⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
The previous part of this series, in which we created a WebHook for our Telegram – bot, was written on December 5th, 2018. This is more than 20 months now without any update to this series – embarrassing ?. My sincere apologies for this! I had some changes in my job and was quite busy catching up.
This time, I will provide you with only a very (!) short update instead of a real article:
Since I recovered from this changed situation in the meantime, I’m planning to pick up this article series again. Since a lot of time has passed and most certainly even the most devoted readers forgot about the progress in previous parts in the meantime, I decided to add something new: A Git Repository which contains all changes so far, structured by tags that allow finding the proper entry point to the code, depending on which article you are reading, currently.
I hope to make the (re-)entry to the series a bit more easy and attractive by this.
I will also add results of future articles to that repository since I had a hard time why something so obvious came to my mind only so late in the article series …
So far, our bot is just capable to receive data. In the next article of this series, we will add a bit more functionality to the bot by adding a reply feature to it. I’m not 100% sure what this will be exactly yet, but I will try to come up with something interesting, educational and useful ?
Also, since this is about to become a bot to store your cash spendings, we will add pattern recognition logic to enable the bot to sum and track your spendings.
Also, when we saw that we ended up with a doubled record in our User table, we realized that the current string representation of the records for that table is not really helpful, displaying the same lines containing “Marc Richter” twice. Having displayed the same name twice made it hard to identify the correct record for deletion. We will change that as well and by that see how to make changes like this to a database that has already records stored to it, using Django’s workflows.
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
When I started to learn Python a few years ago, I often wondered about what’s the “correct” or “best” way to prepare your system’s Python environment for the requirements your software project or some Python-based application you’d like to start using may have: Should I install modules using the package manager of my OS? Or by using Python tools for it like pip? What are “virtual environments” and how do I utilize these for my projects? What’s all this pyenv, pip, pipenv, easy_install, setuptools, anaconda, conda, miniconda …
In this article series, I’d like to introduce the most common tools and techniques on how to do this in the Python world.
At the end of the series, I will share some of my thoughts, doubts, and questions I had back then, tell about some experiences I gathered in the meantime and generally share the outcome of this journey and what my Python-Workflow looks like, nowadays.
Introduction to pyenv ?
This first article is about pyenv, a lightweight, yet powerful, Python version management tool that works in user – scope and does stay out of the way of systems global Python interpreters.
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, we created another database model named Message to hold the message-data from our Telegram – bot. I also explained the process of defining a SQL schema using a Django model, what to consider during that phase and how to bring the Django’s model field reference docs to a good use during that process. Last but not least, we learned what a Heroku “One-Off Dyno” is and how it can be used to execute administrative tasks on our production site like applying outstanding migrations to a database.
This time, I will provide you with the last piece of the puzzle to make your bot available to the world. You will learn how to write and wire the Python code to actually use all that we have prepared so far. At the end of this part, your bot will be able to receive and store each message sent to it by registered users. And since it’s already more than a month since I published the previous article in this series, let’s not waste any more time and jump right in!
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, I explained what a database is good for in general and Django in special. I also told about what relational database systems (RDBS) are supported by Django, what migrations and models are and how to create and apply them. Further, I introduced and explained what the Django Admin-Backend is and how to use it to create, alter or delete data in tables resulting from having applied the migrations to SQL databases from a model definition.
Today, we will create another database model to hold the message-data forwarded to our webhook by the Telegram – bot in the future. I will try my best to make this a play-along part which invites everyone to follow step by step in another console. Hopefully, it gives you an idea what thoughts and considerations are involved in writing a model for a real-world problem and how to involve Django’s documentation resources. Continue reading “Create your own Telegram bot with Django on Heroku – Part 9 – Creating a model for your messages”
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, we had a bit of a term definition to make it easier for beginners of Django to understand what I am talking about. Also, we created a Django – App called “bot” and created a URL routing for it to be available at (https://dry-tundra-61874.herokuapp.com)/bot/* (or whatever your URL looks like) and how to direct URLs to a view.
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, we started with the basics for kicking off a new Django project. We prepared our virtualenv, installed needed modules to it, created and integrated a new Heroku – project for it and learned how to work with variables in Heroku to control our application with an easy example. We also learned how to check our results locally before we publish it to our production space and how we can add an addon to our Heroku project by adding a PostgreSQL database to it.
