How Python and Guido got me my first job and many afterwards

Recently, Guido van Rossum, creator and leader of the Python programming language announced, quite out of the blue for the distant pythonista, that he was resigning from his role as the leader of the project, the well-known BDFL.

This is no small news as Guido has created the language far back in the early 90s and has stuck with its community forever since.

What saddened me was to see that he left to a certain degree due to the harhness of the the discussion around PEP 572. All things considered this PEP doesn’t seem like it should have involved such an outcome, yet it did. As a community we ought to reflect on this event. I assume this is not just a single PEP that forced GvR to make that decision but after years of fighting, maybe that one went too far.

I started with Python back in 2001 with a first personal project, an IRC client for Python. The code is long gone (and that’s probably better for my ego). But, I do recall joining the #python channel back then, asking a newbie question and being left with a feeling I was stupid. I left that channel and never came back. But I stuck with Python, as a language, because it is such a pleasure to work with (even though, back then I mostly had to work on Zope and Plone. Ouch).

When I joined the CherryPy project a year later, I found a very welcoming community and, when I created the according IRC channel a couple of years later, I always made sure that newbies wouldn’t felt the way I had. If a given question is asked repeatedly, I think it’s best to question our documentation quality rather than the person who asked. That led me to propose a new documentation for the project a few years ago.

In all those years, I worked at various companies and learnt different new languages, some I had a lot of fun with (for instance erlang was really sweet to learn from and a couple of years ago, I played a little with Clojure with some interests). But, for my personal projects, I always ended up with Python. This language is so powerful and versatile. It’s not better in every contexts but does a fine job to find the balance between capabilities, readability, maintanability and performance. Its ecosystem is rich and some of its communities are really nice and kind.

Python landed me a few jobs and, today is the backbone of my own company’s products at ChaosIQ such as the Chaos Toolkit and ChaosHub.

So, thank you Mr van Rossum and all the folks leading the project. Not only have you given me tools to build my carreer but you also made it fun for so long and even more so with recent Python 3 versions. I hope you’ll stick by the project for a long time!

Why Python is a great choice for the Chaos Toolkit project!

Russ Miles and I started the chaostoolkit project about three months ago with the idea we wanted to express with code discussions we had been having on Chaos Engineering and its principles. I’ll leave you the opportunity to read Russ’s post about the Chaos Toolkit effort as I would like to focus here on the choice we made to use Python to implement that Open API for Chaos Engineering.

When we started the core implementation, we wondered which language we should pick up for the project. As a long Python developers, I have always felt faster and more at ease with it but I thought it would be wise to give the other existing languages a thought.

At its core, the Chaos Toolkit attempts to define a declarative API to drive your chaos engineering experiments. That experiment is serialized in a JSON file the chaos command ingests and validates it before executing declared activities sequentially.

It felt clear to us that we would be handling a lot of strings so we wanted to have something that was simple, yet powerful, when dealing with them. That alone is not a strong enough requiremement to make a choice. Afterall, most dynamic languages are capable with strings while static languages, although perhaps more verbose, would be able to as well.

We also knew we meant the chaostoolkit to be extended as widely as possible so we looked for a rich ecosystem with various capabilities. It also meant, we should pick up a language that would help us load extensions in a simple fashion (Note that the Chaos Toolkit API describes three extension mechanisms: Python functions, HTTP calls or process. This means that you do not need to write Python code when you want to extend the toolkit).

It was also relevant to select an ecosystem that was well spread to reduce the quantity of steps for users in order to get started.

On the other hand, speed never was a requirement per-se. Indeed, the Chaos Toolkit is a CLI that runs things sequentially from a single machine. There is no need for raw speed because the Chaos Toolkit runs things as fast as they actually need to be.

Language prowesses with paradigm X or Y didn’t matter to us as long as the language was sound and dependable.

Those requirements, if we can call them that, aren’t very strong nor well-defined but they served us as guidelines.

Having worked a little with a variety of languages this past two years, I kept wondering what to select. Afterall, languages such as Clojure, TypeScript or Go all fitted the bill fairly well.

I dismissed Clojure mostly because I didn’t know its ecosystem nor community well-enough and I could not afford the time to thoroughly explore it. However, should have I had known it more, it would have been a strong candidate if only because of its code-as-data approach felt appropriate. Nonetheless, I do wonder still about its ecosystem.

