MongoDB Updates
The newest releases and freshest updates
Optimize Atlas Spending with the Billing Dashboard in Atlas Charts
Managing infrastructure spend across cloud services is a universal challenge for organizations around the world. Teams want to know that their investments in services are driving business value. How much are we spending every month? Where are we spending the most within a given product or service? Are there outliers in our spending we need to take a closer look at? Are there changes that could reduce costs without a negative impact? These are just a few of the many questions you can answer about your Atlas services with the new billing dashboard built on Atlas Charts. Most modern software services give you the basic tools needed to track spending. Information such as: monthly cost, next payment date, billing period, cost by product and/or sub product, past invoice access, and saved payment methods are all common. Atlas itself offers all of these options and more – you can read all about this in our billing page documentation . For digging in even further, that’s where the billing dashboard on Atlas Charts becomes useful. The billing dashboard provides a dedicated space for additional insights into your Atlas spending and it requires minimal setup. If you’re ready to dive in, check out our Github repository for setup instructions, or read on below to learn more about how the dashboard works and how it can help your team find new insights into your Atlas spending. Visualizing your billing data with Atlas Charts The billing dashboard is available as an open source Github project , containing a Realm app to ingest billing data from the Atlas Billing API, an Atlas Charts dashboard to visualize the data, and scripts to help you set this up within your own Atlas organization. If you’re not familiar, Realm is our fully managed solution for edge-to-cloud sync and backend services for quickly building web and mobile apps. Atlas Charts is our native data visualization tool. Charts gives teams the ability to build dashboards from cluster data to quickly answer general questions about your business, to investigate specific questions or issues in your application data, to share dashboards with stakeholders on your team, or even to embed visualizations into your internal or external applications. Take a look at the video below to learn more about setup and see some of the built-in functionality and benefits: The solution comes with a ready-built dashboard that will answer many common questions about your cloud spend. Aggregate metrics such as current monthly spend, your previous month’s spend, and yearly spend provide an overview of your data. Detailed charts break down your projects, clusters, and product categories (i.e. instance, backup, and data transfer) across different time horizons to take your analytics a level deeper. Many Atlas customers further customize charts, and build new charts and dashboards leveraging the same underlying billing data. The dashboard gives you complete flexibility to track the metrics that are most important to your business. For organizations with many projects and clusters inside of Atlas, this flexibility can be invaluable in identifying opportunities to optimize the use of MongoDB. The set up process was recently updated to greatly simplify the steps required to get started. Check out the Github repo for step-by-step instructions. Upleveling your billing insights, for free Gaining additional insight into your Atlas billing will make your team confident that you are doing all you can to best manage your infrastructure spending with MongoDB Atlas. We want you to get the most out of MongoDB, while spending the least! If you need help getting started, feel free to reach out to your customer success manager (CSM). MongoDB’s customer success team works with customers every day to ensure they get the most of the Atlas platform. If you don’t have a CSM and would like to learn more about support at MongoDB, get in touch and we can talk more. Interested in using Atlas Charts for other insights? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.
