Another release of MMS is here!
In the realm of automation, today we are releasing the ability to provision on ephemeral SSD drives for all EC2 types.
Within the MMS UI, users can now search by replica set name, which is particularly helpful if you have hundreds of replica sets.
A new monitoring agent (18.104.22.168) and a new backup agent (22.214.171.124) were released - agents will now identify themselves to the MMS servers using the FQDN of the servers on which they are running.
The outlook for 2015 is “busy” – we’re just starting to roll out 2.8 across all of MMS Systems here internally, and we are sure customers are going to love all the new functionality.
On day one, we plan to have functionality for customers to easily and quickly upgrade to MongoDB, including choosing their storage engine. There will also be some changes to performance metrics in the UI - some metrics have been removed, including the old favorite “Lock %”. This is due to the fact that some metrics are no longer supported in MongoDB 2.8, and the metrics supported also depend on the storage engine chosen. 2.8 will also mean new metrics, so stay tuned for a list of those. In summary, this release was primarily pre-release prep work, which is not yet available in the UI.
Leaf in the Wild: Scaling China’s Largest Car Service App with MongoDB
Leaf in the Wild posts highlight real world MongoDB deployments. Read other stories about how companies are using MongoDB for their mission-critical projects. Kuaidi uses MongoDB at the heart of its taxi hailing service, connecting drivers with passengers up to 6 million times a day, and managing nearly half a billion orders. Kuaidi has scaled MongoDB across 4 geographic regions, serving thousands of reads and writes every second. Following his presentation at last month’s MongoDB Day in Beijing, I sat down with Ouyang Kang, Chief Architect at Kuaidi, to learn more about how China’s leading taxi booking application is using MongoDB, and his recommendations for those getting started with the database. Smartphone based taxi-calling and ride-sharing services are growing at an astounding rate – attracting significant investment (and huge company valuations). They are also intensely competitive. The choice of technology will ultimately drive success or failure in the market. In the world’s most populous country – and one suffering the most severe traffic congestion – the importance of using agile and scalable technology for transportation services is magnified. Please start by telling us a little bit about your company. Kuaidi was founded in 2012 and has grown to become Greater China’s largest car service application 1 , attracting investment from Alibaba and Matrix Partners. In just 2 years, we have attracted 100 million users who place up to 6 million ride requests every day via our smartphone app, connecting them to 3 million drivers in more than in 300 cities across China. And we are continuing to grow fast. The goal of Kuaidi Group is to improve the efficiency of urban transportation and the population’s quality of life. We currently operate 2 branded services – Kuaidi Taxi and Kuaidi ONE – which provide taxi and chauffeured limousine services respectively. Our long term plan is to offer services for every facet of passenger transportation combining location-based mobile technologies, data mining of our huge user base and intelligent routing algorithms. Tell us how you use MongoDB. At heart of our taxi booking application is the location based service, and we rely on MongoDB for this. Using MongoDB’s geospatial indexes and queries we can track the location of our drivers in real time, using it to connect users with their closest taxi, and displaying updates directly to the customer’s app. The location data is constantly being updated and queried. We also use MongoDB as an active archive of our order data. Each time a customer requests a taxi, the journey’s start and end points, the driver identity and fare are stored in a single record. We initially built our archive on top of MySQL, but once our order volume exceeded 100 million records, we hit scaling limits. We knew MongoDB scaled, so we migrated the archive to get the cost and performance benefits of horizontal scale out. What other databases do you use? We use Redis for caching and MySQL to store operational customer and order data. We also replicate data from MongoDB and MySQL into Hadoop for data mining and analytics. Did you consider other databases for your app? What made you select MongoDB? We considered three options for our location based service: Relational solutions based on MySQL and Postgres SOLR (for the search element of the application) MongoDB We evaluated each on multiple criteria, including Performance. We measure performance on multiple dimensions: latency, which is critical for good user experience on mobile apps; and speed of real time updates, so we are always working from the freshest data Scalability. We were confident that the service would quickly gain traction, so knowing we could scale our database on demand was paramount Ease-of-Use. We needed to achieve our performance and scalability goals without burdening our developer and operations team with complexity We evaluated all of the options on this criteria, and found MongoDB to be the best choice for us. It met the performance objectives. We found it easy to develop against. What was really important was that it proved easy to deploy and easy to run at scale . Please describe your MongoDB deployment Our MongoDB database is sharded across four geographic regions. A 7-node replica set is deployed in each region (6 data-bearing nodes and an arbiter). This deployment enables us to place data physically closer to local users for low latency access, as well as provide the scalability and resilience our application needs. We cannot tolerate downtime at all. We use Nagios for monitoring the application and database. Geo-Distributed MongoDB Deployment at Kuaidi We are running MongoDB 2.6 with the Java driver. Are there any metrics you