TypeScript was an interesting one because it does remind me of Python in many respects. Much like Clojure however, I left it out it because I wasn’t sure its ecosystem would be the right one for the sort of experiments that users would care for.

As for Go, it’s mostly the question whether a statically compiled language would fit the requirement for string handling. I don’t mean to say Go is bad at dealing with strings. I made the decision that if I had to end up with empty interfaces, I might as well directly rely on a dynamic language.

I realise you could argue it’s mostly a lack of knowledge of all those languages and ecosystems that made me uneasy about picking them up. Indeed, I certainly think they could all have worked out.

But I settled on Python. In fact, I picked Python 3 specifically. To me Python 2 feels legacy and new projects should not start there any longer (except in rare cases).

Python does check all the boxes the project needed. It’s readily available, has a massive ecosystem, with native libraries for platforms we believe the Chaos Toolkit will provide the most benefit to (such as Kubernetes), and has powerful string handling (specially with the most recent Python 3 releases).

I know most DevOps tools seem to be written in Go these days, but I strongly believe, Python is a sound choice depending on your requirements. Specially Python 3.

Will choosing Python over Go impact the Chaos Toolkit as a project? I certainly hope not and I worked hard to design the code base so that it is comprehensible and easy to ge involved with (well I hope anyhow ;)).

In the end, what tipped it over was that Python is awesome and absolutely fun to work with.

Python has a tremendous ecosystem made of high-quality libraries. The changes in the language in the past years have not just made it more feature-rich but also actually more sound and relevant to new scenarios.

But, at the end of the day, this is just for the little story really, what matters is that we are proud of the Chaos Toolkit and really enjoy using it. We do sincerely hope it will meet its community, so if you fancy a project for 2018 or want to experiment with Chaos Engineering, make sure to join it and say hello! We love a good chat.

Deploying a docker container of a CherryPy application onto a CoreOS cluster

Previously, I presented a simple web application that was distributed into several docker containers. In this article, I will be introducing the CoreOS platform as the backend for clusterizing a CherryPy application.

CoreOS quick overview

CoreOS is a Linux distribution designed to support distributed/clustering scenarios. I will not spend too much time explaining it here as their documentation already provides lots of information. Most specifically, review their architecture use-cases for a good overview of what CoreOS is articulated.

What matters to us in this article is that we can use CoreOS to manage a cluster of nodes that will host our application as docker containers. To achieve this, CoreOS relies on a technologies such as systemd, etcd and fleet at its core.

Each CoreOS instance within the cluster runs a linux kernel which executes systemd to manage processes within that instance. etcd is a distributed key/value store used across the cluster to enable service discovery and configuration synchronization within the cluster. Fleet is used to manage services executed within your cluster. Those services aredescribed in files called unit files.

Roughly speaking, you use a unit-file to describe your service and specify which docker container to execute. Using fleet, you submit and load that service to the cluster before starting/stopping it at will. CoreOS will determine which host it will deploy it on (you can setup constraints that CoreOS will follow). Once loaded onto a node, the node’s systemd takes over to manage the service locally and you can use fleet to query the status of that service from outside.

Setup your environment with Vagrant

Vagrant is a nifty tool to orchestrate small deployment on your development machine. For instance, here is a simple command to create a node with Ubuntu running on it:

Vagrant has a fairly rich command line you can script to generate a final image. However, Vagrant usually provisions virtual machines by following a description found within a simple text file (well actually it’s a ruby module) called a Vagrantfile. This is the path we will be following in this article.

Let’s get the code:

From there you can create the cluster as follows:

I am not using directly vagrant to create the cluster because there are a couple of other operations that must be carried to let fleet talk to the CoreOS node properly. Namely:

  • Generate a new cluster id (via https://discovery.etcd.io/new)
  • Start a ssh agent to handle the node’s SSH identities to connect from the outside
  • Indicate where to locate the node’s ssh service (through a port mapped by Vagrant)
  • Create the cluster (this calls vagrant up internally)

Once completed, you should have a running CoreOS node that you can log into:

To destroy the cluster and terminate the node:

This also takes care of wiping out local resources that we don’t need any longer.

Before moving on, you will need to install the fleet tools.

Run your CherryPy application onto the cluster

If you have destroyed the cluster, re-create it and make sure you can speak to it through fleet as follows:

Bingo! This is the public address we statically set in the Vagrantfile associated to the node.

