Working With Sensitive Data While Keeping It Secure: A Guide to Data Masking
Data masking is a well-established approach to protecting sensitive data in a database while still allowing the data to be usable. By subtly obscuring your data, either temporarily or permanently, data masking allows your engineering teams to use sensitive data while keeping it confidential, secure, and safe. Data masking can also make it easier to comply with regulations such as GDPR and HIPAA. MongoDB supports data masking through the core feature of an aggregation pipeline. In this paper, we explore: How to determine which data should be masked Construction of the aggregation pipeline How to safely expose masked data Additional resources about data masking Download the paper by filling out the form!
4 Common Misperceptions about MongoDB
One year ago, in the middle of the pandemic, Dev Ittycheria, the CEO of MongoDB, brought me on as Chief Technology Officer. Frankly, I thought I knew everything about databases and MongoDB. After all, I’d been in the database business for 32 years already. I’d been on MongoDB’s Board of Directors and used the products extensively. And of course I’d done my due diligence, met the leadership team, and analyzed earnings reports and product roadmaps. Even with all that knowledge, this past year as MongoDB’s CTO has taught me that many of my preconceived notions were just plain wrong. This made me wonder how many other people might also have the wrong impression about this company. And this blog is my attempt to set those perceptions straight by sharing my four major revelations of the last year. My first revelation is that MongoDB is not trying to become this generation’s relational database. For years I assumed that MongoDB basically wanted to be a better, more modern version of Oracle when it grew up. In other words, compete with the huge footprint of Oracle and other commercial RDBMSs that have been the industry archetype for so long. I was way off. The whole point of MongoDB is to leave all those forms of archaic, legacy database technology in the historical dust. This was never supposed to be an evolution, but instead a revolution. Our founders not only envisioned the world's fastest and most scalable persistent store, but also one that would be programmed and operated differently. The combination of embedded documents and structures combined with automatic high availability and almost-infinite distribution capability all add up to a fundamentally different way of working with data, building applications, and running those applications in production. Oracle and (SQL*Server, etc) still hang their hats on E.F. Codd’s 51-year old vision of rows and columns. To obtain high availability and distribution of data, you need add ons, options packages, bailing wire and duct tape. And you need a lot of database administrators. Not cheap. Even after all that, you’re still trailing the technological edge. This is how wrong I was. Our durable competitive advantages over these legacy data stores make competing with those products almost irrelevant. We instead focus on the modern needs of modern developers building modern applications. These developers need to create their own competitive advantage through language-native development, reliable deployments to production, and lightning fast iteration. And the world is noticing; just check out the falling slope of Oracle and SQL*Server and the rising slope of MongoDB on the db-engines website. Which brings me to my second revelation: MongoDB was built for developers, by developers. I always knew that MongoDB was exceedingly fast and easy to program against. One time while I was bored in a meeting (yes, it happens here as well!), I built an Atlas database, loaded it with 350MB of data, downloaded and learned our Compass data discovery tool, built-in analytics aggregation pipelines, and our Charts package, and embedded live charts in a web page. This took me all of 19 minutes, end to end. To build something like that for engineers , it just has to be built by engineers , ones that are free to focus on all the rough edges that creep into products as features are added. I was first exposed to software planning and management over 40 years ago, and my LinkedIn profile shows a pretty diverse tour around the industry. Now, one year in, I can emphatically state that engineering and product at MongoDB are both different and better than any company I’ve ever had the privilege to work at. Our executive leadership gives engineering and product broad brushstokes of goals and desired outcomes, and then we work together to come up with detailed roadmaps, updated quarterly, that meet those goals in the way we think best, with no micromanagement. And we’re not afraid of 