Meet jerg, a JSON Schema to Erlang Records Generator

I'm happy to announce the very first release of my latest Erlang open source project, jerg, a JSON Schema to Erlang Records Generator.

The objective of this project is to be a build tool that parses a rich domain model defined in JSON Schema (as specified in the IETF Internet Draft) in order to generate record definitions that can be used to Erlang applications to both read and write JSON.

I believe that having a formally defined object model for the data that's exchange over web resources is a major plus for APIs. It doesn't necessary imply that consumers have to generate static clients based on such a definition, but as far as the server is concerned, it raises the bar in term of consistency and opens the door for self-generated documentation.

jerg doesn't deal with the actual mapping between JSON and records: there is already a library named json_rec for that. It doesn't also deal with validation as, again, a library named jesse can take care of it.

This first version of jerg generates records with the appropriate type specification to enable type checking with dialyzer. It supports:

• cross-references for properties and collection items (ie the $ref property),
• default values,
• integer enumerations (other types can not be enumerated per limitation of the Erlang type specification).

It also supports a few extensions to JSON schema:

• extends: to allow a schema to extend another one in order to inherit all the properties of its parent (and any ancestors above it),
• abstract: to mark schemas that actually do not need to be output as records because they are just used through references and extension,
• recordName: to customize the name of the record generated from the concerned schema definition.

jerg has a few limitations, like its lack of support for embedded object schemas: pull-requests are more than welcome!

Enough talking: time to get started! Your next stop is jerg's GitHub project. Enjoy!

CRaSH for Mule, an introduction

This blog is the formal introduction to the CRaSH console for Mule on which I've been working for the past month or so. I've decided to interview myself about it because, hey, if I don't do it, who will?

What is CRaSH for Mule?

It is a shell that is running embedded in Mule and that gives command-line access to a variety of Mule internal moving parts. It's built thanks to the excellent CRaSH project, a toolkit built by Julien Viet and sponsored by eXo Platform, which allows the easy creation of embedded shells.

What can we do with it?

Well, it's easy to find it out. Let's connect to CRaSH for Mule and ask for help:



As you can see the range of actions include gathering information, like statistics and names, but also performing actions, like restarting a connector or even stopping the broker.

Why is it better than JMX?

Behind the scene CRaSH for Mule relies on JMX so anything you can do with it could be done with direct JMX interactions (like with JConsole or jmxterm). This said CRaSH for Mule has many advantages over raw JMX, including:

• Adds Mule semantics to JMX. You do not need to fiddle with lengthy object names do achieve what you want: CRaSH for Mule builds the right object names based on your actions.
• Provides auto-completion, this for application names and commands.
• Supports connectivity over telnet and ssh. No need for baroque JMX RMI connection strings.
• All the other CRaSH commands are available. The default set of commands is very rich: to name a few, it comes complete with commands for dealing with the system, the JVM, JDBC and JNDI.

How does it compare to MMC?

Well, it doesn't! The Mule Management Console is way more capable and feature-laden, and comes bundled with the Enterprise Edition of Mule.

CRaSH for Mule focuses on admin-oriented command-line friendly interactions with Mule. And it is fully open sourced.

How do I install it?

Navigate to CRaSH's homepage and locate the Mule distribution download link. You'll get an archive that contains different files, including crash-mule-app.zip that you need to drop in your Mule broker apps directory. This application contains a readme file that explains how to configure it, should the default configuration not satisfy you. You can also read it online.

CRaSH for Mule 1.2.0-cr6-SNAPSHOT has been tested on Mule CE 3.3.1 and EE 3.3.1.

Got into trouble?

Report issues or open feature requests directly to CRaSH's JIRA.

My Final Word (Almost)

In "Final Parameters and Local Variables", Dr. Heinz M. Kabutz rants against the generalized used of the final keyword in Java code. For him, this is a "trend' and an "idiotic coding standard".

I'm a firm believer of the complete opposite.

As a software developer, I spend most of my time reasoning about code. Anything that can make this reasoning easier is welcome. Good practices like short methods and descriptive names fall in this category. I believe immutable variables do too.

Immutable variables simplify reasoning because they ensure a stable state within a scope, whether it's a whole class or a single method. Having established invariants is a tremendous help in understanding code.

Whether it is with my own code or not, I've experienced time and again that my mental load was way lower with immutable variables than mutable ones. Maybe it's just a limitation of my own brain power, but, to me, less mental load translates in deeper understanding. And to the contrary, finding out amid-function that one of its argument has been reassigned creates an intense sense of confusion, prompting to re-read the method again. And again.

