Django Admin and Celery

Published at Feb. 28, 2017 | Tagged with: , ,

A weird race condition

Updated solution, please check below!

Some background: We have a model that is edited only via the Django admin. The save method of the model fires a celery task to update several other records. The reason for using celery here is that the amount of related objects can be pretty big and we decided that it is best to spawn the update as a background job. We have unit and integration tests, the code was also manually tested, everything looked nice and we deployed it.

On the next day we found out that the code is acting weird. No errors, everything looked like it has worked but the updated records actually contained the old data before the change. The celery task accepts the object ID as an argument and the object is fetched from the database before doing anything else so the problem was not that we were passing some old state of the object. Then what was going on?

Trying to reproduce it: Things are getting even weirder. The issues is happening every now and then.

Hm...? Race condition?! Let's take a look at the code:

class MyModel(models.Model):

    def save(self, **kwargs):
        is_existing_object = False if else True

        super(MyModel, self).save(**kwargs)

        if is_existing_object:
So the celery task is called after the "super().save()" and the changes should be already stored to the database. Unless...

The bigger picture: (this is simplified version of what is happening for the full one check Django's source)

    def changeform_view(...):
        with transaction.atomic():
            self.save_model(...)  # here happens the call to

Ok, so the save is actually wrapped in transaction. This explains what is going on. Before the transaction is committed the updated changes are not available for the other connections. This way when the celery task is called we end up in a race condition whether the task will start before or after the transaction is completed. If celery manages to pick the task before the transaction is committed it reads the old state of the object and here is the error.

Solution (updated): The best solution is to use transaction.on_commit. This way the call to the celery task will be executed only after the transaction is completed succesfully. Also, if you call the method outside of transaction the function will be executed immediately so it will also work if you are saving the model outside the admin. The only downside is that this functionality has been added to Django in version 1.9. So it wasn't an option for us. Still, special thanks to Jordan Jambazov for pointing this approach to me, I'll definitely use it in the future.

Unfortunately we are using Django 1.8 so we picked a quick and ugly fix. We added a 60 seconds countdown to the task call giving the transaction enough time to complete. As the call to the task depends on some logic and which properties of the models instance are changes moving it out of the save method was a problem. Another option could be to pass all the necessary data to the task itself but we decided that it will make it too complicated.

However I am always open to other ideas so if you have hit this issue before I would like to know how you solved it.

SQLAlchemy and "Lost connection to MySQL server during query"

Published at July 24, 2016 | Tagged with:

... a weird behaviour when pool_recycle is ignored

Preface: This is quite a standard problem for apps/websites with low traffic or those using heavy caching and hitting the database quite seldom. Most of the articles you will find on the topic will tell you one thing - change the wait_timeout setting in the database. Unfortunately in some of the cases this disconnect occurs much earlier than the expected wait_timeout (default ot 8 hours). If you are in one of those cases keep reading.

This issue haunted our team for weeks. When we first faced it the project that was raising it was still in dev phase so it wasn't critical but with getting closer to the release data we started to search for solution. We have read several articles and decided that pool_recycle is our solution.

Adding pool_recycle: According to SQL Alchemy's documentation pool_recycle "causes the pool to recycle connections after the given number of seconds has passed". Nice, so if you recycle the connection in intervals smaller that the await_timeout the error above should not appear. Let's try it out:

import time

from sqlalchemy.engine import create_engine

url = 'mysql+pymysql://user:pass@'
engine = create_engine(url, pool_recycle=1).connect()

query = 'SELECT NOW();'

while True:
    print('Q1', engine.execute(query).fetchall())
    engine.execute('SET wait_timeout=2')
    print('Q2', engine.execute(query).fetchall())

So what does the code do - we create a connection to a local MySQL server and state that it should be recycled every second(line 7). Then we execute a simple query (line 12) just to verify that the connection is working.
We set the wait_timeout to 2 second and wait for 3. At this stage the connection to the server will timeout, but SQL Alchemy should recycle it, so the last query should be executed successfully and the loop should continue.
Unfortunately the results looks like:

sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError)
(2013, 'Lost connection to MySQL server during query') [SQL: 'SELECT NOW();']

Wait, what happened, why is not the connection recycled?