3-5 year projects, either. For example, multi-cloud was more than three years in the making. Also unlike any other company I’ve been at, we embrace the creation and re-payment of tech debt, rather than sweeping it under the rug. We do this through giving our product and engineering teams huge amounts of context, delivered with candor and openness. And one more essential thing; we have an empowered program management team that improves processes (including killing them) as fast as we create them. In short, we paint the targets for our teams and let them decide how and when to shoot. They even design the arrows and bows. It’s true bottoms-up engineering. Our engineers feel valued and understood. And that, in turn, empowers them to develop features that make our customers feel valued and understood, like a unified query language, or real-time analytics and charting directly in the console, or multi-region/multi-cloud clusters where all the networking cruft is taken care of for you. And this brings me to my third revelation: MongoDB is built for even the most demanding mission critical applications. Fast? Yes. Easy? Of course. But mission-critical? That’s not how I saw MongoDB when I used Version 2 for a massive student data project 10 years ago. While it was the only possible datastore we could have chosen for the amount of data and the speed of ingestion and processing needed, it was pretty hard to set up and use in a 24 x 365 environment. MongoDB had gotten ahead of itself in the early 2010’s. There was a gap between our capabilities and the expectations of the market. And it was painful. Other databases had had more than 30 years to solidify their systems and operations. We’d had five. But with Version 3 we added a new storage engine, full ACID transactions, and search. We built on it with Version 4. And then again with Version 5, released this week at our .Live conference. I knew about all this progress intellectually of course when I joined, but not viscerally. I came to realize that the security, durability, availability, scalability, and operability our platform offers (of course in addition to all the features that developers love too) was ideal for architecting fast-moving enterprise applications. And I found the proof in our customer list. It reads like a Who’s Who of major global banks, retailers, and telecommunications companies, running core systems like payments, IoT applications, content management, and real-time analytics. They use our database, data lake, analytics, search, and mobile products across their entire businesses, in every major cloud, on-premises, and on their laptops. And that leads me to my fourth and final revelation. MongoDB is no longer just a database. Of course, the database is still the core. But MongoDB now provides an enterprise-class, mission-critical application data platform. A cohesive, integrated suite of offerings capable of managing modern data requirements across even the most sprawling digital estates, and scaling to meet the level of any company’s ambition, without sacrificing speed or security. Since the day I was first introduced to MongoDB’s products, I’ve had tremendous respect and admiration for the teams and their work. After all, I’m a developer, first and foremost. And it always felt like they “got” me. But had I known then what I know now, I would have jumped on this train a long time ago. In fact, I might have camped out on their doorstep with my resume in hand. And who knows? Maybe a bunch of people reading this will do just that, and have their own revelations about how fulfilling and exciting it can be to be at a great company, with a great culture, producing great products. I’ll write another letter a year from now, and let you know how it’s going then. In the meantime, please reach out to me here, or at @MarkLovesTech .
MongoDB Atlas 101 eWorkshop
Join the MongoDB Team for a free lunch and virtual hands-on training! This eWorkshop will help you use MongoDB Atlas, our fully-managed database, to increase developer and DevOps productivity by streamlining operations and management. MongoDB Atlas ensures that when you deploy your clusters in the cloud provider of your choice, your databases are run according to operational and security best practices. Register below to learn how you will gain a competitive advantage when moving to MongoDB Atlas. Topics Include: Create a database using MongoDB Atlas Secure the database & Load sample data Perform basic MQL CRUD operations Create indexes and learn about general index building guidelines Create aggregation pipeline queries Begin using MongoDB Charts and create visual representations of your data And so much more! We will be announcing a competition during the session in which you can win prizes!
