Using MMS Backup to Seed New Environments

MongoDB
July 10, 2014
#Cloud

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.

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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

July 4, 2014
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Congratulations to the 2023 APAC Innovation Award Winners

I’m thrilled to announce the nine winners of the 2023 MongoDB APAC Innovation Awards . The MongoDB Innovation Awards honor projects and people who dream big. They celebrate the groundbreaking use of data to build compelling applications and the creativity of professionals expanding the limits of technology with MongoDB. This year, we have broken the awards down regionally to celebrate organizations in APAC, from startups to industry-leading enterprises, across a wide variety of industries, who are delivering big results. We are delighted to announce the winners below: 2023 MongoDB APAC Innovation Award Winners: Positive Impact Open Government Products Open Government Products (OGP) is an in-house team of engineers, designers, and product managers, who is a part of the Singapore Government, and is responsible for building technologies for the public good. OGP used MongoDB’s developer data platform, MongoDB Atlas to create its digital form builder, FormSG. Used by the Singapore government and public healthcare institutions, FormSG securely collects data from residents and businesses and helps public officers to create digital government forms in minutes. It eliminates the use of paper forms and the manual process of transcribing physical documents, which had raised concerns around data privacy and protection. During the pandemic, FormSG enabled public officers to collect more than 100,000 daily temperature declarations nationwide. Today, FormSG has served more than 120,000 public officers from 155 agencies and it has created more than 500,000 digital forms to help the government collect data on travel and health declarations by visitors to the country, applications for COVID-19 swab tests, and applications for financial assistance. Organization Transformation Bendigo and Adelaide Bank Bendigo and Adelaide Bank is one of Australia’s largest banks, with around 7,000 employees helping more than 2.2 million customers achieve their financial goals. The bank has been on a multi-year journey of transformation using MongoDB's developer data platform to improve efficiency and deliver a better customer experience as they fulfill their vision to become Australia’s bank of choice. Recently, the cloud team launched Ready-Set-MongoDB (or RSM). This event-driven framework allows developers to streamline the consumption of internal or external APIs, and applies data transformations and storage automatically within a MongoDB collection of their choice. Using MongoDB Atlas Search, the bank also enabled developers to gain insights across its multi-cloud deployments, identifying cost savings, and providing inventory information to account owners and technical stakeholders. Within the first 18 months of launching these programmes, the automation had saved the organization more than 1,100 developers days. It also helped reduce human involvement, removed stale data, and allowed engineers to focus on the things that matter. The development of Ready-Set-MongoDB is ongoing and improving, as new Bendigo multi-cloud challenges arise and new MongoDB products are released. The application is a perfect representation of how Bendigo's Technology Department is using modern technology, rapid development, and innovation-led problem solving to drive organizational transformation. Heroes in Health Redcliffe Lifetech Private Limited Over the last few years, Redcliffe Labs has become India's fastest growing technology-driven diagnostics service provider. Redcliffe Labs is on a mission to serve 500 Million Indians by 2030 with fusion of technology and world- class laboratories. The company already serves thousands of people daily, with more than 73 labs and close to 1500 walk-in centers across 180 cities. Redcliffe Labs has relied on MongoDB Atlas’ flexible document model to power its innovative Smart Health Report, a patient resource that provides a number of indicators and trackers to gauge holistic health. The MongoDB developer data platform's best in class security, compliance, and privacy controls allows Redcliffe's team to confidently handle even the most sensitive patient data. MongoDB Atlas takes care of many of the traditional database management challenges, which means that developers can spend their time building diagnostics for patients, rather than managing databases. Redcliffe Labs is focusing on incorporating next-generation technologies in the diagnostics space with an AI platform that will make Interactive Diagnostics reports, Advanced Health Profiling and more detailed Diagnostics and Health Alerts. Industry Disruptor Cathay Pacific Cathay Pacific , Hong Kong’s home carrier operating in more than 60 destinations worldwide, has been on an impressive journey to become one of the very first airlines to create a truly paperless flight deck. Until recently, a flight from Hong Kong to New York would require a crew to review more than 150 pages of finely printed text and charts before their flight and make ongoing updates throughout the trip. In 2019, Cathay Pacific conducted the first zero paper flight, removing 50kg of manuals, charts, maps, and flight briefing paperwork. They achieved this enormous feat with the help of one seamless and highly customized iPad application: Flight Folder. Built on MongoDB Atlas, Flight Folder is designed to improve the pilot briefing experience. MongoDB helped consolidate dozens of different information sources into one place, and made it possible for flight crews to easily share their experiences with others. It also included a digital refueling feature that helps crews become much more efficient with fueling strategies – saving significant flight time and costs. The use of MongoDB Device Sync enables seamless syncing and no data loss even when the app goes on- and offline mid-flight. Since the Flight Folder launch, Cathay Pacific has completed more than 340,000 flights with full digital integration in the flight deck. In addition to the greatly improved flight crew experience, flight times have been reduced, and digital refueling saves eight minutes of ground time on average. All these efficiencies have helped the company avoid the release of 15,000 tons of carbon. From Batch to Real-Time Adani Digital Labs Adani Digital Labs