December 13, 2021 | Updated: December 21, 2021
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:
|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 toCVE-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.
PeerIslands Cosmos DB Migrator Tool to MongoDB Atlas on Google Cloud
When you’re in the midst of innovating, the last thing you want to worry about is infrastructure. Whether you’re looking to streamline inventory management or reimagine marketing, you need applications that can scale fast and maintain high availability. That’s where MongoDB Atlas on Google Cloud comes in. With MongoDB Atlas’ general-purpose, document-based database, users can free themselves from the hassle of database management, and give back precious time to developers to focus on innovation. Combine these benefits with Google Cloud’s cloud computing power, high availability, and ability to integrate with tools like BigQuery, Dataflow, Dataproc and more, and it’s hard to find a comparable joint solution. In fact, many current Microsoft Azure Cosmos DB users are now considering making the move to MongoDB. Microsoft’s Cosmos DB only supports single partition transactions, has no schema governance and forces developers to work with five different APIs to deliver full application functionality. Conversely, MongoDB Atlas on Google Cloud supports distributed multi-document ACID transactions, includes schema governance, and offers integrated full-text search, auto-archiving, data lakes, and edge-to-cloud data sync. The following blog illustrates how PeerIslands’ Cosmos DB Migrator tool can help users move from Cosmos DB to MongoDB Atlas on Google Cloud. Why PeerIslands PeerIslands is an enterprise-class digital transformation company composed of a team of polyglots who are comfortable across multiple technologies and cloud platforms. As a services firm, PeerIslands is focused on helping customers with both cloud-native development and application transformation. With best-in-the-industry talent, PeerIslands has been working with the MongoDB team to build a suite of solutions around two key objectives: For a customer evaluating MongoDB, how can we rapidly address common questions? Once a customer has chosen MongoDB, how can we reduce time to value by rapidly migrating workloads to MongoDB? With this in mind, PeerIslands developed a suite of tools around schema generation, understanding MongoDB query performance, as well as helping customers understand code changes required for upgrading MongoDB versions. In terms of workload migrations, PeerIslands developed solutions for both homogenous and heterogenous migrations. The company is also contributing to the open source community with a mobile app for enabling MongoDB admins to manage Atlas on the go. PeerIslands' Cosmos DB migration use case The current approach for migrating data from Cosmos DB to MongoDB is to use MongoDB dump and restore. But there are several problems with this approach. It’s fully manual and CLI-based which creates a poor user experience and requires technical resources even for simple migrations. There’s a lack of change capture capability which requires downtime during the duration of migration. For large Cosmos DB migrations, this causes significant issues. The team is also under pressure to deliver the entire migration in a short period of time. Migrations often get delayed as customers have difficulty identifying the right migration window. The Cosmos to MongoDB tool is a “Live Migrate” like tool that helps perform one-time migrations and change data capture from Cosmos DB (MongoDB model) to MongoDB Atlas and minimizes downtime requirements associated with migrations. The tool is fully GUI-based and nearly everything is automated. All the tasks for infrastructure provisioning, dump & restore, change stream listeners and processors have all been automated with a graphical user interface (GUI). The Cosmos to Mongo migration tool uses native MongoDB tools and the performance is similar to native tools. For change capture, we leverage the native MongoDB change stream APIs. A high level view of the solution is provided in figure 1 below: Figure 1: Solution Map Migration steps: Migration configuration: Provide the name of the migration task, source Cosmos DB details, and target MongoDB details. The tool supports key vault integration as well. Migration infrastructure provisioning: Provide migration infrastructure details required for creating the VM (Virtual Machine) including location, type of VM instance, etc. Migration execution: Allow for automation of the migration once the configuration is complete. The migration is executed in 3 steps: backup, restore and change event processing. As a user, you can initiate the backup process. The change event listener is started in parallel with the backup process and captures all the changes. Once the backup is complete, the user can restore the initial data and then perform change event processing to apply all the changes to MongoDB. Migration validation: The tool also provides facilities for validating the migration. Users can view the total number of documents on both source Cosmos DB collection and target MongoDB collection. They can also compare random documents picked up from Cosmos DB and MongoDB side by side and validate whether the data elements have been loaded correctly. For a more detailed demo and description of events, watch the following video: Migrating to a new database can feel daunting at first, but PeerIslands Cosmos DB migrator makes it easy. Major concerns like delays and downtime are eliminated from the process, helping you run your business smoothly and reap the benefits of MongoDB more quickly. And with PeerIslands suite of tools, you can rapidly address MongoDB-specific questions and accelerate time to value. Reach out today to get started
4 Ways MongoDB Enhances Your Google BigQuery Experience