DocumentDB, MongoDB and the Real-World Effects of Compatibility
If there’s confusion in the market for document databases, it probably has to do with how the products are marketed. AWS claims that DocumentDB, its document model database, comes “with MongoDB compatibility.” But the question of how compatible DocumentDB actually is with MongoDB is worth considering. DocumentDB merely emulates the MongoDB API while running on top of AWS’s cloud-based relational database, Amazon Aurora. And it’s an inconsistent imitator at best, because it fails 62% of MongoDB API correctness tests . Even though AWS claims compatibility with MongoDB 4.0, our tests have concluded that its emulator is a mishmash of features going back to MongoDB 3.2, which we released in 2015. The result is that DocumentDB lacks many of the features that come standard in MongoDB. We’ve already published a side-by-side comparison of the feature sets for each solution. Instead of covering the same ground here, we'll explain how some of those differences play out in real-world scenarios. DocumentDB vs. MongoDB head-to-head comparison Scaling writes, partitioning data, and sharding Native sharding enables you to scale out databases horizontally, across multiple nodes and regions. Atlas offers elastic vertical and horizontal scaling to smooth consumption. DocumentDB does not scale writes or partition data beyond a single node. In order to ensure consistency, MongoDB uses concurrency control measures to prevent multiple clients from modifying the same piece of data simultaneously. Replicate and scale beyond a single region A number of factors are driving the need to distribute workloads to different geographic regions. In some cases, it’s to reduce latency by putting data closer to where it’s being used. In other cases, it’s to store data in a specific geographic zone to help meet data localization requirements. Finally, there’s the need to ensure the availability of data when there’s an outage of an entire AWS region. The flexibility to replicate and move workloads as needed is increasingly seen as a business requirement. But by default DocumentDB limits you to just 15 replicas and constrains you to a single region. Newly introduced Global Clusters may look like an answer, but much like “MongoDB compatibility,” it’s potentially misleading. The Global Clusters feature more closely resembles multi-region replication since it only allows writes to single primaries instead of being able to write to multiple regions. It also requires manual reconfiguration to recover from failures, making it a partial solution, at best. MongoDB Atlas allows true global cluster configurations so you can deliver capabilities to all your users around the world. At a click of a button, you can place the most relevant data near local application servers across more than 80 global regions to ensure low-latency reads and writes. By being able to define a geographic location for each document, your teams are able to more easily meet local privacy and compliance measures. It’s also an insurance policy against being locked into a single public cloud provider. High resilience, rapid failover, retryable writes For critical applications, every second of downtime represents a loss of revenue, trust, and reputation. Rapid failover to a different geographic area is necessary when recovery time objectives (RTO) are measured in seconds. DocumentDB failover SLAs can be as high as two minutes, and multi-region failover is not available. With MongoDB, failover time is typically five seconds, and failover to a different region or cloud provider are also options. Write errors can be as costly as downtime. If a write to increment a field is duplicated because a dropped connection failed to notify the client that the write was executed, that extra increment can be very costly depending on what it represents. With retryable writes, a write can be sent multiple times but applied exactly once. MongoDB has retryable writes. DocumentDB doesn’t. Integrated text search, geospatial processing, graph traversals Integrated text search saves time and improves performance because you can run queries across multiple sources. With DocumentDB, data must be replicated to adjacent AWS services, which increases cost and complexity. MongoDB Atlas combines integrated text search, graph traversals, and geospatial processing features into a single API and platform. Integrated search with MongoDB Atlas helps drive end user behavior by serving up relevant results based on what users are looking for or what businesses want to direct them toward. Hedged reads Geographically distributed replica sets can also be used to scale read operations and intelligently route queries to the replica set that’s closest to the user. Hedged reads is a function that automatically routes queries to the two closest nodes (measured by ping distance), returning results from the fastest replica. This helps minimize situations where queries are waiting on a node that’s