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How I Built an AI Assistant for My Career with Java, Spring AI, and MongoDB

Tue, 15 Sep 2026 11:51:17 +0000

The best way to learn a technology is by putting it into practice in a real system.

A few months ago, I decided to build a virtual assistant that could answer questions about my career, help people learn more about my articles, videos, talks, and projects, and even schedule a call on my calendar.

At first, the idea was simple: give the assistant access to my content and let people ask questions about it. But as the project evolved, I realized that not every question should be handled in the same way. Some questions can be answered with a direct query to the database, while others benefit from semantic search. In other cases, the assistant may need to use a tool or execute multiple steps before reaching an answer.

Agentic WMS — Part 1: Where AI Agents Add Value

Thu, 03 Sep 2026 16:23:14 +0000

I spent a large part of my career working in banking and logistics. In logistics, I spent more than eight years working across different stages of logistics systems, from development to implementation and production support.

That experience shaped the way I think about technology. Knowing a tool or framework is important, but what interests me most is understanding the business problem behind it and where technology can actually add value.

MongoDB Search Server, improved!

Wed, 02 Sep 2026 10:00:00 +0000

In this article, we’re going to revisit and improve the MongoDB Search Server originally described in the article How to Build a Search Service in Java with MongoDB.

First, let’s remind ourselves why an intermediate search service deserves our attention and effort: Search powers the most important part of your application, getting users to the content they need, fast. The search bar often serves as the main entry point to your services. Search results can be returned with all the information needed to present to the user without querying the source database at all. With search serving this important, heavy load on its own, separating the search service into an independently scalable tier allows us to control, scale, and version the search server as needed. Having a gateway from the application to MongoDB Search also allows us to simplify the interface by passing only the key parameters, without getting entangled in the specifics of the aggregation pipeline syntax.

Aggregation Optimization in MongoDB: Sorting With Indexes (Part 5)

Tue, 01 Sep 2026 14:07:36 +0000

And why MongoDB might be a better relational database than you ever realized.

Design reviewsare one-on-one meetings where MongoDB experts deliver advice on data modeling best practices and application design challenges. In this series, we are going to explore common real-life scenarios where design reviews helped developers achieve meaningful success with MongoDB.

In this series, we’ve described our steps to improve the performance of a slow running MongoDB aggregation pipeline. The pipeline was part of a fictional video streaming service application, mapping user profiles to the devices those users were using to access the service, and was based on a real use case I’d encountered during a recent design review.

Aggregation Optimization in MongoDB: Data Duplication to Improve Read Performance (Part 4)

Mon, 31 Aug 2026 14:45:04 +0000

And why MongoDB might be a better relational database than you ever realized.

Aggregation Optimization in MongoDB: Optimizing Many-to-Many Relationships (Part 3)

Fri, 21 Aug 2026 16:00:40 +0000

And why MongoDB might be a better relational database than you ever realized.

In Part 1 of this series, we described a use case based on a recent design review I conducted with a team at a MongoDB customer. The team in question was new to MongoDB, and the approach they had taken to both modeling their data and then subsequently querying it was very “RDBMS-like.” As a result, query performance was significantly slower than their SLA called for.

Introduction to Retrieval-Augmented Generation with Java and MongoDB

Mon, 10 Aug 2026 10:08:50 +0000

Modern organizations store large volumes of information in documents, databases, internal platforms, support systems, policies, and operational tools. However, having data does not guarantee that employees or applications can access the right information when needed. As data grows, traditional search tools often fail to identify context, meaning, and relationships between distributed sources. This results in a growing gap between the information an organization holds and its ability to use that information for effective decisions and actions.

Aggregation Optimization in MongoDB: Unnecessary Unwinds (Part 2)

Thu, 25 Jun 2026 10:01:00 +0000

And why MongoDB might be a better relational database than you ever realized.

This article was written by Graeme Robinson. Find him on LinkedIn.

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

Tue, 23 Jun 2026 13:17:32 +0000

And why MongoDB might be a better relational database than you ever realized.

This article was written by Graeme Robinson. Find him on LinkedIn.

