MongoDB World 2015 Recap

Dana Groce
June 9, 2015 | Updated: January 4, 2016
#Company

Thank you to our 2000+ attendees and 30+ sponsors who attended MongoDB World 2015.

Unable to make the event? We’ve put together our favorite highlights from this year’s conference.

MongoDB World Video Recap

5 New Capabilities in 3.2 Release

We previewed new capabilities coming in MongoDB 3.2, including a new connector for BI and Visualization tools (such as Tableau), encryption at rest, support for dynamic lookups, document validation, and mongoScout, a new graphical interface for MongoDB.

Announced Our Support of Girls Who Code

We’re excited to announce our support of Girls Who Code - a national non-profit organization that works to educate, inspire, and equip high school girls with the skills and resources to pursue opportunities in technical fields.

Beginning on June 1st, over the next 90 days when you download MongoDB, you’ll be given the option to have MongoDB donate $1 to Girls Who Code on your behalf. Read our blog post for full details.

2015 Innovation Award Winners Honored

The annual MongoDB Innovation Awards recognize organizations and individuals that are creating groundbreaking applications. These projects represent the best and most innovative work in the industry over the last year.

A full list of the winners and a brief description of their projects is outlined in our blog post.

Scalability Benchmarks Released

United Software Associates, Inc., an independent benchmarking and performance testing organization, released a benchmark report showing that MongoDB offers up to 13x better, more predictable scaling than niche NoSQL alternatives. More information can be found in the full report.

10,000,000 MongoDB Downloads

We’re thankful to our users for making us a part of their community. Because of your continued support, we’ve reached some new milestones:

  • 10 million downloads
  • 2,000 customers
  • 1,000 partners
  • 300,000 registered students

#MongoDBWorld

Thanks to our great attendees and speakers we were trending on Twitter. Here are some of our favorite conference tweets:

Facebook

Take a look at our Facebook album for highlights of our favorite MongoDB World moments.

Some of our favorite moments from #MongoDBWorld

Posted by MongoDB on Monday, June 1, 2015

Miss MongoDB World or want to relive your favorite talk? Watch the videos below:

MongoDB World 2015 Videos
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How to Change Your Deployment’s Storage Engine with Automation

A killer feature of MongoDB 3.0 is the new WiredTiger storage engine. It improves performance for many different kinds of workloads and its compression helps reduce storage costs. Check out some of our benchmarking results and some information straight from our docs . MMS Automation makes it easier than ever to take your MMAPv1 data and convert it to WiredTiger. How to Convert your Replica Set to WiredTiger Here’s my deployment: I’ve decided to convert my “newMmap” replica set to the WiredTiger storage engine. First I click on the wrench menu for my replica set to bring up the template modifying sidebar. I expand the “Advanced Options” pane, which presents me with the command line options this replica set is using: Now I just click the “Add Option” button, and on the menu that comes up select “engine” which is under “storage”. Once the “engine” option is added, I can select “wiredTiger” and press “Apply”. Once successfully applied, I get the “Unpublished Changes” frame, click “Review and Deploy” and see the nature of my changes: Once I confirm, the automation agent does the following: Connects to a secondary mongod process and shuts it down Empties its data directory (if there’s enough room, it makes a backup of the MMAPv1 files) Changes the config file for the process Starts the mongod process up and waits for it to resync Once the mongod process has completed its sync and is a healthy member of the replica set, it will start over at step 1 until all secondaries are done. Then it will do it once more with the primary, allowing the storage engine switch to lead to only one election. By the way, this process would be the same with a sharded cluster, single member replica set, or even a standalone node. And if I wanted to be more conservative about it, I could have used the wrench for a single process to convert it alone to WiredTiger to see how it behaves before moving the entire replica set. Once this is complete for all my nodes, I will see “Goal State”, and I can enjoy the benefits of WiredTiger without any downtime or ops work beyond a few mouse clicks.

June 5, 2015
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