Of course, this isn't 100% true in Java, mainly because its default data structures are unfortunately mutable. But still, the comfort gained by using the final keyword everywhere, and actually letting your favourite IDE do it for you, far outweighs the small visual clutter it creates.

Unsurprisingly, I'm of the same opinion about early returns and loop breaks, but this is for another debate...

Dr. Kabutz will certainly argue that this is a matter of personal discipline or talent to ensure that one doesn't mess with invariants, because he doesn't "need the compiler to tell [him] this". Again I disagree. I don't trust myself to be on top of things at all time so I want the compiler to tell me everything... and more. I want Findbugs to scrutinize everything I write and break the build if I've been sloppy. I want Checkstyle to reject my code if it isn't compliant to whatever standard is enforced on the project I'm working on.

I do agree on one thing that Dr. Kabutz said though, which is that using the final keyword everywhere in printed books' code snippets is an annoyance. Indeed, books formatting rules constraints on code samples are so stringent (think 71 columns) that the rules of readability are tipped towards "less code as possible".

What is your experience with final variables everywhere? Love, hate or ...

IDlight Launched!

I'm super excited to announce the launch of IDlight, my very first SaaS. I'll promote it further after the week-end, but I wanted my blog readers to be the first to know :)

IDlight is an API that allows applications to retrieve public profile information. Among other things, it uses established and emerging standards like Webfinger, XRD and hCard to retrieve and parse public profiles.

It unifies all the retrieved data under a unique schema, which makes it easy for applications to consume in a consistent manner.

Please give it a try and share your feedback directly on idlight.net.

QCon San Francisco - Day 3

QCon San Francisco 2011 is a wrap. I have mixed feelings about the conference and I believe I'm not the only one. Feedback forms will speak... Here are the last tweets that I favorited and that represent the ideas I grabbed from the sessions I attended. Thank you to all the twitter users I've shamelessly re-used :)

QCon San Francisco - Day 2

My second-hand conference tweeting continues...

QCon San Francisco - Day 1

As I'm getting old and lazy, I've decided to cover QCon by favoriting other people's tweets on sessions I attended and post the result as a daily chunk...

API-First Development

A while back, REST advocate Juergen Brendel twitted:

API-first development: Modern dev. must consider multiple clients. Think about your REST API first, then add front end.

I retwited him because I can't agree more. In fact, even if there is a single client I'm convinced API-First development is a winning approach.


I've been involved in two three API-first projects, all very different in term of domain, size and technology. But in all cases, things turned out pretty well, and, in fact, way better than with traditional approaches to web development.


Clear responsibility delineation - When an API gets defined, client and server responsibilities become easier to define. The API becomes the guardian of this separation of concerns, as any temptation to make concerns of one side permeate to the other will either be impossible or at least extremely cumbersome. The API will resist almost naturally to desires to pervert it.


Better maintainability - Server-side front-end generation usually ends up in a pretty ugly mess where presentation concerns get mixed with service concerns. Even when following the MVC pattern, the sheer fact all artifacts are contained within the same project space opens the door to nasty transfers of responsibility between layers. An API is a contract around which clients and server can evolve harmoniously.


Client liberated - Front-end developers can use rich internet application frameworks and work on a clear interface with the back-end instead of getting constrained by a particular technology and having to deal with data transfer objects they don't fully control and which may change unexpectedly.


Test what matters most - This revolves around my previous rants about what's really important to test. When you expose an API, you can fully test your system as it is experienced by its different clients. There is no need for in-browser testing gimmicks. A simple HTTP client allows you to guarantee that the back-end is performing its duty and offering the front-end the features it needs. And that's what matters most.


Ninjas only - This point is a little harsh but here we go: API-first tolls the bell of tag soup developers. This category of developers, not skilled (or experienced) enough to be doing pure server-side development nor pure front-end one, is not needed anymore. Developers building clients for a server API are experts in their domain, be it HTML5, JavaScript, Flex, Android, iOS, Phonegap or whatnot. They can't rely on "the back-end guys" to pre-chew their job and are fully in charge of what they do.


In API-First development, the time you reach the point of trying a first connection between an actual client and the server is an exhilarating moment. Sure you've been testing the back-end for a while with a simulated front-end, but when they both connect for the first time, it's party time! And amazingly, things work very well right away, all thanks to a well defined API.


