Aggregation Optimization in MongoDB: Unnecessary Unwinds (Part 2)
Table of Contents And why MongoDB might be a better relational database than you ever realized.The video streaming service use case: profiles, devices, and device types (a recap)Optimization, Step 1: removing the $unwind stagesAnd why MongoDB might be a better ...
-
BoxLang 1.14.0 : Query Transformers – Take Full Control of Your Query Results
Table of Contents The Problem with Three Return TypesWhat Is a Query Transformer?The Transformer ContractLive Examples: Inline Closures1. Envelope with Metadata2. Domain Object Hydration3. Tabular Format (Near Zero-Copy)4. Rich Column DescriptorsReusable Class TransformersRegistered App-Level TransformersTransformer Resolution Orderbx:query Component SupportJDBC Metadata: …
-
Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas — Part 2: Conversational Memory
Table of Contents The Problem with Stateless ChatWhat We Are BuildingTwo Different Kinds of MemoryMix & MatchShort-Term Memory: Keeping the Conversation CoherentLong-Term Memory: Carrying Knowledge Across SessionsMemory Consolidation: From Conversation to Durable FactThe Advisor Chain and Why Order MattersThe Atlas …
-
Exploring MongoT (Atlas Search)
Table of Contents Let’s dive in!Simple Example – Text SearchBreakdown Table (for a ~9ms $search aggregation path through MongoT)Local DebuggingSample DataInteresting Example – Faceted Text SearchLucene Indexing Strategy + Benefits over MongoD IndexesVector Search ExampleLocal Grafana MonitoringPerformance Java Code PackagesSo what …
-
Implementing Soft Deletes in Java
Table of Contents What are soft deletes?How can soft deletes be approached?Field FlaggingArchive collectionsImplementation of soft deletesJava driverCode examplesCascading to related collections Cleanup with TTL indexingPros and cons of soft deletesProsConsWrap UpWhat are soft deletes? Usually, when deleting documents from a …
-
Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas — Part 1: RAG Foundation
Table of Contents The problemWhat we are buildingWhy RAG and why MongoDB AtlasHow the Pieces Fit TogetherGetting the Project RunningThe Ingestion PipelineThe Retrieval PipelineThe Atlas Vector Search IndexTrying It OutConclusion and What’s NextThis is the first article in a three-part …
-
UCanAccess: The Modern Pure-Java Bridge to Microsoft Access
Table of Contents A Brief HistoryWhat Problems Does It Solve?The Disappeared BridgeCross-Platform CompatibilitySeamless Integration for ToolingTech Stack & RequirementsGetting StartedAdd the DependencyConnect and QueryWrite Data BackAccess-Specific FunctionsUber JAR for Non-Maven ProjectsQuality & MaintenanceGet InvolvedMicrosoft Access databases are everywhere. Decades of …
-
When Should You Use a Cache With MongoDB?
Table of Contents Why were caches like Memcached & Redis invented, and why do they thrive?So, what’s wrong with having a caching tier?What’s different with MongoDB?What does AI think?SummaryLearn more about MongoDB design reviewsFrom time to time, I’ll run a …
-
Large-Scale ETL Pipeline Architecture
Table of Contents Rethinking ETL for modern systemsArchitectural building blocksEmbracing concurrency with reactive pipelinesBackpressure: the hidden heroDesigning for failure: error handling strategiesRetry and recovery patternsIdempotency: the cornerstone of safe retriesBatching vs streamingParallelizing transformationsIntegrating with messaging systemsObservability and monitoringPutting it all …
-
Building a Personalized Content Delivery System
Table of Contents Prerequisites1. Data model2. Project setup3. Building the content-based recommendation engineUserProfileControllerGameRepositoryRecommendationService core logicRecommendationControllerManual test4. User ratings and affinity adjustmentRatings endpointAffinity adjustment logicMongoDB updateBefore and after demo5. Adding Spring AI embeddings and MongoDB Atlas Vector SearchSpring AI setupGenerating embeddingsDataSeeder …
