Meditation on the Zen of Python

Read it

If you have ever programmed anything in Python, you probably used the “import” statement: the modules of the Python standard library  can be imported into your code or into the interpreter. Take a look at the standard library folders and you’ll find the “” module… what is that? Not much a self-explicative name for a Python module, huh? And you – Java lovers – forget about the Java “this” keyword: you’re far afield.

This module is the mystic “Zen of Python”:

import this

The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

Woohaaaa!!! What?!?! A sort of mantra???

The Pythonic view of the software universe

Kidding apart, the Zen states the high-level development guidelines that were followed in the design of the Python language itself; it was formerly stated into the PEP-20 by Tim Peters, one of the fathers of the language along with Guido Van Rossum (BDFL). Ok, I’m curious about it: I open the code in my favourite text editor and I notice that…

The Zen of Python does not obey the Zen of Python

What??? Here is the source code:

s = """Gur Mra bs Clguba, ol Gvz Crgref

Ornhgvshy vf orggre guna htyl.
Rkcyvpvg vf orggre guna vzcyvpvg.
Fvzcyr vf orggre guna pbzcyrk.
Pbzcyrk vf orggre guna pbzcyvpngrq.
Syng vf orggre guna arfgrq.
Fcnefr vf orggre guna qrafr.
Ernqnovyvgl pbhagf.
Fcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.
Nygubhtu cenpgvpnyvgl orngf chevgl.
Reebef fubhyq arire cnff fvyragyl.
Hayrff rkcyvpvgyl fvyraprq.
Va gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.
Gurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.
Nygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.
Abj vf orggre guna arire.
Nygubhtu arire vf bsgra orggre guna *evtug* abj.
Vs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.
Vs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.
Anzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!"""

d = {}
for c in (65, 97):
for i in range(26):
d[chr(i+c)] = chr((i+13) % 26 + c)

print "".join([d.get(c, c) for c in s])

The first approach to this code might be bewildering… but it’s not so hard to understand, in the end: basically, you have a huge string containing the whole crypted Zen and then you decode it into readable English characters and print it out loud. A few hints:

  • 65 is the ASCII for ‘A’
  • 97 is the ASCII for ‘a’
  • there are 26 letters in the English alphabet
  • the “d” dictionary turns out to have uppercase/lowercase chars as keys and their corresponding translitterated chars as values. The “crypting magic” is given by: i+13 % 26 + c
  • You have that “A”= decrypt[crypt[“A”]] = crypt[crypt[“A”]]Oddity: the Zen does not follow many of its aphorisms! In fact, its code is far from being explicit, and if it’s true that readability counts, well, the Zen doesn’t shine at it. Ok, practicality beats purity but this is complex (not complicated) to read out; in fact the implementation could be simpler to explain, which conveys that this could be done in a better way.

    A metaphor

    My intention is not to disapprove Tim Peters’s work (never be it! I am just a silly rookie!!!) but to show what I think about the Zen: I think that it is a metaphor. It basically poses a problem to its readers, who need to “decipher” it in order to understand how it really works: this is a strong metaphor of life – if you dig deep on problems/difficulties you come up to be sage about them. And so goes for Python design guidelines.

    … and considering that “now is better than never”…

    … while writing this post, I scribbeld (it was funny!) a revised version of the Zen of Python. It shows a few additional features (get random aphorisms, seek for specified keywords) that can help developers to better read and lookup the original Zen of Python. Features that – hopefully – comply with what the Zen itself says Occhiolino

How to use Memcached with PyOWM

This is just a little demonstration on how you can quickly change the basic cache provider provided by the PyOWM library. For this purpose we’ll use Memcached, which – simply put – is a key/value in-memory data store: this turns it into a perfect caching mechanism. I’ve never used Memcached before writing this post: this shall be a good moment for me to get to know it. This demo requires that you work on a Linux env, as Memcached originally is shipped for Unix-like systems via packet distribution systems (but can nevertheless be compiled from source).