already busy. DocumentDB doesn’t offer hedged reads, and it’s more restricted in terms of the number of replica sets it allows and the ability to place workloads in different regions. MongoDB gives you more flexibility when distributing data geographically for hedged reads since it leverages all of the major public cloud providers. Online Archive Putting data in cold storage can be a death knell if accessing it again is too cumbersome or slow. With online archiving, you can tier data across fully managed databases and cloud object storage and query it through a single endpoint. Online archiving automatically archives historical data while reducing operational and transactional data storage costs without compromising on query performance. MongoDB has it. DocumentDB doesn’t. Integrated querying in the cloud Running separate queries for separate data stores can drain resources and slow queries. The best solution is being able to query and analyze data across all the different databases and storage containers at once. You can do this with integrated querying, where you run a single query to analyze live cloud data and historical data together and in-place for faster insights. With DocumentDB, you have to replicate data to adjacent AWS services. With MongoDB, you can query and analyze data across cloud datastores and MongoDB Atlas in its native format. You can also run powerful, easy-to-understand aggregations through a unified API for a consistent experience across data types. On-demand materialized views When you create aggregations, the results are usually put into a new collection every time you create it. The entire collection is regenerated each time you create the aggregation. This process consumes CPU and I/O. With the $merge stage, you can just update the generated results collection rather than rebuild it completely. $merge lets you incrementally update the collection every time you run it. To update it, all you need to do is run the aggregation again and it will update all the values in place. $merge gives you the ability to create collections based on an aggregation and update those collections efficiently. This functionality allows users to create on-demand materialized views, where the content of the output collection is incrementally updated when the pipeline is run. MongoDB has this capability. DocumentDB does not. Rich data types The decimal data type is critical for storing very large or small numbers, like financial and tax computations, where it’s necessary to emulate decimal rounding exactly. DocumentDB does not support decimal data types or, in turn, lossless processing of complex numeric data, which is a problem for financial and scientific applications. MongoDB does support rich data types like Decimal128, giving you 128 bits of high precision decimal representation. Client-side field-level encryption Client-side field-level encryption (FLE) reduces the risk of unauthorized access or disclosure of sensitive data, like personally identifiable information (PII) and protected health information (PHI). Fields are encrypted before they leave the application, which protects data while in transit over the network, in database memory, at-rest in storage, in backup repositories, and in system logs. DocumentDB does not offer client-side FLE. MongoDB’s client-side FLE provides among the strongest levels of data privacy and security for regulated workloads. Platform agility In addition to the feature sets described here, one of the biggest differences between DocumentDB and MongoDB is the degree of freedom you have to move between different platforms. AWS offers seamless movement and minimal friction between services within its own ecosystem. MongoDB makes it easy to replicate data or move workloads to any cloud provider, giving you complete flexibility within the AWS platform as well as outside of it — whether it’s a self-managed MongoDB instance on cloud infrastructure, a full on-premises deployment, or just a local development instance on an engineer’s laptop. Try MongoDB Atlas for free today!
Congratulations to the 2021 Innovation Award Winners!
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 the company received entries across dozens of industries, ranging from disruptive, emerging start-ups to industry-leading global enterprises. We are thrilled to announce the 12 winners who are being honored this year during MongoDB.live. William Zola Award: Michael Höller - An independent software architect, system integrator, and backend developer, Michael is the first MongoDB Champion to earn the rare Evergreen forum badge for his unwavering support of the MongoDB Community. Dual-certified as both a MongoDB Developer and DBA, Michael generously shares his expertise with community members of all levels in the MongoDB forums. He also organizes the DACH Virtual Community User Group, and even finds time to #BuildTogether with MongoDB employees on presentations and in chats during online streams. Michael is one of the