Let’s ensure we have no registered units yet:

Okay, all is good. Now, let’s push each of our units to the cluster:

As you can see, the unit files have been registered but they are not loaded onto the cluster yet.

Notice the naming convention used for webapp_app@.service, this is due to the fact that this is will not be considered as a service description itself but as a template for a named service. We will see this in a minute. Refer to this extensive DigitalOcean article for more details regarding unit files.

Let’s now load each unit onto the cluster:

Here, we asked fleet to load the service onto an available node. Considering there is a single node, it wasn’t a a difficult decision to make.

At that stage, your service is not started. It simply is attached to a node.

It is not compulsory to explicitely load before starting a service. However, if gives you the opportunity to unload a service if a specific condition occurs (service needs to be amended, the chosen host isn’t valid any longer…).

Now ce can finally start it:

You can see what’s happening:

Or alternatively, you can request the service’s status:

Once the service is ready:

Starting a service from a unit template works the same way except you provide an identifier to the instance:

The reason I chose 1 as the identifier is so that it the container’s name becomes notes1 as expected by the load-balancer container when linking it to the application’s container. As described in the previous article.

Start a second instance of that unit template:

That second instance starts immediatly because the image is already there.

Finally, once both services are marked as “active”, you can start the load-balancer service as well:

At that stage, the complete application is up and running and you can go to http://localhost:7070/ to use it. Port 7070 is mapped to port 8091 by vagrant within our Vagrantfile.

No such thing as a free lunch

As I said earlier, we created a cluster of one node on purpose. Indeed, the way all our containers are able to dynamically know where to locate each other is through the linking mechanism. Though this works very well in simple scenarios like this one, this has a fundamental limit since you cannot link across different hosts. If we had multiple nodes, fleet would try distributing our services accross all of them (unless we decided to constraint this within the unit files) and this would break the links between them obviously. This is why, in this particular example, we create a single node’s cluster.

Docker provides a mechanism named ambassador to address this restriction but we will not review it, instead we will benefit from a flat sub-network topology provided by weave as it seems it follows a more traditional path than the docker’s linking approach. This will be the subject of my next article.

A more concrete example of a complete web application with CherryPy, PostgreSQL and haproxy

In the previous post, I described how to setup a docker image to host your CherryPy application. In this installment, I will present a complete – although simple – web application made of a database, two web application servers and a load-balancer.

Setup a database service

We are going to create a docker image to host our database instance, but because we are lazy and because it has been done already, we will be using an official image of PostgreSQL.

As you can see, we run the official, latest, PostgreSQL image. By setting the POSTGRES_USER and POSTGRES_PASSWORD, we make sure the container creates the according account for us. We also set a name for this container, this will be useful when we link to it from another container as we will see later on.

A word of warning, this image is not necessarily secure. I would advise you to consider this question prior to using it in production.

Now that the server is running, let’s create a database for our application. Run a new container which will execute the psql shell:

We have connected to the server, we then create the “notes” database and connect to it.

How did this work? Well, the magic happens through the –link wedb:postgres we provided to the run command. This tells the new container we are linking to a container named webdb and that we create an alias for it inside that new container. That alias is used by docker to initialize a few environment variables such as:

Notice the POSTGRES_ prefix? This is exactly the alias we gave in the command’s argument. This is the mechanism by which you will link your containers so that they can talk to each other.

Note that there are alternatives, such as weave, that may be a little more complex but probably more powerful. Make sure to check them out at some point.

Setup our web application service

We are going to run a very basic web application. It will be a form to take notes. The application will display them and you will be able to delete each note. The notes are posted via javascript through a simple REST API. Nothing fancy. Here is a screenshot for you:

notes_screen

By the way, the application uses Yahoo’s Pure.css framework to change from bootstrap.

Simply clone the mercurial repository to fetch the code.

This will download the whole repository but fear not, it’s rather lightweight. You can review the Dockerfile which is rather similar to what was described in my previous post. Notice how we copy the webapp subdirectory onto the image.

We can now create our image from that directory:

As usual, change the tag to whatever suits you.

Let’s now run two containers from that image:

We link those two containers with the container running our database. We can therefore use that knowledge to connect to the database via SQLAlchemy. We also publish the application’s port to two distinct ports on the host. Finally, we name our containers so that can we reference them in the next container we will be creating.

