MongoDB 3.2.5 is released
MongoDB 3.2.5 is out and is ready for production deployment. This release contains only fixes since 3.2.4, and is a recommended upgrade for all 3.2 users.
Fixed in this release:
- SERVER-23274 Aggregate with out, then stepdown, out collection dropped.
- SERVER-23283 RangeDeleter does not log cursor ids correctly in deleteNow()
- SERVER-22964 IX GlobalLock being held while wating for wt cache eviction
- SERVER-22937 Retry catalog operations whenever possible
- SERVER-22831 Low query rate with heavy cache pressure and an idle collection
- SERVER-21681 In-memory storage engine not reporting index size
All Issues | 3.2 Release Notes | Downloads
As always, please let us know of any issues.
– The MongoDB Team
Oxford Nanopore Technologies & MongoDB: Powering Real-Time Genetic Analysis with Docker, MongoDB, & AWS
Genetic analysis is entering the mobile age. Earlier this year scientific journal Nature published a paper showing how Ebola researchers in Guinea were able to analyse genetic material in hours, rather than the weeks it had previously taken. This increased speed meant doctors could better understand the spread of the disease. Then quickly develop strategies to stop it. The hardware that enabled the genetic analysis is the MinION , from UK-based Oxford Nanopore Technology . The stapler-sized MinION is the data-capture side of the analysis, but for the purposes of this article we’re interested in data processing and analysis. In particular how Oxford Nanopore has been able to build a fast, agile and powerful cloud-based platform that has the potential to deliver biological analyses to any scientist, at any time, anywhere in the world. The applications for this genetic analysis go far beyond the medical field and disease control. Oxford Nanopore is using technologies like MongoDB, Amazon Web Services, and Docker containers in its stated goal: “to enable the real-time analysis of any living thing, by any user, in any environment.” A Billionth of a Meter The MinION does its genetic magic through the use of nanopores. Each nanopore is just a billionth of a meter wide. The technology in the MinION threads the genetic material through the nanopores where tiny differences in each sample can be registered as electrical disruptions. If you want a more detailed explanation of nanopores, check out Oxford Nanopore Technologies’ website . DNA sequencing can be associated with predictive human questions alone, for example “what probability is there that this person will develop a specific disease?” But human genome research is just a part of the equation, and the portable nature of the MinION means it might be suitable for a more diverse range of questions: Is the soup I’m about to eat safe? What type of disease am I looking at? Where did this pathogen originate? How can we grow more resilient plants? Is this hospital ward clean? Crucially, these questions need to be answered quickly, and in a range of environments – from the science lab to the middle of the jungle. Three Billion Bases in the Cloud The cleverest sequencing tool in the world would be worthless if we were unable to process and understand the data it created. To deal with the volume and velocity of processing billions of lines of DNA, Oxford Nanopore Technologies built analysis capabilities offered by Metrichor , on powerful software that can scale seamlessly in the cloud. Richard Carter, Associate Director, Data Integration at Oxford Nanopore gave a presentation at MongoDB Days where he noted: “When we began building Metrichor services, it was clear our data would not fit in the neat rows and columns of a relational database. We needed a database that could look at our complex information in more flexible and dynamic ways. It was a straightforward decision to go with MongoDB. It’s robust, best of breed, and has the data modelling and analytics flexibility we required. We also observed the technology has an incredible community behind it, coupled with extensive documentation and training. All of which enable us to get productive with the technology much faster.” The DNA data is read locally onto the MinION and it’s then sent to an Amazon Web Services cloud. The findings are then analysed before the results are sent back to the user’s laptop or displayed in web reports. All of this is driven by, and stored in the non-relational database MongoDB. Docker containers are used to package, deploy and run the software across the cloud deployment. Carter also noted that: “The biology and hardware is the real trick, of course, but we needed power and scalability to run cloud based services as we wished.” There were other challenges the team had during development of their software. They had a technical goal and a number of ways they could reach it while keeping the focus on the biology. It was essential they had the freedom to experiment and make significant changes as they went along. “Happily, MongoDB supports an evolutionary approach to development.” explained Carter. “We were spinning up instances and working on the science almost instantly. The database got out of the way.” Carter’s team does not have a database administrator. They have found that MongoDB Cloud Manager is able to provide all the monitoring data needed to keep a deployment healthy. Features like simple, automated deployment across any cloud region, continuous backups, and telemetry visualisations also mean administration doesn’t monopolise the developers’ time. Giant Ideas Guinea is just one of the many places where researchers are using Nanopore’s data architecture for analysis. In fact, NASA will soon be using the MinION for testing biological molecules on the International Space Station. Regardless of the location, the combination of rigorous science and the power of cloud computing is ushering in a new way of understanding the world. Read more about MongoDB and its implementation on the AWS cloud platform. MongoDB on AWS: Guidelines and Best Practices About the Author - Mat Keep Mat is director of product and market analysis at MongoDB. He is 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.