MongoDB is often described as a non-relational database, but whenever we store data in a database, there are relationships within that data. Depending on the type of database we use, though, how we model those relationships may change. As my colleague Rick Houlihan has often pointed out, there really is no such thing as non-relational data.

Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas — Part 2: Conversational Memory

Wed, 10 Jun 2026 19:23:46 +0000

This is the second article in a three-part series. Part 1 covered the RAG foundation — loading runbooks into a vector store and wiring them to a language model. Part 3 will introduce stateful workflow checkpointing with pause and resume.

The Problem with Stateless Chat

In the first part of the series, we successfully created a chat interface where an operator can ask questions and receive answers based on the actual content of the runbooks they have uploaded and embedded in the system. For example, they can ask in the chat, “What should I check when my server’s CPU usage exceeds 80%?” and the assistant retrieves the relevant sections from the various runbooks and assembles a coherent and concrete response.

Introduction to CQRS using MongoDB

Tue, 09 Jun 2026 10:00:00 +0000

In enterprise environments, projects often begin with a simple structure: one model, one service, and one document, using a single class and data transfer object for both read and write operations. While this unified approach works at first, it becomes problematic as requirements grow. Operations become more complex, requiring additional validations, rules, and constraints. Over time, read operations may demand different formats, such as aggregations, summaries, or custom views. Relying on a single model for both reading and writing leads to maintenance challenges and inefficient queries. This approach can result in returning unnecessary data or omitting required information, violating the single responsibility principle and making the design less effective.

MongoDB as a Vector Database for AI Agents-MongoDB

Thu, 04 Jun 2026 10:00:00 +0000

Modern artificial intelligence systems are continually evolving. Large Language Models, or LLMs, have become the backbone of modern applications and help build conversational interfaces, like GPS, to more integrated content. However, LLMs lack memory and the capacity to retain content across interactions because they are stateless. And these limitations led to the building of AI agents. These AI agents build beyond simple prompt-response interactions into more autonomous, task-oriented workflows.

These agents are not just model invocations; rather, they are an orchestration layer that combines reasoning with capabilities like retrieval, memory, and tool execution. While developing these agents, a database with the ability to store and retrieve semantically meaningful data is needed, which is where vector databases come into the picture.

What is Sharding in MongoDB and When Should You Use It?

Tue, 02 Jun 2026 22:15:00 +0000

A Practical Introduction to Horizontal Scaling

When building applications, most developers start with a single database server.

At the beginning, everything works perfectly.

Your application might have:

A few thousand users
Manageable traffic
Datasets that easily fit on one machine

But as your application grows, something interesting starts to happen.

Queries take longer.

Write operations slow down.

The database server starts hitting CPU, RAM, or storage limits.

At this stage, many engineers ask an important question:

Exploring MongoT

Thu, 28 May 2026 21:02:49 +0000

Let’s explore this fascinating and awesome Java project from MongoDB - MongoT!

You can check out the source code here:

git clone https://github.com/mongodb/mongot

MongoT is a wrapper around the amazing Java search engine: Lucene.

Lucene is a powerful search toolkit built around an inverted token index structure that enables advanced text search capabilities, including ranked results, autocomplete, synonyms, fuzzy matching, highlighting, and faceting — all with high performance regardless of dataset size. Unlike MongoDB’s native query engine, it can efficiently search across multiple indexes simultaneously by intersecting lists of ordinal document IDs in parallel, using optimization techniques like skip-lists, ordinal compression, and document frequency ordering. It also supports indexing of various field types (integers, dates, keywords, etc.) and has expanded into vector search, enabling semantic similarity search by meaning rather than exact text matching.

AI-Powered Code Review Assistant: Automated Code Analysis with Spring AI and MongoDB

Thu, 14 May 2026 17:09:39 +0000

Code reviews catch bugs before they ship, but they take time. Most teams rely on manual review or basic linters that flag syntax issues but miss deeper problems like subtle resource leaks, poor exception handling, or security anti-patterns. Static analysis tools help, but they work with rigid rules that cannot generalize across code variations. A rule that catches catch (Exception e) {} will miss catch (Throwable t) { return null; }, even though both are the same underlying problem.

Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas — Part 1: RAG Foundation

Thu, 07 May 2026 19:22:11 +0000

This is the first article in a three-part series. Part 2 covers short-term and long-term memory; Part 3 introduces stateful workflow checkpointing with pause/resume.

The problem

It’s 2 a.m. Suddenly, an alert pops up indicating abnormal CPU usage on the payment services. The on-call engineer opens their laptop, logs into the monitoring dashboards, and begins the hunt. One by one, he searches the runbooks on Confluence, checks the Slack chats, and opens the GitHub wikis and documents shared during the design phase. By the time he finds any useful information, ten minutes have already passed.

When Should You Use a Cache With MongoDB?

Tue, 05 May 2026 15:23:30 +0000

From time to time, I’ll run a design review for an application being migrated from a relational database onto MongoDB, where the customer shares an architectural diagram showing a caching layer (typically Redis) sitting between the app server and MongoDB.

I like to keep the architecture as simple as possible—after all, each layer brings its own complexity and management costs—so I’ll ask why the caching layer is there. Of course, the answer is always that it’s there to speed up data access. This reveals a misunderstanding of both the reason why caching layers were created and what MongoDB provides.

Large-Scale ETL Pipeline Architecture

Fri, 01 May 2026 19:23:04 +0000

Modern data-driven systems. ETL pipelines are no longer simply scheduled background processes that run silently overnight, one after another. They are the backbone of real-time analytics, powering operational dashboards and recommendation systems. They enable machine learning workflows.

This evolution, while creating enormous benefits, has—with the increase in data volume and the decrease in latency tolerance—called into question the traditional sequential ETL approach. A bottleneck for the speed is now required.

Building a Personalized Content Delivery System

Thu, 23 Apr 2026 15:10:45 +0000

Recommendation engines have a reputation for requiring specialized ML infrastructure: matrix factorization pipelines, training jobs, and model serving layers. That is one way to do it, but not the only way. If your data already lives in MongoDB and your application runs on Spring Boot, you can build a practical recommendation system using tools you already have. MongoDB aggregation pipelines handle the scoring math server-side, and Atlas Vector Search adds semantic matching without a separate vector database.

Distributed Cache Invalidation Patterns

Tue, 21 Apr 2026 13:52:03 +0000

Caching is one of the most powerful tools developers have at their disposal for optimizing application performance. Caching systems can significantly reduce latency and reduce the load on databases or external systems by storing frequently accessed data as close as possible to the application layer. The result? Improved responsiveness and overall system usability.

In small monolithic applications, cache management is usually very simple. A service retrieves data from a database, stores it in memory, and fulfills subsequent requests by retrieving the data directly from the cache. When the data changes, the cache key is invalidated or updated.

CQRS in Java: Separating Reads and Writes Cleanly

Thu, 16 Apr 2026 20:25:28 +0000

What you’ll learn

How the MongoDB Spring repository can be used to abstract MongoDB operations
Separating Reads and Writes in your application
How separating these can make schema design changes easier
Why you should avoid save() and saveAll() functions in Spring

The Command Query Responsibility Segregation (CQRS) pattern is a design method that segregates data access into separate services for reading and writing data. This allows a higher level of maintainability in your applications, especially if the schema or requirements change frequently. This pattern was originally developed with separate read and write sources in mind. However, implementing CQRS for a single data source is an effective way to abstract data from the application and make maintenance easier in the future. In this blog, we will use Spring Boot with MongoDB in order to create a CQRS pattern-based application.

Building a Kotlin App with Spring Boot and MongoDB Search

Thu, 09 Apr 2026 15:21:05 +0000

One of my favorite activities is traveling and exploring the world. You know that feeling of discovering a new place and thinking, “How have I not been here before?” It’s with that sensation that I’m always motivated to seek out new places to discover. Often, when searching for a place to stay, we’re not entirely sure what we’re looking for or what experiences we’d like to have. For example, we might want to rent a room in a city with a view of a castle. Finding something like that can seem difficult, right? However, there is a way to search for information accurately using MongoDB Search.

