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 (22.214.171.124) and a new backup agent (126.96.36.199) 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
How DataSwitch And MongoDB Atlas Can Help Modernize Your Legacy Workloads
Data modernization is here to stay, and DataSwitch and MongoDB are leading the way forward. Research strongly indicates that the future of the Database Management System (DBMS) market is in the cloud, and the ideal way to shift from an outdated, legacy DBMS to a modern, cloud-friendly data warehouse is through data modernization. There are a few key factors driving this shift. Increasingly, companies need to store and manage unstructured data in a cloud-enabled system, as opposed to a legacy DBMS which is only designed for structured data. Moreover, the amount of data generated by a business is increasing at a rate of 55% to 65% every year and the majority of it is unstructured. A modernized database that can improve data quality and availability provides tremendous benefits in performance, scalability, and cost optimization. It also provides a foundation for improving business value through informed decision-making. Additionally, cloud-enabled databases support greater agility so you can upgrade current applications and build new ones faster to meet customer demand. Gartner predicts that by 2022, 75% of all databases will be on the cloud – either by direct deployment or through data migration and modernization. But research shows that over 40% of migration projects fail. This is due to challenges such as: Inadequate knowledge of legacy applications and their data design Complexity of code and design from different legacy applications Lack of automation tools for transforming from legacy data processing to cloud-friendly data and processes It is essential to harness a strategic approach and choose the right partner for your data modernization journey. We’re here to help you do just that. Why MongoDB? MongoDB is built for modern application developers and for the cloud era. As a general purpose, document-based, distributed database, it facilitates high productivity and can handle huge volumes of data. The document database stores data in JSON-like documents and is built on a scale-out architecture that is optimal for any kind of developer who builds scalable applications through agile methodologies. Ultimately, MongoDB fosters business agility, scalability and innovation. Key MongoDB advantages include: Rich JSON Documents Powerful query language Multi-cloud data distribution Security of sensitive data Quick storage and retrieval of data Capacity for huge volumes of data and traffic Design supports greater developer productivity Extremely reliable for mission-critical workloads Architected for optimal performance and efficiency Key advantages of MongoDB Atlas , MongoDB’s hosted database as a service, include: Multi-cloud data distribution Secure for sensitive data Designed for developer productivity Reliable for mission critical workloads Built for optimal performance Managed for operational efficiency To be clear, JSON documents are the most productive way to work with data as they support nested objects and arrays as values. They also support schemas that are flexible and dynamic. MongoDB’s powerful query language enables sorting and filtering of any field, regardless of how nested it is in a document. Moreover, it provides support for aggregations as well as modern use cases including graph search, geo-based search and text search. Queries are in JSON and are easy to compose. MongoDB provides support for joins in queries. MongoDB supports two types of relationships with the ability to reference and embed. It has all the power of a relational database and much, much more. Companies of all sizes can use MongoDB as it successfully operates on a large and mature platform ecosystem. Developers enjoy a great user experience with the ability to provision MongoDB Atlas clusters and commence coding instantly. A global community of developers and consultants makes it easy to get the help you need, if and when you need it. In addition, MongoDB supports all major languages and provides enterprise-grade support. Why DataSwitch as a partner for MongoDB? Automated schema re-design, data migration & code conversion DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a modern database platform. Our no-code and low-code solutions along with cloud data expertise and unique, automated schema generation accelerates time to market. We provide end-to-end data, schema and process migration with automated replatforming and refactoring, thereby delivering: 50% faster time to market 60% reduction in total cost of delivery Assured quality with built-in best practices, guidelines and accuracy Data modernization: How “DataSwitch Migrate” helps you migrate from RDBMS to MongoDB DataSwitch Migrate (“DS Migrate”) is a no-code and low-code toolkit that leverages advanced automation to provide intuitive, predictive and self-serviceable schema redesign from a traditional RDBMS model to MongoDB’s Document Model with built-in best practices. Based on data volume, performance, and criticality, DS Migrate automatically recommends the appropriate ETTL (Extract, Transfer, Transform & Load) data migration process. DataSwitch delivers data engineering solutions and transformations in half the timeframe of the existing typical data modernization solutions. Consider these key areas: Schema redesign – construct a new framework for data management. DS Migrate provides automated data migration and transformation based on your redesigned schema, as well as no-touch code conversion from legacy data scripts to MongoDB Atlas APIs. Users can simply drag and drop the schema for redesign and the platform converts it to a document-based JSON structure by applying MongoDB modeling best practices. The platform then automatically migrates data to the new, re-designed JSON structure. It also converts the legacy database script for MongoDB. This automated, user-friendly data migration is faster than anything you’ve ever seen. Here’s a look at how the schema designer works. Refactoring – change the data structure to match the new schema. DS Migrate handles this through auto code generation for migrating the data. This is far beyond a mere lift and shift. DataSwitch takes care of refactoring and replatforming (moving from the legacy platform to MongoDB) automatically. It is a game-changing unique capability to perform all these tasks within a single platform. Security – mask and tokenize data while moving the data from on-premise to the cloud. As the data is moving to a potentially public cloud, you must keep it secure. DataSwitch’s tool has the capability to configure and apply security measures automatically while migrating the data. Data Quality – ensure that data is clean, complete, trustworthy, consistent. DataSwitch allows you to configure your own quality rules and automatically apply them during data migration. In summary: first, the DataSwitch tool automatically extracts the data from an existing database, like Oracle. It then exports the data and stores it locally before zipping and transferring it to the cloud. Next, DataSwitch transforms the data by altering the data structure to match the re-designed schema, and applying data security measures during the transform step. Lastly, DS Migrate loads the data and processes it into MongoDB in its entirety. Process Conversion Process conversion, where scripts and process logic are migrated from legacy DBMS to a modern DBMS, is made easier thanks to a high degree of automation. Minimal coding and manual intervention are required and the journey is accelerated. It involves: DML – Data Manipulation Language CRUD – typical application functionality (Create, Read, Update & Delete) Converting to the equivalent of MongoDB Atlas API Degree of automation DataSwitch provides during Migration Schema Migration Activities DS Automation Capabilities Application Data Usage Analysis 70% 3NF to NoSQL Schema Recommendation 60% Schema Re-Design Self Services 50% Predictive Data Mapping 60% Process Migration Activities DS Automation Capabilities CRUD based SQL conversion (Oracle, MySQL, SQLServer, Teradata, DB2) to MongoDB API 70% Data Migration Activities DS Automation Capabilities Migration Script Creation 90% Historical Data Migration 90% 2 Catch Load 90% DataSwitch Legacy Modernization as a Service (LMaas): Our consulting expertise combined with the DS Migrate tool allows us to harness the power of the cloud for data transformation of RDBMS legacy data systems to MongoDB. Our solution delivers legacy transformation in half the time frame through pay-per-usage. Key strengths include: ● Data Architecture Consulting ● Data Modernization Assessment and Migration Strategy ● Specialized Modernization Services DS Migrate Architecture Diagram Contact us to learn more.