Introducing the MongoDB 5.2 Rapid Release
MongoDB allows you to address a wide variety of data workloads using a single API. Our latest rapid release — MongoDB 5.2 — builds upon this vision with improvements to query ergonomics, enhancements to time series collections (introduced in MongoDB 5.0), scaling, operational resilience, and new capabilities that allow teams to execute more sophisticated analytics in-place. Columnar Compression for Time Series Collections Introduced in MongoDB 5.0, time series collections allow you to easily ingest and work with time series data alongside your operational or transactional data, without the need to integrate a separate single-purpose database into your environment. The 5.2 Rapid Release introduces columnar compression for time series collections in MongoDB. Time series use cases — whether it’s device monitoring, trendspotting, or forecasting — require that new data be inserted into the database for every measurement. In cases where data is being continuously created, the sheer amount of data can be staggering, making it difficult to manage an ever growing storage footprint. To help teams achieve high performance while maintaining resource efficiency, we’ve introduced a few capabilities to time series collections. New columnar compression for time series collections will help teams dramatically reduce their database storage footprint by as much as 70% with best-in-class compression algorithms such as delta, delta-of-delta encoding, simple-8b, run-length encoding, and more. For teams using MongoDB Atlas, Atlas Online Archive support for time series collections (introduced with the 5.1 Rapid Release) allows them to define archiving policies to automatically move aged data out of the database and into lower-cost, fully managed cloud object storage. Better Query Ergonomics and Point in Time Queries for Operational Analytics More efficient queries make developers’ lives easier. With the MongoDB 5.2 Rapid Release, we are introducing new operators and enhancements that will increase productivity, query performance and reduce the number of queries needed to unlock insights. This also allows teams to push more work down to the database, reducing the amount of code developers need to write and maintain while limiting the amount of data that has to be pushed back and manipulated in applications. New accumulators & expression to sort arrays MongoDB 5.2 brings new operators that streamline your queries. The $top and $bottom operators allow you to compute the top and bottom elements of a data set and return related fields within the same query without complex logic. For example, let’s say that you were analyzing sales performance and wanted the top salesperson for every region, including their sales. These new operators can help you retrieve the results in a single dataset, including any additional fields from the original dataset. {$group: { _id: "$region", person: {$top: {output: ["$name", “$sales”], sortBy: {“sales”:1}} }} Result: { {_id:’amer’, person: [‘Daniel LaRousse’, 100000]}, {_id:’emea’, person: [‘John Snow’, 1]}, {_id:’latam’, person: [‘Frida Kahlo’, 99]} } We are also introducing $maxN , $minN , and accumulators such as $firstN , $lastN , which return elements while taking into account the current order of documents in a dataset. A highly requested feature, the new $sortArray expression allows you to sort the elements in an array directly in your aggregation pipeline in an intuitive, optimized way. The input array can be as simple as an array of scalars or as complex as an array of documents with embedded subdocuments. Let’s say you had previously sorted product reviews based on timestamp but now want to sort based on user rating. You can now easily do this using the $sortArray operator to change the sorting criteria with no additional code required. Sorting an array of integers $sortArray: { input: [3, 1, 4, 1, 5, 9], sortBy: 1 } Result: [1, 1, 3, 4, 5, 9] Sorting arrays of documents { "team": [ { "name": "kyle", "age": 28, "address": { "street": "12 Baker St", "city": "London" } }, { "name": "bill", "age": 42, "address": { "street": "12 Blaker St", "city": "Boston" } } ] A simple sort: "name" ascending {$project: { _id: 0, result: { $sortArray: { input: "$team", sortBy: {name: 1} } } } Output: { "result": [ { "name": "bill", "age": 42, "address": { "street": "12 Blaker St", "city": "Boston" } }, { "name": "kyle", "age": 28, "address": { "street": "12 Baker St", "city": "London" } } ] } Long-running snapshot queries now generally available Your applications can now execute complex analytical queries against a globally and transactionally consistent snapshot of your live, operational data. Even as data changes beneath you, MongoDB preserves point-in-time consistency of the query results returned to your users without you having to implement complex reconciliation controls back in your code. The default for long-running snapshot queries in MongoDB Atlas is 5 minutes but can be changed with the help of our support team. Queries can span multiple shards, unlocking analytics against large, distributed data sets. By routing long-running queries to secondaries, you can isolate analytics from transactional queries with both workloads served by the same cluster, avoiding slow, complex, and expensive ETL to data warehouses. Query results can be returned directly to the application or cached in a materialized view, providing your users with low latency access to deep analytics. Typical uses include end-of-day reconciliation and reporting, along with ad-hoc data exploration and mining. All of