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, I tried to give you a brief yet thorough introduction to hosting your projects with Heroku.
That part was special because it was a completely optional part of this series; if you prefer to host your applications on a different platform and skipped that article, I’d like to repeat that this is completely OK and that I had shown nothing you will need for anything different but interacting with Heroku. You will hopefully notice no blank spots in the following articles. There is no need to read that article if you do not plan to use Heroku for hosting your bot. But you should be familiar enough with your hosting solution of choice to adopt the Heroku – commands I show here to an adequate setup for your hosting solution.
Today we will finally start creating our bot with Django. What we did up until now was just some kind of preparation and establishing background. In this part, we will finally start with the real stuff. ?
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
In the previous part of this series, I tried to explain the differences in the two modes a Telegram bot supports: pull (getUpdates) and push (Webhook) methods. I also explained what a webhook is and how to easily giving it a test-drive, without any hassle.
Today we will talk about what Heroku is and about how to prepare your workstation to effectively work with that platform.
If you do not like Heroku or if you simply want to use another hosting service for your bot like AWS, OpenShift, Google Cloud Platform, whatever for any reason: That is perfectly OK and you can do so! I will write this and the next article on Heroku in a way that even when you decide to skip it completely, you won’t miss anything from this Telegram/Python/Django article series but the presentation of how to get the bot hosted on Heroku.
The only thing which you won’t be able to follow 1:1 in the upcoming parts of this series is that you can’t use those commands to trigger new deployments to the hosting platform with or do some minor, hosting platform related steps. But if you are advanced and familiar enough with deploying web applications to have a better idea of hosting an application than me, this shouldn’t be much of a problem for you, since most commands for Heroku are pretty straightforward and easy to adapt for a different system.
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
⚠️ This article is outdated and discontinued since Heroku decided to no longer offer their free tiers as this article series suggests to use in August, 2022. Please see this post for details. ⚠️
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
This article describes what “Enterprise Linux” is and how to add a more recent version of Python to it than those available in the base package repository.
What is “Enterprise Linux”?
General definition
CentOS and RedHat Enterprise Linux (RHEL) both are counted as one of the so-called “Enterprise Linux” systems. This term is an artificial noun, which has different meanings. In general, this describes Linux distributions, which are targeted at the commercial market thus putting a strong focus on reliability and long lifecycles.
CentOS, RHEL and SUSE Linux Enterprise Server (SLES) usually maintains a release for 10 years; RHEL and SLES even offer extended support contracts for additional years of support. That means these distributions offer at least twice as long support for a version than Ubuntu LTS versions does (which usually is ~5 years).
This kind of distribution’s biggest strength often is also one of their biggest downsides: If you want to have a more recent version of any of the software they contain, you often have bad luck. More recent versions (if any are available at all) usually come from 3rd party repositories. One of the most famous ones for CentOS is EPEL (Extra Packages for Enterprise Linux), which ports a lot of “had expected that to be available” – like packages from Fedora to Enterprise Linux.
But the more repositories you add, the more unpredictable and unreliable the core becomes.
RedHat based definition
But there’s also another meaning: The term “Enterprise Linux” also has established as a term to group distributions, which are based on blueprints from RedHat Enterprise Linux or build alike it. Some sources refer to this kind of distributions like this (like EPEL, for example). This list normally includes (but is not limited to):
This does not mean in any way, that other distributions, not based on RHEL, are not enterprise class or ready!
What are Enterprise Linux distributions used for?
Enterprise Linux distributions are often used in large-scale IT environments with several hundred or thousand hosts. In this kind of environments, reproducibility (by orchestration/ automatization), reliability, compatibility and hardened concepts and versions are key aspects.
In large orchestrated IT environments, an often selected choice for a distribution as a base is CentOS since, being an Enterprise Linux distribution, its main focus aims at being a rock-stable “enterprise-class” platform prioritized over delivering the latest upstream versions of software selections. It also aims at being binary compatible to RedHat Enterprise Linux (RHEL) while being free of charge and only community supported.
Talking about CentOS …
I will stick to CentOS here since this is the Enterprise Linux I utilize the most. But since we are talking about “Enterprise Linux” here, the following should largely apply for similar distributions, also.
