Announcing the MongoDB World 2016 Diversity Scholarship
April 4, 2016 | Updated: January 20, 2017
As part of MongoDB’s commitment to change the ratio in technology, we’re offering 10 Diversity Scholarships to MongoDB World!
MongoDB’s Diversity Scholarship program supports members of groups who are underrepresented in the technology industry. This includes, but is not limited to, people who identify as African American, Hispanic, LGBTQ, women, low-income, and people with disabilities who may not otherwise have the opportunity to attend MongoDB events.
Eligible candidates can apply online. Hurry, the applications close this Friday, April 8!
Diversity Scholarship recipients receive:
- Complimentary admission to MongoDB World
- Complimentary admission to a pre-conference workshop of their choice
- A MongoDB certification voucher
- Three-month access to paid MongoDB University courses
Additionally, scholarship recipients may be featured in a blog post.
Applicants must be 18 years old or older, and must belong to a group that is underrepresented in the technology industry.
Scholarships are awarded based on a combination of need and impact. Selection will be made by a committee that will review each application. All application info will be kept confidential. Recipients will be notified by April 15.
Don’t qualify, but would like to help? You can contribute to the Diversity Scholarship!
While MongoDB World registration is open, we're raising funds to support Diversity Scholarship recipients. There’s a donation opportunity when registering for the conference. Contributors will be listed as Diversity Champions on our website, unless otherwise requested.
Contact email@example.com with any questions.
Running MongoDB as a Microservice with Docker and Kubernetes
Update – November 2018 This post is now 2.5 years old, and neither MongoDB nor Kubernetes have been standing still! In particular, Kubernetes has introduced StatefulSets and we've introduced the MongoDB Enterprise Operator for Kubernetes . Both of these capabilities make working with MongoDB in Kubernetes much simpler and more robust. Read this post for the state-of-the-art in running MongoDB in Kubernetes . Introduction Want to try out MongoDB on your laptop? Execute a single command and you have a lightweight, self-contained sandbox; another command removes all traces when you're done. Need an identical copy of your application stack in multiple environments? Build your own container image and let your development, test, operations, and support teams launch an identical clone of your environment. Containers are revolutionizing the entire software lifecycle: from the earliest technical experiments and proofs of concept through development, test, deployment, and support. Read the Enabling Microservices: Containers & Orchestration Explained white paper . Orchestration tools manage how multiple containers are created, upgraded and made highly available. Orchestration also controls how containers are connected to build sophisticated applications from multiple, microservice containers. The rich functionality, simple tools, and powerful APIs make container and orchestration functionality a favorite for DevOps teams who integrate them into Continuous Integration (CI) and Continuous Delivery (CD) workflows. This post delves into the extra challenges you face when attempting to run and orchestrate MongoDB in containers and illustrates how these challenges can be overcome. Considerations for MongoDB Running MongoDB with containers and orchestration introduces some additional considerations: MongoDB database nodes are stateful. In the event that a container fails, and is rescheduled, it's undesirable for the data to be lost (it could be recovered from other nodes in the replica set, but that takes time). To solve this, features such as the Volume abstraction in Kubernetes can be used to map what would otherwise be an ephemeral MongoDB data directory in the container to a persistent location where the data survives container failure and rescheduling. MongoDB database nodes within a replica set must communicate with each other – including after rescheduling. All of the nodes within a replica set must know the addresses of all of their peers, but when a container is rescheduled, it is likely to be restarted with a different IP address. For example, all containers within a Kubernetes Pod share a single IP address, which changes when the pod is rescheduled. With Kubernetes, this can be handled by associating a Kubernetes Service with each MongoDB node, which uses the Kubernetes DNS service to provide a hostname for the service that remains constant through rescheduling. Once each of the individual MongoDB nodes is running (each within its own container), the replica set must be initialized and each node added. This is likely to require some additional logic beyond that offered by off the shelf orchestration tools. Specifically, one MongoDB node within the intended replica set must be used to execute the rs.initiate and rs.add commands. If the orchestration framework provides automated rescheduling of containers (as Kubernetes does) then this can increase MongoDB's resiliency since a failed replica set member can be automatically recreated, thus restoring full redundancy levels without human intervention. It should be noted that while the orchestration framework might monitor the state of the containers, it is unlikely to monitor the applications running within the containers, or backup their data. That means it's important to use a strong monitoring and backup solution such as MongoDB Cloud Manager , included with MongoDB Enterprise Advanced and MongoDB Professional . Consider creating your own image that contains both your preferred version of MongoDB and the MongoDB Automation Agent . Implementing a MongoDB Replica Set using Docker and Kubernetes As described in the previous section, distributed databases such as MongoDB require a little extra attention when being deployed with orchestration frameworks