MongoDB and Google Cloud continue to build on their partnership, with MongoDB enhancing Google Cloud with pay-as-you-go abilities, unified billing, and integrations with multiple different GC features, including BigQuery . And, when it comes to data architecture, BigQuery and MongoDB are two products that are better together. Google BigQuery and MongoDB are better together Google’s serverless data warehouse, BigQuery, was launched in 2011 with an aim to enhance business agility as their cloud-native data warehouse. BigQuery allows for fast queries that can uncover insights using familiar SQL. When MongoDB is added to the database technology stack as a complementary technology, it enhances the breadth of capabilities for the developer across a variety of use cases, including the following four examples. Combined impact of the Enterprise Data Warehouse and the Operational Data Store BigQuery is best suited as an Enterprise Data Warehouse (EDW), meaning it is designed to optimize long-running analytics. MongoDB Atlas , on the other hand, is best suited as an Operational Data Store (ODS), designed to optimally support high throughput and highly concurrent real-time operational applications that demand random access to an entity’s data in native JSON. This combination means that BigQuery and MongoDB are complementary technologies that can jointly deliver more value — each delivering on their strongest qualities. BigQuery excels at long-running queries, while Atlas handles the real-time operational application needs with thousands of concurrent sessions and millisecond response times. Enriched end-customer experiences BigQuery enables data scientists and analysts with machine learning (ML) models and BI tools for structured and semi-structured data at scale. For roles that need results with a turnaround time of a day or more, BigQuery is a strong tool for big data queries. With MongoDB Atlas, engineers and development teams can build applications faster and handle highly diverse schema, query, and update patterns, adapting to demanding user needs and competition. Atlas can also deliver the real-time or less than 24-hour queries that are necessary to keep your business operational. Additionally, data can easily move back and forth between the two platforms, creating a prime combination for running analytics on operational data. Being able to unlock the full potential of your data across your organization means that everyone has the insight into the business metrics they need, when they need it. This allows quicker decision making, as well as stronger and more accurate reporting. Extensibility to MongoDB Atlas features On top of the value and synergy that can be realized by a BigQuery+Atlas combination, other Atlas features can help enhance the usefulness and sophistication of a data architecture, such as: Atlas Charts can be leveraged to create rich visualizations of any data stored within Atlas. Atlas Triggers and Alerts can apply database logic in response to events or on a predefined schedule. Atlas Search brings full-text search at scale to all data across MongoDB and BigQuery alike. Atlas Data Federation enables aggregating data across multiple data sources, such as Atlas clusters and HTTPS endpoints, and transforming it into analytical formats (e.g., Parquet). This means you can not only access data in real-time, but you can also analyze it in a visual, user-friendly way. This functionality makes your data more actionable, allowing you not only to answer questions about your business data but also make better predictions and future adjustments based on it. Furthermore, being alerted to certain data-based events and triggering new actions based on that information means you can have your data working more efficiently for you, freeing up time to innovate and focus on core business competencies. Lastly, this approach simplifies your data lifecycle, so JSON data from various applications and endpoints can easily be transformed and consumed for rich analytics. Deeper understanding of your customer Businesses can use fully managed MongoDB Atlas to store customer 360 profiles. A 360-degree view of a customer allows businesses to track an individual customer’s journey across multiple channels, devices, purchases, and interactions, and improves customer satisfaction. With the combination of Atlas and BigQuery, businesses can also use compiled data — such as, transactional data, behavioral data, user profile and segmentations, and business analytics — to match user profiles with products and services using Artificial Intelligence (AI). Vertex AI , a managed machine learning platform, provides all the Google cloud services in one place to deploy and maintain AI models. Being able to easily access a 360 view for each customer and have automation around their customer journey helps with customer engagement and loyalty by improving customer satisfaction and retention through personalization and targeted marketing communications. It also enables retailers to aggregate customer interactions across all channels and identify valuable new customers. Google BigQuery and MongoDB Atlas in the real world Current , a leading U.S. challenger bank, uses innovative approaches, services, and technologies to serve people overlooked by traditional banks, regardless of age or income level, to help improve their financial outcomes. To help create customer experiences that cannot exist in traditional systems, Current chose to leverage Google Cloud, including BigQuery, with MongoDB layering the platform to achieve their goals. Read Full Current Story Are you a Google BiqQuery customer that is curious about how MongoDB Atlas can amplify your existing data warehouse or data lake architecture? Try MongoDB Atlas for free today and spin up your first workload in minutes. Try pay-as-you-go Atlas on GC Marketplace