MongoDB 3.0.13 is out and is ready for production deployment. This release contains only fixes since 3.0.12, and is a recommended upgrade for all 3.0 users.
Fixed in this release:
- SERVER Add Debian 8 (Jessie) builds and associated package repository
- SERVER On RHEL7/Centos7 mongod can't stop if pid location in conf differs from the init.d script
- SERVER Database/Collection drop during initial sync can cause collmod to fail initial sync
- SERVER Update to PCRE 8.39
- SERVER Building 2dsphere index uses excessive memory
- TOOLS No numeric version in --version output
As always, please let us know of any issues.
-- The MongoDB Team
Announcing MongoDB 3.4
Today we are announcing MongoDB 3.4 , another milestone in our march to being the default database for modern applications. 3.4 makes MongoDB more flexible than ever, allowing developers to consolidate even more use cases into their MongoDB deployment, even as we continue to mature the platform and its ecosystem. MongoDB was created to make it easy for developers to work with their data, beginning with introducing the document model itself. Documents are the best rudimentary unit for a data store, because they let you represent any kind of data, and embody their structure however best suits your use case. Whether that means deep or shallow nesting (or no nesting), documents can handle it. The key is being able to add many types of queries and algorithms to the data. MongoDB 3.4 adds a stage to the aggregation pipeline that enables faceted search , greatly simplifying the query load for applications that browse and explore that data. It also adds operators to power graph queries . As we continue to add query features, users can consolidate more uses cases, instead of bloating their application footprint with a proliferation of specialized data stores. Just because it’s easy to work with data in MongoDB, it doesn’t mean we can’t make it easier. In 3.4, the aggregation pipeline continues to mature , with more operators and expressions, enhancing string handling, allowing more sophisticated use of array elements, testing fields for type, and support for branching. Financial calculations are made simple with the addition of a Decimal data type. I think it was John Donne who said: “No database is an island,” but whoever said it, they were very right. A database has to work as the heart of an ecosystem, and in 3.4, we continue to build that thriving ecosystem. Connecting MongoDB to the outside world is better than ever. MongoDB 3.4 introduces a ground-up rewrite of the BI connector , which improves performance, simplifies installation and configuration, and supports Windows. 3.4 also includes an update for our Apache Spark connector , with support for the Spark 2.0. We’ve also extended the platforms that MongoDB runs on, including ARM-64, and IBM’s POWER8 and zSeries platforms. MongoDB Compass is growing up with 3.4. It has new ways to depict data, such as the map view for geographic data, and it has become a data manipulation and performance tuning tool as well. In 3.4, Compass offers visual plan explanations, real-time stats, CRUD operations and index creation, so now you can identify, diagnose, and fix performance and data problems all from within Compass. Of course, MongoDB 3.4 is supported by our trifecta of enterprise-grade ops management platforms: Ops Manager , Cloud Manager , and MongoDB Atlas , each of which add new features with this release. Ops Manager, for example, has improved its monitoring with built in telemetry gathering tailored to each deployment platform, and now allows ops teams to create server pools to serve up database-as-a-service to internal teams. Atlas introduces Virtual Private Cloud (VPC) Peering, allowing teams to use convenient private IPs to talk to their MongoDB service from within their AWS VPC. There’s a ton more than I can fit into a blog post. That’s what release notes are for. But I shouldn’t leave out a few highlights, like: tunable consistency control for replica sets, including linearizable reads; collations for queries and indexes; and read-only views , which enable us to bring field level security to apps handling regulated data. We’re incredibly excited to ship MongoDB 3.4 to you, so it can help your data serve you, not the other way around. Our approach is to build a database that can handle any kind of data, and the capabilities to query that data however you need to. Learn more about MongoDB 3.4, register for our upcoming webinar: Find out what's new About the Author - Eliot Horowitz Eliot Horowitz is CTO and Co-Founder of MongoDB. Eliot is one of the core MongoDB kernel committers. Previously, he was Co-Founder and CTO of ShopWiki. Eliot developed the crawling and data extraction algorithm that is the core of its innovative technology. He has quickly become one of Silicon Alley's up and coming entrepreneurs and was selected as one of BusinessWeek's Top 25 Entrepreneurs Under Age 25 nationwide in 2006. Earlier, Eliot was a software developer in the R&D group at DoubleClick (acquired by Google for $3.1 billion). Eliot received a BS in Computer Science from Brown University.
