I have a fx vol model which I have written in python / questdb on my local machine. The standalone model works as an application in my machine and I also have lots of feeds written which are constantly updating the local machine questdb instance.
Now I want to move to web and have my application and database on the web server away from my machine.
I am not too familiar of web servers and how to install a questdb there.
From my knowledge I will need :
a VPS paid subscription where I have centOS and I have python support.
I need to install questdb here ( using docker ?)
Install all my python model here
Start the questdb in the VPS
Configure my scripts to use the hosted questdb
The model saves output in a table in questdb ; while the scripts also keep on updating new feeds to questdb
Start a webserver which provides a web access to the model results saved in the questdb
7.a I need to provide username and login for the website
7.b I need to use some vitualization
7.c I need the users once they are logged in to run some simulations
What I need some quidance is :
What sort of VPS service to look for
Has any one already installed a questdb in this way
Which webserver is the best for python
Lots of good questions there. Will try to help with all of them (disclaimer, I work for QuestDB as a developer advocate)
What sort of VPS service to look for:
Anything will work, but depending on the volume you are going to ingest/query, you will greatly benefit from a fast drive. In many cases the default disk drive for a VPS/Cloud provider will be slower than the SSD drive on your local development machine. See if the default works for you and otherwise select a better option when starting your virtual machine.
Has any one already installed a questdb in this way
Sure. If you want to install on AWS, Google Cloud, or Digital Ocean, you have detailed instructions at the QuestDB site. If you prefer to use Docker, it is fully supported, but a popular option is installing the binaries and just start QuestDB as a service.
Which webserver is the best for python
This one is tricky, as it really depends on what you will do with it. From your question I am getting the idea you want to run a small user-facing web application that will also run some background jobs. For that scenario a suitable framework can be Flask, which also offers some plugins for user/login management and for background tasks (using redis as a dependency)
Related
I have python scripts for automated trading for currency and I want to deploy them by running on Jupter Lab on a cloud instance. I have no experience with cloud computing or linux, so I have been trying weeks to get into this cloud computing mania, but I found it very difficult to participate in it.
My goal is to set up a full-fledged Python infrastructure on a cloud instance from whichever provider so that I can run my trading bot on the cloud.
I want to set up a cloud instance on whichever provider that has the latest python
installation plus the typically needed scientific packages (such as NumPy and pandas and others) in combination with a password-protected and Secure Sockets Layer (SSL)-encrypted Jupyter
Lab server installation.
So far I have gotten no where. I am currently looking at the digital ocean website for setting jupter lab up but there are so many confusing terms.
What is Ubuntu or Debian? Is it like a sub-variant of Linux operating system? Why do I have only 2 options here? I use neither of the operating system, I use the windows operating system on my laptop and it is also where I developed my python script. Do I need a window server or something?
How can I do this? I tried a lot of tutorials but I just got more confused.
Your question raises several more about what you are trying to accomplish. Are you just trying to run your script on cloud services? Or do you want to schedule a server to spin up and execute your code? Are you running a bot that trades for you? These are just some initial questions after reading your post.
Regarding your specific question regarding Ubuntu and Debian, they are indeed Linux distributions which are popular option for servers. You can set up a Windows server on AWS or another cloud provider, but Linux distributions being much more popular are going to have lots of documentation, articles, stackoverflow posts around a Linux based server.
If you just want to run a script on a cloud on demand, you would probably have a lot of success following Wayne's comment around PythonAnywhere or Google Colab.
If you want your own cloud server, I would suggest starting small and slow with a small or free tier EC2 instance by following a tutorial such as this https://dataschool.com/data-modeling-101/running-jupyter-notebook-on-an-ec2-server/ Alternatively, you could splurge for an AWS AMI which will have much more compute power and be configured.
I have similar problem and the most suiteble solution to me is using docker container for jupyter notebooks. The instructions on how to install Docker can be found at https://docs.docker.com/engine/install/ubuntu/ There is ready to use Docker image docker pull jupyter/datascience-notebook for jupyter notebook python stack. The docker compose files und sone addional insruction you will fid at https://github.com/stefanproell/jupyter-notebook-docker-compose/blob/master/README.md.
I'm moving away from WordPress and into bespoke Python apps.
I've settled on Django as my Python framework, my only problems at the moment are concerning hosting. My current shared hosting environment is great for WordPress (WHM on CloudLinux), but serving Django on Apache/cPanel appears to be hit and miss, although I haven't tried it as yet with my new hosting company. - who have Python enabled in cPanel.
What is the easiest way for me to set up a VPS to run a hosting environment for say, twenty websites? I develop everything in a virtualenv, but I have no experience in running Django in a production environment as yet. I would assume that venv isn't secure enough or has scalability issues? I've read some things about people using Docker to set up separate Django instances on a VPS, but I'm not sure whether they wrote their own management system.
It's my understanding that each instance Python/Django needs uWSGI and Nginx residing within that virtual container? I'm looking for a simple and robust solution to host 20 Django sites on a VPS - is there an out of the box solution? I'm also happy to develop one and set up a VPS if I'm pointed in the right direction.
Any wisdom would be gratefully accepted.
Andy :)
Traditional approach
Virtualenv is good enough and perfectly ready for production use. You can have multiple virtualenv for multiple projects on the same VM.
If you have multiple database engines for multiple projects. Like, MySQL for one, PostgreSQL for another something like this then you just need to set up each individually.
Install Nginx and configure each according to project.
Install supervisor to manage(restart/start/stop) each project individually.
Anything that required by the project.