these use-cases can now be served directly from your transactional data layer, dramatically simplifying the data infrastructure you need to serve multiple classes of workloads. Improving Resilience with Faster Initial Sync via File Copy Initial sync is how a replica set member in MongoDB loads a full copy of data from an existing member. This process occurs when users are adding new nodes to replica sets to improve resilience, or to reduce read latency or improve read scalability with secondary reads. Initial sync is also commonly used to recover replica set members that have fallen too far behind the other members in a cluster. Prior to 5.2, logical initial sync was the only option available for performing an initial sync. With logical initial sync, every collection in the source node is scanned and all documents are then inserted into matching collections in the target node (with indexes being built at the time of document insertion). However, users and customers leveraging logical initial sync, especially those trying to synchronize large data sizes, have reported frustratingly long initial sync times. Starting with the 5.2 Rapid Release, we have added the option of initial sync via file copy to significantly improve the performance of initial syncs. With this method, MongoDB will copy files from the file system of the source node to the file system of the target node. This process can be faster than a logical initial sync, especially at larger data sizes. In our testing with a 630 GB dataset, initial sync via file copy was nearly four times (4X) faster than a logical initial sync on the same dataset. This new capability builds upon the continuous enhancements we’ve made to improve resilience and scalability, including the ability for initial sync to automatically resume after a network failure, and allowing users to specify their preferred initial sync source – both introduced with MongoDB 4.4. For more information, see the documentation on initial sync . Enhanced Developer Experience with MongoDB Analyzer for .NET And finally, we’re pleased to announce the release of the MongoDB Analyzer for .NET , which enables C# developers to more easily troubleshoot queries and aggregations, and prevent errors from cropping up at runtime. The MongoDB Analyzer builds on earlier releases of the MongoDB .NET driver. It makes it easier and faster for developers to use MongoDB with C#, including a fully redesigned LINQ interface. Previously, C# developers were able to interact with MongoDB idiomatically using Builders or LINQ expressions, but there was no easy way to see before running their code if those mapped correctly to the MongoDB Query API. Downloadable as a NuGet package, the MongoDB Analyzer allows developers to easily see if their queries and aggregations correspond to expressions supported by the Query API. By surfacing unsupported expressions during code development, the MongoDB Analyzer ultimately improves developer productivity and reduces the pain of debugging. Getting Started with MongoDB 5.2 MongoDB 5.2 is available now. If you are running Atlas Serverless instances or have opted in to receive Rapid Releases in your dedicated Atlas cluster, then your deployment will be automatically updated to 5.2 starting today. For a short period after upgrade, the Feature Compatibility Version (FCV) will be set to 5.1; certain 5.2 features will not be available until we increment the FCV. MongoDB 5.2 is also available as a Development Release for evaluation purposes only from the MongoDB Download Center. Consistent with our new release cadence announced last year, the functionality available in 5.2 and the subsequent Rapid Releases will all roll up into MongoDB 6.0, our next Major Release scheduled for delivery later this year. Safe Harbour Statement The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.
Introducing Pay as You Go MongoDB Atlas on AWS Marketplace
We’re excited to introduce a new way of paying for MongoDB Atlas . AWS customers can now pay Atlas charges via our new AWS Marketplace listing . Through this listing, individual developers can enjoy a simplified payment experience via their AWS accounts, while enterprises now have another way to procure MongoDB in addition to privately negotiated offers, already supported via AWS Marketplace. Previously, customers who wanted to pay via AWS Marketplace had to commit to a certain level of usage upfront. Pay as you go is available directly in Atlas via credit card, PayPal, and invoice — but not in AWS Marketplace, until today. With this new listing and integration, you can pay via AWS with no upfront commitments . Simply subscribe via AWS Marketplace and start using Atlas. You can get started for free with Atlas’s free-forever tier , then scale as needed. You’ll be charged in AWS only for the resources you use in Atlas, with no payment minimum. Deploy, scale, and tear down resources in Atlas as needed; you’ll pay just for the hours that you’re using them. Atlas comes with a Basic Support Plan via in-app chat. If you want to upgrade to another Atlas support plan , you can do so in Atlas. Usage and support costs will be billed together to your AWS account daily. If you’re connecting Atlas to applications running in AWS, or integrating with other AWS services , you’ll be able to see all your costs in one place in your AWS account. To get started with Atlas via AWS Marketplace, visit our Marketplace listing and subscribe using your account. You’ll then be prompted to either sign in to your existing Atlas account or sign up for a new Atlas account . Try MongoDB Atlas for Free Today!
