MongoDB on Windows Azure

MongoDB
July 19, 2012
#Releases

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

Next →

Collaborating to Build AI Apps: MongoDB and Partners at Google Cloud Next '24

From April 9 to April 11, Las Vegas became the center of the tech world, as Google Cloud Next '24 took over the Mandalay Bay Convention Center—and the convention’s spotlight shined brightest on gen AI. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Between MongoDB’s big announcements with Google Cloud (which included an expanded collaboration to enhance building, scaling, and deploying GenAI applications using MongoDB Atlas Vector Search and Vertex AI ), industry sessions, and customer meetings, we offered in-booth lightning talks with leaders from four MongoDB partners—LangChain, LlamaIndex, Patronus AI, and Unstructured—who shared valuable insights and best practices with developers who want to embed AI into their existing applications or build new-generation apps powered by AI. Developing next-generation AI applications involves several challenges, including handling complex data sources, incorporating structured and unstructured data, and mitigating scalability and performance issues in processing and analyzing them. The lightning talks at Google Cloud Next ‘24 addressed some of these critical topics and presented practical solutions. One of the most popular sessions was from Harrison Chase , co-founder and CEO at LangChain , an open-source framework for building applications based on large language models (LLMs). Harrison provided tips on fixing your retrieval-augmented generation (RAG) pipeline when it fails, addressing the most common pitfalls of fact retrieval, non-semantic components, conflicting information, and other failure modes. Harrison recommended developers use LangChain templates for MongoDB Atlas to deploy RAG applications quickly. Meanwhile, LlamaIndex —an orchestration framework that integrates private and public data for building applications using LLMs—was represented by Simon Suo , co-founder and CTO, who discussed the complexities of advanced document RAG and the importance of using good data to perform better retrieval and parsing. He also highlighted MongoDB’s partnership with LlamaIndex, allowing for ingesting data into the MongoDB Atlas Vector database and retrieving the index from MongoDB Atlas via LlamaParse and LlamaCloud . Guillaume Nozière - Patronus AI Andrew Zane - Unstructured Amidst so many booths, activities, and competing programming, a range of developers from across industries showed up to these insightful sessions, where they could engage with experts, ask questions, and network in a casual setting. They also learned how our AI partners and MongoDB work together to offer complementary solutions to create a seamless gen AI development experience. We are grateful for LangChain, LlamaIndex, Patronus AI, and Unstructured's ongoing partnership. We look forward to expanding our collaboration to help our joint customers build the next generation of AI applications. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with these and other AI partners.

April 23, 2024