rich for systematic retrieval, with McKinsey estimating AI agents could orchestrate $3 trillion to $5 trillion globally by 2030. Retailers need a single enriched catalog that projects consistently to three surfaces: outbound product feeds, agentic commerce endpoints via REST APIs or MCP, and product detail pages with schema.org JSON-LD. MongoDB’s document model supports three tiers of enrichment—identity and transactability, filterable attributes, and agentic context such as use cases and semantic embeddings—to help AI agents reason over products. Change streams and aggregation pipelines keep projections current across surfaces, ensuring consistency that AI agents cross-check. MongoDB Search and Vector Search enable hybrid retrieval for intent-based queries, helping retailers improve discoverability and gain a competitive advantage in the agentic commerce era.


### [MongoDB Extension for Hibernate ORM is Now GA](https://yomu.fyi/post/mongodb-extension-for-hibernate-orm-is-now-ga.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 12, 2026
- Type: Announcement

The MongoDB Extension for Hibernate ORM is now generally available as a first-party, open-source library, giving Java teams a production-ready way to use familiar Hibernate APIs with MongoDB’s document model. Developers can continue using @Entity annotations, HQL, Criteria Queries, and Session APIs while building new applications or migrating existing Hibernate workloads from relational databases. The GA release adds JOIN support (INNER and LEFT OUTER) via standard HQL/JPQL syntax, optimistic locking with @Version, Spring Boot auto-configuration that auto-detects dialect and connection provider from the MongoDB JDBC URL, and clearer exception messaging and logging. The extension transparently translates Hibernate operations into MongoDB aggregation pipelines and requires Hibernate ORM 7.4 LTS. It also supports native embedded documents for faster reads and allows dropping into MongoDB’s native Query API for advanced operations like vector search and geospatial queries.


### [Building A Culture Of Growth: MongoDB's Massive Opportunity In APAC](https://yomu.fyi/post/building-a-culture-of-growth-mongodb-s-massive-opportunity-in-apac.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 12, 2026
- Type: Explainer

MongoDB’s go-to-market organization in APAC has nearly tripled in size over the past three years, driven by the region’s massive database opportunity. IDC forecasts the global database market will grow to $137 billion by 2027, and MongoDB has reached $1 billion in annual recurring revenue, with APAC containing five of the world’s top 13 economies by GDP. To serve diverse markets like India, Australia, and Singapore, MongoDB deploys regionalized business leaders who tailor go-to-market strategies while collaborating on shared best practices at the leadership level. The company’s culture, shaped by six core values including “Embrace the Power of Differences,” emphasizes psychological safety and accountability, encouraging teams to learn from setbacks. MongoDB’s APAC teams prioritize three goals: ensuring customer success, building technical champions, and driving new workloads, as exemplified by Cathay Pacific’s Flight Folder application powered by MongoDB.


### [Code Mode Without the Interpreter: Agents That Write Aggregation Pipelines](https://yomu.fyi/post/code-mode-without-the-interpreter-agents-that-write-aggregation-pipeli.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 7, 2026
- Type: Problem & solution

Fixed-tool document agents suffer multiple round trips and context window bloat because each call returns raw data, and cross-document relationships require pulling all files into the context. The author’s solution lets the agent write a MongoDB aggregation pipeline instead of arbitrary code, avoiding the security risks of a code interpreter. A handler validates the pipeline, prepends a tenancy filter, and executes it server-side, returning only the reshaped result. The agent uses a two-pass approach, often computing booleans or concatenating fields with $addFields so comparisons happen in the database. Limitations include regex not being semantic search and $limit silently truncating results, but the bounded language of nine permitted stages improves both safety and accuracy.


### [Intelligent Workload Management: Keeping Your Clusters Available When It Matters](https://yomu.fyi/post/intelligent-workload-management-keeping-your-clusters-available-when-i.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 7, 2026
- Type: Announcement

MongoDB Atlas announced Intelligent Workload Management (IWM) as GA in MongoDB 9.0, a managed availability capability that keeps clusters responsive and predictable under overload. When demand exceeds safe processing, IWM limits incoming work and queues surplus requests by default, keeps short-running operations moving while managing long-running ones conservatively, and adjusts memory and cache management to preserve cluster health. It works with or without auto-scaling, helping clusters stay stable while additional capacity provisions, closing the gap between a traffic spike and new capacity coming online. Default protections are enabled at no extra cost on eligible dedicated clusters and require no application changes; optional Load Shedding can reject excess operations for sustained overload, and Coinbase’s VP of Engineering cited confidence during peak trading windows. Observability metrics in Atlas track IWM interventions, and a test validates driver compatibility and application handling before enabling Load Shedding in production.


