One of the benefits of using MMS to back up MongoDB is that you have unlimited restores, which means you can use these restores in ways that might not have imagine.
For example, you can use MMS to seed a new replica set member. In fact, it can be faster with very large datasets or on replica sets under heavy load. It will only work if the latest snapshot (or the custom last 24 hour point in time snapshot) is still covered by the oplog window. Then you could seed the new member with the snapshot and then allow it to sync to the other replica set members. The instructions can be found in the docs.
Please note: if you use “Excluded Namespaces” on your MMS Backup (these exclude collections or entire databases from the snapshots), you will not be able to use MMS Backup snapshots to seed.
There are lots of other scenarios in which you might use MMS to build new environments. We covered some of them in a recent webinar. The slides and video are now available.
Dating at eHarmony - 95% Faster on MongoDB
Thod Nguyen, CTO of eHarmony, delivered a fascinating insight into how the world’s largest relationship service provider improved customer experience by processing matches 95% faster and increased subscriptions by 50% after migrating from relational database technology to MongoDB. The full recording and slides from Thod’s MongoDB World session are available now. eHarmony currently operates in North America, Australia and the UK. The company has a great track record of success - since launch in 2000, 1.2 million couples have married after being introduced by the service. Today eHarmony has 55m registered users, a number that will increase dramatically as the service is rolled out to 20 other countries around the globe in the coming months. eHarmony employs some serious data science chops to match prospective partners. Users complete a detailed questionnaire when they sign up for the service. Sophisticated compatibility models are then executed to create a personality profile, based on the user’s responses. Additional research based around machine learning and predictive analytics is added to the algorithms to enhance the matching of prospective partners. Unlike searching for a specific item or term on Google, the matching process used to identify prospective partners is bi-directional, with multiple attributes such as age, location, education, preferences, income, etc. cross-referenced and scored between each potential partner. In eHarmony’s initial architecture, a single monolithic database stored all user data and matches, however this didn’t scale as the service grew. eHarmony split out the matches into a distributed Postgres database, which bought them some headroom, but as the number of potential matches grew to 3 billion per day, generating 25TB of data, they could only scale so far. Running a complete matching analysis of the user base was taking 2 weeks. In addition to the problems of scale, as the data models became richer and more complex, adjusting the schema required a full database dump and reload, causing operational complexity and downtime, as well as inhibiting how quickly the business could evolve. eHarmony knew they needed a different approach. They wanted a database that could: Support the complex, multi-attribute queries that provide the foundation of the compatibility matching system A flexible data model to seamlessly handle new attributes The ability to scale on commodity hardware, and not add operational overhead to a team already managing over 1,000 servers eHarmony explored Apache Solr as a possible solution, but it was eliminated as the matching system requires bi-directional searches, rather than just conventional un-directional searches. Apache Cassandra was also considered but the API was too difficult to match to the data model, and there were imbalances between read and write performance. After extensive evaluation, eHarmony selected MongoDB. As well as meeting the three requirements above, eHarmony also gained a lot of value from the MongoDB community and from the enterprise support that is part of MongoDB Enterprise Advanced . Thod provided the audience with key lessons based on eHarmony’s migration to MongoDB: Engage MongoDB engineers early. They can provide best practices in data modeling, sharding and deployment productization When testing, use production data and queries. Randomly kill nodes so you understand behavior in multiple failure conditions Run in shadow mode alongside the existing relational database to characterize performance at scale Of course, MongoDB isn’t the only part of eHarmony’s data management infrastructure. The data science team integrates MongoDB with Hadoop, as well as Apache Spark and R for predictive analytics. The ROI from the migration has been compelling. 95% faster compatibility matching. Matching the entire user base has been reduced from 2 weeks to 12 hours. 30% higher communication between prospective partners. 50% increase in paying subscribers. 60% increase in unique web site visits. And the story doesn’t end there. In addition to eHarmony rolling out to 20 new countries, they also plan to bring their data science expertise in relationship matching to the jobs market – matching new hires to potential employers. They will start to add geo-location services as part of the mobile experience, taking advantage of MongoDB’s support for geospatial indexes and queries. eHarmony are also excited by the prospect of pluggable storage engines delivered in MongoDB 3.0 . The ability to mix multiple storage engines within a MongoDB cluster can provide a foundation to consolidate search, matches and user data. Whether you’re looking for a new partner, or a new job, it seems eHarmony has the data science and database to get you there. If you are interested in learning more about migrating to MongoDB from an RDBMS, read the white paper below: RDBMS to MongoDB Migration Guide