Here it has a huge drawback. Because you can't use different versions on your database engine for a different project in an easy way. So, containerization is highly recommended.
For simple and robust solution,
Use Docker(docker-compose) for local and production deployment.
Configure uWsgi with Nginx(Available on docker.)
Create a CI/CD pipeline with any tool like Jenkins.
Monitor your projects using any good tool like Raygun.
That's it.
I created a bash script that deploys as many websites as you want on your server. It automatically installs all dependencies on your server, creates a virtual environment, configure Gunicorn, Nginx, and a database for Django, etc. Check it out:
https://github.com/jdbit/django-auto-deploy
I'm relatively new to web development, but I have a couple years experience working with python, so I figured I would build my first production site using django.
I've spent the last few days learning the basics and building a test site running on my local machine. Today, I've been trying to deploy my site to production; however, I've hit a pretty large stumbling block.
The django documentation suggests using mod_wsgi for apache deployments. I followed the install instructions here, only to realize that I don't have access to make any changes to apache - I'm currently on a shared hosting plan.
Apparently, to perform the install, I would have to upgrade to a VPS plan, which costs a lot more.
Any advice for a new web developer trying to get a proof-of-concept web app together (preferably with feedback gathered from real users) on a budget?
I think I have two options:
Eat the cost on my current web hosting plan. Try to find a cheaper host that specializes in django hosting. I've been looking at the following (suggestions here would be wonderful):
Heroku
DigitalOcean
A2Hosting
Try some sort of manual deployment. Is this possible or has anybody ever made this work? I can't seem to find any resources about this.
I was able to install django on my web server and my site just seems like a collection of files at this point. Can I simply move this directory tree to my site using an ftp? And handle the database migration manually?
Can I just run django on my web server via ssh instead of from my local machine? Essentially just rebuild my site in production? I know this isn't a smart idea with a normal production site, but as a proof of concept, I don't mind some downtime if my code is buggy.
Thanks!
If you are just starting Heroku is probably perfect for you. It's super easy to set up, you don't have to handle the server configuration yourself and it has a free tier to do some tests until you are ready to go.
If you are OK with doing some more advanced server/service configuration, you can use Google Cloud, Azure or AWS. This last one has a one year free tier. If you go this route, I suggest using gunicorn/uwsgi + nginx instead of Apache mod_wsgi.
I have an apps coded in python/pandas/scipy which can be launched by anyone authorized. I want to use Google Cloud Platform to host it but I can't find a good way to set up this.
Since I want my app to be a web app, part of this is hosted on google app engine, but since google app engine does not seem compatible with big calculus and scientific libraries, I think I shall send the task to a VM with the scientific libraries.
My questions are:
1- could I create a VM each time the app is launched, in order to save money? But if I do this, I have to setup pandas scipy etc... each time, which should take some time?
2- Am I condemned to have a VM running every time and activated by the app? But if two people launch the calculus together, this can be really bad perf?
3 - Shall I package my app as a .exe and launch it as a standalone?
I am totally lost on how to handle such an architecture, can anyone give me some advices?
Thank you!
You can host your application on Managed VMs. Applications that run on managed VMs are not subject to the restrictions imposed on the sandboxed runtimes (Java, Python, PHP, and Go).
You can also choose the hosting environment (sandboxed or managed VM) separately for each module in your application. This means an application can contain a mix of modules that use different hosting environments. For instance, you might use the sandbox environment for your client-facing frontend, and use a module running in a managed VM for backend processing.
In order to redeploy a GAE application, I currently have to install the GAE deployment tools on the system that I am using for deployment. While this process is relatively straight forward, the deployment process is a manual process that does not work from behind a firewall and the deployment tools must be installed on every machine that will be used for updating GAE apps. A more ideal solution would be if I could update a GAE application from another GAE application that I have deployed previously. This would remove the need to have multiple systems configured to deploy apps.
Since the GAE deployment tools are written in Python and the GAE App Engine supports Python, is it possible to modify appcfg.py to work from within GAE? The use case would be to pull a project from GitHub or some other online repository and update one GAE application from another GAE app. If this is not possible, what is the limiting constraint?
Is it possible? Yes. The protocol appcfg uses to update apps is entirely HTTP-based, so there's absolutely no reason you couldn't write an app that's capable of deploying other apps (or redeploying itself - self-modifying code)! You may even be able to reuse large parts of appcfg.py to do it.
Is it easy? Probably not. It's quite likely you'll need to understand a decent chunk of appcfg's internals, and the RPCs it uses to upload new apps - not a trivial undertaking. You'll also need to store your credentials in the app, in all likelihood - though you can use a role account that is and admin only for the apps it's deploying to minimize risk there.
One limiting constraint could be the protocol that the python sdk uses to communicate with the GAE servers. If it only uses HTTP, you might be OK. but if it's anything else, you might be out of luck because you can't open a socket directly from within GAE.
What problem did you have by trying to update behind a firewall?
I've got some, but finally I manage to work around them.
About your question, the constraint is that you cannot write files into a GAE app, so even though you could possibly pull from the VCS you can't write those pulled files.
So you would have to update from outside the GAE in first place.
Anyway every machine that needs to update the GAE should have the SDK anyway just to see if they changes work.
So, If you really want to do this you have two alternatives:
Host your own "updater" site and istall the SDK there, then when you want to update log into your side ( or run a script ) and do the remote update.
Although I don't know Amazon EC2 well, I think you can do pretty much the same thing as op 1 from there.
Finally I think the password to update has to be typed always. ( you could have the SDK of the App engine and modify that, because it is open source )