Solution: Well, as with all such problems the solution was much simpler compared to the time it took us to find it (we fought with this for days). The only change that solved it was on line 7:

engine = create_engine(url, pool_recycle=1)

# the result
Q1 [(datetime.datetime(2016, 7, 24, 20, 51, 41),)]
Q2 [(datetime.datetime(2016, 7, 24, 20, 51, 44),)]
Q1 [(datetime.datetime(2016, 7, 24, 20, 51, 44),)]
Q2 [(datetime.datetime(2016, 7, 24, 20, 51, 47),)]

Have you spot the difference? We are not calling the connect() method any more.

Final words: To keep it honest, I don't know why this solved the issue. Hopefully someone more familiar with SQL Alchemy will come with a reasonable explanation for it. The bad part is that the examples in the official docs are using "connect". So it is either a bug or a bad documentation. I will send this article to SQL Alchemy's Twitter account so hopefully we will see some input from them. Till then, if any of you have an idea explanation about the behaviour I'll be happy to hear it.

Service Discovery with Smartstack

Published at July 3, 2016 | Tagged with: , ,

A week of innovation

First of all I want to thank Lifesum for having another "Innovation Week", it is a great opportunity and I hope that more companies will start following it. In a few words the idea is to allow everyone from the company to freely pick a project or idea that they want to develop and and work on it for one week. The benefits range from just making people happy because of the break of the routines and the opportunity to work on something a bit different, to seeing some pretty amazing prototypes that can be easily implemented in the company product.

What is Service Discovery?

In summary service discovery is the possibility of the separate services in scalable infrastructure to communicate with each other and to the outside world. In other words - how to route the requests to the corresponding service while providing balanced load on the instances in the pool and monitoring their health. Sounds simple, right?

Well, unfortunately service discovery in the real world is not that simple. In my presentation from the Stockholm Python MeetUp I talked a bit more about the complexity of service discovery, the suboptimal solutions and Smartstack - a solutions invented at AirBnB for simplifying the whole process. You can see more about it in my presentation:

AirBnB service auto discovery from Ilian Iliev

However, the whole idea sounded so awesome that me and my colegue Esma decided to team up on that and try to explore a bit more the opportunities that Smarstack provides for us. In the matter of fact we decided to explore two different approaches: Smartstack and Consul. However we had some issue with the Consul setup and we found that it is not acting exactly the way we so at the end we focused all our attention to Smarstack.

How does it work?

Smartstack consists of two main components - Nerve and Synapse. Never handles the service registration while Synapse reads the information about the available services and configures a local HAProxy that plays as a load balancer for the service pool. For our tests we used Zookeeper as register for the services.

What have we built?

We created a small project consisting of a pool of Zookeeper instances, two node for service A and one node for service B. We tested multiple scenarios of crashes of one or more nodes, both zookepers instances and service instances, how the systems operates during the crashes and how it recovers after the nodes are brought back on.


As a result we created a public repository service-discovery giving details about the whole setup process, our tests and their results. Actually now is the time to praise Esma and her awesome work on conducting the tests and writing the great documentation from the repo above. So, if you have ever wondered about how service discovery works or you just want to test Smartstack, just clone the repo and follow the instruction. We would be happy to hear about your experience.

Django interview questions ...

Published at May 24, 2015 | Tagged with: ,

... and some answers

Well I haven't conducted any interviews recently but this one has been laying in my drafts for a quite while so it is time to take it out of the dust and finish it. As I have said in Python Interview Question and Answers these are basic questions to establish the basic level of the candidates.

  1. Django's request response cycle
    You should be aware of the way Django handles the incoming requests - the execution of the middlewares, the work of the URL dispatcher and what should the views return. It is not necessary to know everything in the tiniest detail but you should be generally aware of the whole picture. For reference you can check "the live of the request" slide from my Introduction to Django presentation.
  2. Middlewares - what they are and how they work
    Middlewares are one of the most important parts in Django. Not only because they are quite powerfull and useful but also because the lack of knowledge about their work can lead to hours of debugging. From my experience the process_request and process_response hooks are the most frequently used and those are the one I always ask for. You should also know their execution order and when their execution starts/stop. For reference check the official docs.
  3. Do you write tests? How?
    Having well tested code benefits you, the project and the rest of the team that is going to maintain/modify your code. Django's built-in test client, Django Webtest, Factory boy, Faker or whatever you use, I would like to hear about it and how you do it. There is not only one way/tool for it so I would like to know what are you using and how. You should show that you understand how important is to test your code and how to do it right.
  4. select_related and prefetch_related
    Django's ORM (as every other) can be both a blessing and a curse. Getting a foreign key property can easily lead you to executing million queries. Using select_related and prefetch_related can come as a saviour so it is important to know how to use them.
  5. Forms validation
    I expect from the candidates that they know how to build forms, to add custom validation for one field and how to validate fields that depend on each other.
  6. Building a REST API with Django
    The most popular choices out there are Django REST Framework and Django Tastypie. Have you used any of them, why an how. What issues have you faced? Here is the place to show that you understand the concept of REST APIs, the correct request/response types and the serialisation of the data.
  7. Templates
    Building website using the built-in template system is often part of your daily tasks. This means that you should understand the template inheritance, how to reuse the block and so on. Having experience with sekizai is a plus.