I’ll use Ubuntu, with Memcached 1.4.6 and PyOWM 0.4.0. Let’s dive into it.

First we install Memcached and the relative Python bindings:

sudo apt-get install memcached python-memcache

Then we install PyOWM library and check the installation:

sudo pip install pyowm
ls /usr/local/lib/python2.6/dist-packages # check installation

(in my distro, Python packages are installed by pip in the /usr/local/lib/python2.6/dist-packages folder: change accordingly to yours) In order to “plug” Memcached support into PyOWM we are going to leverage the installed Python bindings by creating an adapter class that can conform the SW interface that PyOWM expects into the Memcached API for getting/setting cache elements. Fortunately, the Memcached API is very close to the PyOWM expected interface (which is stated into the pyowm.abstractions.owmcache.OWMCache class), so we have chances that our adapter will be simple enough. Let’s name it ““: you can put it anywhere, provided that this anywhere is “seen” by the Python intepreter: in example, you can put it into a folder listed into the PYTHONPATH variable or you can place it directly into the PyOWM install folder. I did the latter:

cd /usr/local/lib/python2.6/dist-packages/pyowm
sudo vim

The module will contain the MemcachedAdapter class:

#!/usr/bin/env python

class MemcachedAdapter():
  Realizes the pyowm.abstractions.owmcache.OWMCache interface
  adapting a memcache.Client object
  __ITEM_LIFETIME_MILLISECONDS = 1000*60*10 # Ten minutes

  def __init__(self, hostname="",
    port="11211", item_lifetime=__ITEM_LIFETIME_MILLISECONDS):
    from memcache import Client
    self._memcached = Client([hostname+":"+port])
    self._item_lifetime = item_lifetime

  def get(self, request_url):
    return self._memcached.get(request_url)

  def set(self, request_url, response_json):
    self._memcached.set(request_url, response_json, self._item_lifetime)

I wrote this adapter in 5 minutes, so please don’t blame me for errors 😉 It can surely be improved. Now what is left to do is to tell the PyOWM library how to use the adapter: this is done via configuration. The library uses OWMCache concrete instance which is created into a configuration file and injected into the code; a separate configuration file exist for the code supporting each Open Weather Map web API version. Currently only API version 2.5 is supported, so we’ll put our adapter into the file, commenting out the default cache object:

# Cache provider to be used
# cache = NullCache()  # <-- comment this line
from memcachedadapter import MemcachedAdapter
cache = MemcachedAdapter("", "11211")

As you can see, we are adapting a local Memcached instance listening on the default 11211 port, but you can change this configuration as needed. Now let’s try it out – let’s start Memcached and use the PyOWM library:

memcached &

in example:

>>> from pyowm import OWM
>>> owm = OWM()
>>> f = owm.daily_forecast("London,uk")  # This first call to the API is not cached, obviously
>>> g = owm.daily_forecast("London,uk")  # This second call is cached

Time saving should be at a glance.

In a similar way it is possible to write adapters for plugging other cache/storage providers (Redis, MongoDB, etc..) into the PyOWM library.

EDIT: this post stimulated me to write more adapters, you can find them here.

Web APIs design: an improvable example

Today I want to speak about the OWM web API 2.5 and its design.

First let me clearly state that I’m writing this post as a “gathering of thoughts” I’ve had during the first draft development of the PyOWM library, and it is not meant to be a negative criticism – but rather a positive review – to the  API architects/developers. I just want to write here my ideas so that the OWM API can be improved in future versions – and I will commit myself to help with this process, if needed.

I found this activity also very educational because it made me think again to all that books and articles I’ve read on the Internet about API design and – good grief – they were damn right!

As I said, developing the PyOWM library I had to write code interfacing with the OWM web API, which basically meant I had to setup an HTTP client and some kind of parsing module in order to read the API’s responses, squeeze useful data out of them and inject data into my custom object model to the benefit of users. These funny tasks lead me, nevertheless, to crash onto a few improvable API design features that made my work unreasonably more complicated and error-prone. And a bug also came into sight.

