- Mastering Docker Enterprise
- Mark Panthofer
- 338字
- 2021-07-02 12:30:01
New era for app Dev, DevOps, and IT operations
Using containers and orchestrators changes the way we look at building software and defining a software delivery pipeline. Container-based development fundamentally supports what the DevOps folks call a shift left, where developers of distributed systems become more accountable for the quality of the overall solution, meaning the binaries and how they are connected. Hence, wiring up my services in no longer the networking, integration, or operations teams' problem; it belongs to the developers. In fact, the YAML specification for connecting and deploying their application is now an artifact that gets checked into source code control!
Faster deployment of fixes and enhancements is a prime motivation for containerizing monolithic web applications built with job and .NET. Containerization allows each team to operate independently and deploy its application as soon as it is ready to go, no longer having to wait for all of the other application teams or the next quarterly release cycle.
Containerizing applications can be really helpful for breaking up the organization log jams associated with pre-container monolithic deployments, as each application gets its own runtime container. This container includes all of their specific runtime dependencies, such as Java and Tomcat, for the application to run. Because we are using containers, we become less concerned about the overhead associated with starting and operating similar containers in production by remembering how Docker isolates application execution, while sharing common layers from the filesystem for fast start times and efficient resource utilization. So, rather then having to coordinate across all of the teams involved in a deployment, each team has its own isolated stack of dependencies, which allows them to deploy and test on their own schedule. Not surprisingly, after applications are containerized, it is much easier to independently refactor them.
- Big Data Analytics with Hadoop 3
- Circos Data Visualization How-to
- WOW!Illustrator CS6完全自學寶典
- IoT Penetration Testing Cookbook
- 統計學習理論與方法:R語言版
- 軟件構件技術
- Mastering Exploratory Analysis with pandas
- 大數據:引爆新的價值點
- Practical AWS Networking
- Kubernetes on AWS
- 數據清洗
- 數字多媒體技術與應用實例
- 計算機應用基礎學習指導與練習(Windows XP+Office 2003)
- Hands-On Microservices with C#
- Hands-On Data Analysis with NumPy and pandas