There are a lot of other things to ask about internationalisation(i18n), localisation(l10n), south/migrations etc. Take a look at the docs and you can see them explained pretty well.

PyCon Sweden 2015

Published at May 19, 2015 | Tagged with: ,

In a few words PyCon Sweden 2015 was awesome. Honestly, this was my first Python conference ever but I really hope it won't be the last.

Outside the awesome talks and great organisation it was really nice to spend some time with similar minded people and talk about technology, the universe and everything else. I have met some old friends and made some new ones but lets get back to the talk. Unfortunately I was not able to see all of them but here is a brief about those I saw and found really interesting:

It all started with Ian Ozsvald and his awesome talk about "Data Science Deployed" (slides / video). The most important point here were:

  • log everything
  • think about data quality, don't use everything just what you need
  • think about turning data into business values
  • start using your data

Then Rebecca Meritz talked about "From Explicitness to Convention: A Journey from Django to Rails" (slides / video). Whether the title sounds a bit contradictive this was not the usual Django vs Rails talk. At least to me it was more like a comparison between the two frameworks, showing their differences, weak and strong sides. Whether I am a Django user, I am more and more keen to the position that none of the frameworks is better than the other one, they are just two different approaches for building great web apps.

Flavia Missi and "Test-Driven-Development with Python and Django" (slides / video). TDD will help you have cleaner, better structured and easy to maintian code. If you are not doing it the best moment to start is now. Whether it is hard at the beginning you will pretty soon realise how beneficial it is. Especially if someone pushed a bad commit and the tests saved your ass before the code goes to production.

Later Dain Nilsson talked about "U2F: Phishing-proof two-factor authentication for everyone" (video). Whether I don't use two-factor authentication at the moment I am familiar with the concept and I really like it. The U2F protocol looks like a big step towards making it more applicable over different tools and applications and the key holding devices are more and more accessible nowadays. Maybe it is time for me to get one )))

The second day started with Kate Heddleston who talked about ethics in computer programming (video). About how social networks can be used as a tool for ruining peoples lifes and that we as a developers should take a responsibility and work towards making the internet a safer place for everyone. A place where you can have your privacy and have protection if harassed. It is a big problem which won't be solved in a night, but talking about it is the first step towards solving it.

"Why Django Sucks" by Emil Stenström. Don't rush into raging cause this was one of best talks at the conference. Emil showed us the parts in Django where he sees flaws and that need improvement. The main point was the lack of common template language between Django and Javascript and what possible solutions are available or can be made.

Daniele Sluijters reminded us how easy is to work with "Puppet and Python" (video). No more fighting with Ruby, you can easily use your favorite language to build your own tools and helpers

Dennis Ljungmark and "Embedded Python in Practice". The last time I programmed embedded devices was 15 years ago as a part of short course in the high school. Dennis' work is much more complex than what I did then but his talk reminded me of things that are applicable to general programming. Whether using non-embedded systems we often have much more memory and processing power available that does not mean that we should waste it. So think when you code - RAM is not endless and processors are not that fast as we often wish. Also don't forget that Exceptions wil sooner or later occur so make your code ready to handle them.

"How to Build a Python Web Application with Flask and Neo4j" (video) by Nicole White. Well I have heard about Neo4J, but I have never used it or seen it in action so this one was really exciting. Neo4J offers you a whole new perspective about building databases and relations between objects but it is much far from panacea. Actually I can see it it is more like a special tool then as a general replacement of a relation database but it still worths to be tried. Oh, and the Neo4J browser - totally awesome.

In the lightning talks Tome Cvitan talked about Faker. If you are still not familiar with it now is a good time to try it. Especially if you are writing tests.

