Last Updated: May 15, 2019
· teleyinex

Crowdcrafting stack

Putting all your heart in what you do makes the difference. Why? Because as the
Baron says in the movie, The Cat
, the creation is given a

On December 4 of 2014, we got the very wonderful news that
Crowdcrafting was recognized as one of the social
technological companies of the year

Wining this price has been amazing, a recognition to our really hard to make our
Crowdcrafting site robust, scalable and stable.

TL;DR as this is going to describe our current infrastructure and how we run
Crowdcrafting, so you are advised!

HTTP Load Balancer

We host all our services in Rackspace. The reason? Well, they've a handy
calculator that allows us to estimate how much is going to cost us running our
services there, and I love it. Basically, because they don't lie, and the
numbers fit.

One of the nice features that Rackspace offers, is the option to enable an
HTTTP load balancer for your cloud servers. This simplifies a lot our set up,
and we've configured it to balance the incoming requests to our PyBossa

Therefore, when a user requests a page from Crowdcrafting, the first service
that it's contacted is the load balancer. The balancer will distribute the
requests to our PyBossa servers.

Nginx & uWSGI

Once the request has been redirected to one of the PyBossa servers, the request
hits the Nginx server. Nginx, checks its sites enabled, and directs the request
to our PyBossa Flask application written in Python. At this point, the server
is contacted and served via the uWSGI middleware, that will take care of moving
the request through our infrastructure.

Hence, Nginx takes care of serving static files, while uWSGI takes care of the

In the very beginning of Crowdcrafting we used Apache2 and mod_wsgi, but we
changed to Nginx and uWSGI because:

  • Nginx is really simple to configure.
  • uWSGI gives the best performance.

When we were running on Apache2 and mod_wsgi the
performance was suboptimal (we tested it with Locust.io) and we could see a
clear detriment in the number of requests per second that we were delivering in
comparison with the current setup. For this reason, we looked for new solutions
and we found that the best match for us is Nginx + uWSGI.

While we've been developing PyBossa we've always keep in mind that PyBossa
should be able to scale horizontally without problems. While this seems easy
to achieve
the truth is that there are so many options out there that at the
end it becomes a nightmare to decide which one is the best solution.

For example, serving avatars from N different servers should always return the
same image from all of them to the clients.

In our case we decided to keep things as simple as possible, (the KISS principle).

For this reason, we've enabled the Rackspace CDN support in PyBossa (it can be
easily extended to any other CDN as we've a generic class that can be
inherited) for serving files from a central place. This solution allows us to
grow horizontally without taking care of where the files are being served.

Additionally, if someone does not want to enable the CDN, they can configure
PyBossa to use the local uploader and use a Glusterfs to distribute the files
across all the servers. We didn't like this solution as it added another point for
failure in our systems and we've to take care of it ourselves, while the CDN does
this for us automagically.

Once the request is in the uWSGI middleware, the PyBossa server will probably
need access data in the database, so it can render the HTML and return the
response back to the client. The next section explains how we handle this part
of the request.

PostgreSQL & PgBouncer

Once the request hits the Flask app, usually it will involve a query to the
database. We use PostgreSQL 9.3 and we're really impressed by the quality,
performance and community around it. We LOVE IT! Best DB ever.

As we will have lots of connections coming from different servers, we wanted to
improve how we handle those connections to the DB to reduce overhead and timing
establishing connections, closing, etc. For this issue we're using in each
PyBossa server PgBouncer for pooling the connections to two PostgreSQL servers:

  • Master node accepting read and write queries, and
  • Slave node accepting only read queries.

PyBossa establishes two different connections to the databases in order to use
read-only connections when we want to grab just information, or write
connections when we've to write something back to the DB.

While PgBouncer pools connections, it does not load balance them, so for this
reason we use HAProxy to load balance the READ queries between the master and
slave nodes transparently. The best part of this configuration is that
everything is completely handled automagically and transparently by HAProxy, so
PyBossa does not know anything about it.

Thanks to this set up we can add more slave nodes horizontally, scaling and load
balancing our infrastructure easily.

