Background

At $work I have always spent most of my time in the backend and infrastructure side of things but sometimes, especially when working on side projects, I have to go "full stack" and take care of frontend tasks, even if I kind of suck at it. For this reason I am always inclined to start with some sort of backend code, starting with a data model, core business logic and a REST API to (eventually) build a frontend on top of it...which most of the times means I will lose interest/momentum and the project dies.

At some point something clicked

One day (probably a lazy Sunday) I had an idea: I want an incredibly stupid way to collect random thoughts, links or any kind of text blob, I want to own it, it must live on my VPS and I have to finish it today! This time thoughts decided to follow a different path:

• how important what I am going to write here is? can I afford to lose data?

• what if I don't need to store anything in a durable storage?

• ok, aim for least amount of features and see later if I need more

The outcome is a service that will keep all data in volatile memory, losing it at each restart and this is perfectly fine for what I needed at that time! I think this small tool planted the seed for what, I think, will be be my preferred method for future projects.

What has been so different with development of this tool is that I have tried and (more or less succeeded) to address the problem at hand without much ceremony, and it served its purpose quite well. At some point I've started to add more features "just because" but, at the end of the day, the core remained the same and now I even want to remove these new features because they are not adding value but are, effectively, distracting.

Next project

Fast forward few months, I am again thinking about a new project. My girlfriend needed/wanted a tool to make it easy to share a watermarked version of her drawings, even better if the shared link required a password to access the image.

Compared to the previous project, this one is slightly more complex, especially the user facing part, but I am confident that if I address the most basic requirements first, I should be able to give her a rough beta in few hours. Here are the features I have decided to include in the "MVP":

• user can upload a file

• user can chose a watermark size

• user can eventually chose a password to protect the image

• once the user clicks "Upload", the system applies the watermark and

• render a page with the list of uploaded/watermarked files, each with a link to it

So far so good, the tool is there, ugly but working. What if we open this tool to other people? WOW! We can make our own product! Lets do it!

Armed with great energy I have started working on the new version but this time I have started building a full featured backend even if, at this point, is not 100% clear how a user is going to interact with it, how valuable it could be and all the usual unknowns. So why I am insisting with the old approach when it showed, multiple times, it is quite ineffective? I guess it is a matter of habits (because I don't want to call it stupidity).

A new approach

I often fail to make progress on my projects when it comes the turn of the frontend code, after having written most of the backend, maybe this time I can try the other way around! Common sense suggests to create mocks of the interface using tools like Balsamiq, Moqups and so on but, for some reasons I am totally unable to use those kind of tools plus I would like to have a taste of how to UI feels in the hands a user...what should I do now?

Recently I have started experimenting with ClojureScript and Rum (a library that wraps React) and so far I feel quite productive with it, why should I not use these tools to write the mock? The plan now looks like this:

• quickly build an ugly but functional UI

• mock the app state/data to reflect what I need in the UI

• understand what feels wrong, get feedback from other people

• plan next iteration

• repeat until happy

Essential to this approach is the quick feedback loop you can get by using ClojureScript + Rum (or any other React wrapper like Reagent), immutable persistent data structures and a REPL; I "suspect" that, if we exclude the REPL, a similar setup can be achieved with other stacks but I am quite happy with the tools I have chosen.

How has it been so far?

I am not a frontend developer and every time I try to create some sort of UI I am pretty sure I will fail, lose interest and put the project on hold but, this time, it has been quite different. Being able to iterate quickly and having a fast feedback loop, helped me a lot to proceed at "great speed" and not losing focus. Each time I wanted to add a new feature or change how a component had to work, the process has been pretty straightforward:

• create a new branch

• try out the new idea by small, even tiny iterations with live feedback

• adjust the data model as needed

• repeat

• if happy merge to master otherwise scrap everything and forget

Managing the app state

One of the points that I would like to spend so time on is the app state. Even if keeping all the state in one big map is quite handy and flexible, at some point, it becomes harder and harder to maintain, especially in the case where there are many ( > 4 or 5) entity types with cross references and the size of the data gets bigger and bigger; it can still be done but the state management code starts to get bigger and harder to maintain compared to the UI code; for my case, the UI code had to know too much about the data model itself impacting the separation of concerns and this was driving away the focus on the main goal which is to explore the space of possibilities for the UI I want to build.

Introducing Datascript into my tool-set

With the growing pain of state management I have decided to invest a little bit of time to find out which solutions are already available in this space and fortunately I have stepped into Datascript, an in memory database developed by the same person who gave us Rum :) This database has all the features I need plus some more; I am sure that I am still missing some handy features but so far I have been able to:

• build a consistent data store

• which supports cross references between entities

• which I can easily query (using Datalog)

• with support to joins

Datalog itself is quite simple even if a bit "alien" until you know how to use it; instead of writing about it here I prefer to give a link to the best learning material I have found http://www.learndatalogtoday.org/.