At the final Kenneth Reitz told us about "Python for Humans"(video). About the not that obvious things in Python and what solutions are out there. And also about The Hitchhiker’s Guide to Python! a great place for beginners and not only and shared the idea to make Python easier and more welcoming by introducing better tools for higher level of operations.

Finally I want to thank to everyone - the organisers, the speakers, the audience and practically everyone who was a part of the conference. Without you it would be the same (or be at all). Thanks, keep up the good work and hopefully we will sea each other again.

P.S. Have I mentioned that the whole conference was recorded on video so hopefully we will see be able to see all the talks pretty soon. I will try to keep this post updated with the links to the videos and/or slides when they become available. Of course if you know about any published slides from the conference that are not linked here please let me know.

The full set of videos are available at PyCon Sweden's Youtube channel.

Django, pytz, NonExistentTimeError and AmbiguousTimeError

Published at March 29, 2015 | Tagged with: , , ,

Brief: In one of the project I work on we had to convert some old naive datetime objects to timezone aware ones. Converting naive datetime to timezone aware one is usually a straightforward job. In django you even have a nice utility function for this. For example:

import pytz
from django.utils import timezone

timezone.make_aware(datetime.datetime(2012, 3, 25, 3, 52),
# returns datetime.datetime(2012, 3, 25, 3, 52, tzinfo=<DstTzInfo 'Europe/Stockholm' CEST+2:00:00 DST>)

Problem: You can use this for quite a long time until one day you end up with something like this:

timezone.make_aware(datetime.datetime(2012, 3, 25, 2, 52),

# which leads to
Traceback (most recent call last):
  File "", line 1, in 
  File "/home/ilian/venvs/test/lib/python3.4/site-packages/django/utils/", line 358, in make_aware
    return timezone.localize(value, is_dst=None)
  File "/home/ilian/venvs/test/lib/python3.4/site-packages/pytz/", line 327, in localize
    raise NonExistentTimeError(dt)
pytz.exceptions.NonExistentTimeError: 2012-03-25 02:52:00

Or this:

timezone.make_aware(datetime.datetime(2012, 10, 28, 2, 52),

Traceback (most recent call last):
  File "", line 1, in 
  File "/home/ilian/venvs/test/lib/python3.4/site-packages/django/utils/", line 358, in make_aware
    return timezone.localize(value, is_dst=None)
  File "/home/ilian/venvs/test/lib/python3.4/site-packages/pytz/", line 349, in localize
    raise AmbiguousTimeError(dt)
pytz.exceptions.AmbiguousTimeError: 2012-10-28 02:52:00

Explanation: The reason for the first error is that in the real world this datetime does not exists. Due to the DST change on this date the clock jumps from 01:59 standard time to 03:00 DST. Fortunately (or not) pytz is aware of the fact that this time is invalid and will throw the exception above. The second exception is almost the same but it happens when switching from summer to standard time. From 01:59 DST the clock shifts to 01:00 standard time, so we end with a duplicate time.

Why has this happened(in our case)? Well we couldn't be sure how exactly this one got into our legacy data but the assumption is that at the moment when the record was saved the server has been in different timezone where this has been a valid time.

Solution 1: This fix is quite simple, just add an hour if the exception occurs.

    date = make_aware(
        datetime.fromtimestamp(date_time, timezone=pytz.timezone('Europe/Stockholm'))
except (pytz.NonExistentTimeError, pytz.AmbiguousTimeError):
    date = make_aware(
        datetime.fromtimestamp(date_time) + timedelta(hours=1),

Solution 2: Instead of calling make_aware call timezone.localize directly.

    date = make_aware(
        datetime.fromtimestamp(date_time, timezone=pytz.timezone('Europe/Stockholm'))
except (pytz.NonExistentTimeError, pytz.AmbiguousTimeError):
    timezone = pytz.timezone('Europe/Stockholm')
    date = timezone.localize(datetime.fromtimestamp(date_time), is_dst=False)

The second solution probably needs some explanation. First lets check what make_aware does. The code bellow is take from Django's sourcecode as it is in version 1.7.7

def make_aware(value, timezone):
    Makes a naive datetime.datetime in a given time zone aware.
    if hasattr(timezone, 'localize'):
        # This method is available for pytz time zones.
        return timezone.localize(value, is_dst=None)
        # Check that we won't overwrite the timezone of an aware datetime.
        if is_aware(value):
            raise ValueError(
                "make_aware expects a naive datetime, got %s" % value)
        # This may be wrong around DST changes!
        return value.replace(tzinfo=timezone)    

To simplify it, what Django does is to use the localize method of the timezone object(if it exists) to convert the datetime. When using pytz this localize method takes two arguments: the datetime value and is_dst. The last argument takes three possible values: None, False and True. When using None and the datetime matches the moment of the DST change pytz does not know how to handle the datetime and you get one of the exceptions shown above. False means that it should convert it to standard time and True that it should convert it to summer time.

