MongoDB 3.3.9 is released
MongoDB 3.3.9 has been released. As a reminder, 3.3.9 is a development release and is not intended for production use.
This version of MongoDB includes a preview of some of the new features coming in MongoDB 3.4 as announced by Eliot Horowitz at the MongoDB World 2016 Keynote:
- Faceted search, via the $facet aggregation stage
- Recursive lookup, via the $graphLookup aggregation operator
Other features and bug fixes in this release include:
- SERVER-5905 Add data collection and command to get histogram of query response times
- SERVER-9566 Add support for position independent executables
- SERVER-18966 Allow exclusion in $project stage of aggregation pipeline
- SERVER-23265 Consistent diagnostics for aggregate command
- SERVER-23816 Add $sortByCount aggregation stage
- SERVER-23836 add $count stage to aggregation
- SERVER-24582 MongoDB secondary node crashes randomly when primary node is killed/dies
- TOOLS-349 mongoimport may report incorrect number of imported documents
- WT-2664 Change eviction so any eviction thread can find candidates
- WT-2702 Under high thread load, WiredTiger exceeds cache size
As always, please let us know of any issues.
-- The MongoDB Team
Getting Started With MongoDB Atlas for Cloud Manager Users
Jay Gordon is a Technical Account Manager with MongoDB and is available via our chat to discuss MongoDB Cloud Products at https://cloud.mongodb.com . MongoDB recently released our new Database as a Service (DBaaS) platform known as Atlas. MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery – we’ve got you covered. As a Cloud Manager user, you may want to reduce your operational overhead and move to Atlas. No longer will you need to manage server hardware or your cloud instances. You only need to select your requirements from the Atlas configuration and begin working. Authentication configuration, SSL encryption as well as monitoring and backups are all managed by MongoDB. The very first step in this process for existing Cloud Manager users is signing up for a new group. Navigate your browser to https://cloud.mongodb.com and log in with your normal credentials, once you’ve done this you should return to your deployment page: Next step, we’ll click “Settings” and then “My Groups” on the left side of the page, we’ll find our existing Cloud Manager groups: As you see on the right side, there’s a green “Add Group” button. Go ahead and select this, you’ll be given options now to begin creating your MongoDB Atlas Group or another Cloud Manager Group. Today we’re going to work with Atlas. Click the “MongoDB Atlas" option: Our buddy the Automation Robot will begin the process of setting up your new Atlas group in a few seconds! Once the Automation Robot is done you’ve now entered Atlas! Welcome to your new Clusters Page: To start building your Atlas clusters, click the green button that says “Build a New Cluster.” If you have a promotional or coupon code, you can apply it by entering it in the bottom of the payment details form under “Coupon or Activation Code”. Click save at the bottom of the screen and you’re ready to start building something GIANT using MongoDB Atlas!
How Edenlab Built a High-Load, Low-Code FHIR Server to Deliver Healthcare for 40 Million Plus Patients
The Kodjin FHIR server has speed and scale in its DNA. Edenlab, the Ukrainian company behind Kodjin , built our original FHIR solution to digitize and service the entire Ukrainian national health system. The learnings and technologies from that project informed our development of the Kodjin FHIR server. At Edenlab, we have always been driven by our passion for building solutions that excel in speed and scale. With Kodjin, we have embraced a modern tech stack to deliver unparalleled performance that can handle the demands of large-scale healthcare systems, providing efficient data management and seamless interoperability. Eugene Yesakov, Solution Architect, Author of Kodjin Built for speed and scale While most healthcare projects involve handling large volumes of data, including patient records, medical images, and sensor data, the Kodjin FHIR server is based on a system developed to handle tens of millions of patient records and thousands of requests per second, to ensure timely access and efficient decision-making for a population of over 40 million people. And all of this information had to be processed and exchanged in real-time or near real-time, without delays or bottlenecks. This article will explore some of the architectural decisions the Edenlab team took when building Kodjin, specifically the role MongoDB played in enhancing performance and ensuring scalability. We will examine the benefits of leveraging MongoDB's scalability, flexibility, and robust querying capabilities, as well as its ability to handle the increasing velocity and volume of healthcare data without compromising performance. About Kodjin FHIR server Kodjin is an ONC-certified and HIPAA-compliant FHIR Server that offers hassle-free healthcare data management. It has been designed to meet the growing demands of healthcare