Last week over 1,100 developers came together for MongoSV, the largest MongoDB conference to date. 10gen kicked off MongoSV with our inaugural MongoDB Masters program, which brought together MongoDB evangelists from around the world.
At the opening keynote, 10gen CTO Eliot Horowitz demoed a twitter app for #mongoSV tweets, featuring the new aggregation framework expected for the MongoDB 2.2 release. These gather all the tweets sent out with the hashtag #mongoSV and organizes them in by recency and most retweets. Get the source code for the demo app here
Highlights from MongoSV include presentations on X.commerce’s new open source developer platform, MongoDB’s integration with Azure, MongoDB’s new aggregation framework, How Disney manages their deployment of 1400 Mongo instances and more
the 10gen booth at MongoSV
10gen President Max Schireson welcomes the Speakers and Masters to MongoSV
Voting Open for the MongoDB Community Awards
For three weeks, we invited members of the MongoDB community to nominate candidates for awards in three categories—Community Champion, Innovative Application, and MongoDB Contributor. Dozens of nominations were submitted from MongoDB users around the world. After considerable deliberation, 10gen employees picked finalists in each of the three categories. However, it will once again be left to the MongoDB user community to choose the grand prize winners via online voting. The competition for recognition is expected to be fierce, and each vote is important. After reading the following list of award candidates, we invite you to vote on category winners . Community Champion This award recognizes an individual for their efforts evangelizing and growing the MongoDB community. Nathen Harvey is the manager of Web Operations for CustomInk.com and the co-organizer of the Washington DC MongoDB Users Group and DevOps DC. As organizer of the DC MUG, Nathen has been instrumental in growing the group to 250 members in one year through consistent meetings, detailed event summaries, and good beer. Takahiro Inoue is the leader of the MongoDB user community in Japan, having founded the Japan MongoDB User Group, now at over 600 members, and has organized seven MongoDB seminars in Tokyo. Takahiro blogs about MongoDB frequently, is working on the first Japanese-language MongoDB book, and helped develop Treasure Data ’s Fluentd , an advanced open-source log collector. Karl Seguin is a developer with experience across various fields and technologies. With respect to MongoDB, he was a core contributor to the C# MongoDB library NoRM, wrote the interactive tutorial mongly , the Mongo Web Admin and the free Little MongoDB Book . Rick Copeland is a Lead Software Engineer at SourceForge, where he developed the Python ODM Ming , led the effort to rewrite and open source Allura (the developer tools portion of the SourceForge site on the Python/MongoDB platform), and created the Zarkov realtime analytics framework. He is a frequent speaker at MongoDB events and an avid MongoDB enthusiast. Innovative Application This award recognizes a company or individual who has built an innovative application using MongoDB. MongoPress is an open source, MongoDB-based CMS, developed by Mark Smalley. MongoPress uses PHP and jQuery to offer a NoSQL alternative which is easy to use (even for beginners) and offers a high-performance and more lightweight alternative to WordPress. Cascade is a tool developed by the NYTimes R&D Lab that links browsing behavior on a site to sharing activity to create a map of information as it is spread and shared through social networks. Initially applied to New York Times stories and information, the tool is widely applicable and can help us to understand how messages spread in the online space. Cube is an open-source system for visualizing time series data, built on MongoDB, Node and D3. If you send Cube timestamped events (with optional structured data), you can easily build realtime visualizations of aggregate metrics for internal dashboards. Cube was developed and open sourced by Square Inc . MongoDB Contributor This award recognizes a community member for significant contribution to the codebase of the MongoDB core server, language drivers, or tools. Gustavo Niemeyer is a developer at Canonical, and in his free time, Gustavo is a contributor to Google’s Go language and the author of the mgo (mango), the MongoDB driver for Go. He also designed the Geohash concept that is used internally by MongoDB. Nat Lueng is a Singapore-based MongoDB user. In addition to bug fixes and small enhancement in the MongoDB core, C# driver, and Java driver, Nat is prolific on the free support forums, including 2,700+ posts to date. LearnBoost is an education startup built on node.js and MongoDB. The team, particularly Guillermo Rauch and Aaron Heckmann, built Mongoose , a popular MongoDB object modeling tool designed to work in an asynchronous environment. Visit our voting form to weigh in on these candidates until 1:30 PST on December 9th. We’ll announce the winners at the conclusion of MongoSV .
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 .