### [The Ground Beneath the Database](https://yomu.fyi/post/the-ground-beneath-the-database.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 7, 2026
- Type: Explainer

On September 29th, MongoDB announced the public preview of Atlas Infinite, a new deployment option built on disaggregated storage where compute and storage run on separate machines. The shared storage layer consists of three services: a Log Service for write consensus, a Page Service keeping materialized pages warm, and an Object Index Service managing object storage. This architecture was necessary because WiredTiger’s speculative eviction and deferred writes made conventional approaches impossible. Data is encrypted on the compute node before reaching the storage layer, preventing shared services from reading documents. Benefits include unlimited storage scaling, read replicas without disk copying, and constant-time snapshots.


### [Data Modeling and Schema Design for MongoDB Search Part 1: Practical Considerations](https://yomu.fyi/post/data-modeling-and-schema-design-for-mongodb-search-part-1-practical-co.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 6, 2026
- Type: Explainer

Data modeling for MongoDB Search requires balancing conceptual, logical, and physical schema decisions against workload demands and system constraints. The $search stage must be the first in an aggregation pipeline, queries a single Search index, and runs against a single collection, with results returned in relevancy order. The underlying Lucene engine supports two mappings for arrays of sub-documents: default document mapping flattens fields into one index object, while embeddedDocuments mapping creates separate Lucene index objects per sub-document. Embedded mapping enables cross-field matching within sub-documents but explodes the index object count and causes heavy reindexing on any update, as the entire block is deleted and re-added. This post establishes the foundational concepts and physical constraints that will inform schema patterns in Part 2.


### [Data Modeling and Schema Design for MongoDB Search Part 2: Use Cases and Patterns](https://yomu.fyi/post/data-modeling-and-schema-design-for-mongodb-search-part-2-use-cases-an.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 6, 2026
- Type: Explainer

The post presents best practices for data modeling and schema design in MongoDB Search, covering use cases and patterns such as the Entity Pattern to guide developers in structuring collections for effective search. It emphasizes that searchable items, like movies, cast members, or genres, should be modeled as first-class documents in their own collections rather than as nested structures or ambiguous attributes. The Entity Pattern addresses ‘find anything anywhere’ and as-you-type suggestions by representing all domain entities as documents in a specialized collection with stable identifiers and a type field for filtering. The Attribute Pattern is explicitly discouraged for search because its key-value structure prevents type-sensitive field mapping, making it better to model attributes as straightforward fields. The post details a multi-indexing strategy on the name field that applies left-edge, phonetic, shingled, and language-agnostic analysis to enable flexible matching and relevancy scoring, with optional type-specific metadata nested in sub-documents.


### [Data-Driven, Interoperable Core Banking: A BIAN and MongoDB Perspective](https://yomu.fyi/post/data-driven-interoperable-core-banking-a-bian-and-mongodb-perspective.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 6, 2026
- Type: Explainer

Core banking modernization is hindered by semantic fragmentation, where the same banking concepts are defined differently across systems, creating duplicated logic and fragile integrations. The Banking Industry Architecture Network (BIAN) provides a shared semantic foundation by defining common banking domains, boundaries, and service interactions, enabling a composable architecture. MongoDB’s document model, using JSON as both storage and interchange format, allows banks to represent business domains flexibly and evolve incrementally without schema migrations, eliminating the ORM impedance mismatch. MongoDB ensures financial integrity through single-document atomicity for common ledger updates and multi-document ACID transactions for complex cross-domain workflows, while Change Streams enable event-driven operations. Together, BIAN and MongoDB create an operating model for continuous transformation, where teams modernize domain by domain, supported by a shared business language and a scalable, flexible data platform.


### [MongoDB Queryable Encryption: Prefix, Suffix, and Substring Queries Are Now GA](https://yomu.fyi/post/mongodb-queryable-encryption-prefix-suffix-and-substring-queries-are-n.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 5, 2026
- Type: Announcement

MongoDB announced general availability of prefix, suffix, and substring query support in Queryable Encryption, extending beyond equality and range queries to production workloads at no additional cost in Atlas, Enterprise Advanced, and Community Edition. These capabilities enable expressive text searches on encrypted data without decryption, with the database server never seeing plaintext during query processing. The open-source encryption scheme is independently verifiable, works without specialized hardware, and protects data at rest, in transit, and in use. The new query types enable partial-name lookups, autocomplete, and partial-word filtering on encrypted fields via the native query engine, requiring no external indexes or middleware. This helps meet HIPAA, PCI-DSS, and GDPR compliance by protecting data in use — a stage other encryption methods cannot cover — reducing audit gaps and eliminating workarounds.


