MongoDB on Windows Azure

MongoDB
July 19, 2012

This post originally appeared on the Microsoft Interoperability Blog.

Do you need to build a high-availability web application or service? One that can scale out quickly in response to fluctuating demand? Need to do complex queries against schema-free collections of rich objects? If you answer yes to any of those questions, MongoDB on Windows Azure is an approach you’ll want to look at closely.

People have been using MongoDB on Windows Azure for some time (for example), but recently the setup, deployment, and development experience has been streamlined by the release of the MongoDB Installer for Windows Azure. It’s now easier than ever to get started with MongoDB on Windows Azure!

MongoDB

MongoDB is a very popular NoSQL database that stores data in collections of BSON (binary JSON) objects. It is very easy to learn if you have JavaScript (or Node.js) experience, featuring a JavaScript interpreter shell for administrating databases, JSON syntax for data updates and queries, and JavaScript-based map/reduce operations on the server. It is also known for a simple but flexible replication architecture based on replica sets, as well as sharding capabilities for load balancing and high availability. MongoDB is used in many high-volume web sites including Craigslist, FourSquare, Shutterfly, The New York Times, MTV, and others.

If you’re new to MongoDB, the best way to get started is to jump right in and start playing with it. Follow the instructions for your operating system from the list of Quickstart guides on MongoDB.org, and within a couple of minutes you’ll have a live MongoDB installation ready to use on your local machine. Then you can go through the MongoDB.org tutorial to learn the basics of creating databases and collections, inserting and updating documents, querying your data, and other common operations.

MongoDB Installer for Windows Azure

The MongoDB Installer for Windows Azure is a command-line tool (Windows PowerShell script) that automates the provisioning and deployment of MongoDB replica sets on Windows Azure virtual machines. You just need to specify a few options such as the number of nodes and the DNS prefix, and the installer will provision virtual machines, deploy MongoDB to them, and configure a replica set.

Once you have a replica set deployed, you’re ready to build your application or service. The tutorial How to deploy a PHP application using MongoDB on Windows Azure takes you through the steps involved for a simple demo app, including the details of configuring and deploying your application as a cloud service in Windows Azure. If you’re a PHP developer who is new to MongoDB, you may want to also check out the MongoDB tutorial
on php.net.

Developer Choice

MongoDB is also supported by a wide array of programming languages, as you can see on the Drivers page of MongoDB.org. The example above is PHP-based, but if you’re a Node.js developer you can find a the tutorialNode.js Web Application with Storage on MongoDB over on the Developer Center, and for .NET developers looking to take advantage of MongoDB (either on Windows Azure or Windows), be sure to register for the free July 19 webinar that will cover the latest features of the MongoDB .NET driver in detail.

The team at Microsoft Open Technologies is looking forward to working closely with 10gen to continue to improve the MongoDB developer experience on Windows Azure going forward. We’ll keep you updated here as that collaboration continues!

