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.
4 Critical Features for a Modern Payments System
The business systems of many traditional banks rely on solutions that are decades old. These systems, which are built on outdated, inflexible relational databases, prevent traditional banks from competing with industry disruptors and those already adopting more modern approaches. Such outdated systems are ill-equipped to handle one of the core offerings that customers expect from banks today — instantaneous, cashless, digital payments . The relational database management systems (RDBMSes) at the core of these applications require breaking data structures into a complex web of tables. Originally, this tabular approach was necessary to minimize memory and storage footprints. But as hardware has become cheaper and more powerful, these advantages have also become less relevant. Instead, the complexity of this model results in data management and programmatic access issues. In this article, we’ll look at how a document database can simplify complexity and provide the scalability, performance, and other features required in modern business applications. Document model To stay competitive, many financial institutions will need to update their foundational data architecture and introduce a data platform that enables a flexible, real-time, and enriched customer experience. Without this, new apps and other services won’t be able to deliver significant value to the business. A document model eliminates the need for an intricate web of related tables. Adding new data to a document is relatively easy and quick since it can be done without the usually lengthy reorganization that RDBMSes require. What makes a document database different from a relational database? Intuitive data model simplifies and accelerates development work. Flexible schema allows modification of fields at any time, without disruptive migrations. Expressive query language and rich indexing enhance query flexibility. Universal JSON standard lets you structure data to meet application requirements. Distributed approach improves resiliency and enables global scalability. With a document database, there is no need for complicated multi-level joins for business objects, such as a bill or even a complex financial derivative, which often require object-relational mapping with complex stored procedures. Such stored procedures, which are written in custom languages, not only increase the cognitive load on developers but also are fiendishly hard to test. Missing automated tests present a major impediment to the adoption of agile software development methods. Required features Let’s look at four critical features that modern applications require for a successful overhaul of payment systems and how MongoDB can help address those needs. 1. Scalability Modern applications must operate at scales that were unthinkable just a few years ago, in relation to both transaction volume and to the number of development and test environments needed to support rapid development. Evolving consumer trends have also put higher demands on payment systems. Not only has the number of transactions increased, but the responsive experiences that customers expect have increased the query load, and data volumes are growing super-linear. The fully transactional RDBMS model is ill suited to support this level of performance and scale. Consequently, most organizations have created a plethora of caching layers, data warehouses, and aggregation and consolidation layers that create complexity, consume valuable developer time and cognitive load, and increase costs. To work efficiently, developers also need to be able to quickly create and tear down development and test environments, and this is only possible by leveraging the cloud. Traditional RDBMSes, however, are ill suited for cloud deployment. They are very sensitive to network latency, as business objects spread across multiple tables can only be retrieved through multiple sequential queries. MongoDB provides the scalability and performance that modern applications require. MongoDB’s developer data platform also ensures that the same data is available for use with other frequent consumption patterns like time series and full-text search . Thus, there is no need for custom replication code between the operational and analytical datastore. 2. Resiliency Many existing payment platforms were designed and architected when networking was expensive and slow. They depend on high-quality hardware with low redundancy for resilience. Not only is this approach very expensive, but the resiliency of a distributed system can never be reached through redundancy. At the core of MongoDB’s developer data platform is MongoDB Atlas , the most advanced cloud database service on the market. MongoDB Atlas can run in any cloud, or even across multiple clouds, and offers 99.995% uptime. This downtime is far less than typically expected to apply necessary security updates to a monolithic legacy database system. 3. Locality and global coverage Modern computing demands are at once ubiquitous and highly localized. Customers expect to be able to view their cash balances wherever they are, but client secrecy and data availability rules set strict guardrails on where data can be hosted and processed. The combination of geo-sharding, replication, and edge data addresses these problems. MongoDB Atlas in combination with MongoDB for Mobile brings these powerful tools to the developer. During the global pandemic, more consumers than ever have begun using their smartphones as payment terminals. To enable these rich functions, data must be held at the edge. Developing the synchronization of the data is difficult, however, and not a differentiator for financial institutions. MongoDB for Mobile, in addition with MongoDB’s geo-sharding capability on Atlas cloud, offloads this complexity from the developer. 4. Diverse workloads and workload isolation As more services and opportunities are developed, the demand to use the same data for multiple purposes is growing. Although legacy systems are well suited to support functions such as double entry accounting, when the same information has to be served up to a customer portal, the central credit engine, or an AI/ML algorithm, the limits of the relational databases become obvious. These limitations have resulted in developers following what is often called “best-of-breed” practices. Under this approach, data is replicated from the transactional core to a secondary, read-only datastore based on technology that is better suited to the particular workload. Typical examples are transactional data stores being copied nightly into data lakes to be available for AI/ML modelers. The additional hardware and licensing cost for this replication are not prohibitive, but the complexity of the replication, synchronization, and the complicated semantics introduced by batch dumps slows down development and increases both development and maintenance costs. Often, three or more different technologies are necessary to facilitate the usage patterns. With its developer data platform, MongoDB has integrated this replication, eliminating all the complexity for the developers. When a document is updated in the transactional datastore, MongoDB will automatically make it available for full-text search and time series analytics. The pace of change in the payments industry shows no signs of slowing. To stay competitive, it’s vital that you reassess your technology architecture. MongoDB Atlas is emerging as the technology of choice for many financial services firms that want to free their data, empower developers, and embrace disruption. Replacing legacy relational databases with a modern document database is a key step toward enhancing agility, controlling costs, better addressing consumer expectations, and achieving compliance with new regulations. Learn more by downloading our white paper “Modernize Your Payment Systems."