Be warned: this is a quite long post 😉

Design oddities

I found the following ones:

  1. mismatch between endpoint naming and features that endpoints implement
  2. inconsistent formatting of JSON data returned by different endpoints when queried for the same (or similar) data entities
  3. lack of use of proper status codes in HTTP headers for error signaling
  4. certain endpoints map on 200 (Ok) HTTP status codes also 404 (Not Found) error conditions

And there is something more… I won’t blame anybody  of these, but they really should be taken into account:

  • the API is poorly documented
  • the API is not RESTful

Let’s dig into each point.

Endpoints: naming vs features

The API lets you basically retrieve different weather datasets (observations, forecasts, historic data) about places in the world. The most simple and natural feature for the API is to let you query for the currently observed weather in a location: this can be specified in a twofold manner, either by passing the API a toponym or a lat/lon couple. The related endpoints are:

#Feature: retrieve current weather - location is specified via toponym,uk
#Feature: retrieve current weather - location is specified via lat/lon

Here the naming seems to quite proper with regards to the implemented feature: great!

Things worsen when you consider these API features: find the current weather conditions in all the locations having certain characteristics, such as having toponyms that are similar/equal to a given string or being in the neighborhood of a given lat/lon couple. Here are the related endpoints:

#Feature: retrieve current weathers in all the locations whose names exactly equal to the string "London"
#Feature: retrieve current weathers in all the locations whose names contains the string "London"
#Feature: retrieve current weathers in all the locations in the neighborhoods of a lat/lon couple

Now, this lays down three questions:

  1. what the heck does “neighborhood” mean? The API documentation is silent about this topic. One could suppose that the API is performing a geographic search based on a circle with center into the specified lat/lon couple and a certain radius – and this is effectively what has been done in a prior (and now dismissed) version of the API. But nobody knows if this guess is true and – above all – what is the value for the radius , as it cannot be specified by the user.
  2. we know for sure that behind the API a geocoder is in action (for those who don’t know what a geocoder is: it is a SW module that performs direct mapping between geographic labels – such as addresses, city names, toponyms into a geographic coordinates couple or even a geographic feature on a map. Sometimes geocoders also perform the reverse mapping): for this reason, we have a “smell” here… the “find” endpoint is implementing a geocoder-like feature: it should not be responsibility of the API to behave like that, or at least, if this responsibility is implemented it should be kept separate from the weather data provisioning. So, in my opinion, there should be an endpoint providing geocoding queries and another one providing current weather data on a single location: then, queries for current weather data on X multiple locations can be done with X API requests for current weather on a each single location. You think that users won’t do that? Yes, they shouldn’t: an automatic HTTP client should. Indeed, that’s what APIs are meant to do: automatize 🙂
  3. isn’t the “accurate” parameter unnecessary? A “like” query should also give as results literal word matchings!

Another feature: retrieve weather forecasts for a location. You can get forecasts for every 3-hours of the next 5 days or for every of the next 14 days. Here are the endpoints:

#Feature: retrieve 3h weather forecast for a location
#Feature: retrieve daily weather forecast for a location

Again, questions:

  1. good naming here, but it can be improved. I would use “/forecast/daily” for daily forecast and “/forecast/3h” for 3-hours forecast. A viable alternative could be using a single endpoint “/forecast” along with a “interval=[daily|3h]” query parameter.
  2. only results coming from the daily forecast query can be paged by the user: the user can control how many results are returned by the API through an optional parameter named “cnt”. This is not possible with regards to the 3-hours forecast query: why?
  3. why isn’t it possible to specify a forecast through a lon/lat couple? Maybe it is a design decision, but it creates asimmetry with the previously described API features.
  4. why isn’t it possible to query for weather forecasts for all the locations having name similar to a given string or being in the neighborhood of a specific lon/lat couple? Guess it’s due to API designers laziness…

