Cloud Partner Integration With Docker Containerization and Orchestration Technology

Introduction

In 2013, Docker made containerization, a simple Linux kernel technology, accessible to everyone. Today, Docker offers something better — container orchestration. Container orchestration is used to deploy complex multi-container apps on many machines, as opposed to deploying containers individually, on a single host.

Container orchestration runs on a distributed platform, independent of infrastructure, and stays online throughout the lifetime of the application. Docker makes this possible, enabling all users to access container orchestration without locking them in, i.e., depending on experts to create such a complex system. Thanks to its easy and fast implementation, portability, resilience, and better security, Docker can now offer container orchestration through cloud partner integration.

Integration changes

Multi-host and multi-container orchestration has been made easier in the latest version of Docker, 1.12. New API objects (e.g. Service and Node) allow usage of Docker API in deployment and management of apps on a group of Docker Engines, referred to as Docker Swarm. Docker Swarm orchestration has been integrated with Docker Engine, and it’s set to be commercially supported in the second half of 2016.

Docker Engine is Docker’s core software and runtime that creates containers within an operating system. When Docker Engine is run in Swarm mode, it depends on a distributed database spread among engines to maintain the state and define services made up of containers.

Docker Swarm is a tool for scheduling and clustering containers. As you know, clustering combines a group of systems to provide redundancy if one or more nodes fail. In this case, it also allows administrators to add/subtract container iterations with change in computing demands.

Docker has partnered with various cloud partners to easily and quickly get Docker containers up and running in their respective clouds. Examples include Docker for Amazon Web Services (AWS), Docker for Azure (Microsoft) and Digital Ocean.

How this affects DBAs

The integration of Docker containerization plus orchestration with prominent cloud partners will offer DBAs portability for the database infrastructure in their cloud providers. Docker containerization enables easy deployment of microservices-based architecture common for cloud-native web and database applications. This makes it possible for such applications to be easily combined with other database-related technologies like Modulus.io (PaaS for applications running on MongoDB and Node.js). Nodes in Docker run containers enabling modular units and more fine-grained scalability control over database applications.

Docker enables automation of resource management, where predefined library images are pretested and developers can easily deploy them. DBAs can apply this for their database infrastructure.

Pros and cons

Pros:

  •       Ready availability of containerization technology exponentially increases the economic advantage of microservices-based architecture systems
  •       In comparison to virtual machines, Docker containers are more efficient because each container is not running its own OS
  •       Since containers run in user space, there is less possibility of blockage, corruption or crashing at the kernel level
  •       It’s easy to copy, cache, and readily spin up and down containers
  •       It’s easy to fully build containers anywhere

In summary, containers are designed to be stateless and contain data that requires no protection.

Con:

  • There is less isolation between applications than in virtual machines

Effects of the cloud partner integration on its uses and applications

Docker provides an environment for the application within container, which is also portable from one platform to another. In this case, the container takes the app from the underlying cloud platform, thus handling the differences in the cloud platforms and not the application itself.

Docker is not the best for all applications, therefore it’s very important to choose the right ones. Docker is good for these three application types:

  •       Applications running on more than one cloud
  •       Applications benefitting from DevOps
  •       Application based on microservices-based architecture

Distributed nature of Docker enables Docker containers to run on any cloud platform, enabling them to locate each other and make up distributed apps. This also allows load sharing, application server reuse, and controlling containers orchestration.

Conclusion

In conclusion, Docker is helping us build a more agile, fluid and convoluted environment through combination of containerization and orchestration. Enterprises are yet to fully embrace Docker since it’s still a new technology. However, they cannot ignore the very attractive benefits of containers and also the emerging container data management products.

Docker may slowly phase out virtualization since it offers better-distributed application workload and lighter weight, and is more cost-effective. Cloud-based companies selling virtualization technology may consider Docker a threat.

To learn more, contact Datavail today. With more than 600 database administrators worldwide, Datavail is the largest database services provider in North America. As a reliable provider of 24×7 managed services for applications, BI/Analytics, and databases, Datavail can support your organization, regardless of the build you’ve selected.

Contact Us
Chuck Ezell
Vice President – Data Integration Practice
With almost 20 years of development experience, Chuck Ezell leads the Development, Tuning and Automation Practice (DTA). Bringing his knowledge from supporting some of the world's largest infrastructures in database & application performance tuning. With a broad background in process automation, database application development, optimizing database performance and many DevOps tools. Although he and his team excel at optimizing and customizing Oracle systems, Chuck works with .NET, T-SQL, C#, PHP, javascript, Java, ANSI SQL, PL/SQL, APEX, and many other languages on systems by Oracle, HP, IBM, Linux and Windows. He uses SSMS, Oracle OEM, AppDynamics, Splunk, Visual VM along with many others, for performance tuning operations.

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