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
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.
Revolutionizing Data Storage and Analytics with MongoDB Atlas on Google Cloud and HCL
Every organization requires data they can trust—and access—regardless of its format, size, or location. The rapid pace of change in technology and the shift towards cloud computing is revolutionizing how companies handle, govern and manage their data by freeing them from the heavy operational burden of on-premise deployments. Enterprises are looking for a centralized, cost-effective solution that allows them to scale their storage and analytics so they can ingest data and perform artificial intelligence (AI) and machine learning (ML) operations, ultimately expanding their marketing horizon. This blog post explores why companies should partner with MongoDB Atlas on Google Cloud to begin their data revolution journey, and how HCL Technologies can support customers looking to migrate. MongoDB Atlas as the distributed data platform MongoDB Atlas is the leading database-as-a-service on the market for three main reasons: Unparalleled developer experience - allows organizations to bring new features to market at a high velocity Horizontal scalability - supports hundreds of terabytes of data with sub-second queries Flexibility - stores data to meet various regulatory, operational, and high availability requirements. The versatility offered by MongoDB’s document model makes it ideal for modern data-driven use cases that require support for structured, semi-structured, and unstructured content all within a single platform. Its flexible schema allows changes to support new application features without costly schema migrations typically required with relational databases. MongoDB Atlas extends the core database by offering services like Atlas Search and MongoDB Realm that are a necessity for modern applications. Atlas Search provides a powerful Apache Lucene-based full text search engine that automatically indexes data in your MongoDB database without the need for a separate dedicated search engine or error-prone replication processes. Realm provides edge-to-cloud sync and backend services to accelerate and simplify mobile and web development. Atlas’ distributed architecture supports horizontal scaling for data volume, query latency, and query throughput which offers the scalability benefits of distributed data storage alongside the rich functionality of a fully-featured general purpose database. MongoDB Atlas is unique in its ability to provide the most wanted database as a managed service and is relied on by the world’s largest companies for their mission-critical production applications. Innovation powered by collaboration with HCL Technologies MongoDB’s versatility as a general-purpose database, in addition to its massive scalability, makes it a perfect foundation for analytics, visualization, and AI/ML applications on Google Cloud. As an MSP partner for Google Cloud, HCL Technologies helps enterprises accelerate and risk-mitigate their digital agenda, powered by Google Cloud. We’ve successfully implemented applications leveraging MongoDB Atlas on Google Cloud, building upon MongoDB’s flexible JSON-like data model, rich querying and indexing, and elastic scalability in conjunction with Google Cloud’s class-leading cloud infrastructure, data analytics, and machine learning capabilities. HCL is working with some of the world’s largest enterprises in building secure, performant, and cost-effective solutions with MongoDB and Google. Possessing technical expertise in Google Cloud, MongoDB, machine learning, and data science, our dedicated team developed a reference architecture that ensures high performance and scalability. This is simplified by MongoDB Atlas’ support for Google Cloud services which allows it to essentially operate as a cloud-native solution. Highlighted features include: Integration with Google Cloud Key Management Service Use of Google Cloud’s native storage snapshot for fast backup and restore Ability to create read-only MongoDB nodes in Google Cloud to reduce latency with Google Cloud-native services regardless of where the primary node is located (even other public cloud providers!) Integrated billing with Google Cloud Ability to span a single MongoDB cluster across Google Cloud regions worldwide, and more As represented in Figure 1 below, MongoDB Atlas on Google Cloud can be used as a single database solution for transactional, operational, and analytical workloads across a variety of use cases. Figure 1: MongoDB's core characteristics and features The following architecture in Figure 2 demonstrates the ease of reading and writing data to MongoDB from Google Cloud services. Dataflow, Cloud Data Fusion, and Dataproc can be leveraged to build data pipelines to migrate data from heterogeneous databases to MongoDB and to feed data to create interactive dashboards using Looker. These data pipelines support both batch and real-time ingestion workloads and can be automated and orchestrated using Google Cloud - native services.. Figure 2: MongoDB Atlas' integration with core Google Cloud services A data platform built using MongoDB Atlas and Google Cloud offers an integrated suite of services for storage, analysis, and visualization. Address your business challenges with HCL: Industry use cases Data-driven solutions built with MongoDB Atlas on Google Cloud have multiple applications across industries such as financial services, media and entertainment, healthcare, oil and gas, energy, manufacturing, retail, and the public sector. Every industry can benefit from this highly integrated storage and analytical solution. Use Cases and Benefits Data lake modernization with low cost and high availability for media and entertainment customers: Maintaining high availability and a low-cost data lake is an obstacle for any online entertainment platform that builds mobile or web ticketing applications. However, building on Google App Engine with MongoDB Atlas Clusters in the backend allows for a high-availability, low-cost data platform that seamlessly feeds data to downstream analytics platforms in real time. Unified data platform for retail customers: The retail business frequently requests an agile environment in order to encourage innovation among its engineers. With its agility in scaling and resource management, seamless multi-region clusters, and premium monitoring, running MongoDB Atlas on Google Cloud is a fantastic choice for building a single data platform. This simplifies the management of different data platforms and allows developers to focus on new ideas. High-speed real-time data platform of supply chain system for manufacturing units: By having real-time visibility and distributed data services, supply chain data can become a competitive advantage. MongoDB Atlas on Google Cloud provides a solid foundation for creating distributed data services with a unified, easy-to-maintain architecture. The unrivaled speed of MongoDB Atlas simplifies supply chain operations with real-time data analytics. The way forward Even in just the past decade, organizations have been forced to adapt to the extremely fast pace of innovation in the data analytics landscape: moving from batch to real-time, on-premise to cloud, gigabytes to petabytes, and the increased accessibility of advanced AI/ML models thanks to providers like Google Cloud. With our track record of success in this domain, HCL Technologies is uniquely positioned to help organizations realize the joint benefits of building data analytics applications with best-of-breed solutions from Google Cloud and MongoDB. Visit us to learn more about the HCL Google Ecosystem Business Unit and how we can help you harness the power of MongoDB Atlas and Google Cloud Platform to change the way you store and analyze your data through these solutions.