first-timers, consulting on MongoDB projects since 2014. Customer-first Award: Luma Health - As COVID-19 took the world by storm, Luma Health jumped into action to partner with health systems and providers to leverage their platform to run some of the largest mass vaccination sites and help clinics scale from hundreds to thousands of appointments, ultimately leading to nearly 2 million vaccination appointments. Data for Good Award: Journey Foods - This company solves food science and supply chain inefficiencies with software in order to help companies feed 8 billion people better. To date, Journey Foods has established a database of over 11 billion ingredient insights. From Batch to Real Time Award: CSX - A leading provider of transportation and supply chain solutions, CSX is redefining freight rail. Embracing event-driven architecture, the company has improved engagement with safety information produced by Positive Train Control (PTC) systems by putting PTC data on MongoDB. Leveraging MongoDB, CSX receives the data real-time – enabling smarter and faster decision making and better ensuring safety regulations are met for the company’s around-the-clock operations. Front Line Heroes Award: Ahmad Awais for The “CORONA CLI” Project - Awais built a CLI command-line tool to track COVID-19 in March 2020. As COVID-19 spread, the project termed “corona-cli” became the number one trending repository on GitHub. To date, this project has served several billions of API requests making COVID stats accessible throughout the world with 53 different releases and extensive functionality built/contributed by 15+ developers. Going Global Award: Riot Games - Founded in 2009 to change the ways games were developed, Riot has created the most-played PC game in the world and expanded to 20+ offices worldwide in only 12 years. A game platform developer and his team migrated their data to MongoDB Atlas to manage B2B billing and player IP validation data for all of their games globally. Industry Transformation Award: American Airlines - As a network air carrier, American’s purpose is to care for people on life’s journey. During COVID-19, American Airlines passionately pursued efficiencies, particularly those enabled by technology. American Airlines created an operational data layer on MongoDB in the cloud for critical flight information, which enabled other services to move to the cloud and consume data from the modern cloud-based data fabric. Jackpot Award: Cisco Systems - Cisco is the worldwide leader in technology that powers the Internet. This global brand completed the Cloud native migration of its highly critical Commerce Quoting platform, which serves more than 225K users and 4M application hits daily worldwide. The result has been no application downtime for releases, improved performance, lower TCO, and significantly better developer productivity. Savvy Start-Up Award: Blerp - The audio expression platform that makes it easy to enhance any moment with sound clips. Millions of Blerps are being shared on their two largest integrations on Twitch and Discord. Unbound Award: Yodel - Yodel is an independently owned parcel carrier, delivering around 190 million parcels each year for many of the UK’s leading retailers and businesses. An early adopter of Realm Sync following its GA release in February 2021, Yodel uses MongoDB to sync parcel-scanning data from employee devices up to Atlas - and in the opposite direction, pushing down large data volumes to devices via the MongoDB Kafka Connector. By streamlining the process of scanning parcels and reducing the time drivers need to spend in service centers, Yodel expects to achieve increased productivity and cost savings. Certified Professional of the Year Award: Sydney Herrera - After becoming certified and while assisting a large governmental organization in a mainframe modernization effort - which involved transforming multiple massive, disparate mainframe datastores into a cohesive and application-focused MongoDB data warehouse, Sydney was faced with the challenge of assisting developers with building efficient applications. He created a tool called proactive query analyzer (PQA) that was rolled out into the organization. PQA is an automated tool that analyzes queries sent to MongoDB and provides feedback and suggestions before queries are implemented to aid developer teams. For the People Award & Innovator of the Year Award: The Department for Work and Pensions (DWP) is the UK’s biggest public service department responsible for distributing over £190 billion annually in welfare, pensions and child maintenance to over 20 million citizens. With an unprecedented spike in demand due to the COVID-19 crisis, the Universal Credit platform was able to scale seamlessly, underpinned by MongoDB databases, to meet the tenfold spikes in claims from people who needed DWP’s support. The information contained in the above descriptions was provided by the relevant award winners or obtained from publicly available information.