At this stage, you ought to see that your application is running by going either to http://localhost:8080/ or http://localhost:8081/.

Setup a load balancer service

Our last service – microservice should I say – is a simple load-balancer between our two web applications. To support this feature, we will be using haproxy. Well-known, reliable and lean component for such a task.

Tak some time to review the Dockerfile. Notice how we copy the local haproxy.cfg file as the configuration for our load-balancer. Build your image like this:

And now run it to start load balancing between your two web application containers:

In this case, we will be executing the container in the background because we are blocking on haproxy and it won’t lok to the console anyway.

Notice how we link to both web application containers. We set short alias just by pure lazyness. We publish two ports to the host. The 8090 port will be necessary to access the stats page of the haproxy server itself. The 8091 port will be used to access our application.

To understand how we reuse the the aliases, please refer to the the haproxy.cfg configuration. More precisely to those two lines:

We load-balance between our two backend servers and we do not have to know their address at the time when we build the image, but only when the container is started.

That’s about it really. At this stage, you ought to connect to http://localhost:8091/ to see use your application. Each request will be sent to each web application’s instances in turn. You may check the status of your load-balancing by connecting to http://localhost:8090/.

Obviously, this just a basic example. For instance, you could extend it by setting another service to manage your syslog and configure haproxy to send its log to it.

Next time, we will be exploring the world of CoreOS and clustering before moving on to service and resource management via Kubernetes and MesOS.

Create a docker container for your CherryPy application

In the past year, process isolation through the use of containers has exploded and you can find containers for almost anything these days. So why not creating a container to isolate your CherryPy application from the rest of the world?

I will not focus on the right and wrongs in undertaking such a task. This is not the point of this article. On the other hand, this article will guide you through the steps to create a base container image that will support creating per-project images that can be run in containers.

We will be using docker for this since it’s the hottest container technology out there. It doesn’t mean it’s the best, just that it’s the most popular which in turns means there is high demand for it. With that being said, once you have decided containers are a relevant feature to you, I encourage you to have a look at other technologies in that field to draw your own conclusion.

Docker uses various Linux kernel assets to isolate a process from the other running processes. In particular, it uses control groups to constraints the resources used by the process. Docker also makes the most of namespaces which create an access layer to resources such as network, mounted devices, etc.

Basically, when you use docker, you run an instance of an image and we call this a container. An image is mostly a mille-feuille of read-only layers that are eventually unified into one. When an image is run as a container, an extra read-write layer is added by docker so that you can make changes at runtime from within your container. Those changes are lost everytime you stop the running container unless you commit it into a new image.

So how to start up with docker?

Getting started

First of all, you must install docker. I will not spend much time here explaining how to go about it since the docker documentation does it very well already. However, iI encourage you to:

  • install from the docker repository as it’s more up to date usually than official distribution repositories
  • ensure you can run docker commands as a non-root user. This will make your daily usage of docker much easier

At the time of this writing, docker 1.4.1 is the latest version and this article was written using 1.3.3. Verify your version as follow:

Docker command interface

Docker is an application often executed as a daemon. To interact with it you use the command line interface via the docker command. Simply run the following command to see them:

Play a little with docker

Before we move on creating our docker image for a CherryPy application, lets play with docker.

The initial step is to pull an existing image. Indeed, you will likely not create your own OS image from scratch. Instead, you will use a public base image, available on the docker public registry. During the course of these articles, we will be using a Ubuntu base image. But everything would work the same wth Centos or something else.

Easy right? The various downloads are those of the intermediary images that were generated by the Ubuntu image maintainers. Interestingly, this means you could start your image from any of those images.

Now that you have an image, you may wish to list all of them on your machine:

Notice that the intermediate images are not listed here. To see them:

Note that, in the previous call we didn’t specify any specific version for our docker image. You may wish to do so as follow:

Let’s pull a centos image as well for the fun:

Let’s now run a container and play around with it:

In the previous command, we start a bash command executed within a container using the Centos image tagged 7. We name the container to make it easy to reference it afterwards. This is not compulsory but is quite handy in certain situations. We also tell docker that it can dispose of that container when we exit it. Otherwise, the container will remain.

This is interesting because it shows that, indeed, the container is executed in the host kernel which, in this instance, is my Ubuntu operating system.