### [MongoDB 9.0: This Is What a Database Should Be](https://yomu.fyi/post/mongodb-9-0-this-is-what-a-database-should-be.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 5, 2026
- Type: Announcement

MongoDB 9.0 is the most advanced release in MongoDB’s history, designed to eliminate the tradeoffs that force organizations to add infrastructure, compromise security, or rethink architecture as applications grow. Transactional workloads achieve up to 20% higher throughput compared to 8.0, with find-one queries up to 35% faster and update-one queries up to 30% faster, and large deployments see nearly 2x higher throughput. Queryable Encryption now supports prefix, suffix, and substring queries, enabling partial-match searches on encrypted data throughout its lifecycle. Intelligent Workload Management queues transient traffic spikes during overload to keep clusters responsive, buying time for auto-scaling or operator intervention. The release also includes redesigned Atlas Metrics UI, enhanced OpenTelemetry integration for streaming metrics to platforms like Datadog and Prometheus, and a MongoDB extension for Hibernate ORM to ease modernization.


### [Tag, You're It: Using MongoDB Labels To Drive DataDog Business Logic](https://yomu.fyi/post/tag-you-re-it-using-mongodb-labels-to-drive-datadog-business-logic.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 5, 2026
- Type: Announcement

MongoDB Atlas’s integration with DataDog now sends custom cluster-level labels as tags on DataDog metrics objects, allowing users to organize and categorize monitoring data in meaningful ways. Users define key-value labels in MongoDB at the cluster level using the Data API or configuration endpoints, and these labels automatically appear as tags in DataDog. These tags can filter metrics in the Metrics Explorer, group data for dashboards, and set alert priorities based on attributes such as environment, application, or region. For instance, tag-based monitors can trigger a P2 alert for production clusters exceeding query targeting thresholds while assigning lower priority to non-production clusters. This enhancement, available on Atlas M10 cluster tiers and higher, leverages existing MongoDB labeling infrastructure to improve observability and drive business-specific alerting and dashboarding logic.


### [How Atlas Agent Engine Powers MongoDB’s Internal AI Strategy](https://yomu.fyi/post/how-atlas-agent-engine-powers-mongodb-s-internal-ai-strategy.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 30, 2026
- Type: Announcement

MongoDB announced the public preview of Atlas Agent Engine, a secure platform for building, deploying, and governing AI agents with built-in memory, retrieval, and open flexibility to choose any LLM or framework. The company validated the platform internally with Holly, a unified agentic workspace that replaces seven disconnected tools for sales reps and is powered by Atlas Agent Engine’s runtime, orchestration, and governance. Compared to manually hopping between tools, Holly cuts the time to research and build an outbound prospecting list by roughly 95%, reducing account prep from hours to minutes. Atlas Agent Engine enforces centralized controls and governance while granting developer autonomy, addressing CIO fears about agents running amok in enterprise systems. It is part of a trio of launches alongside MongoDB 9.0 and Atlas Infinite, giving builders a governed platform to run agents grounded in real-time operational data.


### [AI Demand is Constant, Unpredictable, and Extreme. MongoDB is Built for It.](https://yomu.fyi/post/ai-demand-is-constant-unpredictable-and-extreme-mongodb-is-built-for-i.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 30, 2026
- Type: Announcement

AI-driven demand is constant, unpredictable, and extreme, unlike earlier internet and cloud eras where spikes were more predictable. Enterprises need a data layer that can absorb sudden bursts of traffic without re-architecture. MongoDB announced Atlas Infinite, a new deployment option within MongoDB Atlas that independently scales compute and storage within minutes. Atlas Infinite supports replica sets with 10x the data capacity of Atlas Core and delivers 189% more throughput per dollar in internal tests. MongoDB 9.0 was also released, offering up to 35% faster find-one queries and new security features like queryable encryption with prefix, suffix, and substring searches.