Log4Shell Vulnerability (CVE-2021-44228, CVE-2021-45046 and CVE-2021-45105) and MongoDB
When MongoDB became aware of the Log4Shell vulnerability ( CVE-2021-44228 , CVE-2021-45046 and CVE-2021-45105 ), we began an investigation to determine whether there had been any impact to our products, services or internal systems. As of December 20, 4pm ET, the following is the status of our investigation: table { border-spacing: 0px; } th, td { border-bottom: 1px solid black; margin: 0px; padding: 4px; } . Product Status MongoDB Atlas Search Update - Dec 18: Confirmed log4j removal from production Environment. Atlas Search is no longer affected. Dec. 17: Patched to log4j v.2.16.0 in response to CVE-2021-45046 Dec. 12: Patched to log4j v.2.15.0 in response to CVE-2021-44228 No evidence of exploitation or indicators of compromise prior to the patches were discovered. All other components of MongoDB Atlas (including Atlas Database, Data Lake, Charts) Not affected MongoDB Enterprise Advanced (including Enterprise Server, Ops Manager, Enterprise Kubernetes Operators) Not affected MongoDB Community Edition (including Community Server, Cloud Manager, Community Kubernetes Operators) Not affected MongoDB Drivers Not affected MongoDB Tools (including Compass, Database Shell, VS Code Plugin, Atlas CLI, Database Connectors) Not affected MongoDB Realm (including Realm Database, Sync, Functions, APIs) Not affected We continue to monitor our system and services for any updates. If you have any questions, please visit the MongoDB Community Forums . If you are a MongoDB Commercial Support subscriber and have questions related to your deployments, please open a support case.
Introducing the MongoDB Atlas Data API, Now Available in Preview
As the leading application data platform , we’re hyper-focused on accelerating and simplifying how developers leverage their application data. This has led to the introduction of features like serverless instances and the Atlas Triggers that minimize the operational burden associated with traditional database workloads. Today, we’re excited to announce the next step forward in this mission with the introduction of the MongoDB Atlas Data API – a fully managed, REST-like API for accessing your Atlas data. The Data API makes it easy to perform CRUD and aggregations on your data in minutes and allows you to query MongoDB from your backend in any language, without the need for drivers. The next level of data access Organizations are increasingly relying on operational data layers to build distributed architectures like microservices for their modern applications to speed-up development and stay competitive in rapidly changing markets. These stacks often require scalable, highly available, and secure access to the data layer. The most popular way to architect these data services is to build APIs that communicate with MongoDB data over HTTPS using REST or similar protocols. However, creating a custom-built API typically takes a lot of time and effort. It's a painful process that introduces unnecessary operational burdens like provisioning additional servers, connection management, and scaling. With the Atlas Data API, customers can generate a fully managed, REST-like API for their Atlas data in seconds. Developers no longer need to worry about the underlying infrastructuring of their APIs, and instead can enjoy the efficiency of intuitive, out-of-the box data access, while still being able to leverage the always-on and highly available qualities of Atlas as the underlying database. This unlocks a whole new level of developer productivity for use cases that were previously time consuming to accomplish – such as building data-centric microservices, simplifying access from serverless functions, and integrating with third party services . The API even has built-in support for aggregation pipelines to use with services like Atlas Search . Try the Atlas Data API All customers now have the ability to enable the Data API for their Atlas deployment. We invite you to try it out today with a new or existing Atlas account. It’s incredibly easy to get started: simply choose the cluster you’d like to connect to and generate an API key. That’s all it takes to set up and start accessing Atlas data. Have questions? Check out our documentation or head over to our community forums to get answers from fellow developers. What's next for the Atlas Data API This preview release is just the beginning. Support for services like Data Lake and Serverless Instances will be added over the coming months. And, long term, we see the Data API as the next step in our journey to abstract and automate infrastructure decisions – to help developers build the future faster. Atlas Data API documentation can be found here