Same conceptual entities, different JSON representations

Clients of data API expect returned data to be structured using some kind of language or convention and  they also expect that structured data is organized in chunks or logical groups  that clearly convey cohesion and hierarchy. In our specific case, the description language used by the API can either be JSON or XML (but we will only rely on JSON from now on): this is a consolidated practice among the web APIs  and this sounds good. At this point, we want to inspect the JSON returned by a query for current weather data on London,UK:

#Payload of response to request:
  "coord": {
     "lon": -0.12574,
     "lat": 51.50853 },
  "sys": {
     "country": "GB",
     "sunrise": 1378877413,
     "sunset": 1378923812 },
  "weather": [{
     "id": 521,
     "main": "Rain",
     "description": "proximity shower rain",
     "icon": "09d" }],
  "base": "gdps stations",
  "main": {
       "temp": 288.88,
       "pressure": 1021,
       "humidity": 63,
       "temp_min": 287.15,
       "temp_max": 290.37 },
  "wind": {
       "speed": 4.6,
       "deg": 330 },
  "clouds": {
       "all": 75 },
  "dt": 1378899070,
  "id": 2643743,
  "name": "London",
  "cod": 200

The “coord”, “country”, “id” and “name” JSON root elements refer to a single logical entity: the location for which the current weather is given (London, UK). Can you see it? Wouldn’t have it been better to group all the location info into a single JSON element? For example, like this:

  "coord": {
     "lon": -0.12574,
     "lat": 51.50853 },
  "country": "GB",
  "name": "London",
  "id": 2643743

Another legitimate question is: why location information spread out from the data regarding current weather? Here the API is clearly returning more data than it has been asked for. But what is really obscure is that location info are structured in different ways if returned by different endpoints. In example, if we ask for the daily weather forecast on London,UK we get:

#Payload of response to request:
"city" : {
  "coord" : {
     "lat" : 51.50853,
     "lon" : -0.12573999999999999 },
   "country" : "GB",
   "id" : 2643743,
   "name" : "London",
   "population" : 1000000},

Now data is structured! Magic? No: an evil art! And it drives me – and everyone else who is developing  a client library for this API – literally mad to parse the JSON: each endpoint, in practice, needs a specific JSON parser in order to parse the same data entities, and this introduces complexity into the code. Had the data been structured uniformly across the different endpoints, just one parser would be needed.

HTTP status codes are neglected

This is one of the main shortcomings of this API: it does not convey error conditions through a proper use of HTTP status codes. The API users want to receive a 200 (OK) status code in the HTTP response’s header – along with data – whenever a GET request is a hit: this means that the endpoint exists and is correctly invoked by the clients; the same way, users want to receive a 4xx or 5xx status code whenever something goes wrong with their request: this can happen for several reasons, either due to clients or the server itself. But, to be short: a user expects a non-200 status code to be returned whenever something goes wrong with its request.

The OWM API always returns a 200 HTTP status code, no matter what happens. But, if something goes wrong with a client’s request, it returns the right HTTP status code and an explanation message into the HTTP response’s payload! An example: we want to query for current weather on a non-existent location (the folkloristic: “sev082hfrv2qvf2vunr”)

#HTTP request
GET /data/2.5/weather?q=sev082hfrv2qvf2vunr HTTP/1.1
#HTTP response headers
HTTP/1.1 200 OK
Server: nginx
Content-Type: application/json; charset=utf-8
#HTTP response payload
{"message":"Error: Not found city","cod":"404"}

The JSON payload is clear: the location has not been found and a 404 (Not Found) error code has been returned. However the code is returned into the payload, so the clients have to first presume that the request was a success, then parse the payload and find out that it wasn’t! The API is mis-using the HTTP protocol, which is a very nasty behaviour for clients and blows the API away from RESTfulness, as well.