MongoDB Partner of the Year Award Winners 2021
The last 12 months have been a remarkably challenging time, with widespread changes affecting the entire world—and certainly the IT industry—like never before. However, the pandemic has also brought customers and partners further together to solve common problems, and in particular, one major trend we’ve seen accelerated is the move toward cloud modernization. At MongoDB, we’re committed to help our customers evolve to this next stage of necessary innovation and are so grateful to our 1000+ Partners who help make it happen. A handful of strategic partners went above and beyond and I would like to congratulate Featurespace, Zaloni, Carahsoft, TCS, PeerIslands, Alibaba, AWS, and Google Cloud as our Partners of the Year! I’m excited to have shared this year’s winners during my Partner Keynote at MongoDB.live. You can still head to MongoDB.live to watch it on demand. Independent Software Vendor (ISV) Partner of the Year Feature Space is a disruptive company in the security space and the inventor of Adaptive Behavioral Analytics and Automated Deep Behavioral Networks technology for fraud and financial crime management. Trusted by the world’s largest banks, insurance companies and gaming organizations, Feature Space embeds MongoDB as their database platform, for both on-premise and SaaS deployment offerings. Learn more about the partnership here . Technology Partner of the Year Zaloni is a MongoDB data supply chain and modernization partner that helps its customers catalogue and transform their data to enhance analytics and real-time applications. A recent engagement with a fleet management client required Zaloni to move and modernize four billion records of data built on SQL to new microservices in the cloud. Using MongoDB Atlas, Zaloni helped their client successfully scale and reach new levels of customer satisfaction. Learn more about the partnership here . Reseller Partner of the Year Carahsoft is a trusted, long-standing partner and they continue to be one of MongoDB’s largest resellers in the world. Carahsoft’s depth and reach in the Public Sector market helps government agencies leverage Open Source technologies to drive innovation, maximize cost efficiencies and achieve success for their digital modernization initiatives. They’ve been critical to growing MongoDB’s agency, civilian and DOD business and we’re excited for what’s next. Learn more about the partnership here . Global System Integrator Partner of the Year Tata Consultancy Services , a leading multinational IT services and consulting company, leverages its IP-based solutions to accelerate and optimize service delivery. TCS MasterCraft™ TransformPlus uses intelligent automation to modernize and migrate enterprise-level mainframe applications to MongoDB’s leading-edge architecture and database. Together, TCS and MongoDB simplify and accelerate the customer journey to the cloud. Learn more about the partnership here . Boutique System Integrator Partner of the Year PeerIslands is a boutique consultancy focused on helping clients accelerate product development with cloud-based solutions. An invaluable MongoDB partner with expertise in ecommerce and inventory management modernization, PeerIslands has helped MongoDB’s ISV and retail customers modernize, moving software built for on-prem to SaaS environments more conducive to cloud environments. Learn more about the partnership here . Cloud (Emerging) Partner of the Year Alibaba Cloud is one of MongoDB’s largest Cloud OEM partners in the world. In our second year of our partnership with Alibaba Cloud as an authorized MongoDB-as-a-service solution, MongoDB has seen some of our strongest adoption numbers yet in mainland China. With this partnership, Alibaba Cloud ensures end-to-end management and support for customers on current and future versions of MongoDB, with the ability to escalate bug fixes and support issues on their behalf. Users of Alibaba Cloud’s platform offering receive easy access to the latest MongoDB features and capabilities, backed by comprehensive support from Alibaba Cloud and MongoDB, and we remain thrilled to scale this partnership. Learn more about the partnership here . Cloud (Co-Sell) Partner of the Year AWS provides a massive global cloud infrastructure that allows customers to rapidly innovate and iterate. Over the past year, MongoDB and AWS sellers came together to help customers modernize, accelerate consumption of EDPs, and close on major cross-industry and cross-vertical deals around the world. We’ve partnered together to help customers build modern, event-driven serverless applications. And as recently as April 2021, we launched a new AWS Quick Start for MongoDB Atlas, which allows AWS customers to quickly and easily launch a basic MongoDB Atlas deployment from the AWS CLI or console, taking advantage of AWS CloudFormation’s seamless automation. We also released integrations for Atlas with Amazon EventBridge, Amazon Kinesis, AWS App Runner, AWS PrivateLink, AWS Wavelength, and more in the last year. With additional integrations in the pipeline, we know there’s so much more to come with our partnership. Learn more about AWS and MongoDB here . Cloud (Marketplace) Partner of the Year Google Cloud views the database as an essential building block of cloud infrastructure. Since launching MongoDB Atlas on Google Cloud Marketplace over the past two years, our partnership has seen rapid adoption and acceleration across industries such as gaming, retail, healthcare, financial services, and automotive. In the last 12 months, we have integrated Atlas with more Google services, including DataStream, BigQuery, DataFlow, Cloud Run, App Engine, and Cloud Functions, helping our joint customers innovate faster. Additionally, Google Cloud’s mainframe modernization solutions now support MongoDB Atlas and help customers convert legacy COBOL code on mainframes into modern Java-based applications built on MongoDB. Together, G4 and MongoDB Atlas accelerate the modernization and migration process for organizations moving their business-critical workloads to the cloud. More and more of Google Cloud’s customers are choosing to run MongoDB Atlas for a variety of needs, such as managing large-scale product catalogs of popular e-commerce websites, building great customer experiences by unifying disparate pieces of data, or building modern global web and mobile applications. Learn more about the partnership here .
