Nicholas Tang, who leads 10gen’s North American support team, recently presented on Performance Tuning and Monitoring Using MongoDB Management Service (MMS) in a live webcast. In the session, Nicholas explained what MMS is, why you should use it, how you can set it up and how to use it for performance tuning. He then gave some examples of real-world scenarios where he worked with customers to use MMS to diagnose and debug performance issues using MMS. The video and slides are now available online below.
To start monitoring your MongoDB deployment today, sign up for a free MMS account at mms.10gen.com.
The MongoDB Java Driver 3.0
By Trisha Gee , MongoDB Java Engineer and Evangelist You may have heard that the JVM team at 10gen is working on a 3.0 version of the Java driver. We’ve actually been working on it since the end of last year, and it’s probably as surprising to you as it is to me that we still haven’t finished it yet. But this is a bigger project than it might seem, and we’re working hard to get it right. So why update the driver? What are we trying to achieve? Well, the requirements are: More maintainable More extensible Better support for ODMs, third party libraries and other JVM languages More idiomatic for Java developers That’s all very nice, but it’s a bit fluffy. You can basically summarise that as “better all round”. Which is probably the requirement of any major upgrade. Since it’s too fluffy to guide us in our development, we came up with the following design goals. Design Goals Consistency Cleaner design Intuitive API Understandable Exceptions Test Friendly Backwards compatible Consistency Java developers using the driver will have encountered a number of inconsistencies: the way you do things in the shell, or in other drivers, is not always the same way you do things in the Java driver. Even using just the Java driver, methods are confusingly named (what’s the difference between createIndex and ensureIndex , for example?); the order of parameters is frequently different; often methods are overloaded but sometimes you chain methods; there are helpers such as QueryBuilder but sometimes you need to manually construct a DBObject , and so on. If you’re working within the driver, the inconsistencies in the code will drive you mad if you’re even slightly OCD: use of whitespace, position of curly braces, position of fields, mixed field name conventions and so on. All of this may seem pedantic to some people, but it makes life unnecessarily difficult if you’re learning to use the driver, and it means that adding features or fixing bugs takes longer than it should. Cleaner Design It’s easy to assume that the driver has a single, very simple, function - to serialise Java to BSON and back again. After all, its whole purpose is to act as a facilitator between your application and MongoDB, so surely that’s all it does - turn your method call and Java objects into wire-protocol messages and vice versa. And while this is an important part of what the driver does, it’s not its only function. MongoDB is horizontally scalable, so that means your application might not be talking to just a single physical machine - you could be reading from one of many secondaries, you could be writing to and reading from a sharded environment, you could be working with a single server. The driver aims to make this as transparent as possible to your application, so it does things like server discovery, selects the appropriate server, and tries to reuse the right connection where appropriate. It also takes care of connection pooling. So as well as serialisation and deserialisation, there’s a whole connection management piece. The driver also aims to provide the right level of abstraction between the protocol and your application - the driver has a domain of its own, and should be designed to represent that domain in a sane way - with Documents, Collections, Databases and so on exposed to your application in a way that you can intuitively use. But it’s not just application developers that are using the driver. By implementing the right shaped design for the driver, we can make it easier for other libraries and drivers to reuse some of the low-level code (e.g. BSON protocol, connection management, etc) but put their own API on the front of it - think Spring Data , Morphia , and other JVM languages like Scala. Instead of thinking of the Java driver as the default way for Java developers to access MongoDB, we can think of this as the default JVM driver, on top of which you can build the right abstractions. So we need to make it easier for other libraries to reuse the internals without necessarily having to wrap the whole driver. All this has led us to design the driver so that there is a Core, around which you can wrap an API - in our case, we’re providing a backward-compatible API that looks very much like the old driver’s API, and we’re working on a new fluent API (more on that in the next section). This Core layer (with its own public API) is what ODMs and other drivers can talk to in order to reuse the common functionality while providing their own API. Using the