Finally below, let’s see the network configuration:

Note that the eth0 interface is attached to the bridge the docker daemon created on the host. The docker security scheme means that, by default, nothing can reached that interface from the outside. However the docker may contact the outside world. Docker has an extensive documentation regarding its networking architecture.

Note that you can see containers statuses as follow:

Exit the container:

Run again the command:

As we can see the container is indeed gone. Let’s now rewind a little and do not tell docker to automatically remove the container when we exit it:

Let’s see if the container is there:

Nope. So what’s different? Well, try again to start a container using that same name:

Ooops. The container is actually still there:

There you go. By default docker ps doesn’t show you the containers in the exit status. You have to remove the container manually using its identifier:

I will not go further with using docker as it’s all you really need to start up with

A word about tags

Technically speaking, versions do not actually exist in docker images. They are in fact tags. A tag is a simple label for an image at a given point.

Images are identified with a hash value. As with IP addresses, you are not expected to recall the hash of the images you wish to use. Docker provides a mechanism to tag images much like you would use domain names instead of IP address.

For instance, 14.10 above is actually a tag, not a version. Obviously, since tags are meant to be meaningful to human beings, it’s quite sensible for Linux distributions to be tagged following the version of the distributions.

You can easily create tags for any images as we will see later on.

Let’s talk about registries

Docker images are hosted and served by a registry. Often as it’s the case in our previous example, the registry used is the public docker registry available at : https://registry.hub.docker.com/

Whenever you pull an image from a registry, by default docker pulls from that registry. However, you may query a different registry as follow:

Basically, you provide the address of your registry and a path at which the image can be located. It has a similar form to an URI without the scheme.

Note that, as of docker 1.3.1, if the registry isn’t served over HTTPS, the docker client will refuse to download the image. If you need to pull anyway, you must add the following parameter to the docker daemon when it starts up.

Please refer to the official documentation to learn more about this.

A base Linux Python-ready container

Traditionnaly deploying CherryPy application has been done using a simple approach:

  • Package your application into an archive
  • Copy that archive onto a server
  • Configure a database server
  • Configure a reverse proxy such as nginx
  • Start the Python process(es) to server your CherryPy application

That last operation is usually done by directly calling nohup python mymodule.py &. Alternatively, CherryPy comes with a handy script to run your application in a slightly more convenient fashion:

This runs the Python module mymodule as a daemon using the given configuration file. If the -P flag isn’t provided, the module must be found in PYTHONPATH.

The idea is to create an image that will serve your application using cherryd. Let’s see how to setup an Ubuntu image to run your application.

First we create a user which will not have the root permissions. This is a good attitude to follow:

Next, we install a bunch of libraries that are required to deploy some common Python dependencies:

Then we create a virtual environment and install Python packages into it:

These are common packages I use. Install whichever you require obviously.

As indicated by Tony in the comments, it is probably overkill to create a virtual environment in a container since, the whole point of a container is to isolate your process and its dependencies already. I’m so used to using virtual env that I automatically created one. You may skip these steps.

Those operations were performed as the root user, let’s make the web user those packages owner.

Good. Let’s switch to that user now:

At this stage, we have a base image ready to support a CherryPy application. It might be interesting to tag that paricular container as a new image so that we can use it various contexts.

We take the docker container and we commit it as a new image. We then tag the new created image to make it easy to reuse it later on.

Let’s see if it worked. Exit the container and start a new container from the new image.

Well. We are ready to play now.

Run a CherryPy application in a docker container

For the purpose of this article, here is our simple application:

Two important points:

  • You must make sure CherryPy listens on the eth0 interface so just make it listen on all the container interfaces. Otherwise, the CherryPy will listen only on 127.0.0.1 which won’t be reachable from outside the container.
  • Do not start the CherryPy engine yourself, this is done by the cherryd command. You must simply ensure the application is mounted so that CherryPy can serve it.

Save this piece of code into your container under the module name: server.py. This could be any name, really. The module will be located in /home/web.

You can manually test the module:

The second line tells us the IPv4 address of this container. Next point your browser to the following URL: http://localhost:9090/

“What is this magic?” I hear you say!

If you look at the command we use to start the container, we provide this bit: -p 9090:8080. This tells docker to map port 9090 on the host to port 8080 on the container alllowing for your application to be reached from the outside.

And voilà!

Make the process a little more developer friendly

In the previous section, we saved the application’s code into the container itself. During development, this may not be practical. One approach is to use a volume to share a directory between your host (where you work) and the container.