Armed with Datascript (for the data store) and Datalog (for the queries) the UI code can now interact with the app state without knowing much about it! This is a great relief because now I can focus again on building a UI for my project and spend the right amount of time (< 5%) on the data store.

Another good point of this approach is that, when the backend will be ready, it will be a matter of translating what I will get out of its APIs to the UI's internal data store and everything will continue to work as expected.

Summary

The goal of this post was to write down my current approach to software development with the hope to start a discussion with other developers which may be interested to explore and talk about novel methodologies which have worked great for them. If by reading this blob of text you are now also curious about ClojureScript and its ecosystem, I am even more happy!

Links

• ClojureScript: https://clojurescript.org/

• Rum: https://github.com/tonsky/rum

• DataScript: https://github.com/tonsky/datascript

• Datalog: https://en.wikipedia.org/wiki/Datalog

• Datalog course: http://www.learndatalogtoday.org/

• Reagent: https://reagent-project.github.io/

• Balsamiq: https://balsamiq.com/wireframes

• Moqups: https://moqups.com/

Solution

We have evaluated a couple of solutions:

• per message priority

• dedicated low priority queue for high volume automaticly generated notifications using task routing

The first solution is a generic one but as far as we have seen it is not easy to have the guarantie that a high priority message will be delivered in our desired time frame and we opted for the second solution because we don't need a fine grained prioritization system (maybe in the future) but just a way to continue to deliver user generated notifications when we are sending our automated high volume notifications during the morning.

Implementation

Our stack is based on Celery and it is composed mainly by two parts:

• the notifications service thats send messages to a queue

• a pool of workers that fetch messages from the queue and deliver the notifications

To achieve our goal we only had to change the way that notifications service sends messages to the queue by specifing the low or default priority queue based on the message type and by running a specific pool of workers bound to each priority queue.

Example code with routing:

from celery import Celery

celery = Celery()
celery.main = 'Test app'
celery.config_from_object('celeryconfig')
celery.autodiscover_tasks(
    ['tasks'],
    force=True
)


@celery.task
def task(prio, message):
    print(f'{prio}: {message}')

# calling the task specifying the queue
task.apply_async(args=('default', next(generator)),
                 queue='default')

How to run a worker specifing the queue::

$ celery -A tasks worker --loglevel=INFO -Q default

This solution works but there is an efficency problem since when the low priority queue will be empty the low priority workers will be idle wasting precious (and paid) resources; fortunately there is a simple solution for this because it is possible to specify more than one queue using the -Q parameter. This way we will have a dedicated pool of workers that will work on messages generated by user activity and a second pool of workers that will handle the automated messages and, when those will be finished, these workers can help with default priority messages.

An example implementation is provided in this repo with instruction to run the different use cases.

P.S. We are hiring!

List of available resources

Right now the best collection of rust game development related project can be found in the "Are we game yet" page<http://arewegameyet.com/>. this web site maintain a list of frameworks and engines fro the lowest level flavour to the most complete game engine.

Of all the available solutions ggez<https://github.com/ggez/ggez/> (Good Game Easy) is the one which attracts me the most because I like to create small prototypes and not having the knowledge overhead required by other full featured engines or frameworks like piston<https://github.com/PistonDevelopers/piston> or amethyst<https://github.com/amethyst/amethyst> it is helpful when experimenting; it also feels like a microframework to which you can add the components you need without having to comply with other people choices and taste.

Brief description of the problem

The software is a desktop application used to upload email attachments to a web based data store; the application talk to the web app using a "RESTy" api, and fetches the file from a email address; the GUI is written with the beatiful PyQT5 (someone may prefere PySide) and usually this dependency is a pain to work with but I have to admit that the newer version installs gracefuly.

OS version and things to download

The person using this software needs to run it from a Windows OS and at this time Windows 7 is the oldes worsion of the OS I want to support (no Vista, please) since addressing Windows 10 will produce an executable with no retro compatibily and Windows 8 have less installs than 7.

The first software needed to be dowloaded is obviusly the Python 3.5 interpreter and after its installation came the first surprise, it doesn't run because of some missing dlls; after some research I have downloaded the redistribuitable runtime and it started to work as expected.

After Python was ready I've had to install the pip utility which was not automatically installed, maybe I have forgotten to chek the corresponding option in the installer; anyway everything installed correctly using it, this is a brief list of dependencies:

• requests: OK

• paramiko: OK

• imapclient: OK

• PyQT: (surprisingly!) OK

• cx_Freeze: Not ok at all..

cx_Freeze requires Widonws build tools that can freely (as in beer) downloaded and installed but even after that I was not able to install it from pip; fortunately some kind people provided a procompiled wheel for it but be sure to download the 5.0 version because the 4.x was not able to produce a proper executable.

Additional dependencies and some setup.py notes

Having not touched I tought that the setup script was soemwhat correct since the last build was succesful, nothing more far from the truth; the first thing has been a nice surprise, cx_Freeze recognized correctly all the dlls to use in the final package and referencing additional dlls is not needed anymore, good work cx_Freeze guys!