same core across multiple JVM drivers and libraries should give consistency to how the driver communicates with the database, while allowing application developers to use the library with the most intuitive API for their own needs. Intuitive API We want an API that: Feels natural to Java developers Is logical if you’ve learnt how to talk to MongoDB via the shell (since most of our documentation references the shell) Is consistent with the other language drivers. Given those requirements, it might not be a surprise that it’s taking us a while to come up with something that fits all of them, and this process is still in progress. However, from a Java point of view, we would like the following: Static typing is an advantage of Java, and we don’t want to lose that. In particular, we’re keen for the IDE to help you out when you’re trying to decide which methods to use and what their parameters are. We want Cmd+space to give you the right answers. Generics. They’ve been around for nearly 10 years, we should probably use them in the driver We want to use names and terms that are familiar in the MongoDB world. So, no more DBObject , please welcome Document . More helpers to create queries and objects in a way that makes sense and is self-describing The API is still evolving, what’s in Github WILL change. You can take a look if you want to see where we are right now, but we make zero guarantees that what’s there now will make it into any release. Understandable Exceptions When you’re troubleshooting someone’s problems , it becomes obvious that some of the exceptions thrown by the driver are not that helpful. In particular, it’s quite hard to understand whether it’s the server that threw an error (e.g. you’re trying to write to a secondary, which is not allowed) or the driver (e.g. can’t connect to the server, or can’t serialise that sort of Java object). So we’ve introduced the concept of Client and Server Exceptions. We’ve also introduced a lot more exceptions, so that instead of getting a MongoException with some message that you might have to parse and figure out what to do, we’re throwing specific exceptions for specific cases (for example, MongoInvalidDocumentException ). This should be helpful for anyone using the driver - whether you’re using it directly from your application, whether a third party is wrapping the driver and needs to figure out what to do in an exceptional case, or whether you’re working on the driver itself - after all, the code is open source and anyone can submit a pull request. Test Friendly The first thing I tried to do when I wrote my first MongoDB & Java application was mock the driver - while you’ll want some integration tests, you may also want to mock or stub the driver so you can test your application in isolation from MongoDB. But you can’t. All the classes are final and there are no interfaces. While there’s nothing wrong with performing system/integration/functional tests on your database, there’s often a need to test areas in isolation to have simple, fast-running tests that verify something is working as expected. The new driver makes use of interfaces at the API level so that you can mock the driver to test your application, and the cleaner, decoupled design makes it easier to create unit tests for the internals of the driver. And now, after a successful spike , we’ve started implementing Spock tests, both functional and unit, to improve the coverage and readability of the internal driver tests. In addition, we’re trying to implement more acceptance tests (which are in Java, not Groovy/Spock). The goal here is to have living documentation for the driver - not only for how to do things (“this is what an insert statement looks like”) but also to document what happens when things don’t go to plan (“this is the error you see when you pass null values in”). These tests are still very much a work in progress, but we hope to see them grow and evolve over time. Backwards Compatible Last, but by no means least, all this massive overhaul of design, architecture, and API MUST be backwards compatible. We are committed to all our existing users, we don’t want them to have to do a big bang upgrade simply to get the new and improved driver. And we believe in providing users with an upgrade path which lets them migrate gradually from the old driver, and the old API, to the new driver and new API. This has made development a little bit more tricky, but we think it’s made it easier to validate the design of the new driver - not least because we can run existing test suites against the compatible new driver (the compatible-mode driver exposes the old API but uses the new architecture), to verify that the behaviour is the same as it used to be, other than deprecated functionality . In Summary It was time for the Java Driver for MongoDB to have a bit of a facelift. To ensure a quality product, the drivers team at 10gen decided on a set of design goals for the new driver and have been hard at work creating a driver that means these criteria. In the next post, we’ll cover the new features in the 3.0 driver and show you where to find it.