You can then work on your application and the container will see those changes immediatly.

Automate things a bit

The previous steps have shown in details how to setup an image to run a CherryPy application. Docker provides a simple interface to automate the whole process: Dockerfile.

A Dockerfile is a simple text file containing all the steps to create an image and more. Let’s see it first hand:

Create a directory and save the content above into a file named Dockerfile. Create a subdirectory called webapp and store your server.py module into it.

Now, build the image as follow:

Use whatever tag suits you. Then, you can run a container like this:

That’s it! A docker container running your CherryPy application.

In the next articles, I will explore various options to use docker in a web application context. Follow ups will also include an introduction to weave and coreos to clusterize your CherryPy application.

In the meantime, do enjoy.

“Robot Framework Test Automation” book review

From time to time PacktPub will request a book review of one of their Python-related titles. This time around it was regarding their “Robot Framework Test Automation” book they recently released. Since I’ve been using this awesome acceptance testing tool at work for more than two years, I was happy to comply.

In a nutshell, Robot Framework provides a great interface that acts as the middle-man between variour stakeholders. Indeed, tests are written in plain text (though other formats are supported, I never use them) with a rather minimal set of rules making it (almost) straightforward to read even by non-technical persons. The dirty technical details being hidden away and implemented in Python and executable in one of the various Python VM (CPython, Jython, IronPython are supported out of the box).

Most of the time, the basics of the Robot Framework data model and workflow can be taught in a couple of hours. However, being efficient with it will take a little more time. Still, people don’t have to learn a complete programming language (Python) itself and that’s a relief meaning they are happy to work with Robot Framework sometimes cumbersome syntax.

In spite of having a rather extensive documentation available online, the project did lack a good, straight to the point summary that takes you by the hand. Moreover, the documentation’s style of the project is fairly dry and Unix-style making it tedious to browse sometimes. Still, the content is there and it rarely failed me. With that said, having a friendly book on the subject is a great thing. Kudos to PacktPub. Now about the book…

The good

The book provides an introduction to the tool, its most common usages and even tries to guide you getting more from it. It’s a short book, 83 pages, that will not bore you with complex details. In other words, it’s a good companion of the online documentation if you start with Robot Framework.

Sumit Bisht, the author, does a good job keeping a neutral point of view in regards to how you should use Robot Framework. Indeed, depending on your software under test, you might want to have a more data-oriented approach (ala fitness), a behavior-driven testing approach or even a more assert-oriented style. Not many software can deal with all of them equally and it depends also on how testing is perceived in your organisation. Robot Framework can cope with all of them.

The bad

Though I could understand it’s only an introduction, it feels like some concepts are not properly explored. The idea behind keywords, the internal data model, dynamic libraries, etc. In other words, you will not really understand the underlying blocks and axioms that are the pedestal of the whole tool, you’ll rather learn the basics of using it. In fact, the only section where the book goes into more technical details (with a good example on using sikuli) will probably confuse you since it failed to properly introduce the principles behind them.

The ugly

There isn’t anything particulary that bad with this book, again it should be considered as a friendly introduction. I do not agree with a few minor points Sumit makes but they hardly matter and aren’t wrong anyway, just a matter of opinion. Note also that the book lacks examples a couple of times where it would have mattered but I don’t believe this makes the book any less useful.

The only thing that annoys me really is that PacktPub book’s layout still looks so unprofesionnal. They should really make an effort as the code is, most of the time, too hard to read (actually on this one item, it wasn’t that bad).

Final note

I think this book is ideal if you are about to start with Robot Framework as it will speed up the basics. If you’re already used to the tool, I am not sure it will help very much.

 

 

 

ws4py – WebSocket client and server library for Python

Recently I released ws4py, a package that provides client and server WebSocket support for Python 2.6 and 2.7.

Let’s first have a quick overview of what ws4py offers for now:

  • WebSocket specification draft-10 of the current specification.
  • A threaded client. This gives a simple client that doesn’t require an external dependency.
  • A Tornado client. This client is based on Tornado 2.0 which is quite a popular way of running asynchronous networking code these days. Tornado provides its own server implementation so I didn’t include mine in ws4py.
  • A CherryPy extension so that you can integrate WebSocket from within your CherryPy 3.2.1 server.
  • A gevent server based on the popular gevent library. This is courtesy of Jeff Lindsay.
  • Based on Jeff’s work, a pure WSGI middleware as well (available in the current master branch only until the next release).
  • ws4py runs on Android devices thanks to the SL4A package

Hopefully more client and servers will be added along the way as well as Python 3.x support. The former should be rather simple to add due to the way I designed ws4py.