“Not found” is different from “Found but no data available”

The improper usage of HTTP status codes is particularly problematic in the case of queries to historic weather data registred by meteostations. A meteostation is identified by a unique integer number and when historic data is queried, the API returns a JSON list of data measurements for the desired meteostation. So, an empty list means: no data for the desired meteostation. Now, if I want to get historic data for a station that is not present in the API’s meteostations collection (like, say, station number -2147483648), I get:

#HTTP request
GET /data/2.5/history/station?id=-2147483648&type=tick HTTP/1.1
#HTTP response headers
HTTP/1.1 200 OK
Server: nginx
Content-Type: application/json; charset=utf-8
#HTTP response payload
"calctime":" tick = 0.294 total=0.2954",

Again, a 200 status code (that means: “Ok, everything went smoothly”) and – surprisingly – an empty data list. What I would have expected is a 404 HTTP status code, telling me: “Hey, this station is not listed in my meteostations database”! So in this case, an error condition is wrongly mapped onto a non-error condition. And what if I query for an existing meteostation and it has no data available? How can I discern the “not found” case from the “found but no data available” case?

My first GitHub project

It’s been a while since I wrote here:  lately I’ve spent a lot of my spare time to organize and code my first  GitHub project ever.

Why an open source project on GitHub?

The reasons I decided to setup a GitHub account and launch an open source project are quite simple:

  • I’ve been living on the shoulders of the open source community for years and I’ve always been proud of what it gave me. The best way to be thankful is to give my commitment and code for free to everyone!
  • GitHub is a nice place where programmers can show their skills to the world (friends, fellow programmers, potential new employers). I mean: not only coding skills, also organizational and communication skills, as well as mind openness.
  • My desire is to use GitHub to link and cohoperate with others like me, sharing my same interests
  • I’m sure that open source cohoperation will teach me a lot of things: I have a lot to learn from the code masters
  • Last, but not least, it’s a good chance to practice with a few languages – first of all, Python

The PyOWM library

So the question was: what will my open source project be about? A few minutes after that question raised in my mind I ran into the OpenWeatherMap website, which basically is a webportal disseminating world weather data that are openly contributed by the user community. I noticed that the site provided a data web API, that had been created ages before and, of course, lots of code projects have been popping out since regarding this API. I took a look at the client wrapping libraries that have been created for the API and noticed that no Python client wrapper were mentioned; I also googled a bit and I found that only one attempt of Py-wrapping this API had been made since (pretty rough, not supporting the latest API version and its last commit dates back to more to the beginning of the year).

So, it was a deal: a Python client wrapping library that could allow users to interact with the OpenWeatherMap web API via a simple object-oriented model.

The PyOWM library was conceptually born.

State of the art

I worked hard to shape the library, and now most of the web API features are covered. I’ve developed it using a Test-Driven approach and keeping it as minimalist as possible. I hope this work will be useful to as many people as possible.

Now I need to “sponsorize” my creation with the OpenWeatherMap keepers, the OWM community users and gather help to test and improve the library.

How to contribute

Do you want to help my open source project grow? There are infinite ways you can help: report issues, submit new feature requess, test on specific architectures, port to different Python versions, mention it in your blogs/user communities… and of course help in coding if you are able to!

Visit the GitHub page of the PyOWM library

Thank you and cheers! 😉

How to convert VMWare virtual machines to Virtual Box

Where I started from

This is my situation: I’m on a Windows7 x86 host, and I have an old Ubuntu 10.04 virtual machine with VMWare Tools installed on it.

My need is to turn it into an OVF appliance, so that I can run it on Virtual Box, no matter where what architecture it will be run on.