Announcing Google Private Service Connect (PSC) Integration for MongoDB Atlas
We’re excited to announce the general availability of Google Cloud Private Service Connect (PSC) as a new network access management option in MongoDB Atlas . Announced alongside the availability of MongoDB 5.1 , Google Cloud PSC is GA for use with Altas. See the documentation for instructions on setting up Google Cloud PSC for Atlas, or read on for more information. MongoDB Atlas is secure by default . All dedicated Google Cloud clusters on Atlas are deployed in their own VPC. To set up network security controls, Atlas customers already have the options of an IP Access List and VPC Peering . The IP Access List in Atlas is a straightforward and secure connection mechanism, and all traffic is encrypted with end-to-end TLS. But you must be able to provide static public IPs for your application servers to connect to Atlas, and to list those IPs in the Access List. If your applications don’t have static public IPs or if you have strict requirements on outbound database access via public IPs, this won’t work for you. The existing solution to this is VPC Peering, which allows you to configure a secure peering connection between your Atlas cluster’s VPC and your own Google Cloud VPC(s). This is easy, but the connections are two way. Atlas never has to initiate connections to your environment, but some Atlas users don’t want to use VPC peering because it extends the perceived network trust boundary. Access Control Lists (ACLs) and IAM Groups can control this access, but they require additional configuration. MongoDB Atlas and Google Cloud PSC Now, you can use Google Cloud Private Service Connect to connect a VPC to MongoDB Atlas. Private Service Connect allows you to create private and secure connections from your Google Cloud networks to MongoDB Atlas. It creates service endpoints in your VPCs that provide private connectivity and policy enforcement, allowing you to easily control network security in one place. This brings two major advantages: Unidirectional: connections via PSC use a private IP within the customer’s VPC, and are unidirectional. Atlas cannot initiate connections back to the customer's VPC. This means that there is no extension of the perceived network trust boundary. Transitive: connections to the PSC private IPs within the customer’s VPC can come transitively from an on-prem data center connected to the PSC-enabled VPC with Cloud VPN . Customers can connect directly from their on-prem data centers to Atlas without using public IP Access Lists. Google Cloud Private Service Connect offers a one-way network peering service between a Google Cloud VPC and a MongoDB Atlas VPC Meeting security requirements with Atlas on Google Cloud Google Cloud PSC adds to the security capabilities that are already available in MongoDB Atlas, like Client Side Field-Level Encryption , database auditing , BYO key encryption with Google Cloud KMS integration , federated identity , and more. MongoDB Atlas undergoes independent verification of security and compliance controls , so you can be confident in using Atlas on Google Cloud for your most critical workloads. To learn more about configuring Google PSC with MongoDB Atlas, visit our docs . If you’re already managing your Atlas clusters with our API, you can add a private endpoint with the documentation here . For more information about Google Cloud Private Service Connect, visit the Google Cloud docs or read the Introducing Private Service Connect release announcement. Try MongoDB Atlas for free today!