such as Kubernetes. This section goes to the next level of detail, showing how this can actually be implemented. We start by creating the entire MongoDB replica set in a single Kubernetes cluster (which would normally be within a single data center – that clearly doesn't provide geographic redundancy). In reality, little has to be changed to run across multiple clusters and those steps are described later. Each member of the replica set will be run as its own pod with a service exposing an external IP address and port. This 'fixed' IP address is important as both external applications and other replica set members can rely on it remaining constant in the event that a pod is rescheduled. The following diagram illustrates one of these pods and the associated Replication Controller and service. **Figure 1:** MongoDB Replica Set member configured as a Kubernetes Pod and exposed as a service Stepping through the resources described in that configuration we have: Starting at the core there is a single container named mongo-node1 . mongo-node1 includes an image called mongo which is a publicly available MongoDB container image hosted on Docker Hub . The container exposes port 27107 within the cluster. The Kubernetes volumes feature is used to map the /data/db directory within the connector to the persistent storage element named mongo-persistent-storage1 ; which in turn is mapped to a disk named mongodb-disk1 created in the Google Cloud. This is where MongoDB would store its data so that it is persisted over container rescheduling. The container is held within a pod which has the labels to name the pod mongo-node and provide an (arbitrary) instance name of rod . A Replication Controller named mongo-rc1 is configured to ensure that a single instance of the mongo-node1 pod is always running. The LoadBalancer service named mongo-svc-a exposes an IP address to the outside world together with the port of 27017 which is mapped to the same port number in the container. The service identifies the correct pod using a selector that matches the pod's labels. That external IP address and port will be used by both an application and for communication between the replica set members. There are also local IP addresses for each container, but those change when containers are moved or restarted, and so aren't of use for the replica set. The next diagram shows the configuration for a second member of the replica set. **Figure 2:** Second MongoDB Replica Set member configured as a Kubernetes Pod 90% of the configuration is the same, with just these changes: The disk and volume names must be unique and so mongodb-disk2 and mongo-persistent-storage2 are used The Pod is assigned a label of instance: jane and name: mongo-node2 so that the new service can distinguish it (using a selector) from the rod Pod used in Figure 1. The Replication Controller is named mongo-rc2 The Service is named mongo-svc-b and gets a unique, external IP address (in this instance, Kubernetes has assigned 22.214.171.124 ) The configuration of the third replica set member follows the same pattern and the following figure shows the complete replica set: **Figure 3:** Full Replica Set member configured as a Kubernetes Service Note that even if running the configuration shown in Figure 3 on a Kubernetes cluster of three or more nodes, Kubernetes may (and often will) schedule two or more MongoDB replica set members on the same host. This is because Kubernetes views the three pods as belonging to three independent services. To increase redundancy (within the zone), an additional headless service can be created. The new service provides no capabilities to the outside world (and will not even have an IP address) but it serves to inform Kubernetes that the three MongoDB pods form a service and so Kubernetes will attempt to schedule them on different nodes. **Figure 4:** Headless service to avoid co-locating of MongoDB replica set members The actual configuration files and the commands needed to orchestrate and start the MongoDB replica set can be found in the Enabling Microservices: Containers & Orchestration Explained white paper . In particular, there are some special steps required to combine the three MongoDB instances into a functioning, robust replica set which are described in the paper. Multiple Availability Zone MongoDB Replica Set There is risk associated with the replica set created above in that everything is running in the same GCE cluster, and hence in the same availability zone. If there were a major incident that took the availability zone offline, then the MongoDB replica set would be unavailable. If geographic redundancy is required, then the three pods should be run in three different availability zones or regions. Surprisingly little needs to change in order to create a similar replica set that is split between three zones – which requires three clusters. Each cluster requires its own Kubernetes YAML file that defines just the pod, Replication Controller and service for one member of the replica set. It is then a simple matter to create a cluster, persistent storage, and MongoDB node for each zone. **Figure 5:** Replica set running over multiple availability zones Next Steps To learn more about containers and orchestration – both the technologies involved and the business benefits they deliver – read the Enabling Microservices: Containers & Orchestration Explained white paper . The same paper provides the complete instructions to get the replica set described in this post up and running on Docker and Kubernetes in the Google Container Engine. Interested in learning more about Microservices? Microservices Resources About the Author - Andrew Morgan Andrew is a Principal Product Marketing Manager working for MongoDB. He joined at the start last summer from Oracle where he spent 6+ years in product management, focused on High Availability. He can be contacted @andrewmorgan or through comments on his blog ( clusterdb.com ).
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 .