Adventures & experiences in contemporary technology
This is the second post on Ansible following my main system’s upgrade to Linux Mint 21. Then, I manually ran some Ansible playbooks only to spot messages that I had not noticed before. Here, I discuss two messages issued because of an issue with an inventory file, which is where one defines lists of servers against which playbooks are executed. The default is called hosts and is located at /etc/ansible but the system upgrade had renamed the existing one so Ansible could not find it. The solution was to take a copy and put somewhere safer. Then, I needed to add the location of the new file to the affected ansible-playbook commands using the following construct:
ansible-playbook [playbook path] -i [inventory file path]
Before I did this, I was seeing messages including the text “Could not match supplied host pattern” or others with the following text:
[WARNING]: No inventory was parsed, only implicit localhost is available
[WARNING]: provided hosts list is empty, only localhost is available. Note that the implicit localhost does not match 'all'
The cause was the same in each case and attending to the inventory file got rid of the unwanted messages. The new file also should not be affected by system upgrades in the future.
My primary use for Ansible is doing system updates using the inbuilt apt module. Recently, I updated my main system to Linux Mint 21 and a few things like Ansible stopped working. Removing instances that I had added with pip3 sorted the problem but I then ran playbooks manually only for various warning messages to appear that I had not noticed before. What follows below is one of these.
[WARNING]: The value True (type bool) in a string field was converted to u'True' (type string). If this does not look like what you expect, quote the entire value to ensure it does not change.
The message is not so clear in some ways, not least because it had me looking for a boolean value of True when it should have been yes. A search on the web revealed something about the apt module that surprised me.: the value of the upgrade parameter is a string when others like it take boolean values of yes or no. Thus, I had passed a bareword of yes when it should have been declared in quotes as “yes”. To my mind, this is an inconsistency but I have changed things anyway to get rid of the message.
While I have been programming with SAS for a few decades and it remains a lynchpin in the world of clinical development in the pharmaceutical industry, other technologies like R and Python are gaining a foothold. Two years ago, I started to look at those languages with personal projects being a great way of facilitating this. In addition, I got to hear of Julia and got to try that too. That journey continues since I have put it into use for importing and backing up photos, and there are other possible uses too.
Recently, I updated Julia to version 1.8.2 but ran into a problem with the DataArrays package that I had installed so I decided to remove it since it was added during experimentation. The Pkg package that is used for package management is documented but I had not gotten to that so some web searching ensued. It turns out that there are two ways of doing this. One uses the REPL: after pressing the ] key, the following command gets issued:
rm DataArrays
When all is done, pressing the delete or backspace keys returns things to normal. This also can be done in a script as well as the REPL and the following line works in both instances:
using Pkg; Pkg.rm("DataArrays")
The semicolon is used to separate two commands issued in the same line but they can be on different lines or issued separately just as well. Naturally, DataArrays is just an example here so you just replace that with the name of whatever other package you need to remove. Since we can get carried away when downloading packages, there are times when a clean-up is needed to remove redundant packages so knowing how to remove any clutter is invaluable.
In the BASH shell used on Linux and UNIX, the history command calls up a list of recent commands used and has many uses. There is a .bash_history file in the root of the user folder that logs and provides all this information so there are times when you need to exclude some commands from there but that is another story.
The Julia REPL environment works similarly to many operating system command line interfaces, so I wondered if there was a way to recall or refer to the history of commands issued. So far, I have not come across an equivalent to the BASH history command for the REPL itself but there the command history is retained in a file like .bash_history. The location varies on different operating systems though. On Linux, it is ~/.julia/logs/repl_history.jl while it is %USERPROFILE%\.julia\logs\repl_history.jl on Windows. While I tend to use scripts that I have written in VSCode rather than entering pieces of code in the REPL, the history retains its uses and I am sharing it here for others. In the past, the location changed but these are the ones with Julia 1.8.2, the version that I have at the time of writing.
Recently, I got to experimenting with Ansible after reading about the orchestration tool in a copy of Admin magazine. It came in handy for updating a few web servers that I have as well as updating my main Linux workstation. For the former, automated entry of SSH passwords sufficed but the same did not apply for sudo usage on my local machine. This meant that I needed to use Ansible Vault to store the administrator password and doing so opened up a file in the Vi editor. since I am not familiar with Vi and wanted to get things sorted quickly, I fancied using something more user-friendly like Nano.
Doing this meant adding the following line to .bashrc:
export EDITOR=nano
Saving and closing the file followed by reloading the session set me up for what was needed.
While I continue to use Spyder as my preferred Python code editor, I also tried out Visual Studio Code. Handily, this Integrated Development Environment also has facilities for working with R and Julia code as well as MarkDown text editing and adding the required extensions is enough for these applications; it helps that there is an unofficial Grammarly extension for content creation.
My Python code development makes use of the Pylance extension and it works a little differently from Spyder when it comes to including files using import statements. Spyder will look into the folder where the base script is located but the default behaviour of Pylance is that it looks in the root path of your workspace. This meant that any code that ran successfully in Spyder failed in Visual Studio Code.
The way around this was to add the required location using the python.analysis.extraPaths setting for the workspace. That meant opening Settings by navigating to File > Preferences > Settings in the menu system and entering python.analysis.extraPaths into the search box. That took me to the section that I needed and I then clicked on Add Item before entering the required path and clicking on the OK button. That was enough to fix the problem and all worked as it should after that.
