alpine | Docker image for multiple architectures | Continuous Deployment library

I'm trying to docerize my NodeJS API together with a MySQL image. Before the initial run, I want to run Sequelize migrations and seeds to have the tables up and ready to be served.

Here's my docker-compose.yaml:

I've used a web API to import data from a specific website. I was able to import the data in JSON format. I am very new to python, hence finding hard to transform it to a tabular format which I can use it for my data analysis. Here's my sample code;

I'm starting to use gitlab CI/CD pipeline but have some doubts regarding the output of the building process if i was to have a project(Repo) and inside this project I have the front and backend separated by the project structure, ex:

.gitlab-ci.yml

|__FrontEndCarProject

|__BackendCarProject

let's say that every time I change something in the frontend I would need to build it and deploy it to S3, but there is no need to build the backend (java application) and deploy it to elastic beanstalk (and vice versa for when i change the backend)..Is there a way to check where the changes have been made(FrontEndCarProject/BackendCarProject) using GitLab and redirect the .gitlab-ci.yml to a script file depending on if a have to deploy to S3 or elastic beanstalk?

Just trying

Note: another way is just to manually change the yml file depending on where i want to deploy..but is there a way to autodetect this and automated?

Just to get the idea, heres an example that would run in a linear way, but how can i conditionally build/deploy(depending on my front or backend)? should i keep them in different repos for simplicity? is it a good practice?

There is a Java 11 (SpringBoot 2.5.1) application with simple workflow:

1. Upload archives (as multipart files with size 50-100 Mb each)
2. Unpack them in memory
3. Send each unpacked file as a message to a queue via JMS

When I run the app locally java -jar app.jar its memory usage (in VisualVM) looks like a saw: high peaks (~ 400 Mb) over a stable baseline (~ 100 Mb).

When I run the same app in a Docker container memory consumption grows up to 700 Mb and higher until an OutOfMemoryError. It appears that GC does not work at all. Even when memory options are present (java -Xms400m -Xmx400m -jar app.jar) the container seems to completely ignore them still consuming much more memory.

So the behavior in the container and in OS are dramatically different. I tried this Docker image in DockerDesktop Windows 10 and in OpenShift 4.6 and got two similar pictures for the memory usage.

Dockerfile

I'm trying to follow instructions on this guide but under docker.

I set up a folder with:

I encountered a permission error while trying to build a docker container in a React app.
I tried to make use of the community answers, but didn't help.
Following related discussion I tried:

• I get the current user: id -un
• tried this: sudo chown -R myUser:myUser /usr/local/lib/node_modules
• this also threw the same error: sudo chown -R ownerName: /usr/local/lib/node_modules
• same with this: sudo chown -R $USER /usr/local/lib/node_modules
• adding a user didn't help: sudo chown -R $USER /app/node_modules
• tried to give permission installing this: sudo npm install -g --unsafe-perm=true --allow-root
• another try was to remove node_modules and install specifying sudo: sudo npm install

Adding this to docker-compose file, didn't help either:

I am using this image which has bash v4.3.48 and curl v7.56.1:

https://hub.docker.com/r/bizongroup/alpine-curl-bash/tags?page=1&ordering=last_updated

Inside the docker I write the following script:

I have a hyper table for exchange candle data set up using TimescaleDB.

• TimescaleDB official image timescale/timescaledb:latest-pg12 set up and running with Docker with the exact version string starting PostgreSQL 12.6 on x86_64-pc-linux-musl, compiled by gcc (Alpine 10.2.1_pre1) 10.2.1 20201203, 64-bit

• Python 3 client

• The table has 5 continuous aggregate views set up like here and around 15 colums

Running the following query is slow (count query generated with SQLAlchemy):

I'm currently trying to introduce docker compose to my project. It includes a golang backend using the redis in-memory database.