Introducing the MongoDB 5.1 Rapid Release
Arriving just a few months after the General Availability of 5.0, MongoDB 5.1 is our first Rapid Release which brings more native time series enhancements, richer analytics, new security options, and overall improvements to platform resilience and developer productivity. Launching alongside MongoDB 5.1 are new capabilities in Atlas Search which will make it easier for users to build fast and rich application search experiences. MongoDB 5.1 marks our accelerated release cadence designed to get new database features and improvements into your hands faster than ever before. MongoDB 5.1 and all future rapid releases will be fully supported on MongoDB Atlas and are available as development releases from our Download Center. Native Time Series Enhancements With optimized time series collections, clustered indexes, and window functions, MongoDB 5.0 made it faster, easier, and lower cost to serve the industry’s fastest growing, data intensive use cases such as IoT platforms and real-time financial analytics. Now with MongoDB 5.1, you can globally distribute your time series applications and further simplify their development: More developer velocity Time series collections can now take advantage of MongoDB’s native sharding to horizontally distribute massive data sets and co-locate nodes with data producers to support local write operations and to enforce the data sovereignty controls. It is common for time series data to be uneven, for example a sensor goes offline and several readings are missed. But in order to perform analytics and ensure correctness of results data needs to be continuous. With densification you can now handle missing data better and build time series apps and analytics faster putting less burden on the developer. Time series collections now also support delete operations . While most time series applications are append-only, users need to be able to invoke their right to erasure so we are giving developers an easy way to comply with modern data privacy regulations. Complete data lifecycle From medical sensors to market data fluctuations, time series means hundreds of millions data points per day. You need to process these massive volumes fast, distill valuable insights then continue to retain the full data set for regulatory purposes - possibly for years - all without incurring skyrocketing costs and data movement complexity. With Atlas Online Archive support for time series, now available in preview, you can do exactly that and seamlessly and economically manage your entire time series data lifecycle. Simply define your own archiving policy, and Atlas handles all data movement for you by tiering aged time series data out of your database into lower cost, fully managed cloud object storage. Rather than delete anything, you can retain all your time series data, preserving the ability to query it at any time alongside your live data for long term trend analytics and machine learning, or for compliance purposes. Support for online archiving is available for MongoDB 5.0 and above. Broader platform support for Time Series Data Our native time series capabilities are supported across the entire MongoDB application data platform making it easy to work with time series data in any context. You can now create time series collections directly from Atlas Data Explorer, MongoDB Compass or MongoDB for VS Code. With support for date binning, date filtering options, and value comparison, Atlas Charts lets you create graphs and dashboards from any Atlas times series collection, easily share insights, and embed visualizations into your applications for a rich user experience. Richer and More Flexible Analytics and Full-Text Search Many developers start out with MongoDB for their operational use cases, and then expand to leverage our platform's versatility in powering analytics and search as well. MongoDB 5.1 includes new features and enhancements that make it easier to unlock insights from your data and improve user experience. Cross-shard joins and graph traversals For most transactional and operational workloads, the document data model largely eliminates the need to join data from different collections. This is because related data can be embedded in sub-documents and arrays within a single, richly structured document – following the principle that what is accessed together is often best stored together. However analytical applications can sometimes require joins to be executed – for example bringing together customers and orders from separate collections. Through the $lookup aggregation pipeline stage, you can have the database join collections for you. The $graphLookup stage gives you the ability to traverse related data, performing “friend-of-friend” type queries to uncover patterns and surface previously unidentified connections in your data. In MongoDB 5.1 we now allow you to use $lookup and $graphLookup to combine and analyze data that is distributed across shards which was not previously possible. Our design gives you even more precision in your code by enabling you to target individual shards as needed. However you don’t need to understand sharding or even know your collection is sharded to run these queries as there is no new syntax for developers to learn. Materializing results for operational analytics The $merge and $out aggregation stages can be used to write the results of an aggregation pipeline in order to create a new collection or create/update an on-demand materialized view . These stages enable users to reduce processing overhead by reading pre-computed results instead of re-running the aggregation each time, and by writing only incremental results when the aggregation results change. Users often want to run resource-intensive analytical queries on secondary nodes in order to avoid performance impacts on the primary — but since only primaries can serve writes, aggregations including $out or $merge could not previously