One thing that is very handy for shell scripting is to have automated entry of passwords for logging into other servers. This can involve using plain text files, which is not always ideal so it was good to find an alternative. The first step is to use the keygen tool that comes with SSH. The command is given below and the -t switch specifies the type of key to be made, RSA in this case. There is the option to add a passphrase but I decided against this for sake of convenience and you do need to assess your security needs before embarking on such a course of action.
ssh-keygen -t rsa
The next step is to use the ssh-copy-id command to generate the keys for a set of login credentials. For this, it is better to use a user account with restricted access to keep as much server security as you can. Otherwise, the process is as simple as executing a command like the following and entering the password at the prompt for doing so.
ssh-copy-id [user ID]@[server address]
Getting this set up has been useful for running a file upload script to keep a web server synchronised and it is better to have the credentials encrypted rather than kept in a plain text file.
Recently, I tried using Commento with a static website that I was developing and this needed PostgreSQL rather than MySQL or MariaDB, which many content management tools use. That meant a learning curve that made me buy a book as well as the creation of a system account for administering PostgreSQL. These are not the kind of things that you want to be too visible so I wanted to hide them.
Since Linux Mint uses AccountsService, you cannot use lightdm to do this (the comments in /etc/lightdm/users.conf suggest as much). Instead, you need to go to /var/lib/AccountsService/users and look for a file called after the user name. If one exists, all that is needed is for you to add the following line under the [User] section:
SystemAccount=true
If there is no file present for the user in question, then you need to create one with the following lines in there:
[User]
SystemAccount=true
Once the configuration files are set up as needed, AccountsService needs to be restarted and the following command does that deed:
sudo systemctl restart accounts-daemon.service
Logging out should reveal that the user in question is not listed on the logon screen as required.
There is a simple principal with the 7G Firewall from Perishable press: it is a set of mod_rewrite rules for the Apache web server that can be added to a .htaccess file and there also is a version for the Nginx web server as well. These check query strings, request Uniform Resource Identifiers (URI’s), user agents, remote hosts, HTTP referrers and request methods for any anomalies and blocks those that appear dubious.
Unfortunately, I found that the rules heavily slowed down a website with which I tried them so I am going have to wait until that is moved to a faster system before I really can give them a go. This can be a problem with security tools as I also found with adding a modsec jail to a Fail2Ban instance. As it happens, both sets of observations were made using the GTmetrix tool so it seems that there is a trade off between security and speed that needs to be assessed before adding anything to block unwanted web visitors.
My response to changes in the technology stack used in clinical research is to develop some familiarity with programming and scripting platforms that complement and compete with SAS, a system with which I have been programming since 2000. One of these has been R but Python is another that has taken up my attention and I now also have Julia in my sights as well. There may be others to assess in the fullness of time.
While I first started to explore the Data Science world in the autumn of 2017, it was in the autumn of 2019 that I began to complete LinkedIn training courses on the subject. Good though they were, I find that I need to actually use a tool in order to better understand it. At that time, I did get to hear about Python packages like Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn and Beautiful Soup though it took until of spring of this year for me to start gaining some hands-on experience with using any of these.
During the summer of 2020, I attended a BCS webinar on the CodeGrades initiative, a programming mentoring scheme inspired by the way classical musicianship is assessed. In fact, one of the main progenitors is a trained classical musician and teacher of classical music who turned to Python programming when starting a family so as to have a more stable income. The approach is that a student selects a project and works their way through it with mentoring and periodic assessments carried out in a gentle and discursive manner. Of course, the project has to be engaging for the learning experience to stay the course and that point came through in the webinar.
That is one lesson that resonates with me with subjects as diverse as web server performance and the ongoing pandemic pandemic supplying data and there are other sources of public data to examine as well before looking through my own personal archive gathered over the decades. Some subjects are uplifting while others are more foreboding but the key thing is that they sustain interest and offer opportunities for new learning. Without being able to dream up new things to try, my knowledge of R and Python would not be as extensive as it is and I hope that it will help with learning Julia too.
In the main, my own learning has been a solo effort with consultation of documentation along with web searches that have brought me to the likes of Real Python, Stack Abuse, Data Viz with Python and R and others for longer tutorials as well as threads on Stack Overflow. Usually, the web searching begins when I need a steer on a particular or a way to resolve a particular error or warning message but books always are worth reading even if that is the slower route. Those from the Dummies series or from O’Reilly have proved must useful so far but I do need to read them more completely than I already have; it is all too tempting to go with the try the “programming and search for solutions as you go” approach instead.
To get going, many choose the Anaconda distribution to get Jupyter notebook functionality but I prefer a more traditional editor so Spyder has been my tool of choice for Python programming and there are others like PyCharm as well. Spyder itself is written in Python so it can be installed using pip from PyPi like other Python packages. It has other dependencies like Pylint for code management activities but these get installed behind the scenes.
The packages that I first met in 2019 may be the mainstays for doing data science but I have discovered others since then. It also seems that there is porosity between the worlds of R an Python so you get some Python packages aping R packages and R has the Reticulate package for executing Python code. There are Python counterparts to such Tidyverse stables as dply and ggplot2 in the form of Siuba and Plotnine, respectively. The syntax of these packages are not direct copies of what is executed in R but they are close enough for there to be enough familiarity for added user friendliness compared to Pandas or Matplotlib. The interoperability does not stop there for there is SQLAlchemy for connecting to MySQL and other databases (PyMySQL is needed as well) and there also is SASPy for interacting with SAS Viya.
Pyhton may not have the speed of Julia but there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have there uses for dealing with data volumes that make Pandas code crawl. As if to prove that there are plenty of libraries for various forms of data analytics, data science, artificial intelligence and machine learning, there also are the likes of Keras, TensorFlow and NetworkX. These are just a selection of what is available and there is no need not to check out more. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurst to keep an open mind either.