Yes, here we go again: listings of ten characteristics, advantages or attributes, yadda, yadda, yadda. Even I am not a fan of these types of articles. I have recently been transferred from one company to another and as a result of it I had to switch almost all of my cloud work from AWS to GCP. I must say that it has been an exciting journey so far, so I decided to point out some things where these two platforms differ, and I hope that it might be useful to others. (more…)
Try to Google a bit and find some blog posts about adoption of microservices, Docker, Kubernetes and other “new” stuff in the traditional environments. Almost every post is like “yeah, microservices are kinda cool, but monitoring and overall observability is very challenging.”
Well, that’s not true anymore. And today I’m gonna show you one important pillar of this shift from “challenging” to “absolutely possible.” Please make some noise for the technology which can’t be missing in your microservice stack – distributed tracing.
As we’ve shared in our previous DevOps posts, we mainly use Jenkins for our common CI and CD tasks. Nowadays, Jenkins is still industry standard, there are heaps of resources tutorials and Stackoverflow threads about (almost) every conceivable issue. I’ve written “almost” back there, I know. Well, sometimes you have to dive in to Java code and figure out what the hell that XYZ plugin actually does. And things can get messy. But enough of complaints.
We have almost 50 developers working with different app development technologies to create, test and ship apps for our demanding clients. With multiple git pushes and merge requests per hour there is a need for fast and optimized flow. Automating the CI/CD with multiple technologies and clouds/other deployment targets is critical for us. This is how we use powerful Jenkins Pipeline with Shared Pipeline Library with Jenkins and Gitlab.
We have tested two MongoDB cluster solutions deployed on Google Cloud Platform: Google Launcher’s MongoDB cluster and our MongoDB cluster Docker container running on Google Container Engine. We wanted to simply compare the performance of both solutions using a standard mongo performance benchmark. What are the benchmark results?
During app development at Ackee, we have tested two database-as-a-service solutions from two major players: Google Cloud SQL Second Generation, Amazon RDS for MySQL and official MariaDB Docker container running as a single instance without any replication on Google Container Engine. MariaDB was used in the test as a baseline, only to see if there is any significant performance overhead of replication and the difference between MySQL and MariaDB. What are the benchmark results?