The main idea is to make a distinction between the bytes provider and the bytes processing. The former is essentially reading and writing bytes from the connected socket. The latter is the function of making something out of the received bytes based on the WebSocket specification. In most implementations I have seen so far, both are rather heavily intertwined making it difficult to use a different bytes provider.

ws4py tries a different path by relying on a great feature of Python: the possibility to send data back to a generator. For instance, the frame parsing yields the quantity of bytes each time it needs more and the caller feeds back the generator those bytes once they are received. In fact, the caller of a frame parser is a stream object which acts the same way. The caller of that stream object is in fact the bytes provider (a client or a server). The stream is in charge of aggregating frames into a WebSocket message. Thanks to that design, both the frame and stream objects are totally unaware of the bytes provider and can be easily adapted in various contexts (gevent, tornado, CherryPy, etc.).

On my TODO list for ws4py:

  • Upgrade to a more recent version of the specification
  • Python 3.x implementation
  • Better documentation, read, write documentation.
  • Better performances on very large WebSocket messages

Running CherryPy on Android with SL4A

CherryPy runs on Android thanks to the SL4A project. So if you feel like running Python and your own web server on your Android device, well you can just do so. You’ve probably not heard something that awesome since the pizza delivery guy rung the door.

How to get on about it? Well that’s the surprise, CherryPy in itself doesn’t need to be patched. Granted I haven’t tried all the various tools provided by CherryPy but the server and the dispatching works just fine.

First, you need get the CherryPy source code, build and copy the resulting cherrypy package into the SL4A scripts directory.

Once you’ve plugged your phone to your machine through USB, run the next commands:

Just change the path to match your environment. That’s it.

Now you can copy your own script, let’s assume you use something like below:

As you can see we must disable the multiprocessing logging since the multiprocessing package isn’t included with SL4A.

Save that script on your computer as cpdroid.py for example. Copy that file into the scripts directory of SL4A.

Unplug your phone and go to the SL4A application. Click on the cpdroid.py script, it should start fine. Then from your browser, go to http://phone_IP:8080/ and tada! You can also go to the /location path to get the geoloc of your phone.

Integrating SQLAlchemy into a CherryPy application

Quite often, people come on the CherryPy IRC channel asking about the way to use SQLAlchemy with CherryPy. There are a couple of good recipes on the tools wiki but I find them a little complex to begin with. Not to the recipes’ fault, many people don’t necessarily know about CherryPy tools and plugins at that stage.

The following recipe will try to make the example complete whilst as simple as possible to allow folks to start up with SQLAlchemy and CherryPy.

The general idea is to use the plugin mechanism to register functions on an engine basis and enable a tool that will provide an access to the SQLAlchemy session at request time.

Using Jython as a CLI frontend to HBase

HBase, the well known non-relational distributed database, comes with a console program to perform various operations on a HBase cluster. I’ve personally found this tool to be a bit limited and I’ve toyed around the idea of writing my own. Since HBase only comes with a Java driver for direct access and the various RPC interfaces such as Thrift don’t offer the full set of functions over HBase, I decided to go for Jython and to directly use the Java API. This article will show a mock-up of such a tool.

The idea is to provide a simple Python API over the HBase one and couple it with a Python interpreter. This means, it offers the possibility to perform any Python (well Jython) operations whilst operating on HBase itself with an easier API than the Java one.

Note also that the tool uses the WSPBus already described in an earlier article to control the process itself. You will therefore need CherryPy’s latest revision.

To test the tool, you can simply grab the latest copy of HBase and run:

Then you need to configure your classpath so that it includes all the HBase dependencies. To determine them:

Copy the full list of jars and export CLASSPATH with it. (This is from the HBase wiki on Jython and HBase).

Next you have to add an extra jar to the classpath so that Jython supports readline:

Make sure you’ll install libreadline-java as well.

Now, that your environment is setup, save the code above under a script named stave.py and run it as follow:

You can import any Python module available to your Jython environment as well of course.

I will probably extend this tool over time but in the meantime I hope you’ll find it a useful canvas to operate HBase.