Using MongoDB Skill Scanner to Build Better Training Programs
Technology leaders know that transformation is about more than just adopting modern technologies like MongoDB. The entire organization has to rally behind change — which is no easy task. The skills that modern development teams need are evolving faster than ever, and hiring to fill skills gaps can be too time-consuming and expensive of a process for many organizations. So it’s imperative that we plan for how we want to bring our people with us on our modernization journey, and proactively upskill them on the technologies we’re betting on. Because what happens if you choose MongoDB, but your developers don’t know how to use it? CIOs know that training programs are easier said than done. EY reported that 30% of CIOs acknowledge that their training programs are ineffective, and that they’re struggling to retain talent because of it. These leaders come to us to help them build and execute their MongoDB training programs , and seek advice on two extremely common yet critical challenges: How do we get away from the less effective one-size-fits-all approach? How do we measure the ROI of our training program and connect it to business impact? How we use MongoDB Skill Scanner to overcome training challenges Our Professional Services team uses a tool called MongoDB Skill Scanner to address both of these challenges. This tool helps us provide these three benefits to our customers looking to build a training program: Improve MongoDB proficiency: Teams can use Skill Scanner to quickly and easily assess the MongoDB skill gaps of their team members and gain a comprehensive understanding of their team’s MongoDB skills baseline. Increased productivity and accuracy: When team members have a comprehensive understanding of MongoDB, they are able to work more quickly and accurately on projects, leading to increased productivity and a higher quality of work. Save time and money with targeted Training: Using Skill Scanner, customers can avoid wasting time and money on trial-and-error learning. Instead, they can focus on improving their skills in a more targeted and efficient way with right-sized training plans. By leveraging this data, our customers’ engineers can engage in the right training at the right time, targeted for their job role and specific skill shortages. When a training program is built this way, engineers maximize their knowledge retention and minimize time away from their projects. Skill Scanner includes three role-based assessments, one for developers, database administrators, and DevOps respectively. Through a series of multiple choice questions, Skill Scanner provides customers with a clear understanding of their level of expertise across a set of technical skills that are critical for success in their role. After submitting the assessment, engineers will get results in each skill area outlining if they are beginner, intermediate, or advanced. Why data-driven training programs matter We’ve learned that it’s not enough to just tell teams to go watch training videos or webinars on their own, or to place everyone in the same one-size-fits-all program. Skills gaps vary from team to team, and individual to individual. The one-size-fits-all approach of some programs may not address individual learners' needs, wasting time and making it difficult for them to acquire new skills. By using Skill Scanner, we’re able to interpret this data to help determine which training courses your team should take. But we don’t only capture this data before doing training; we use Skill Scanner again after training programs are completed to see where immediate improvements have been made. This helps technology leaders prove the impact and ROI of their training, and gives them the confidence that their teams are ready to be successful with MongoDB. Developing a Precision Learning Program To go even further, our team can work with you to build a Precision Learning Program, where we use Skill Scanner data to build learning schedules that are unique to each individual. These schedules include a variety of short, blended, learning events such as classes, technical workshops, self-paced exercises, and project coaching. We’ve seen PLP lead to higher knowledge retention and of course, measurable project results. A customer who recently concluded their PLP saw a 43% increase in knowledge retention. Getting started building a personalized training program Skill gaps aren’t a novel problem IT leaders are facing. But with new digital courses, training, and technologies, the resources to close these gaps are at your fingertips. Skill Scanner and Precision Learning Program have been specifically designed to empower teams by offering targeted training that enhances their understanding of MongoDB. These short training events are carefully crafted to close skill gaps without compromising developer productivity. We’ve seen a variety of customers use this tool to help train their team’s individual needs, from needing to upskill new hires on their teams, projects with new MongoDB products, migrating to MongoDB Atlas, and more. It also saves your business the hours developers would've wasted searching for answers (and developers don’t want to spend their time that way, either). “We need help getting from point A to point B and feel MongoDB is uniquely positioned to help” — CTO at large insurance firm If you're interested in trying out MongoDB Skill Scanner or want to explore the MongoDB Precision Learning Program further, you can reach out to your account representative or contact us directly .