run on a secondary node. Soon, such pipelines will run, performing their query execution work on a secondary node, then automatically directing any writes to the primary. This allows you to offload computationally expensive analytics work to secondary nodes while still being able to materialize the results of that work. This will be accessible via drivers in their upcoming releases. Full-Text Search Facets: now in public preview Faceted search allows users to filter and quickly navigate search results by categories and see the total number of results per category for at-a-glance statistics. With our new facet operator , facet and count operations are pushed down into Atlas Search’s embedded Lucene index and processed locally, taking advantage of 20+ years of Lucene optimizations. This makes workloads such as ecommerce product catalogs, content libraries, and counts run up to 100x faster . Learn more from our Atlas Search facets blog post . New and Enhanced Security Options End-to-end encryption for confidential computing Extending beyond cloud provider Key Management Services (KMS), MongoDB’s unique Client-Side Field Level Encryption will support any KMIP-compliant KMS . This functionality is being released in new versions of drivers that will be available soon. Client-Side FLE delivers some of the strongest privacy and security controls available anywhere today. By using the MongoDB drivers to encrypt the most sensitive fields in your documents before they leave the application you can do three things that are not possible with in-flight or at-rest encryption alone: Protect data while it is in-use, in the memory of your active database instance. The database never sees plaintext, but data remains queryable. Make data unreadable to anyone running the database for you, or who has access to the underlying database infrastructure — this includes MongoDB SREs running the Atlas services as well as cloud provider personnel. Simplify the process of enforcing right to erasure (sometimes called right to be forgotten) mandates in modern privacy regulations such as the GDPR or the CCPA. This is because you simply destroy the key encrypting a user’s PII, and their data is rendered unreadable and unrecoverable — in-memory, at-rest, in backups, and in logs. Google Cloud Private Service Connect We’ve also added a new network security option to MongoDB Atlas with the availability of Google Private Service Connect (PSC). Private Service Connect allows you to create private and secure connections from your Google Cloud networks to MongoDB Atlas. It creates service endpoints in your VPCs that provide private connectivity and policy enforcement, allowing you to easily control network security in one place. Along with VPC Peering, Google Cloud PSC makes it easy to connect your applications and services in Google Cloud to Atlas. Platform Resilience MongoDB 5.1 continues to build out controls for reliability and availability with the following enhancements: We've made a number of changes to WiredTiger internals that improve backups, including minimizing the checkpoints pinned while a backup cursor is open and improving handling of backup cursors that are open for long periods. These improvements will reduce both the operational overhead and storage consumption on the replica node from which the backup is taken. This improvement is available for backups taken from MongoDB Atlas and from self-hosted deployments controlled by Ops Manager or Cloud Manager, and has been backported to MongoDB 4.2 and above. In addition to enhancements affecting backups, WiredTiger checkpointing and locking have been improved to enhance performance when MongoDB is managing many concurrently active collections in a single instance. This is especially useful to multi-tenant applications built on MongoDB. We'll also be adding improvements in upcoming versions of our drivers that support mongos controls to mitigate connection storms in sharded clusters, especially during failover events. These include preferentially connecting to nodes that have existing idle connections that can be reused, improving the matching of connection pool sizing across replica set members, limiting the rate of new connections, and adding a mechanism to limit the number of mongos servers used when connecting to sharded clusters via SRV records. Improved Productivity for C# Developers Making it easier for developers to query and manipulate data is at the core of our mission as the modern application data platform. For C# developers the LINQ API serves as the main gateway between the language and database. In MongoDB 5.1 we are improving developer productivity for our C# community with a completely redesigned LINQ interface that lets developers write all of their MongoDB queries as well as build sophisticated aggregation pipelines natively in C#. Getting Started with MongoDB 5.1 You can learn more about all of the new features and enhancements in MongoDB 5.0 and 5.1 from our Guide to What’s New . MongoDB 5.1 is available now. If you are running Atlas Serverless instances or have opted in to receive Rapid Releases in your dedicated Atlas cluster, then your deployment will be automatically updated to 5.1 starting today. For a short period after upgrade, the Feature Compatibility Version (FCV) will be set to 5.0; certain 5.1 features will not be available until we increment the FCV. MongoDB 5.1 is also available as a Development Release for evaluation purposes only from the MongoDB Download Center. Consistent with our new release cadence announced last year, the functionality available in 5.1 and the subsequent Rapid Releases will all roll up into MongoDB 6.0, our next Major Release scheduled for delivery in 2022. I really look forward to hearing what you think about MongoDB 5.1, and can’t wait to tell you what’s new in the 5.2 Rapid Release scheduled for next quarter. Safe Harbour Statement The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.