This is what I’ve done:

  • I made sure (e.g: using VMWare Player or Workstation) that the virtual appliance is powered off;
  • Opened a Command Prompt, moved to the VMWare Player/Workstation installation dir and executed the OVF conversion tool. Be aware that this conversion may take some time, depending on how big is your VMX appliance. I did it as follows (replace the paths as needed):
cd "C:\Program Files\VMware\VMware Workstation\OVFTool"
ovftool.exe "C:\Users\claudio\Documents\Virtual Machines\ubuntu1004\ubuntu1004.vmx" "C:\Users\claudio\Documents\Virtual Machines\converted-to-virtualbox\ubuntu1004.ovf"
  • When the conversion process was over, I imported the “ubuntu1004.ovf” appliance into Virtual Box by using the “File” > “Import virtual appliance ..” menu element  and leaving all the defaults;
  • Then I booted up the “ubuntu1004.ovf” appliance and performed VMWare Tools uninstallation by opening up an SSH shell and executing:
sudo /usr/bin/
  • Then I finished the whole process by executing the “Device” > “Install Guest Additions” menu item: a virtual CD is then mounted and I launched the installation process from a shell
cd /media/VBOXADDITIONS_4.2.12_84980
sudo bash


Not as difficult as it may seem…. Hope this helps!

Command-line software design: 5 more advices

Ok, folks, ready to take off with 5 more CLMs (Command-Line Modules) design advices?  This is part II of a posts strip, part I (with the first 5 advices) is in my previous post.

1. Provide meaningful messages

AKA: “What am I doing? I am existing…”

Your CLM should provide insight into what it is currently doing. The difficult part is to decide how much detail you want to provide to the user…and you might argue: “Ok, but you can always use log level filtering and then let the user decide the verbosity” – this is perfectly right, but I’m talking about on-screen messages. My advice is to print out a specific message which conveys what the CLM is currently doing, with a detail level which should be just enough for the user not to say “It is talking rubbish”! So, what is really vital is that you avoid using simple and generalistic messages like “Computing” or “Executing” and – on the other hand – that you avoid using hyper-detailed expressions such as “Inverting matrix – computing determinant of the 3rd 2×2 submatrixif they are not meaningful to the user. Of course if the focus of your CLM is matricial inversion that shall be fine, but it shouldn’t be if your CLM is – in example – focused on a higher-level problem which is solved using matricial inversion.

…And, please, never print out the raw counters in nested for loops. It happened to me just a couple of days ago to run an image-processing CLM provided by a project partner: this was the output of a successful run

claudio@laptop:~$ python input.tif output.tif
Conversion to 8bit took 23.567 seconds
#2000 or so more lines
The variance computation took 367.145 seconds

Each and every row index is printed out….It is just irritating!!!

2. Gracefully fail

AKA: “I don’t want to see each blood drop spreading from your wound”

As a CLM user, would you prefer seeing this:

claudio@laptop:~$ python /var/clmdata/testoutdir  #we are missing the first parameter
  Traceback (most recent call last):
  File "", line 3, in <module>
  inputfile = sys.argv[1]
  IndexError: list index out of range
claudio@laptop:~$ echo $?

..or this?

claudio@laptop:~$ python /var/clmdata/testoutdir  #we are missing the first parameter
  ERROR: you must specify an input file
  Usage: <inputfile_path> <output_path>
claudio@laptop:~$ echo $?

The correct answer would be: none of them! But you can’t expect that your CLM is working fine every time. So it is important to let users know what reasons made the CLM stop running. A nice design choice is to detect possible error conditions and treat them so that your CLM “says something of interest” and terminates with a known exit status: this can be done quite easily if you use languages (eg: Java, Python, etc..) that provide formal exception/error handling constructs – in other terms, the usual try/catch blocks.
Graceful failures are delightful for the user, but may not the best approach to handle error situations while you are still writing your CLM because they may not give you enough information if you need to debug. So my advice is to add them only when you are pretty sure that you won’t make further heavy changes or do any more refactoring on your CLM.