After the first build ihave started a cycle of try, fix retry until the application could as expected, here is a list of additional dependencis that I had to install and reference in the setup.py file:

• appdir

• packaging

• atexit

appdir and atexit only need to be referenced as packages instead packaging requires some more fine tuining so I had to add this additional sub_packages to the includes settings of the build_exe_options dictionary:

• packaging.version

• packaging.specifiers

• packaging.requirements

Final words

It took me a couple of hours of trial/error to be able to ship the build and I hope to not have to repeat this madness again soon; if I'll need to create a new build in the future I hope that this little post will halp me not to waste my time again.

Scenario

Imagine you have a slew of models in your application, at some point you feel the need to log somewhere creation, modification or deletion of data belonging to these models. How to proceed without having to modify the classes one by one?

What's on sqlalchemy

SQLAlchemy (http://sqlalchemy.org) offers a couple of interesting mechanisms: the first concerns the possibility to hook to some event listeners such as before_insert, before_update, before_delete and the corresponding after_*. Additional help is provided by sqlalchemy the opportunity to work on a model after its definition by overriding the method __declare_last__. Using these facts, and assuming that you have defined a model named MyModel, if we wanted to intercept the event "after_insert" we could write the following code:

class MyModel(object):
#lets pretend to have defined our model

  def after_insert(mapper, connection, target):
    #do some stuff
    pass

  @classmethod
  def __declare_last__(cls):
    event.listen(cos, "after_insert", cls.after_insert)

Whenever an object of class MyModel will be entered into the database after_insert method will be called , passing as parameters the mapping of the model, the connection and the target is none other than the object that has just been entered into the database.

In the event that you are intercepting the creation or deletion of an object is sufficient to access its primary key to identify it in your log, but if we wanted to know which fields have been modified, with new and old values, as a result of an update it gets a little more complicated, but not too much. In fact sqlalchemy allows us, quite easily , to check the status of the fields of an object using the function sqlalchemy.orm.attributes.get_history (http://docs.sqlalchemy.org/en/latest/orm/session.html#sqlalchemy.orm.attributes.get_history). This function is called for each field, it returns an object of type History (http://docs.sqlalchemy.org/en/latest/orm/session.html#sqlalchemy.orm.attributes.History) which we will use the method has_changes() to check for changes, and if there were, getting the new and old values of the field that we are analyzing, for example:

h = get_history(target, "a_field")
if h.has_changes():
  #do something using h.deleted list to get the old values
  #do something using h.added list to get the new values

LoggableMixin

Clearly to do this for all models of an application may be costly in terms of time and code maintenance (and extremely annoying) so you might think about creating a generic Mixin with which to extend the models of our application. Below is the skeleton for the implementation of the above mixin, omitting the details of where and how the logs are stored:

class LoggableMixin(object):

  def after_insert(mapper, connection, target):
    #do some stuff for the insert
    pass

  def after_update(mapper, connection, target):
    #do some stuff for the update, maybe saving the changed fields values using get_history
    pass

  def after_delete(mapper, connection, target):
    #do some stuff
    pass

  @classmethod
  def __declare_last__(cls):
    event.listen(cos, "after_insert", cls.after_insert)
    event.listen(cos, "after_update", cls.after_update)
    event.listen(cos, "after_delete", cls.after_delete)

so, for each model we want to log changes it will be sufficient to inherit from LoggableMixin:

class MyModel(SomeSuperClass, LoggableMixin):
  pass

Improvements

One of the first improvements you can make to the class LoggableMixin could be the separation of the class in three different classes eg . LogInsertMixin, LogUpdateMixin LogDeleteMixin, in my case I preferred to have it all together given the small size of the class. A second improvement would be the generalization of mixin allowing you to specify which functions (or methods) to be assigned to different listeners, and once more the specific needs of the application I'm working on does not require this level of abstraction and can live well with this approach.

Conclusions

SQLAlchemy provides a number of services to work with the model, the system just described would not have been so easy to implement if it were not for the quality of the API of sqlalchemy. I invite anyone to go deeper in the documentation for sqlalchemy (http://docs.sqlalchemy.org) because within it are preserved gems of great value. For those wishing to see a concrete implementation of the topics discussed in this post they can take a look at the file sysgrove/models.py in the repository at https://bitbucket.org/sysgrove/sysgrove

What is buildbot

BuildBot (http://buildbot.net/) is a framework created to simplify the development of automation solutions in the field of software development. Unlike other systems such as Jenkins (http://jenkins-ci.org/) or TravisCI (https://travis-ci.org/) buildbot only provides a framework on which to build the infrastructure that you need . The project has client-server architecture where the server sends to the client the work to be performed, the server is called master and the clients are called slaves; the framework supports multiple masters and multiple slaves, in this way the system architecture can easily grow with the needs of your project. Buildbot is available for virtually any platform, even if the current version 0.8.x platform support win32-64 is not exactly the best though still usable with some changes (see http://trac.buildbot.net/wiki/RunningBuildbotOnWindows )