Keeping a software application up and running is an interesting task. The operations engineers responsible for maintaining back-end infrastructure have a difficult job, and it’s even more challenging when each change the developers want to roll out requires changes to the infrastructure and its configuration. Recently, though, many organizations have begun to adopt container technology as a consistent, pre-configured delivery mechanism that makes deployment simpler and less error-prone. Containers have occurred as a new way to deliver application and service amount of work (i.e., an executable unit of code that performs work—in this setting as an application) across the continuous integration/continuous delivery (CI/CD) development channel and well into production environments.
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The Open Container Initiative is tasked with making an industry-standard container format, which will eventually be abundant. The setup will permit for complete workload portability, freeing developers to focus less on development and tools selection, subsequently, all types of acquiescent container technologies will provide the similar container capabilities, such as orchestration and scaling.
There are real-world methods you can use to optimize containers for a robust DevOps pipeline.
Multiple management aspects
During workload development and operation, many things must be measured, such as product quality and systems security and health. These features are handled by tools that have their exact configuration in proprietary and private file formats and repositories, frequently apart from the workload code and binaries. Once a container is at a given phase in the pipeline, you should be using testing or monitoring instructions that identify the workload code. Though, the parting between the workload and testing or monitoring instructions often leads to errors and cumbersome integration procedures, such as when it’s required to match precise automated tests with a specific workload version.
The microservice complexity issue
As the speed of innovation accelerates and more and more workloads are being developed in equivalent using microservices architecture, IT professionals are wrapping them in containers and quickly forceful them down the development pipeline and into production systems. This increased pace creates a high burden on the whole pipeline and the many tools that are a portion of it.
Put your workload alongside its test and monitoring
Products that check the user interface (UI) or API functionality have to stock testing directions in their inner data depositories, which are frequently overseen by configuration systems that alteration from the code they test. This has the potential to reason regular misalignment between source-code evolution into executable binaries and the supporting testing directions. Current testing products are not created on a descriptive testing model that can be competently managed as code, much less be kept and used in the container.
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Embed test as code in the container
The file format for container images is created on a file system that stores workload binaries and any mandatory dependencies and configuration information. To leverage that, you can make a new folder within the container image file system to cloud the testing instructions. The developer who formed the feature/capability should initially make these instructions. Once a CI system deploys the set of containers on a target staging or production environment, it hands the deployed container locations to the testing tool and requests that testing commence. The testing tool examines each container for fixed testing instructions, states the instruction set, and then executes them on the aim container under test.
Monitoring operations as code
Embedding monitoring requirements and specifications together with the workload in the container also have the advantage of keeping both development and operations in sync. The workload growths in features and functions such that the observing instructions (which should also be stated by the developer in a comparable way to the testing instructions I described above) are maintained in sync with the workload. This serves to codify the operations of the new functionality while it progresses down the development pipeline.
Modernize your CI/CD pipeline
In a container-optimized pipeline, the container itself could be attached to serve more of a determination than just stock the workload. By embedding pipeline phase instructions for testing, monitoring, and other instructions, as well as storing phase results within the container, you are effectively embedding the pipeline state machine within the container itself. At any given pipeline checkpoint, you can inspect the container to validate which phases it has undergone and what the results were, calling up additional CI and delivery pipeline phases based on what passed or failed.
While containers are a cooler and fewer error-prone way to deliver applications, the Open Container Initiative must not let test and monitoring instructions get left after on the journey from Dev to Ops. Until container testing and monitoring are standardized though, the techniques discussed above will help you on your way to creating a highly efficient DevOps pipeline.