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mongo, the MongoDB Shell

The MongoDB shell ( mongo ) is an extended SpiderMonkey (JavaScript) shell, so you can use it to execute JavaScript code just like you’re used to writing. The shell is best at things like testing out queries, examining specific records, configuring replica sets and sharding, and administrative tasks like creating indexes. Many objects in the shell have help functions in case you forget how to do things. When in doubt: > help > db.help() DB methods: db.addUser(username, password[, readOnly=false]) db.auth(username, password) ... > db.demo.help() DBCollection help db.demo.find().help() - show DBCursor help ... Some of the most common tasks the shell is needed for involve sending commands to the MongoDB server - like changing profiler settings, losetting. These kinds of functions are performed by using the shell to send database commands for example db.runCommand("shutdown") or db.runCommand({profile:-1}) . You can get info on these commands and how to use them from the shell like this: > db.listCommands() // print a listing of all the available commands > db.commandHelp("compact") // show details about how to use the "compact" database command. Sometimes, it’s useful to see how a particular shell function works - you can do this by leaving off the ( ), which forces the shell to print the source code of the function instead of just executing it. For example: > db.printReplicationInfo() // print info about current replication status ... > db.printReplicationInfo // print the source code of the printReplicationInfo function function () { ... } In the shell, the default behavior when executing a query is to print the output, unformatted. db.posts.find() { "_id" : ObjectId("4e697832c67f0623d40000ad"), "content" : "lorem ipsum" } . . . { "_id" : ObjectId("4e697832c67f0623d40000f0"), "content" : "four score and 7 years ago" } has more Try adding .pretty() to the function to format the output in a more readable matter, like this: db.posts.find().pretty() By default, queries with lots of results only print a limit of 20 results at a time - type it at the shell to show the next batch of 20. You can adjust this size limit by setting the value of DBQuery.shellBatchSize . When the mongo shell starts, it will read and execute any JavaScript code in the file .mongorc.js in your home directory. By adding your settings, utility functions, or tweaks into this file you can make them available in every shell session. For example, add this snippet of code to the file, and it will allow you to run the inspect() function on any javascript object in the shell, which will print information about its properties: function inspect(o, i) { if (typeof i == "undefined") { i = ""; } if (i.length > 50) { return "[MAX ITERATIONS]"; } var r = []; for (var p in o) { var t = typeof o[p]; r.push(i + "\"" + p + "\" (" + t + ") => " + (t == "object" ? "object:" + xinspect(o[p], i + " ") : o[p] + "")); } return r.join(i + "\n"); } You can also add snippets of code in your .mongorc.js file to do other cool stuff, like customize your shell prompt . In addition, you can execute any file containing JavaScript code from within the shell by calling load(filename) . Frequently it’s useful to execute some mongo shell commands without leaving your operating system shell - for example, to pipe the output to another process or redirect to a file. This can be done easily by just calling the shell command with the --eval option followed by the JavaScript you want to execute. Just be aware that since the output isn’t being automatically printed by the shell process with this approach, so if you need to print the JSON representation of documents in your queries, you will need to explicitly use the printjson() function to generate correct output. For example: $ mongo --eval "db.posts.find().forEach(function(x){printjson(x)})" { "_id" : ObjectId("4fbec0b9f3ecac6f43bc1c13"), "x" : 10 } ... When writing statements in the shell that leave an open bracket, parenthesis, or quote, hitting enter will prompt you with “…” for more input. So if you need to write a long block of code, you can let it span multiple lines: > for(var i=0;i<100;i++){ … If you screw up or want to cancel it and start over, just hit enter twice - the entire block of code will be aborted. A new shell feature available in versions 2.1.x and later is the ability to edit blocks of code using a text editor. Use the “edit” keyword with the name of a function, and it will invoke your editor with the block of source code for that function: > edit testfunc // now we get dropped into an editor where we can edit code for the function > testfunc // show the source of the function we just wrote function testfunc() { print("hello world!"); } > testfunc() hello world!

July 17, 2012

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Better Digital Banking Experiences with AI and MongoDB