is the India-based digital innovation arm of the larger Adani group. The lab’s team's mission is to create one single platform – a SuperApp called AdaniOne – to empower a billion stories in India. To address several use cases and the huge scale that will be required by the superapp, the Adani Digital team selected MongoDB Atlas as its the main transactional database that will further enhance the application. A key component of the app is how it can bring together disparate data in order to provide a single view of activity across the application. In the first process, developers had taken out the data in batches and sent it to their database However, this was too slow and unpredictable as far as business requirements are concerned. Also, the consolidated view of customer history, orders, inventory, and supply chain network updates was likely to impact their customer's ability to generate revenue. Therefore, in order to find a better solution, Adani Digital Labs built a more modern architecture in line with MongoDB. Using MongoDB's Change Streams and the data platform's native Kafka connector, they created an event-based architecture that pushes the data out in real-time for analysis. Adani Digital Labs is still in the early phases of the SuperApp's rollout and collaborating with MongoDB as its developer data platform continues to help the firm to grow and deliver insights in real time. Industry 4.0 Dongwha Founded in 1948, the Dongwha Group has evolved from a singular focus on the wood and timber industry into a global leader across a number of sectors including building materials, chemicals and media. As part of its wider digital transformation strategy, Dongwha required smarter factories that would improve and optimize their production efficiency. Dongwha built an innovative Smart Factory Software platform that collects and analyzes data to enhance quality and production management capabilities. Originally, the platform was built with the community version of MongoDB. However, in order to scale and adapt, the team recently migrated to MongoDB Atlas in the cloud. This enabled them to store large volumes in the fastest and most secure way, optimize their solution for time series data, and make it easy to run machine learning across their data. Dongwha completed the migration seamlessly, without any disruption or downtime to their factories, and it has now been launched across five different sites. Over the last year, the application has significantly increased its availability and reliability while performance has improved by as much as 6x . As they look to the future, Dongwha plans to roll out the software to more of its international factories. Digital Native myBillBook India is home to more than 60 million small and medium-sized businesses (SMBs) but only a small portion of those SMBs are taking advantage of digitization and many still operate using pen and paper. In addition, many businesses in India still struggle with fluctuations in internet services, outages, and latency. FloBiz is on a mission to change that with myBillBook , a one-stop solution that helps SMBs create professional invoices, manage stock, collect payments, automate reminders through smart banking, engage with their customers, manage staff attendance and payroll and generate more than 25 business reports for accounting and decision making. The app is also mobile-first, so businesses can access them from their mobile devices and allows users to manage billing and inventory in both online and offline environments. The myBillbook app is powered by MongoDB Atlas, providing the flexible and scalable foundation for the business to do everything from building new features to performing complex analytical queries. In addition, MongoDB Realm, the mobile database within the data platform, supports offline usage and syncing to ensure there is never data loss or functionality for users due to poor internet connection. Because of its success in supporting customers with business critical operations, more than 6.5 million business owners in India are now using myBillbook for their billing, accounting, collection and business growth. Customer Focused KASIKORN Business-Technology Group Established in 1945, Kasikornbank (KBank) is one of the largest and oldest banks in Thailand. Their mission is to strive towards service excellence and empower every customer’s life and business. One of KBank’s subsidiaries, KASIKORN Business-Technology Group (KBTG) , developed a mobile banking application – MAKE by KBank. MongoDB Atlas’ flexibility and ease of development enabled MAKE’s development team to choose the best type of database for its tasks, to automate data tiering with Atlas Online Archive, and to reduce hours spent on operational maintenance. With more time to focus on delivering new innovations to customers, they created unique features like Cloud Pocket which can allocate funds into unlimited customizable pockets for separate usage. They also built Pop Pay, a feature that allows users to easily search for nearby friends and transfer money by clicking their profile picture as well as “Expense Summary" a spending analysis services that helps inform and manage users’ financial habits. As of January 2023, MAKE has acquired more than 1 million users, and increased the number of transactions in MAKE from 900,000 to more than 7.5 million in a span of one year. Massive Scale China Mobile China Mobile provides mobile voice and multimedia services via its nationwide mobile telecommunications network across mainland China and Hong Kong. It is the world's largest mobile network operator by total number of subscribers. The telecommunications leader is using MongoDB to support one of its largest and most critical push services, which sends out billing details to more than 1 billion users every month. Prior to MongoDB, the tech team relied on Oracle, but as the user numbers increased, performance degraded. Despite large investments, it was still taking too long to do basic requests like finalize and deliver bills to users. In 2019, after comprehensive testing, China Mobile migrated to MongoDB. By taking advantage of MongoDB's native sharding, they were able to improve performance by 80% and go from 50 Oracle machines, to just 12 machines for the same workload. The service now handles all current requirements and is set up to scale with future growth. With the support of MongoDB, China Mobile is growing steadily,with more than 168 million monthly users and has one of the highest customer satisfaction scores in the China Mobile group.

March 30, 2023