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Articles related to "docker"


Adventures with Dockerised Headed Chrome, Extensions and Selenium-Python

  • These checks were generally executed manually by the engineer following the post-release run-book, its essentially a high level integration test, to ensure that the telephony, data protection act verification and CRM systems are working together as expected.
  • Python Selenium can provide the Chromedriver a list of packed extensions, which will be loaded into the temporary profile used by Chrome.
  • To solve this we downloaded the internal extension to a known path at Docker build time, and loaded it in as an unpacked extension with the load-extension=/path/to/extension argument to Chrome.
  • So a work around was need, it seems simple enough: connect to the inbox via IMAP, wait for an email from Salesforce, load the email then grab the code via regex and pass it back into Selenium.
  • This means in the end we ended up using another chrome extension to fill in the authentication details, again, loaded via a an argument to chrome at start time.

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Getting started with Docker and Kubernetes: a beginner's guide

  • Docker makes it easier to create, deploy, and run applications with the use of containers.
  • It’s important to get your application containerized, but don’t forget the next step in the process; how will you run your containers at scale in production?
  • In a production environment that runs containers hosting critical applications, you would rather have your favorite admins install Docker Enterprise.
  • If you have an older macOS version, you may use the older Docker Toolbox based on VirtualBox. As we saw earlier, containers are created from images.
  • A Docker image is created using the docker build command and a Dockerfile file.
  • Now, let’s create a basic image for a container that displays a “hello world” message when its run.
  • The CMD instruction specifies which executable is run when a container is created using your image and provides optional arguments.

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Accepting An Amazon Job Offer And Filling In My Skill Gaps

  • As I am going to be working with a lot of Cloud concepts, I have started to flesh out my understanding of containers: what they are, what they are used for, and how I go about making use of them myself.
  • The latter of the two was especially interesting, as it allowed me to use the free-tier AWS EC2 service while I was following along in the tutorials in building the container swarm.
  • I think learning MongoDB while also working on learning more about Docker and Kubernetes actually makes a lot of sense.
  • My work on my side projects has been a bit slower than I would have liked it to be, but I suppose I should give myself some slack given that I just went through the existential crisis of finding (and deciding to accept) a dev job.

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Running Jenkins and Persisting state locally using Docker

  • Another benefit of using containers is persisting the state of your Jenkins server using Docker volumes.
  • In the command we ran, /var/jenkins_home is the path to where the Jenkins state is stored on our container instance.
  • After confirming that the job runs, we want to make sure that we can recover our Jenkins configuration if needed.
  • In order to check if the persistence works correctly, let's destroy our current docker container and see if we can log in as an admin and view our job build history.
  • Run the same command we used to create the container instance in order to recover the Jenkins instance.
  • You can easily run a Jenkins instance as a Docker container and persist your Jenkins server state using Docker Volumes.
  • In case you need to restart or recover your Jenkins instance, all of the state is stored inside the Docker Volume.

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Kafdrop

  • When it comes to Kafka topic viewers and web UIs, the go-to open-source tool is Kafdrop.
  • And there’s a reason behind that: Kafdrop does an amazing job of filling the apparent gaps in the observability tooling of Kafka, solving problems that the community has been pointing out for too long.
  • Conveniently, Kafdrop displays the computed lag for each partition, which is aggregated at the footer of each topic table.
  • In Kafka, this period is usually in the order of tens or hundreds of milliseconds, depending on both the producer and consumer client options, network configuration, broker I/O capabilities, the size of the pagecache and a myriad of other factors.
  • It’s exactly what you’d expect — a chronologically-ordered list of messages (or records, in Kafka parlance) for a chosen partition.

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Kafdrop

  • When it comes to Kafka topic viewers and web UIs, the go-to open-source tool is Kafdrop.
  • And there’s a reason behind that: Kafdrop does an amazing job of filling the apparent gaps in the observability tooling of Kafka, solving problems that the community has been pointing out for too long.
  • Conveniently, Kafdrop displays the computed lag for each partition, which is aggregated at the footer of each topic table.
  • In Kafka, this period is usually in the order of tens or hundreds of milliseconds, depending on both the producer and consumer client options, network configuration, broker I/O capabilities, the size of the pagecache and a myriad of other factors.
  • It’s exactly what you’d expect — a chronologically-ordered list of messages (or records, in Kafka parlance) for a chosen partition.

save | comments | report | share on


Kafdrop

  • When it comes to Kafka topic viewers and web UIs, the go-to open-source tool is Kafdrop.
  • And there’s a reason behind that: Kafdrop does an amazing job of filling the apparent gaps in the observability tooling of Kafka, solving problems that the community has been pointing out for too long.
  • Conveniently, Kafdrop displays the computed lag for each partition, which is aggregated at the footer of each topic table.
  • In Kafka, this period is usually in the order of tens or hundreds of milliseconds, depending on both the producer and consumer client options, network configuration, broker I/O capabilities, the size of the pagecache and a myriad of other factors.
  • It’s exactly what you’d expect — a chronologically-ordered list of messages (or records, in Kafka parlance) for a chosen partition.

save | comments | report | share on