The 5 Phases of Banking Modernization
Accelerate your digital transformation while minimizing risk If banks are eager to modernize, and their customers are demanding modern banking experiences, what’s taking banks so long to move away from the legacy systems that are restricting their ability to innovate? And why do so many legacy modernization efforts fall short? In this white paper, we’ll answer those questions and provide a path forward using an iterative approach to banking modernization. Our five-phase approach enables banks to see rapid improvements in a relatively short time while preserving the legacy components for as long as they’re needed to keep the business running. Download this white paper to gain a detailed understanding of the following: The differences between application-driven modernization, data-driven modernization, and a blended approach (iterative modernization) -How to create an operational data layer (ODL) to act as a bridge between the legacy system and the new architecture -How to rank data sources and applications to create a blueprint for modernizing We hope you enjoy our white paper, if you would like to learn more, take a look at our MongoDB for Financial Services page.
Fine-Tune Relevance in MongoDB Atlas Search with Function Scoring and Synonyms
MongoDB Atlas Search is an embedded full-text search solution in MongoDB Atlas that gives developers a seamless and scalable experience for building fast, relevance-based application features. We announced its general availability last year at MongoDB.live 2020 and over the past year we’ve introduced many new features, including a visual index builder, search query tester, custom analyzers , and wildcard path queries . This year at MongoDB.live 2021 , we’re excited to highlight two new capabilities that help developers tune the relevance of search results. See how easy it is to get started with MongoDB Atlas Search in this demo video by Marcus Eagan, Senior Product Manager for Atlas Search. Building relevance into search results Understanding the behavior of your users is essential when thinking about search result relevance. People don’t always tell you what they want, and they sometimes use words or phrases that don’t match your content exactly. To cover these scenarios, you can use full-text search features like function scoring and synonyms. Influence search rankings with function scoring There are often multiple factors that influence how search results should be ranked. For example, let’s say you have a restaurant finder application. The explicit inputs are things like the user’s location and what they’re searching for, but what’s implied is that they likely want to see highly rated restaurants or ones with more reviews. What’s Cooking: a sample restaurant finder application using MongoDB Atlas Search Function scoring allows you to influence the order of results returned by manipulating the score of each result. In Atlas Search, that means you can use a numeric field in a document and apply a mathematical expression to it. For example, you might want to increase the score of restaurants that are sponsored or have higher star ratings. This can easily be accomplished within the same search query by simply adding the function option to the score parameter of your query. Learn more about how to use function scores in our developer tutorial . Show results for more search queries with synonyms Synonyms are often used to define terms that are semantically similar to each other to improve search results. For example, someone searching for “noodles” might want to find results for “spaghetti”, “chow mein”, or “pad thai”. Synonyms can also help with typos, especially on mobile and small keyboards. In Atlas Search, you can define collections of synonyms for a search index via the API. Synonyms can be explicit (one-way) or equivalent (two-way). Explicit synonyms are good for defining relationships between terms that are subsets of each other, like the noodle example above: “spaghetti”, “chow mein”, and “pad thai” are all explicit synonyms for “noodles”, but not each other (you don’t want results for “chow mein” in a search for “spaghetti”). Equivalent synonyms are often used for terms that have regional variations or are otherwise interchangeable both ways, like soda and pop, or Kleenex and tissues. What's next for Atlas Search Developers are increasingly turning to full-text search to make content more discoverable and relevant for application end users. With Atlas Search, we hope to not only make building full-text search easier, but also more powerful and expressive. Join our community to ask questions and find out what other developers are building with Atlas Search and let us know what you think we should build next in our feedback forums .