projects, allowing for the efficient handling of increasing data volumes and concurrent requests. Its architecture, built on a horizontally scalable microservices approach, utilizes cutting-edge technologies such as the Rust programming language, MongoDB, ElasticSearch, Kafka, and Kubernetes. These technologies enable Kodjin to provide users with a low-code approach while harnessing the full potential of the FHIR specification. A deeper dive into the architecture approach - the role of MongoDB in Kodjin When deciding on the technology stack for the Kodjin FHIR Server, the Edenlab team knew that a document database would be required to serve as a transactional data store. In an FHIR Server, a transactional data store ensures that data operations occur in an atomic and consistent manner, allowing for the integrity and reliability of the data. Document databases are well-suited for this purpose as they provide a flexible schema and allow for storing complex data structures, such as those found in FHIR data. FHIR resources are represented in a hierarchical structure and can be quite intricate, with nested elements and relationships. Document databases, like MongoDB, excel at handling such complex and hierarchical data structures, making them an ideal choice for storing FHIR data. In addition to supporting document storage, the Edenlab team needed the chosen database to provide transactional capabilities for FHIR data operations. FHIR transactions, which encompass a set of related data operations that should either succeed or fail as a whole, are essential for maintaining data consistency and integrity. They can also be used to roll back changes if any part of the transaction fails. MongoDB provides support for multi-document transactions , enabling atomic operations across multiple documents within a single transaction. This aligns well with the transactional requirements of FHIR data and ensures data consistency in Kodjin. Implementation of GridFS as a storage for the terminologies in Terminology service Terminology service plays a vital role in FHIR projects, requiring a reliable and efficient storage solution for terminologies used. Kodjin employs GridFS , a file system within MongoDB designed for storing large files, which makes it ideal to handle terminologies. GridFS offers a convenient way to store and manage terminology files, ensuring easy accessibility and seamless integration within the FHIR ecosystem. By utilizing MongoDB's GridFS, Kodjin ensures efficient storage and retrieval of terminologies, enhancing the overall functionality of the terminology service. Kodjin FHIR server performance To evaluate the efficiency and responsiveness of the Kodjin FHIR server in various scenarios we conducted multiple performance tests using Locust, an open-source load testing tool. One of the performance metrics measured was the retrieval of resources by their unique ids using the GET by ID operation. Kodjin with MongoDB achieved a performance of 1721.8 requests per second (RPS) for this operation. This indicates that the server can efficiently retrieve specific resources, enabling quick access to desired data. The search operation, which involves querying ElasticSearch to obtain the ids of the searched resources and retrieving them from MongoDB, exhibited a performance of 1896.4 RPS. This highlights the effectiveness of polyglot persistence in Kodjin, leveraging ElasticSearch for fast and efficient search queries and MongoDB for resource retrieval. The system demonstrated its ability to process search queries and retrieve relevant results promptly. In terms of resource creation, Kodjin with MongoDB showed a performance of 1405.6 RPS for POST resource operations. This signifies that the system can effectively handle numerous resource-creation requests. The efficient processing and insertion of new resources into the MongoDB database ensure seamless data persistence and scalability. Overall, the performance tests confirm that Kodjin with MongoDB delivers efficient and responsive performance across various FHIR operations. The high RPS values obtained demonstrate the system's capability to handle significant workloads and provide timely access to resources through GET by ID, search, and POST operations. Conclusion Kodjin leverages a modern tech stack including Rust, Kafka, and Kubernetes to deliver the highest levels of performance. At the heart of Kodjin is MongoDB, which serves as a transactional data store. MongoDB's capabilities, such as multi-document transactions and flexible schema, ensure the integrity and consistency of FHIR data operations. The utilization of GridFS within MongoDB ensures efficient storage and retrieval of terminologies, optimizing the functionality of the Terminology service. To experience the power and potential of the Kodjin FHIR server firsthand, we invite you to contact the Edenlab team for a demo. For more information On MongoDB’s work in healthcare, and to understand why the world’s largest healthcare companies trust MongoDB, read our whitepaper on radical interoperability .