Why isn't this fixed in Django? The simple answer is "because this is how it should work". For a bit longer check the respectful ticket.

Reminder: Do not forget that the "fix" above does not actually care whether the original datetime is during DST or not. In our case this was not criticla for our app, but in some other cases it might be, so use it carefully.

Thanks: Special thanks to Joshua who correctly pointed out in the comments that I have missed the AmbiguousTimeError in the original post which made me to look a bit more in the problem, research other solutions and update the article to its current content.

Working with intervals in Python

Published at Feb. 22, 2015

Brief: Working with intervals in Python is really easy, fast and simple. If you want to learn more just keep reading.

Task description: Lets say that the case if the following, you have multiple users and each one of them has achieved different number of points on your website. So you want, to know how many users haven't got any point, how many made between 1 and 50 points, how many between 51 and 100 etc. In addition at 1000 the intervals start increasing by 100 instead of 50.

Preparing the intervals: Working with lists in Python is so awesome, so creating the intervals is quite a simple task.

intervals = [0] + \  # The zero intervals
            [x * 50 for x in range(1, 20)] + \  # The 50 intervals
            [x * 100 for x in range(10, 100)] + \  # The 100 intervals
            [x * 1000 for x in range(10, 102)]  # the 1000 intervals

So after running the code above we will have a list with the maximum number of points for each interval. Now it is time to prepare the different buckets that will store the users count. To ease this we are going to use a defaultdict.

from collections import defaultdict

buckets = defaultdict(lambda: 0)

This way, we can increase the count for each bucket without checking if it exists. Now lets got to counting

for user in users:
        bucket = intervals[bisect.bisect_left(intervals, user.points)]
    except IndexError:
        # we are over the last bucket, so we put in in it
        bucket = intervals[-1]

buckets[bucket] += 1

How it works: Well it is quite simple. The bisect.bisect_left uses a binary search to estimate the position where an item should be inserted to keep the list, in our case intervals sorted. Using the position we take the value from the invervals that represent the bucker where the specified number should go. And we are ready. The result will looks like:

        1: 10,
        10: 5,
        30: 8,
        1100: 2

Final words: As you see when the default dict is used it does not have values for the empty buckets. This can be good or bad depending from the requirements how to present the data but it can be esily fixed by using the items from the intervals as keys for the buckets.

P.S. Comments and ideas for improvement are always welcome.


Published at Feb. 22, 2015

Well, as some of you may have seen this blog was on hold for quite a long time. There were multiple reasons mainly my Ph.D. and changing my job but it is back online.

So, what is new?

As a start this blog is no longer running on wordpress. The reason is that I had some issues with wordpress - the blog was hacked twice due to security holes in wordpress/plugins, it was terribly slow and the code looked like shit. Lots of inline styles and javascript etc. So I made a simple Django based blog that generates static content. Alse we have new design and new domain, the last one much easier to remember )))

Also the comments are now handled by Disquss and the search functinality is provided by Google. The code of the blog, needs some minor cleaning and then it will be released publicly in the next few weeks. Meanwhile you can check my latest post Working with intervals in Python.

P.S. I have finally finished my Ph.D. so no more university/reasearch job and hopefully more time for blogging.

Python Interview Question and Answers

Published at April 28, 2013 | Tagged with:

Update 2: You can also check the follow up post Django interview questions.

For the last few weeks I have been interviewing several people for Python/Django developers so I thought that it might be helpful to show the questions I am asking together with the answers. The reason is ... OK, let me tell you a story first.
I remember when one of my university professors introduced to us his professor - the one who thought him. It was a really short visit but I still remember one if the things he said. "Ignorance is not bad, the bad thing is when you do no want to learn."
So back to the reason - if you have at least taken care to prepare for the interview, look for a standard questions and their answers and learn them this is a good start. Answering these question may not get you the job you are applying for but learning them will give you some valuable knowledge about Python.
This post will include the questions that are Python specific and I'll post the Django question separately.