How the Austin Chapter of MongoDB’s Women’s Group Built Community During the Pandemic
MongoDB is on a mission to create an inclusive workplace where every single employee can thrive. With a range of established affinity groups — and new ones forming regularly — MongoDB looks for ways to amplify those groups’ efforts and help support their overall mission. When the COVID-19 pandemic forced offices to shut down and employees to work from home, our affinity groups were challenged to find creative ways to support and grow their now-remote communities. As leaders of the MongoDB Women’s Group Austin chapter, we share how we pivoted this challenge into an opportunity. First, What's the MongoDB Women's Group The MongoDB Women’s Group is a community of MongoDB employees identifying as women, nonbinary, or trans. Our mission is to create a bold, visible, and united force for gender equality. To help us get there, the MongoDB Women’s Group hosts monthly members-only meetings as well as events open to both members and allies. Relaunched in 2018, the Austin-based chapter connects women and allies in our Austin office to a community of local companies and women’s groups that can support their growth within the tech industry. Pre-COVID, we gained a lot of momentum with our events, which included a live speaker series in the office, yoga, and events focused on subjects such as fertility and imposter syndrome. When COVID-19 hit, we faced a new challenge: how do we create a sense of community for our members when everyone works completely remote? Although initially daunting, the challenge of organizing remote events was an opportunity in disguise. It enabled us to kick off a speaker series for all employees, featuring prominent women in leadership positions across the country. Enter Angie Brown, from The Home Depot. Angie was the first woman to join our remote speaker series, and we couldn’t have asked for a better person to kick it off. She began her career at The Home Depot in 1998 as an entry-level software developer and now is Vice President of Technology — Merchandising, leading a team that develops solutions to support cataloging, pricing, and assortment capabilities at the giant retail chain. She also helps to mentor aspiring leaders in a number of ways, including actively participating in Atlanta’s Women in Technology association. Here, we share some highlights from our fireside chat with Angie during which she discussed her career and provided advice on what women can do to set themselves up for success. Fireside Chat with Angie Brown MongoDB: What advice do you have for those just starting off in their careers? Angie Brown: Opportunities can look like problems and not everyone wants to run into the fire, but avoiding problems can really be a missed opportunity. That’s one important lesson I’ve learned throughout my career. Although you should have a general idea of where you want to go, you also need to be willing to flex. Things might unfold in ways you didn’t expect. If you’re too prescriptive, you might miss out on them. So, you need to find a way to strike a balance. MongoDB: You took a role in leadership fairly early. How did you change your skills and evolve as you moved up? AB: When I talk to people considering moving into management, I ask them to look at the job and determine if the required qualities and responsibilities would make them happy. It’s not just about the title and pay increase. When you pivot from being an individual contributor to being in a leadership role, servant leadership is a huge part of it. If you look at management as a way to control, you won’t be happy. If you look at it as a way to serve others and help them be successful, then you’ll find joy in that career shift. I didn’t prethink this when I first moved into management and had a little bit of an identity crisis. I was used to being the one who got things done. All of a sudden, my role and life was all about going to meetings, and I didn’t look at meetings as tangible work. I was over it. Where was the joy in this? If your joy comes from having your hands on the keyboard and needing to do things your way, then being in management would be like fitting a square peg in a round hole. At first I felt invalidated and unsure of myself because it wasn’t my hands on the keyboard. I had to work through that and do a little soul-searching. I reframed my thinking to be happy leading a team and helping them solve their problems, even if it meant I wasn’t solving them myself. I had a lightbulb moment when I moved into a director role when I realized I was still solving big problems by helping my team tackle them. There’s nothing wrong with where you find your joy and no judgement if your passion aligns as an individual contributor; we need amazing developers! Always be aware of the work itself and make sure it aligns with what you enjoy. MongoDB: How have mentors played a role in your success? AB: I wish I had invested in mentors much sooner. In the early stages of my career, I didn’t think I needed help and believed I could just figure it all out on my own. I thought asking for help was a sign of weakness. In hindsight, my mentors have absolutely formed part of who I am today. I don’t have just one mentor. Instead, I look at a topic and focus on finding a mentor for that specific topic. With that approach, I have ended up having a number of mentors. Thank you again to Angie Brown! We appreciate your insight and inspiration. If you are interested in joining MongoDB, explore our career opportunities and join an innovative team that is disrupting the database industry every day.