Digital Transformation with MongoDB Atlas and Accenture Cloud First: Three Use Cases for Cloud Modernization
Choosing which providers to partner with when moving to the cloud is a critical first step in pivoting to meet this new field of opportunity. Together, MongoDB and Accenture deliver deep expertise, proven success, and the right balance of experience across industries. Here’s how—and where—we can help. Journey to the cloud: Three value-unlocking use cases typically prioritized by CIOs and CTOs Accenture and MongoDB have identified three common use case patterns across industries, each worth examining further: Building out APIs for modern application development Moving from monolith to microservice for modernization Offloading from legacy or mainframe systems These scenarios typically come to the forefront of the CIO/CTO agenda as they aim to accelerate their journey to the cloud and to release gridlock from old but valuable business applications. A common mistake is believing that the business case for this migration is entirely IT-driven. The true motivation must come from understanding that the legacy technology is stunting the ability for applications to keep up with the increasing pace of change in the business. Additionally, and especially for mainframe applications, the labor force segment that knows these business critical applications is rapidly approaching retirement, leaving a critical knowledge gap in supporting and extending these applications. Let’s take a deeper look. 1. Building out APIs for modern application development Innovative applications require businesses to react and adapt quickly. Development needs to be quick, the architecture must be loosely coupled, and the deployment model must have scalability built into its core. Technical challenges facing today’s applications include: Incoming digital requests can vary and follow trends, in an often quick-bursting and even disruptive manner Businesses need to be able to adapt on the fly – and, therefore, so does the data structure Innovation today requires data models that can be extended to meet future demands The weight of these requirements has been exacerbated as businesses become more and more structured for our on-demand world. Mobile applications at a bank, for example, need to be able to service customers in real-time and 24/7. And it’s not just banks—demand volatility is on the rise across industries and customer expectations continue to evolve. The secret sauce in these new applications is to build the data to fit the application – rather than dealing with the constraints of the relational data models of the previous generation applications. With the flexible data structures and ease-of-change with MongoDB, developers can build and adjust the data structures to meet the rapidly evolving needs of new applications, delivering the speed and agility needed to create disruptive change. Furthermore, you can scale out quickly with the horizontal scaling capabilities of the leading NoSQL database, while uniquely still having access to ACID database properties where needed. Finally, with MongoDB Atlas , the fully managed MongoDB solution, the needs for scale-on-demand and ready-to-use secure production infrastructure in Azure, AWS, and Google Cloud, peace of mind has never come easier when preparing for an industry-changing innovation. 2. Moving from monolith to microservice for modernization Not all innovation comes in a brand-new application. In order to innovate with old applications, sometimes they need to be extended in ways that were not contemplated when leading architectures centered around monolithic application configurations and deployments. Many of our clients experience challenges with their application portfolios because they have not kept pace with the rapid evolution of technology, driving one critical business problem: the inability to scale at pace. Digital Decoupling is an Accenture solution designed to extend the life of critical business applications while augmenting them with new functionality – often through the modern development techniques and architecture as described in section 1. Initially, this will mean that there is yet another technology pattern included in the footprint of the business application. However, the difference here is that the transformation from “legacy java” to modern architectures can be accelerated. Once you have experienced the simplicity of data that matches exactly what your application needs, it will be difficult to stop! Diagram A: Monolith to microservices transformation The diagram above (reference Diagram A ) illustrates the historical move to a more ideal data architecture from monolith to microservices. However, in the real world, companies often find completely moving over to a microservice layer to be too expensive or too much effort. That is why over the last 5 years, Accenture has developed its Digital Decoupling approach to solving this problem. More details can be found here . With Accenture’s Smart Data Mover data can be moved from the relational database into MongoDB and code can be substantially ported as-is while simplifying the data access layer, accelerating organizations in their onramp to rapid change and innovation. The size and shape of the new services can adjust from microservices for rapidly evolving functions to coarse-grained services that don’t have much change and don’t warrant further investment. MongoDB is designed to work well as the storage layer of a microservice or API architecture and can mitigate risk during the refactoring phase, so that businesses can meet demands that ebb, flow and ideally grow in traffic. 