3. Organize your CLM folder

AKA: “I am the Borg … I bring order to chaos” (Borg Queen – Star Trek: First Contact)

Order in organizing your code is good. This translates directly into the fact that a well-structured CMS is easy to understand and modify, and can be efficiently used in a small amount of time. My advice is to adhere to widely adopted or standard program folder structuration patterns: I usually have my CLM’s folder in this fashion

  |--bin/     #Binaries: main CLM program and dependancies
  |--doc/     #Documentation about CLM usage/installation
  |--src/     #Source files
  |--static/  #Static data: config files, static inputs, etc.
  `--test/    #Tests

4. Minimize filesystem usage and leverage temporary folders

AKA: “Forbidden: you don’t have enough permissions to write the file”

As a general advice, don’t rely on the safety of filesystem operations. If your CLM needs to store intermediate data try to do that in-memory, and if it’s not possible and therefore you are compelled to use the filesystem, your target should be to put the least complexity between your CLM and your data. Reading data from filesystems seldom is a problem, but writing often is, and the amount of adversities you might face depends on a variety of factors such as the architecture (never tried to write in a folder for which you don’t have ‘w’ permissions?), the possible concurrency in data modification, the remoteness of the target filesystem and so on.

Another misused – but smooth and clever – technique is to leverage temporary folder support provided by the operating systems. In my experience with bash programming, I’ve always seen people doing local computations as follows: input files are copied into the same folder of the executing binaries, then intermediate files are written in that folder (usually, a lot  of files), and in case of successful CLM end intermediate files are deleted. This always made me angry, because often their programs were  buggy and therefore never got to their natural end, which forced me to press CTRL+C…leaving all of those intermediate files undeleted in the folder. And this meant: I myself would have to delete them!!! :-$ To solve this issue, I simply suggested those people to leverage the “mktemp” Linux command, which creates a temporary folder with a pseudo-random name under /tmp and returns its name: one can then use this folder to do whatever she/he likes – i.e. writing the CLM execution’s intermediate rubbish.

It’s as easy as follows:

claudio@laptop:~$ tempdir=$(mktemp)
claudio@laptop:~$ echo $tempdir

5. Leverage absolute paths

AKA: “Time – as well as folder location – is relative”

When you provide paths as arguments for CLMs it is a very good practice to give them in an absolute fashion. If you give absolute paths, there’s a pretty good chance that your CLM  addresses files and folders in the right way. And my advice is: always handle absolute paths internally to your command-line softwares…in fact, this will prevent you from using terrible solutions like the “cd” (change directory) command, which will mess the whole thing up if you are using relative paths because the root folder they are resolved against changes!

A little coding exercise: let us write a small bash script (copier.bash) that takes reads a file and echoes its contents to a file named “results.out” which will be created in a directory of our choice. We want it to have this interface:

copier.bash <inputfile_path> <output_path>

and here is the code (as you can see I’m using the “cat” executable which lies in the /bin path on my Linux system):




cd "$bindir"
cat "$inputfile" > "$outputdir/result.out"

Now if we setup the environment like this:

claudio@laptop:~$ cd /opt/copier
claudio@laptop:~$ mkdir output  #we create the output folder
claudio@laptop:~$ tree .
|-- copier.bash
`-- output
1 directory, 1 file
claudio@laptop:~$ echo "italia has got talent" > input.txt #we create the input file
claudio@laptop:~$ bash copier.bash input.txt output        #we run the script
copier.bash: line 9: output/result.out: No such file or directory

As we expected, the “cd” inside our script is messing up everything and the bash shell is complaining about the fact that after it, it is impossible to find the “output” subfolder (which, in absolute terms, is: “/bin/output” !!!)

Also the following command-line fail:

claudio@laptop:~$ bash copier.bash input.txt /opt/copier/output
cat: input.txt: No such file or directory

This time it’s the “cat” executable complaining for the missing input.txt file, which it expects to be here: “/bin/input.txt

The right way of running this script would be:

claudio@laptop:~$ bash copier.bash /opt/copier/input.txt /opt/copier/output
claudio@laptop:~$ cat output/result.out
italia has got talent

You can see that: one must know in advance that absolute paths must be used. And consider that we were lucky to have a textual CLM, what if we had a compiled one? Lesson learn: never use “cd” and leverage absolute paths!