Should I Begin a Pre-Sales Career at MongoDB? Insights from Our Remote Solutions Center
Do you have a technical background and enjoy working with customers? Have you considered beginning a career in pre-sales? Aicha Sarr, Solutions Architect at MongoDB, shares insights into how our Remote Solutions Center offers a path to building a career in pre-sales. Read on to learn more about how our Remote Solutions Center team builds off of each other’s strengths, applies their technical expertise, and focuses on customer success. Diverse backgrounds, shared attributes Our Remote Solutions Center team values both diverse backgrounds and sharing common attributes. Team members possess a blend of technical expertise and a customer-focused mindset with a strong affinity for technology and an inherent curiosity to grasp knowledge related to MongoDB, databases, and complex technical concepts. We all come from a variety of backgrounds, such as data engineers, software developers, cloud architects, and sales engineers. While direct experience with MongoDB is beneficial, it's not mandatory. Similarly, a background as an engineer can be advantageous, but it's not strictly necessary. To succeed on the team, one needs a keen interest in the technology and architecture of systems, an appetite for learning, and a commitment to ensuring customer success. We are trusted advisors, collaborating closely with customers to design and implement reliable systems, making a strong technical alignment between the platform and customer needs that are pivotal for success. Hands-on application of technical skills I’m frequently engaged in projects that require me to apply my technical skills. Many of these projects have been focused on building reusable demos that are distributed to our global team, providing an opportunity for my work to be showcased during technical calls or workshops. For instance, a teammate and I developed a mobile application centered around MongoDB Atlas for the Edge , our edge computing solution. The project included demonstrating the synchronization of user data between intermittently internet-connected devices and a central database, MongoDB Atlas. This demo showcased the practical application of Atlas Edge computing, maintaining data consistency across various devices, reflecting the real-world implications of mobile application development and data synchronization challenges. Additionally, I’ve developed an application illustrating the implementation of a Client-Side Field Level Encryption (CSFLE)-enabled application using Amazon Web Services Key Management Service with Java Spring Boot as the programming language. This hands-on experience allowed me to showcase the robust security features of CSFLE, a feature that enables the encryption of data in an application before it is sent over the network to MongoDB - a tangible example of how to enhance application security within a cloud environment. In my customer calls, I’ve used both my demos and demos built by my colleagues. Hence, these hands-on experiences play a crucial role in enhancing our team's collective knowledge, capabilities, and resources. This collaboration between team members fosters a shared understanding and serves as a valuable learning opportunities, allowing us to disseminate the acquired knowledge internally and promote a culture of continuous learning and skill development. The customer engagement journey Our customer engagement journey begins with establishing strong relationships during initial contact and holding in-depth discussions to grasp the customer's business requirements. This phase serves as the foundation for crafting tailored proposals that outline effective solutions. Technical presentations elucidate proposed solutions' intricacies, involving iterative, collaborative discussions and adjustments based on continuous feedback from customer stakeholders, including IT teams and decision-makers. Transitioning to the implementation phase, Proof of Concept projects may be initiated for real-world solution applications. The seamless move to implementation teams ensures ongoing support, addressing post-implementation issues for continuous customer satisfaction. This holistic approach emphasizes effective communication, active listening, and building lasting partnerships grounded in trust. Engaging with diverse customer personas, each playing a distinct role in decision-making, involves tailored interactions. Technical decision-makers engage in in-depth technical discussions, addressing integration concerns and compatibility. Business decision-makers require presentations emphasizing business value, ROI, and alignment with overarching goals. Interactions with developers or data engineers involve technical implementation discussions and collaborative problem-solving in specific programming languages. These interactions with end users, project managers, and technical teams are essential for gathering insights into practical requirements, project timelines, and seamless implementation. The shared goal is to create solutions that meet business and technical requirements, ensuring feasibility, efficiency, and alignment with customer capabilities. Conclusion: A tapestry of skills and collaboration The success of our Remote Solutions Center lies in the tapestry of diverse backgrounds, shared attributes, and a commitment to continuous learning. The opportunities for hands-on application of technical skills not only strengthen our individual capabilities but also contribute to a collective knowledge pool within our team. The customer engagement process is a collaborative journey, with an emphasis on the importance of understanding and adapting to evolving customer needs. This holistic approach to team dynamics, skill development, and customer engagement positions our Remote Solutions Center for success in a rapidly changing technological landscape. Learn more about careers in solutions consulting at MongoDB.