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 firstname.lastname@example.org 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 ).
Splitit & MongoDB Atlas: Racing to Capture a Global Opportunity
Splitit is a global payment solution that allows businesses to offer installment plans for their customers. Unlike with other buy now, pay later (BNPL) solutions, Splitit shoppers can split their online purchases into monthly installments by using their existing credit, without the need for registration, application, or approval. “We have a very different proposition than others in this space,” says Splitit’s CTO, Ran Landau. “We’re not a financing company. We utilize the customer’s existing credit card arrangement, which allows us to accommodate smaller average deal values and a broader range of installment schedules.” Splitit works with online retailers across all market sectors and diverse price points, and recently raised $71.5 million in investment to fund global expansion. Following its IPO in January 2019, the business had seen strong growth as more consumers moved from brick and mortar to ecommerce. Then COVID-19 hit, and online shopping boomed. Landau recognized that the company needed to quickly scale its infrastructure in order to capture this large opportunity. The Need for Speed Landau joined Splitit in May 2019 and worked to modernize the company’s infrastructure. At the time, the team was using a traditional relational database. “As tech leaders, we need to make the right decision,” he says. “When I came to Splitit, I knew I needed a powerful NoSQL server so that my developers could develop faster and so that we could scale – both things that our relational databases were failing to deliver.” In the interest of getting up and running quickly, Ran’s team thought that they could move faster using a cloud-provider database that mimicked MongoDB functionality. He had used MongoDB before and saw that this solution offered the same drivers he was familiar with and claimed compatibility with MongoDB 3.6. Initially, the new solution seemed fine. But as the team started to migrate more data into the database, however, Landau noticed a few missing features. Scripts for moving documents from one collection to another were failing, and overall performance was deteriorating. The application became slow and unresponsive even though the load on the database was normal. “We were having issues with small things, like renaming collections. I couldn’t search or navigate through documents easily,” recalls Landau. Offline Database: A Breaking Point Then one day, the application was unable to communicate with the database for 20 minutes, and when the database finally came back online, something wasn’t right. Landau contacted support, but the experience was not very helpful. “We were not pleased with the response from the database vendor,” he explains. “They insisted that the issue was on our side. It wasn’t so collaborative.” Fortunately, he had taken a snapshot of the data so Splitit was able to revert back to an earlier point in time. But the incident was troubling. Other teams also had been complaining about how difficult it was to debug problems and connect to the database successfully. Landau knew he needed to find a better solution as soon as possible. MongoDB Atlas: A Reliable, Scalable Solution Landau believed that MongoDB was still the right choice for Splitit, and investigated whether the company offered a cloud solution. He discovered MongoDB Atlas and decided to give it a try. “The migration to MongoDB Atlas was so simple. I exported whatever data I had, then imported it into the new cluster. I changed the connection strings and set up VPC peering in all of my environments,” says Landau. “It was incredibly easy.” Not only was MongoDB Atlas built on actual MongoDB database software, but it was also secure, easy to use, and offered valuable features such as Performance Advisor . “It can tell you which indexes need to be built to increase speed. It’s such a powerful tool — you don’t need to think; it analyzes everything for you,” explains Landau. Another great feature was auto-scaling. “My biggest concern as I scale is that things keep working. I don’t have to stop, evaluate, and maintain the components in my system,” says Landau. “If we go back to doing database operations, we can’t build new features to grow the business.” Auto-archival Made Easy with Online Archive As a business in the financial services industry, Splitit needs to comply with various regulations, including PCI DSS . A key requirement is logging every transaction and storing it for auditing purposes. For Splitit, that adds up to millions of logs per day. Landau knew that storing this data in the operational database was not a cost-effective, long-term solution, so he initially used an AWS Lambda function to move batches of logs older than 30 days from one collection to another periodically. A few months ago, he discovered Online Archive , a new feature released at MongoDB.live in June 2020. With it, Landau was able to define a simple rule for archiving data from a cluster into a more cost-effective storage layer and let Atlas automatically handle the data movement. “The gem of our transition to Atlas was finding Online Archive,” says Landau. “There’s no scripting involved and I don’t have to worry about my aging data. I can store years of logs and know that it’s always available if I need it.” Online Archive gives me the flexibility to store all of my data without incurring high costs, and feel safe that I won't lose it. It's the perfect solution. Ran Landau, CTO, Splitit With federated queries, the team can also easily analyze the data stored in both the cluster and the Online Archive for a variety of use cases. Ready for Hypergrowth and Beyond Looking back, Landau admits that he learned his lesson. In trying to move quickly, he selected a solution that appeared to work like MongoDB, but ultimately paid the price in reliability, features, and scalability. You wouldn't buy a fake shirt. You wouldn't buy fake shoes. Why buy a fake database? MongoDB Atlas is the real thing. Ran Landau, CTO, Splitit Landau is confident that his investment in MongoDB puts in place a core building block for the business’ continued success. With a fully managed solution, his team can focus on building features that differentiate Splitit from competitors to capture more of the market. “We saw our growth triple in March due to COVID-19, but the sector as a whole is expanding,” he says. “Our technology is patent protected. Everything we build moving forward will be on MongoDB. As a company that’s scaling rapidly, the most important thing is not having to worry about my scaling. MongoDB Atlas takes care of everything.”