At the time of this writing, CentOS 7, is the latest release of the distribution and was released in 07/2014. It will receive full updates until Q4/2020 and stay maintained (provided with Critical impact Security Advisories (RHSAs) and selected Urgent Priority Bug Fix Advisories (RHBAs) only) until 07/2024. CentOS 6 also is still around; released in 07/2011, it’s maintenance will be continued until 11/2020. CentOS 5 (released in 04/2007) exceeded it’s maintenance timeframe in 03/2017 and is considered unsupported.
Thus, I will only consider CentOS 7 and 6 here.
For details, please see CentOS Product Specifications and Red Hat Enterprise Linux Life Cycle.
Talking about Python …
System Default and base repo
CentOS utilizes Python a lot for its command line tools. In fact, it’s primary package manager yum is heavily depending on Python 2 (2.7.5 in CentOS 7, 2.6.6 in CentOS 6 – provided by the package named “python” from “base” repo) and uninstalling it by force is a reliable way to render your package management useless. This version of Python also comes with some modules installed, which are hard to find in the most common locations like PyPI, including:
yum-metadata-parser
slip.dbus
In short: You do not want to mess around with this system interpreter! ?
What about Python 3?
Python 3 is not available in either CentOS 6 or 7.
Python 3.0 was released in 12/2008.
CentOS 6 was released in 07/2011.
CentOS 7 was released in 07/2014.
Normally one would consider that to be a fair amount of time to add any release of a major technique to even an Enterprise Linux; especially with the Python 2 End of Life in sight for 2020. But – as you can see, it hasn’t been done yet. Remember what I said about the downsides of an Enterprise Linux? Here’s an example ?
But you still have choices.
Using EPEL
The most convenient way to get Python 3 for any supported Enterprise Linux is by adding the EPEL repository to your system. This also has the benefit that its usage is quite common and so the risk to end up with a too customized system is not that huge.
Also, EPEL’s Guidelines and Policies aim to not interfere with any base package and also have quite a strict upgrade policy.
Also, the project is somewhat very close to RedHats own development, since it was born out of Fedora and Fedora is sponsored by RedHat and it is aimed to be used in their Enterprise Distribution, also.
EPEL provides Python 3.4.5 for both, CentOS 6 and 7.
If that satisfies your needs, this is quite a low hanging fruit. You add EPEL and install Python 3 from it like this:
centos_version=6 # Set this to your CentOS version (6 or 7)
yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-${centos_version}.noarch.rpm
yum install -y python34
Using IUS
IUS (Inline with Upstream Stable) is a project which is sponsored by Rackspace. It aims at providing more recent versions of some major key software packages, including Python.
It’s goals and philosophies are very close to those of EPEL in not to interfere with base packages. It’s naming convention makes sure that even if a package equivalent will ever show up in base, it will never interfere with those provided by IUS.
It considers itself a “SafeRepo” and compares itself with EPEL here; feel free to read this resource if want to learn more.
IUS provides several versions of Python:
3.4.7 (package python34u)
3.5.4 (package python35u)
3.6.4 (package python36u)
If this satisfies your needs, this is also quite easy to achieve by issuing the following:
centos_version=6 # Set this to your CentOS version (6 or 7)
yum install -y https://centos${centos_version}.iuscommunity.org/ius-release.rpm
yum install -y python36u
Installing from source
This is by far the most flexible approach while also the most cumbersome one. Also, this is not an “Enterprise Linux” specific task but can be done on any Linux system in the more or like the same way.
Please consider the fact that this is some kind of an anti-pattern if you think of what’s the goal and philosophy behind Enterprise Linux distributions.
By compiling from source, you can freely decide which version of Python you want to use. But at the same time, you sacrifice all benefits package managers have to offer, including:
Ease of installation
Package QA and reviewing workflow
Being supplied with security updates
Integration and availability to the package manager, often integrated into other orchestration tools
Born in 1982, Marc Richter is an IT enthusiast since 1994. He became addicted when he first put his hands on their family’s PC and never stopped investigating and exploring new things since then.
He is married to Jennifer and a proud father of two wonderful children.
His current professional focus is DevOps and Python development.
