This iteration includes plenty of eye candy for the visually inclined as well as bug fixes. The biggest item is the new view of MongoDB hosts - we’ve added a new view of MongoDB Deployments which shows the topology of your server environment, along with whether or not its healthy. The tabular view of hosts is still available and can be viewed via switching the view toggle. We also rolled out new dark themes for the Host Detail page.
Monitoring MongoDB continues to be priority one for our users, and this iteration we added a couple of key features:
- A new history of alert configurations
- Provide audit trail of who acknowledged an alert or deleted it, including the optional comment
- Implemented LDAP auth between Monitoring Agent and monitored hosts
MongoDB Backup functionality also received some enhancements:
- Implemented LDAP auth between Backup Agent and backed-up host
- We now allow Point in Time restores to be specified using a BSON timestamp
- Tweaked UI to show retired shards and configs so that users can still access backups for the retired members
The monitoring and backup agents also received new versions:
Monitoring Agent (version 184.108.40.206) was upgraded to Go 1.3, an updated mgo driver, which includes a fix for DNS lookup timeouts (MGO-34) and added support for LDAP authentication.
Backup Agent (version 220.127.116.11) includes the upgrade to Go 1.3, support for LDAP authentication, additional logging for when the backup agent manipulates the balancer; and http proxy configuration via the config file.
Have an issue or a bug or a feature request? File a ticket in our feature request queue!
Intern Spotlight: Russell Kaplan
This year, MongoDB welcomed 33 university students to our intern program in Engineering, Marketing and Education. In this series, we'll introduce you to the talented students who are helping us transform development and operations for how we run applications today. We had the chance to sit down with intern Russell Kaplan, who is working on the C++ Driver team. Where do you go to school, what is your major, and what year are you in? I go to Stanford, where I am a computer science major and a rising sophomore. What is your role at MongoDB? I work on the C++ driver team, building a geospatial API. How did you find out about the internship program at MongoDB? Why did you choose to come to MongoDB? I met MongoDB at PennApps . The App I made there won the prize for best use in the MongoDB category. It was called screenshades, and was a chrome extension that figured out what TV shows you watch and hides spoilers for them from your twitter stream. It worked with machine learning, so we needed a lot of training data, which we scraped from Twitter and Reddit for spoiler hashtags and built a dataset off of. We then used that as a classifier. I chose to come to MongoDB because I already had a lot of experience with front-end development and building web-apps and wanted to learn more about the back-end of development. What’s your hometown? My hometown is NYC. Best city in the world! Did you have previous experience using MongoDB before you arrived? If so, how are things different now that you work at MongoDB? If not, how did you learn MongoDB and how was the education process? I used it at hackathons before. But I only really used its basic features. I learned a lot more about it after getting here. It’s really simple to use for quickly getting started with web applications. Bike or public transportation to work? Subway. What’s a typical day (or week) for you? I get into the office by 10am. Eat some breakfast in the café, catch up on emails for a bit and then get to coding. I code until lunch, have some seamless, play a game of ping pong and then code for the rest of the day. What do you love most about MongoDB? I love the people I get to work with. It’s a lot of really smart high-energy people that I have so much to learn from. What’s the most challenging aspect of your job? Because it’s a database and an open source company, the code really has to be production quality in a way that class work doesn’t. It’s a much more rigorous standard of development. That’s something that’s really cool to learn but challenging at times. What do you hope to accomplish while you’re here? I hope to have my code integrated into the rest of the MongoDB code base. I hope that the people who use the C++ driver appreciate the work I’ve done. What’s your favorite Seamless lunch order? Chop’t steak salad. Name one secret skill you have, unrelated to work. I can beat box. A little bit, I’m an amateur. Whose your favorite tennis player? Djokovic, he’s incredible. He also has a hilarious sense of humor and isn’t afraid to make jokes about himself and other players. Kindle or book? What’s your favorite book? Books. I’m old school. My favorite book is probably 1984. Describe your perfect weekend. Oh man. Sleep in late Saturday morning and then go play some tennis with some friends. Discover some obscure yet delicious restaurant for dinner, and then go see a Death Cab for Cutie concert. All while getting to hang-out with friends and family. Want to help build the next revolution in database technology? MongoDB offers summer internships and new graduate opportunities to foster computer science talent across the country. Learn more about the MongoDB University Relations program .
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.