  1. How are arguments passed - by reference of by value?
    The short answer is "neither", actually it is called "call by object” or “call by sharing"(you can check here for more info). The longer one starts with the fact that this terminology is probably not the best one to describe how Python works. In Python everything is an object and all variables hold references to objects. The values of these references are to the functions. As result you can not change the value of the reference but you can modify the object if it is mutable. Remember numbers, strings and tuples are immutable, list and dicts are mutable.
  2. Do you know what list and dict comprehensions are? Can you give an example?
    List/Dict comprehensions are syntax constructions to ease the creation of a list/dict based on existing iterable. According to the 3rd edition of "Learning Python" list comprehensions are generally faster than normal loops but this is something that may change between releases. Examples:
    # simple iteration
    a = []
    for x in range(10):
    # a == [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
    # list comprehension
    a = [x*2 for x in range(10)]
    # dict comprehension
    a = {x: x*2 for x in range(10)}
    # a == {0: 0, 1: 2, 2: 4, 3: 6, 4: 8, 5: 10, 6: 12, 7: 14, 8: 16, 9: 18}
  3. What is PEP 8?
    PEP 8 is a coding convention(a set of recommendations) how to write your Python code in order to make it more readable and useful for those after you. For more information check PEP 8.
  4. Do you use virtual environments?
    I personally and most(by my observation) of the Python developers find the virtual environment tool extremely useful. Yeah, probably you can live without it but this will make the work and support of multiple projects that requires different package versions a living hell.
  5. Can you sum all of the elements in the list, how about to multuply them and get the result?
    # the basic way
    s = 0
    for x in range(10):
        s += x
    # the right way
    s = sum(range(10))
    # the basic way
    s = 1
    for x in range(1, 10):
        s = s * x
    # the other way
    from operator import mul
    reduce(mul, range(1, 10))

    As for the last example, I know Guido van Rossum is not a fan of reduce, more info here, but still for some simple tasks reduce can come quite handy.
  6. Do you know what is the difference between lists and tuples? Can you give me an example for their usage?
    First list are mutable while tuples are not, and second tuples can be hashed e.g. to be used as keys for dictionaries. As an example of their usage, tuples are used when the order of the elements in the sequence matters e.g. a geographic coordinates, "list" of points in a path or route, or set of actions that should be executed in specific order. Don't forget that you can use them a dictionary keys. For everything else use lists.
  7. Do you know the difference between range and xrange?
    Range returns a list while xrange returns a generator xrange object which takes the same memory no matter of the range size. In the first case you have all items already generated(this can take a lot of time and memory) while in the second you get the elements one by one e.g. only one element is generated and available per iteration. Simple example of generator usage can be find in the problem 2 of the "homework" for my presentation Functions in Python
  • Tell me a few differences between Python 2.x and 3.x
    There are many answers here but for me some of the major changes in Python 3.x are: all strings are now Unicode, print is now function not a statement. There is no range, it has been replaced by xrange which is removed. All classes are new style and the division of integers now returns float.
  • What are decorators and what is their usage?
    According to Bruce Eckel's Introduction to Python Decorators "Decorators allow you to inject or modify code in functions or classes". In other words decorators allow you to wrap a function or class method call and execute some code before or after the execution of the original code. And also you can nest them e.g. to use more than one decorator for a specific function. Usage examples include - logging the calls to specific method, checking for permission(s), checking and/or modifying the arguments passed to the method etc.
  • The with statement and its usage.
    In a few words the with statement allows you to executed code before and/or after a specific set of operations. For example if you open a file for reading and parsing no matter what happens during the parsing you want to be sure that at the end the file is closed. This is normally achieved using the try... finally construction but the with statement simplifies it usin the so called "context management protocol". To use it with your own objects you just have to define __enter__ and __exit__ methods. Some standard objects like the file object automatically support this protocol. For more information you may check Understanding Python's "with" statement.
  • Well I hope this will be helpful, if you have any question or suggestion feel free to comment.

    Update: Due to the lots of comments on Reddit and LinkedIn, I understood that there is some misunderstanding about the post. First, the questions I have published are not the only ones I ask, the interview also includes such related to general programming knowledge and logical thinking. Second the questions above help me get a basic understanding of your Python knowledge but they are not the only think that makes my decision. Not answering some of them does not mean that you won't get the job, but it may show me on which parts we should to work.

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