At some point in your development journey, you might have heard of the design principles — S.O.L.I.D during your work. There is no shortage of resources and examples on the web regarding these 5 principles, and in this post, I would like to just document and contribute my own understanding of these principles in the Python language. I will also share some references which I found very useful to help me better understand these principles at the end.
A class should have only one reason to change
My Understanding:Each class or module should have one responsibility, thus one reason…
docker push <gcr-hostname>/<project-name>/<image-name>:<tag>gcloud run deploy <service-name> --image gcr.io/<project-name>/<image-name>:<tag>
You are building a fancy web app or API in your local machine. Everything is going smoothly, and you are ready for deploying the app to production. You are thinking one step ahead of packaging the app into a Docker container, so that you can eventually use the even fancier Kubernetes to orchestrate and manage your containers.
You have built the Docker container and are ready to go, and suddenly, you realized that you need to first set up the Kubernetes Cluster. Choosing which instance type for the cluster is…
Disclaimer: This article is written in collaboration with Dashbird.io. The information provided is solely based on my personal usage and opinion on the platform.
Using Dashbird.io allows us to monitor our AWS Serverless resources better and helps us nailed down on specific errors quickly and more efficiently
As a startup, we always want to focus on the most important thing — to deliver value to our customers. For that reason, we are a huge fan of the serverless options provided by AWS (Lambda) and GCP (Cloud…
In the previous article, I shared 2 approaches on how to overcome the AWS Lambda deployment size limit, i.e. using S3 or AWS EFS to store large data files/dependencies. In this article, I will share how we can use Lambda with container image to achieve the same.
A bit of context, AWS announced at the end of 2020 that AWS Lambda will now provide container image support. With that, a user can now deploy Lambda functions up to 10GB in size, vastly increased from the existing 250MB limit. AWS provides base images that we can use for all the supported…
For engineers that frequently deploy serverless function to AWS Lambda, there will be a point in time where you get hit by the following errors:
In the previous medium posts, I shared how we can deploy GCP Cloud Functions via Bitbucket Pipelines using the native gcloud command. In this post, I will share how we can use the Serverless Framework to achieve the same.
There are few advantages when using Serverless Framework in developing and deploying serverless applications, mainly:
Reposting this from my personal blog which was written 2 years back, as I am looking to consolidate some of the articles and potentially retiring my personal website and focus on writing on Medium. Nonetheless, reading back the old article brings back some good ol’ memories and reminds me of the technological advancement that I have seen and experienced in the country.
It has been a while since I took my solo trip. Most of my travel mates don’t seem to express interest to travel to China, so the idea of traveling together (to China) has been called off several…
In the previous article, I posted on how engineers can leverage on Bitbucket Pipelines as a CI/CD tool to automate the integration and deployment process. Specifically, using Bitbucket Pipelines to deploy serverless cloud function code to GCP.
In this article, we will discuss how we can do the same to deploy serverless code to AWS Lambda.
Having a CI/CD pipeline setup can save software engineers tons of time, making sure the deployment steps are consistent and reduce potential errors by automating repeatable steps. In our company Interviewer.AI, we are using Bitbucket as our version control repository hosting service and deploying various serverless applications and functions to GCP Cloud Function and AWS Lambda. This post will be explaining the steps to configure Bitbucket Pipelines as a CI/CD tool to deploy function code to GCP Cloud Function.