Interactive banking represents a new era in financial services where customers engage with digital platforms that anticipate, understand, and meet their needs in real-time. This approach encompasses AI-driven technologies such as chatbots, virtual assistants, and predictive analytics that allow banks to enhance digital self-service while delivering personalized, context-aware interactions. According to Accenture’s 2023 consumer banking study , 44% of consumers aged 18-44 reported difficulty accessing human support when needed, underscoring the demand for more responsive digital solutions that help bridge this gap between customers and financial services. Generative AI technologies like chatbots and virtual assistants can fill this need by instantly addressing inquiries, providing tailored financial advice, and anticipating future needs. This shift has tremendous growth potential; the global chatbot market is expected to grow at a CAGR of 23.3% from 2023 to 2030 , with the financial sector experiencing the fastest growth rate of 24.0%. This shift is more than just a convenience; it aims to create a smarter, more engaging, and intuitive banking journey for every user. Simplifying self-service banking with AI Navigating daily banking activities like transfers, payments, and withdrawals can often raise immediate questions for customers: “Can I overdraft my account?” “What will the penalties be?” or “How can I avoid these fees?” While the answers usually lie within the bank’s terms and conditions, these documents are often dense, complex, and overwhelming for the average user. At the same time, customers value their independence and want to handle their banking needs through self-service channels, but wading through extensive fine print isn't what they signed up for. By integrating AI-driven advisors into the digital banking experience, banks can provide a seamless, in-app solution that delivers instant, relevant answers. This removes the need for customers to leave the app to sift through pages of bank documentation in search of answers, or worse, endure the inconvenience of calling customer service. The result is a smoother and user-friendly interaction, where customers feel supported in their self-service journey, free from the frustration of navigating traditional, cumbersome information sources. The entire experience remains within the application, enhancing convenience and efficiency. Solution overview This AI-driven solution enhances the self-service experience in digital banking by applying Retrieval-Augmented Generation (RAG) principles, which combine the power of generative AI with reliable information retrieval, ensuring that the chatbot provides accurate, contextually relevant responses. The approach begins by processing dense, text-heavy documents, like terms and conditions, often the source of customer inquiries. These documents are divided into smaller, manageable chunks vectorized to create searchable data representations. Storing these vectorized chunks in MongoDB Atlas allows for efficient querying using MongoDB Atlas Vector Search , making it possible to instantly retrieve relevant information based on the customer’s question. Figure 1: Detailed solution architecture When a customer inputs a question in the banking app, the system quickly identifies and retrieves the most relevant chunks using semantic search. The AI then uses this information to generate clear, contextually relevant answers within the app, enabling a smooth, frustration-free experience without requiring customers to sift through dense documents or contact support. Figure 2: Leafy Bank mock-up chatbot in action How MongoDB supports AI-driven banking solutions MongoDB offers unique capabilities that empower financial institutions to build and scale AI-driven applications. Unified data model for flexibility: MongoDB’s flexible document model unifies structured and unstructured data, creating a consistent dataset that enhances the AI’s ability to understand and respond to complex queries. This model enables financial institutions to store and manage customer data, transaction history, and document content within a single system, streamlining interactions and making AI responses more contextually relevant. Vector search for enhanced querying: MongoDB Atlas Vector Search makes it easy to perform semantic searches on vectorized document chunks, quickly retrieving the most relevant information to answer user questions. This capability allows the AI to find precise answers within dense documents, enhancing the self-service experience for customers. Scalable integration with AI models: MongoDB is designed to work seamlessly with leading AI frameworks, allowing banks to integrate and scale AI applications quickly and efficiently. By aligning MongoDB Atlas with cloud-based LLM providers, banks can use the best tools available to interpret and respond to customer queries accurately, meeting demand with responsive, real-time answers. High performance and cost efficiency: MongoDB’s multi-cloud, developer-friendly platform allows financial institutions to innovate without costly infrastructure changes. It’s built to scale as data and AI needs to grow, ensuring banks can continually improve the customer experience with minimal disruptions. MongoDB’s built-in scalability allows banks to expand their AI capabilities effortlessly, offering a future-proof foundation for digital banking. Building future-proof applications Implementing generative AI presents several advantages, not only for end-users of the interactive banking applications but also for financial institutions: Enhanced user experience encourages customer satisfaction, ensures retention, boosts reputation, and reduces customer turnover while unlocking new opportunities for cross-selling and up-selling to increase revenue, drive growth and elevate customer value. Moreover, adopting AI-driven initiatives prepares the groundwork for businesses to develop innovative, creative, and future-proof applications to address customer needs and upgrade business applications with features that are shaping the industry and will continue to do so, here are some examples: Summarize and categorize transactional information by powering applications with MongoDB’s Real-Time Analytics . Understand and find trends based on customer behavior that could positively impact and leverage fraud prevention , anti-money laundering (AML) , and credit card application (just to mention a few). Offering investing, budgeting, and loan assessments through AI-powered conversational banking experience. In today’s data-driven world, companies face increasing pressure to stay ahead of rapid technological advancements and ever-evolving customer demands. Now more than ever, businesses must deliver intuitive, robust, and high-performing services through their applications to remain competitive and meet user expectations. Luckily, MongoDB provides businesses with comprehensive reference architectures for building generative AI applications, an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a coordinated support system through the MongoDB AI Applications Program (MAAP) . By building AI-enriched applications with the leading multi-cloud developer data platform, companies can leverage low-cost, efficient solutions through MongoDB’s flexible and scalable document model which empowers businesses to unify real-time, operational, unstructured, and AI-related data, extending and customizing their applications to seize upcoming technological opportunities. Check out these additional resources to get started on your AI journey with MongoDB: How Leading Industries are Transforming with AI and MongoDB Atlas - E-book Our Solutions Library is where you can learn about different use cases for gen AI and other interesting topics that are applied to financial services and many other industries.

November 26, 2024