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 184.108.40.206 ) 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 ).
MACH Aligned for Retail (Microservices, API-First, Cloud Native SaaS, Headless)
Across the Retail industry, MACH principles and the Mach Alliance are becoming increasingly common. What is MACH and why is it being embraced for Retail? The MACH Alliance is a non-profit organization fostering the adoption of composable architecture principles. It stands for Microservices, API-First, Cloud-Native SaaS and Headless. The MACH Alliance’s Manifesto is to: “Future proof enterprise technology and propel current and future digital experiences" The MACH Alliance and the creation of this set of principles originated in the Retail Industry. Several of the 5 co-founders of the MACH Alliance are technology companies building for retail use cases: for example commercetools is a composable commerce platform for retail (built completely on MongoDB). MongoDB has been a member of the MACH Alliance since 2020, as an “enabler” member, meaning use of our technology can enable the implementation of the MACH principles in application architectures. This is because a data layer built on MongoDB is ideal as the basis for a MACH architecture. Members of our Industry Solutions team sit on the MACH technology, growth and marketing councils, and actively are involved with furthering the adoption of MACH across the Retail Industry What is MACH, why is it important for retail? The retail industry has long been a fast adopter of technology and a forerunner in technology trends. This is because of the competitive nature of the business leading a drive towards innovation- its vital that retails are able to react quickly to new technologies (e.g. NFTs, VR, AI) to capture market share and stay ahead of the competitors. Retailers have realized that to be able to deliver new and value-add experiences to their customers, they have to cut back on operational overhead that leads to increased cost and build standard functionality that can either be bought or re-used. This is where the benefits of MACH comes in- it's all about increasing the ability to deliver innovation quickly while lowering operational costs & risk. Microservices: An approach to building applications in which business functions are broken down into smaller, self-contained components called services. These services function autonomously and are usually developed and deployed independently. This means the failure or outage of one microservice will not affect another and teams can develop in parallel, increasing efficiency. API-First: A style of development where the sharing and use of the data via API (application programming interface) is considered first and foremost in the development process. This means that services are designed to aid the easy sharing of information across the organization and simple interconnectivity of systems. Cloud-Native SaaS: Cloud-native SaaS solutions are vendor-managed applications developed in and for the cloud, and leveraging all the capabilities the cloud has to offer, such as fully managed hosting, built-in security, auto-scaling, cross-regional deployment and automatic updates. These are a good fit for a MACH architecture as adopting them can reduce operational costs and frees up developers for value-add work like new unique customer experiences. Headless: Decoupling the front end from the back-end so that front ends (or “heads”) can be created or iterated on with no dependencies on the back end. The fact that the layers are loosely coupled decreases time to market for new front ends, and encourages the re-use back-end services for multiple purposes. It also de-risks change in the long term as services can function independently. Where does MongoDB come in? MongoDB is an enabler for MACH, meaning that using MongoDB as your data layer helps retailers and retail software companies. achieve MACH compliance. Our data model, architecture and functionality empower IT organizations to build in line with these architecture principles. During a digital transformation, where a retailer is modernizing a monolith into a microservices based architecture, they're looking for a data layer which will enable speed of development & change. MongoDB is the "most wanted" database 4 years running on Stack Overflow's developer survey- this is because our document model maps to the way developers are thinking & coding, and the flexibility allows for iterative change of the data layer. When looking at API based communication, the standard format for APIs is JSON, which again maps to MongoDB's document model. The idea with API-first development is to develop with the API in mind- why not store the data the way you're going to serve it by API. This reduces complexity and increases performance. Cloud Native and SaaS products have become the norm as retailers wish to reduce maintenance and management work. MongoDB Atlas, provides a database-as-a-service, guaranteeing 99.995% uptime, automatic failover and self-healing and allowing DevOps engineers to spin up databases in minutes or by API/ script. Many retail software companies are also built on MongoDB Atlas- for example commercetools, which provides an ecommerce solution as a SaaS product. Headless architectures require a data layer that is able to adapt and change for new workloads. The ability to change the schema at runtime, with no downtime, makes MongoDB's document model ideal for this. Performance and the ability to scale for new "heads" is also important. MongoDB is known as a high performance database and can scale vertically automatically or scale out horizontally seamlessly. So MongoDB becomes a great choice for retailers choosing to adopt a MACH architecture (see figure 1 below). As a general purpose database with high performance, a rich expressive query language and secondary indexing, MongoDB is a really good fit as a data layer as it is capable of handling operational and analytical needs of the application. FIgure 1: Example of a MACH architecture Want to know more? Are you interested in a transition to MACH? Dive into our four part blog series exploring each topic in detail and how MongoDB supports each of these principles: Microservices API-First Cloud-Native SaaS Headless