### [Building the Agentic Ecosystem: Partner Updates from MongoDB.local NYC](https://yomu.fyi/post/building-the-agentic-ecosystem-partner-updates-from-mongodb-local-nyc.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 30, 2026
- Type: Announcement

At MongoDB.local NYC, MongoDB launched Atlas Agent Engine in Public Preview, providing a single foundation for building, deploying, and governing AI agents. Six global systems integrators—Accenture, Capgemini, Cognizant, Infosys, TCS, and Tech Mahindra—are launch partners, integrating the engine into their AI offerings. The platform is model- and framework-neutral, with partners like Arize AI offering trace monitoring for production reliability. MongoDB also announced Devin for MongoDB Modernizations, leveraging Cognition’s AI software engineer to automate legacy code migration while AMP handles data movement. New partnerships with Emergent, Monk.io, and Retool extend MongoDB’s reach into AI application development and internal tools, reinforcing that the agentic era requires an ecosystem of partners with industry expertise.


### [Context Engineering Has a Retrieval Issue: Introducing rerank-3 and Native Reranking](https://yomu.fyi/post/context-engineering-has-a-retrieval-issue-introducing-rerank-3-and-nat.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 30, 2026
- Type: Announcement

AI agents require precise context retrieval, not larger windows, and MongoDB’s new Rerank-3 and Rerank-3-lite models outperform Cohere Rerank v4.0 Pro by 2.14% and Qwen3-Reranker-8B by 3.31% overall, with greatest improvements on long documents and code. The $rerank native aggregation stage rescores candidate sets index-free within MongoDB, while Automated Embedding generates and refreshes vectors near-real-time to prevent model version drift. This unified platform eliminates separate vendors and external API calls, supporting lexical, vector, and hybrid search directly against operational data without moving or syncing. The result is simpler retrieval architecture that improved Gravity’s accuracy and cut latency by roughly 50% by better discriminating between strong and weak matches. Retrieval quality forms one layer of MongoDB’s broader agent platform, which also includes performance scaling with Atlas Infinite and agent memory with Atlas Agent Engine.


### [The Intelligent Data Platform for the AI Era](https://yomu.fyi/post/the-intelligent-data-platform-for-the-ai-era.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 29, 2026
- Type: Announcement

MongoDB announced the largest platform expansion yet, including MongoDB 9.0, Atlas Infinite, and Atlas Agent Engine. MongoDB 9.0 delivers up to 2x higher throughput, 35% faster reads, and 30% faster updates over version 8.0. Atlas Infinite is a new deployment option that allows storage and compute to scale independently, with time to scale more than 96% faster and each shard holding 10x more storage. Atlas Agent Engine provides built-in memory, retrieval, identity, and governance for production agents, operating on the same data platform as operational data. The company argues that as AI moves from generating answers to taking actions, the data layer must provide live, fresh, and retrievable operational data rather than separate AI infrastructure.


### [How to test that a dozen MongoDB drivers, in a dozen languages, conform to a dozen specs](https://yomu.fyi/post/how-to-test-that-a-dozen-mongodb-drivers-in-a-dozen-languages-conform.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 23, 2026
- Type: Problem & solution

MongoDB develops a dozen native client library drivers in a dozen languages, each idiomatic to its language but needing consistent API behavior. Instead of relying on English specs or Cucumber tests, engineers created a YAML-based domain-specific test language interpreted by runners in each driver. The Unified Test Format, built on this YAML language, now contains nearly 1,500 files testing conformance to specs covering retries, transactions, client-side encryption, and more. A natural experiment showed four of five drivers had lower nonconformance bug rates after adopting YAML tests; Node.js improved after fixing its test runner. The IEEE ISSRE 2026 accepted a paper on this approach, which has saved over 22,000 lines of test-runner code across seven drivers.


### [Data Sovereignty by Design: Five Practical Evaluation Lenses for IT Leaders](https://yomu.fyi/post/data-sovereignty-by-design-five-practical-evaluation-lenses-for-it-lea.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 22, 2026
- Type: Explainer

Data sovereignty is a market expression, not a universal legal standard, and IT leaders should evaluate it through architecture and governance questions rather than a single label. The assessment covers where data resides, who can access it, how it moves, and whether control is retained, with considerations varying by jurisdiction, industry, and risk posture. Physical location is only one part of a broader control boundary that includes operations, key custody, and access paths. The article proposes five technical lenses for evaluation: jurisdiction-native data placement, in-use encryption, customer-held keys, network isolation and identity-based access, and forensic-grade audit evidence. These lenses are practical reference points for architecture decisions, not a legal test or certification checklist, and organizations should assess their own requirements with qualified counsel.


[Older posts](https://yomu.fyi/topic/mongodb/page/2.md)