3. Offloading from legacy or mainframe systems As more and more companies migrate to the cloud, a common (and often painful) use case is the requirement to offload applications from mainframes and other legacy data stores. It is painful for many reasons, most predominantly because no one within the organization has the institutional knowledge required to maintain and operate the legacy application, and because many times the movement of the data from the system involves several complex technical hurdles to implementation. To solve this, Accenture’s Digital Decoupling approach extends to the mainframe as well. This approach within the mainframe offload scenario is located below (reference Diagram B ). Diagram B: Mainframe offload reference architecture with MongoDB Atlas: When transitioning from a legacy or mainframe system, there are several aspects to consider: How will the data and code be migrated? If moving to a more scalable data solution, how will code in triggers and procedures get ported? How will the new data platform solution deliver on required flexibility, scale (and not just for read-only copies of the data) in addition to resilience non-functional requirements? The last point is the pivot of our conversation. To gain a true advantage, most organizations will need to preserve traditional enterprise capabilities, but also meet the needs of a modern day and age where storage is no longer a concern. To help move the data over, Accenture Smart Data Mover can get the job done seamlessly. CDC tools and/or Kafka can also be leveraged for continuous update, if the preferred source of truth for some applications still needs to be the mainframe. Please check out this solutions guide for more information about best practices on offloading from the mainframe with MongoDB. Why MongoDB? MongoDB’s document-based, distributed database provides users with the versatility to build sophisticated applications that can respond and adapt to changing customer demands and market trends. As the leading choice for general-purpose databases, MongoDB reduces time spent on development cycles and empowers developers with flexible schema and the tools they need to innovate. Furthermore, MongoDB’s fully managed database-as-a-service option, MongoDB Atlas , is the only multi-cloud document database available in the market, and delivers the most advanced security and data distribution capabilities of any fully-managed service. MongoDB has also spent years building out a full modernization program to help customers and their architects with their journey to the cloud. This program includes training, tools, and best practices that have been co-developed with System Integrators, especially Accenture. This is why MongoDB is partnering heavily with Accenture in helping customers move to the cloud. Why Accenture Cloud First? Accenture Cloud First is a multi-service group of 70,000 cloud professionals across the globe that brings together the full power and breadth of Accenture’s industry and technology capabilities to help move organizations to the cloud with greater speed and achieve greater value, faster. The Cloud First team combines world-class cloud and cloud native engineering, learning and talent development expertise, deep experience in cloud change management, and cloud-ready operating models with a commitment to responsible business by design. Security, data privacy, responsible use of artificial intelligence, sustainability, and ethics and compliance are built into the fundamental changes Accenture helps companies achieve. How MongoDB and Accenture can help Our clients have taken a deep, insightful look at their application portfolios and uniformly decided that they need to accelerate their migrations to the cloud under the realization that their data center isn’t where they want their employees, i.e. their most precious resource, to spend most of their time. This realization has pushed Cloud Migration and Modernization efforts front and center for the C-Suite. Accenture has developed an end-to-end Value-Led Modernization methodology that analyzes each business case to deliver the most value possible for our clients. We built this methodology in order to be laser-focused on delivering the right outcome: increased value to the organization vs. just doing modernization for modernization’s sake. Core to our methodology is the belief that modernization initiatives should take a lean engineering approach to the work itself while simultaneously enabling new value streams for the business. To enable this dual reality that CIOs and CTOs find themselves in today, we architecturally focus on three tenants: Minimal invasive modernization efforts using our digital decoupling techniques Establishment of an enterprise, event-driven architecture as the core of communication Establishment of a modern data architecture to underpin our new value propositions while simultaneously supporting existing value cases. To remain competitive in today’s ever-changing marketplace, you need to be able to scale quickly and securely while enabling access to one of the business’ most important assets: its data. Together, Accenture and MongoDB have made several investments to date and continue to partner to bring the best to our clients. Accenture and MongoDB have launched another joint solution, our “Modernizer Tool,” to help customers modernize as they migrate to the cloud. The Modernizer tool is an asset that identifies relevant information and speeds up feeding and integration and migration process definition by standard techniques. The tool aims to mitigate data modeling and integration challenges by applying a metadata-driven approach which anticipates key risks. Looking Forward Bottom line? Your organization needs a modern database—one that can allow it the speed and agility to keep up with ever changing business needs. Further investment in modernization today isn’t an option, it’s a necessity to remain competitive, to realize your digital transformation goals and to provide your organization with the foundation necessary to innovate. Accenture and MongoDB will continue to partner in making investments in Cloud Enablement, Cloud Migration and Modernization that will enable you to realize your goals. We look forward to working with our customers on cloud modernization projects in the field. Find out more about the MongoDB & Accenture partnership here .