MongoDB Atlas for Government Achieves "FedRAMP In-process"
We are pleased to announce that MongoDB Atlas for Government has achieved the FedRAMP designation of “ In-process ”. This status reflects MongoDB’s continued progress toward a FedRAMP Authorized modern data platform for the US Government. Earlier this year, MongoDB Atlas for Government achieved the designation of FedRAMP Ready . MongoDB is widely used across the Federal Government, including the Department of Veterans Affairs, the Department of Health & Human Services (HHS), the General Services Administration, and others. HHS is also sponsoring the FedRAMP authorization process for MongoDB. What is MongoDB Atlas for Government? MongoDB Atlas for Government is an independent environment of our flagship cloud product MongoDB Atlas. Atlas for Government has been built for US government needs. It allows federal, state, and local governments as well as educational institutions to build and iterate faster using a modern database-as-a-service platform. The service is available in AWS GovCloud (US) and AWS US East/West regions. MongoDB Atlas for Government Highlights: Atlas for Government clusters can be created in AWS GovCloud East/West or AWS East/West regions. Atlas for Government clusters can span regions within AWS GovCloud or within AWS. Atlas core features such as automated backups, AWS PrivateLink, AWS KMS, federated authentication, Atlas Search, and more are fully supported Applications can use client-side field level encryption with AWS KMS in GovCloud or AWS East/West. Getting started and pricing MongoDB Atlas for Government is available to Government customers or companies that sell to the US Government. You can buy Atlas for Government through AWS GovCloud or the AWS marketplace . Please fill out this form and a representative will get in touch with you. To learn more about Atlas for Government, visit the product page , check out the documentation , or read the FedRAMP FAQ .
Serverless Instances Now Offer Extended Regional and Cloud Provider Support
Today’s applications are expected to just work, regardless of time of day, user traffic, or where in the world they are being accessed from. But in order to achieve this level of performance and scale, developers have to meticulously plan for infrastructure needs, sometimes before they even know what the success of their application may be. In many cases, this is not feasible and can lead to over provisioning and over paying. But what if you could forgo all of this planning and the database would seamlessly scale for you? Well, now you can - with serverless instances on MongoDB Atlas. Since we announced serverless instances in preview at MongoDB.live we have been actively working toward implementing new functionality to make them more robust and widely available. With our most recent release, serverless instances now offer expanded cloud providers and regions, and support MongoDB tools. Deploy a serverless instance on the cloud provider of your choice With our dedicated clusters on MongoDB Atlas, you have the flexibility to run anywhere with global reach on the cloud provider of your choice, so you can deliver responsive and reliable applications wherever your users are located. Our goal is to provide this same flexibility for serverless instances. We’re happy to announce that you can now deploy a serverless instance in ten regions on AWS, Google Cloud, and Azure. You’ll see when deploying a serverless instance there are now more regions supported on AWS, as well as two available regions on both Google Cloud and Azure - so you can get started with the cloud provider that best suits your needs or the region that’s closest to you. We will be continuing to add new regions over time to ensure coverage where you need it most. Easily import your data with MongoDB tools With this release, we have also made it easier to work with your data. You can now easily import data from an existing MongoDB deployment using the MongoDB Tools including mongodump, mongorestore, mongoexport , and mongoimport . In order to use MongoDB tools with serverless instances, you will need to be using the latest version . If you have additional feature requests that would make your developer experience better, share them with us in our feedback forums . Database deployment made simple With serverless instances, you can get started with almost no configuration needed - MongoDB Atlas will automatically scale to meet your workload needs, whether you have variable traffic patterns or you’re looking for a sandbox database for your weekend hobby project. If you haven’t yet given serverless instances a try, now is a great time to see what they can offer. 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. Create your own serverless instance on MongoDB Atlas. Try the Preview .