Manage HTTP Sessions with Spring Session MongoDB

Tue, 07 Apr 2026 15:19:10 +0000

Spring Session MongoDB is a library that enables Spring applications to store and manage HTTP session data in MongoDB rather than relying on container-specific session storage. In traditional deployments, session state is often tied to a single application instance, which makes scaling across multiple servers difficult. By integrating Spring Session with MongoDB, session data can be persisted beyond application restarts and shared across instances in a cluster, enabling scalable distributed applications with minimal configuration.

Java Faceted Full-Text Search API Using MongoDB Atlas Search

Thu, 02 Apr 2026 16:27:30 +0000

This is going to be a fun, practical tutorial demonstrating how to build a Java faceted full-text search API (like the ones powering sites like Amazon)!

We’ll use an interesting dataset which showcases how you can effectively pair machine learning/AI-generated data with more traditional search to produce fast, cheap, repeatable, and intuitive search engines.

TL;DR: If you (like me!) are less about words and more about code, you can jump straight in. Check it out and run it locally like this:

MongoDB Search Score Breakdown

Tue, 31 Mar 2026 15:05:08 +0000

Full-text search powers all of our digital lives — googling for this and that; asking Siri where to find a tasty, nearby dinner; shopping at Amazon; and so on. We receive relevant results, often even in spite of our typos, voice transcription mistakes, or vaguely formed queries. We have grown accustomed to expecting the best results for our searching intentions, right there, at the top.

But now it’s your turn, dear developer, to build the same satisfying user experience into your Atlas-powered application.

Modeling One-to-Many Relationships in Java with MongoDB

Thu, 26 Mar 2026 15:50:30 +0000

In a relational database, modeling a one-to-many relationship is straightforward: you create two tables and connect them with a foreign key. When you need the data together, you write a JOIN. In MongoDB, you have a choice, and that choice has a direct impact on your application’s performance, scalability, and maintainability.

Consider a common scenario: a BlogPost that has many Comment objects. In Java, this is a natural List<Comment> field on the post. But when it comes time to persist that relationship in MongoDB, you need to decide how to store it. Should the comments live inside the blog post document? Or should they sit in their own collection, connected by references?

Clean Architecture with Spring Boot and MongoDB

Tue, 24 Mar 2026 16:43:14 +0000

Most Spring Boot tutorials tightly wire everything together. Controllers call services, services call repositories, and MongoDB annotations like @Document and @Field sit right next to your business logic. It works until you need to swap the database, test logic in isolation, or reuse domain rules in a different context.

Clean Architecture enforces one rule: source code dependencies always point inward. Your business logic never imports Spring or MongoDB classes. The database becomes a pluggable detail at the outermost layer, something you can replace without rewriting core application code.

Building Reactive Data Streams with Project Reactor

Thu, 19 Mar 2026 15:13:31 +0000

Creating Non-Blocking Streaming Endpoints for High-Throughput Applications

There are problems that only occur in production. Or rather, we only notice them in production.

We have created an application that exposes clean APIs, according to all standards. You have modeled the domain elegantly and efficiently: all load tests show reassuring data. CPU usage is reasonable, to say the least. However, there is a problem: when traffic exceeds a threshold, the system slows down. Response time latency becomes inconsistent; threads stack up on top of each other. Response times increase unpredictably, and application customers begin to complain about this situation.

Language Learning Flashcard System - Part 1

Tue, 17 Mar 2026 16:39:58 +0000

My native language is Spanish. I’ve been learning (and butchering) English my whole life. But at some point, I felt confident with English and wanted to learn Japanese. Big mistake. This is a totally different beast: three writing systems (Hiragana ひらがな, Katakana カタカナ and Kanji 漢字), a completely different grammar, no relation whatsoever with European languages… I needed help. And tools to learn. And one of them is Space Repetition System based apps.

Atlas Online Archive: Efficiently Manage the Data Lifecycle

Thu, 12 Mar 2026 14:55:00 +0000

Problem statement

In the production environment, in a MongoDB Atlas database, a collection contains massive amounts of data stored, including aged and current data. However, aged data is not frequently accessed through applications, and the data piles up daily in the collection, leading to performance degradation and cost consumption. This results in needing to upgrade the cluster tier size to maintain sufficient resources according to workload, as it would be difficult to continue with the existing tier size.