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.
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. “Then one day, my data was gone.” Lost Data: A Breaking Point The application was unable to communicate with the database for 20 minutes, and when the database finally came back online, a large collection had vanished. 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 we deleted the data ourselves. It wasn’t so collaborative.” Fortunately, he had taken a snapshot of the data so Splitit was able to recover the 60 million missing documents. 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.”