can share? Yes. MongoDB is serving 50,000 operations per second (split 80:20 between reads and writes) Our database has grown to just under half a billion documents and continues to scale Do you have plans to use MongoDB for other applications? Our marketing team stores all of its promotions and messaging in MySQL, but is starting to hit scaling limits. As a result, it is not keeping pace with their demands. We are evaluating migrating this to MongoDB as well. What feature of the forthcoming MongoDB 3.0 release are you most looking forward to? It has to be document level concurrency control. As our service continues to grow, we need to scale to keep pace – especially writes. This is something we believe MongoDB 3.0 with its new WiredTiger storage engine will allow us to do. What advice would you give someone who is considering using MongoDB for their next project? Don’t just follow the crowd. Don’t just choose the same technology you have always chosen. There is so much innovation happening today, and the databases of the last decade are not always the right choice. Once you have a short-list of potential technologies, test them with your app, your queries and your data. It is the only way to be sure you are choosing the right technology going forward. Ouyang, thank you for your time, and sharing your experiences with the MongoDB community. Thinking about migrating from a relational database? Read the MongoDB white paper to get started: Migrating from RDBMS to MongoDB 1 Based on market share and transaction volume About the Author - Mat Keep Mat is part of the MongoDB product marketing team, responsible for building the vision, positioning and content for MongoDB’s products and services, including the analysis of market trends and customer requirements. Prior to MongoDB, Mat was director of product management at Oracle Corp. with responsibility for the MySQL database in web, telecoms, cloud and big data workloads. This followed a series of sales, business development and analyst / programmer positions with both technology vendors and end-user companies. < Read About Our William Zola Award for Community Excellence
MongoDB at AWS re:Invent 2020
While 2020 has been a challenging year, it has also given rise to new levels of innovative collaboration and agile thinking. Where better to experience both than at AWS re:Invent 2020? At MongoDB, we’re excited to partner with AWS on this free, 3-week virtual event, providing unlimited access to hundreds of sessions led by Cloud experts. Although we’ll miss the grand, buzzing halls of the Venetian Hotel and the celebratory sounds of slot machines this year, it’s still important to approach AWS re:Invent with a focused plan. Think of this year’s event as an opportunity to curate your own perfectly tailored experience. Check out this page for details of our fresh new lineup of deep-dives, targeted jam sessions and — of course — the annual MongoDB late-night party. Here are some of the highlights. AWS Jam — "Excel isn't a database!" Imagine this: It's your first week in a new job, and the VP of sales has already given you an important data task. The good news? From the start of the year, all your current sales data has been stored in MongoDB Atlas — allowing operational and analytical workloads to run on the live data set. The not-so-good news? That wasn't always the case. For years before they switched, their database (well, ”database”) of choice was… Excel. Fortunately someone took the initiative to export that data in CSV format and store it in S3, but now the sales team needs your help to analyze that data — and they need it fast. In our “Excel isn’t a database!” Jam Session, you’ll test and upgrade your skills by connecting MongoDB Atlas Data Lake to CSV data that’s been languishing in an S3 bucket. Then you’ll run an aggregation to complete the challenge and claim points. Game on! This jam session will be available on-demand for the duration of AWS re:Invent Databases & S3: Auto-archiving Breakout Session Databases are built for fast access, but this can also make them resource-intensive. As data grows, you may want to optimize performance (or cost) by migrating old or infrequently used data into cheap object storage. But this presents its own problems: automating the archival process, ensuring data consistency during failures, and either querying two data stores separately or building a query federation system. In this talk, you’ll learn about how we approached these problems while building Online Archive and Federated Query features into MongoDB Atlas, lessons learned from the experience, and how you can do the same. MongoDB Late Nite That’s right: it’s a party! In the spirit of Vegas, MongoDB will be hosting an interactive late-night bash complete with throw-back entertainment at our virtual after-hours event. Like Vegas, there’s something for everyone. Unlike Vegas, the odds are actually on your side. Get your adrenaline going and dial in for exclusive swag at our Home Shopping Network. Just sign on and dial into our custom QVC-reboot every hour for a chance to snag some really cool limited-release items. Stay tuned to the event website to find out what you can win, and when! Are you a Jeopardy lover? MongoDB Late Nite is your time to shine. Exercise your mental reflexes and get those synapses firing with hundreds of other party people inside episodes of dev-focused live trivia. And what kind of revelry is complete without a resident psychic on board? Join us at the Future of Coding for an interactive reading by a VERY accurate psychic. So kick back, grab a beverage and join us at the party from home. Let’s get in the spirit together! Sponsor Page/Online Booth Pop into our virtual sponsor booth at your convenience. Our product experts will be there to answer your questions one-on-one. Alternatively, if casually exploring resources is more your style, check out our self-serve content playlists. View these to dig deeper into MongoDB education, glean customer success stories and get up to speed on the latest product features.