While this solution is great, some queries need to be cached before hitting the
database as they take time to be processed (i.e. statistics for Crowdcrafting
projects). For this reason we're using Redis and Sentinel to cache almost

Redis & Sentinel

If we're in love with PostgreSQL what can we say about Redis and Sentinel: we
love them too :-)

Since the very beginning PyBossa has been using Redis and Sentinel to build a
load-balance high-available cache solution.

There set up is pretty simple: one Redis master node that accepts read and write
queries, while almost every other node in our infrastructure has a slave node.

Additionally Sentinel takes care of handling all these nodes for us
transparently, and we don't have to do anything ourselves. This solution has
been working great for us, and thanks to it we're saving lots of queries from
the DB, improving our performance.

More over, we are using Redis also for background jobs (i.e. exporting results,
computing statistics, sending emails, etc.) thanks to
Python-RQ and rq-scheduler
to run periodic jobs.

We checked Celery but it was overkilling for what we are
building and we decided again to keep things simple.

Python-RQ and
rq-scheduler are small libraries that can be easily adapted to our
needs, plus we already have in our systems Redis so it was the best candidate
for us.


In summary, we're using micro frameworks to build our project paired with
a very simple infrastructure that allows us to grow
horizontally without problems and load balance our incoming traffic efficiently.

The next picture shows how a request goes through our current setup:

Infrastructure Diagram src="/assets/img/blog/infrastructurediagram.png"></p>

UPDATE: Some people have asked about our numbers. The truth is that
the current setup can serve up to 2.5k rpm in less than 200ms for 1500
users browsing the site at the same time (we've 2 PyBossa servers with 2GB of RAM
and 2 cores each, while the DBs have 4GB of RAM and 4 cores -master and slave).

In August 2014 we managed to store in our servers more than 1.5 datum per second
one day. At that moment the DB servers have only 1GB of RAM,
and taking into account that the OS takes around 200MB of it,
the DBs were using only 800MB of RAM.

Deployments & Ansible

Up to now we've been managing all our infrastructure by hand. However, in the
last weeks we've been migrating our infrastructure to be completely
controlled via Ansible.

Additionally, we've developed our own in-house solution
for automatic deployments for all the team integrated with Github Deployments
API and Slack to get notifications in our own team chat channels. Doing a
deployment right now consist in merging and closing a pull request. As simple
as that.

Using Ansible for everything has helped us to have similar playbooks reused
across different clients, allowing us to do faster deployments that are easy to
maintain, debug and deploy.

On the other hand the automatic deployments solution uses the same playbooks,
so everything runs on the same tools and technologies.

We checked different solutions like HUBot, but we decided again to have a very
simple solution to integrate all these tools in our toolchain. The deployments
server has less than 300 lines of code, is 100% fully tested and covered, so
it's really simple to adapt it and fix it. Moreover, it runs in the same
services that we are currently using: Nginx + uWSGI, so we don't have to add
anything different to our stack.

NOTE: I'll write a blog post about the deployments solution :-)
EDIT: You can read about the deployment solution

Continuous integration and code quality

We take really seriously code quality and tests. Right now we've almost 1000
tests (953 at the time of the writing) covering almost all the source code (97%
covered) and with a health quality of 94%.

Our deployments solution uses the Travis-CI Github Statuses API to do the
deployments, so we can know for sure that it will work in our production

We follow the Github Flow more or less, as we don't have a versioning schema
per se for our PyBossa software. What we do is that everything that it is in
master is stable, as our main service runs directly from it. For this reason,
we take really seriously the quality of our software as a bug or an issue will
break our Crowdcrafting platform.

We usually do several deployments per week, adding new features, bug fixes,
etc. to PyBossa and therefore Crowdcrafting, as all the team has deployment
rights. This has proven to be an amazing feature, as we deliver really fast
following the RERO principle: Release Early Release Often.

And that's all! I hope you like it. If you have questions, please, use the
comments section below and I'll try to answer you. Now:

NOTE: This blog post was orignally published in my blog the 10 of Feb of 2015.