Introducing Serverless Instances on MongoDB Atlas, Now Available in Preview
Since we first launched MongoDB Atlas in June 2016, we’ve been working towards building a cloud database that not only delivers a first-class developer experience, but also simply just works: no setup, tuning, or maintenance required. Over the years, this has led to features like auto-scaling and click-to-create index suggestions , along with numerous optimizations to our automation engine. We’re excited to announce that we’re one more step closer to realizing this vision with the introduction of serverless databases on MongoDB Atlas . Think less about your database, and more about your data Serverless computing and NoOps have emerged as popular trends in modern application development. Cloud functions are commonly used to power business logic in applications, and many teams rely on completely automated IT operations. The appeal of serverless technology is hard to deny: elastic scaling eliminates the need for upfront resource provisioning and ongoing maintenance, and consumption-based pricing means paying only for resources that are used. It abstracts and automates away many of the lower-level infrastructure decisions that developers don’t want to have to learn or manage so they can focus on building differentiated features. When it comes to databases, compute and storage resources have traditionally been tightly coupled. Applying a serverless model to databases means decoupling them and changing the way engineering teams think about infrastructure. Rather than asking a developer to predict an application’s future workload patterns, break them down into individual resource requirements, and then map them to arbitrary units of database instance sizes, serverless databases offer a much simpler experience: define where your data lives, and get a database endpoint you can use. This not only streamlines the database deployment process, it also eliminates the need to monitor and adjust capacity on an ongoing basis. Developers are free to focus on thinking about their data rather than their databases, and leave the lower-level infrastructure decisions to intelligent, behind-the-scenes automation. Serverless instances on MongoDB Atlas All customers now have the ability to create a serverless database on MongoDB Atlas with the introduction of serverless instances , announced at MongoDB.live 2021 . It’s incredibly easy to get started: simply choose a cloud region and you’ll receive an on-demand database endpoint for your application. Serverless instances always run on the latest MongoDB version so you never have to worry about backwards compatibility or upgrades. You can view and manage them using the same UI and API as your existing database deployment on Atlas (i.e., clusters), and they come with end-to-end security, continuous uptime, metrics, alerts, and backups. Watch this demo of how to create a serverless instance on MongoDB Atlas This new deployment type will be available in preview, so it doesn’t yet support all of the features and capabilities available on clusters today. It’s ideal for infrequent or sparse workloads, or development and testing workloads in the cloud. If you’re running a high-throughput production workload, dedicated clusters are still the recommended deployment option. A hands-free database experience This is the first of many releases, and we have an ambitious roadmap ahead. We will continue to invest in making working with data ever more seamless and delightful for developers, from adding support for newer Atlas capabilities like full-text search and native visualizations , to even more intelligent automation and optimization. Create your own serverless instance on MongoDB Atlas. Try the Preview If you have feedback or questions, we’d love to hear them! Join our community forums to meet other MongoDB developers and see what they’re building with serverless instances. What's next for MongoDB Atlas Serverless instances are just one of many new additions to Atlas that we hope will make developers’ lives easier. Earlier this year, we added index removal suggestions to Performance Advisor and released a quick start for creating and managing clusters via the command line with the MongoDB CLI . We are also working on integrations with Vercel and Netlify , two popular serverless application platforms, to give developers an easy way to get started on MongoDB Atlas. What would make your development experience better on MongoDB Atlas? Share your feature requests in our feedback forums .