Before closing I should mention a downside for which I haven't found a satisfactory answer yet: against what back-end should client developers work? It is sure easy enough to expose a development-grade server to use while developing the front-end. But it's not always practical. A local server is an option, a client-specific stub is another one. Or maybe a state machine simulating the server API and allowing the front-end developer to change its state at will to easily simulate test scenarios?


So, what's your experience with API-first development? Any blazing success or horror story to share?

Service Oriented Organizations

Earlier this year, I twitted this:

and that:

Though seemingly disconnected these tweets are actually related. So here we are, in the blink of an eye, almost five months later and I can finally find some time to circle back to these ideas and expand them a little.

Both these tweets are related to the problem of growth and the pains a software company goes through when it expands.

At the beginning of a company, life is good, sharing code is easy. Whether everybody sits in the same room or not, the number of people involved in coding the product is so limited that synchronization is easy. Friction is limited, things go fast. Conflicts can be resolved with beer and pizza.

If by accident (!) the company ends up being successful, things change, sometimes very quickly and most of the time not for the best. So the team grows, divides into groups, and once the 150 persons mark gets passed, people start losing track of who's who.

The traditional path to a graceful handling of growth consists in adding layers of management. Whether the control this approach brings is actual or illusionary, the fact of the matter is that it increases the distance between teams. Here I'm not talking about physical distance, though it may be the case, but really about the perceived distance, the kind of distance from which the "us versus them" mindset stems.

Independently of all that, code still gets written. As the business has grown, so did the code base. Teams are sharing code. But now any code change must go through several layers of management, both up and down for each team. Fluidity has been lost in favor of process, which protects the business stability by doing what process does best: slowing things down, if possible to the point nothing happens thus preventing anything bad to happen.

At this point, the sheer fact of sharing code becomes a heavy burden. In software companies, shared code doesn't typically consist of pure libraries, like Apache Commons. Shared code more often than not involves shared dependencies on enterprise resources, like databases. High level of coupling, if not tangling, exists in the shared codebase. The mismatch between the boundaries that have been cut across teams and the actual software artifacts' delineation becomes an impediment to progress.

This is where the idea for a Service Oriented Organization comes from (notice how I avoided Service Oriented Business for obvious "acronymistic" reasons). It is not about getting rid of management or meetings. It is about organizing teams around tangible boundaries that fit the needs of sustainable software development.

In a Service Oriented Organization teams interact around well defined contracts: APIs and SLAs are the promises teams make to each other. And they fully define the actual extent of their commitments to each other. Teams do not share code but services.

Having clear objectives in order to achieve common goals without exposing any gory details is beneficial both for people management and software development. Whether APIs expose fine-grained technical methods or coarse-grained business ones, teams will have the rabid desire to provide the best service possible for what they're responsible of, and this for functional and non-functional qualities.

Of course, all this sounds nice and rosy and quite possibly the wishful thinking of an utopian. The quest that many people have embarked upon is to find ways for corporates not to succumb to the illusion of control and grow themselves into dumbness.

Service Oriented Organizations have been discussed before. This is just my two cents about the concept. Please share yours.

SNMP Monitoring for Scout

Half a year ago, I created a JMX plug-in for the Scout monitoring platform in the cloud. This time, I've created a plug-in for reading values from SNMP-enabled applications or systems.

The plug-in is available on GitHub.

It's capable of reading multiple values or walking the tree and gather all read values.

Let me know what you think of it: post bugs or feature requests here or directly in GitHub.

RSB (R Service Bus) at the Vancouver R Users Group

R Service Bus

View more presentations from David Dossot

The loggr Erlang Client is out!

I've just released the very first version of loggErL, my Erlang client for loggr. In case you do not know, loggr is a sweet piece of SaaS that offers a great deal of compelling log-related features wrapped in beautiful UI.

loggErL offers direct API calls to the loggr API, including support for optional fields:

loggErL comes complete with a Log4Erl appender that allows sending log events to loggr, again optionally supporting extra fields:

Feel free to fork this project on GitHub: https://github.com/ddossot/loggErL.

Mounting Resque Web Server in Ruby on Rails 3

If you want to load Resque's web server on a URL path alongside your main Ruby on Rails 3 application, no need to mess with config.ru or Rack::URLMap, as shown in different places.

The solution is way simpler and consists in using the RoR3's capacity to mount Rack applications directly in the routes table, as shown here:



Yep, it's that simple. Enjoy!

Erlang Monads FTW!

A few days ago, the big brains at RabbitMQ have released Erlando, a nifty pair of parse transformers that add support for cuts and do-syntax monads in Erlang. Like many others I'm sure, I've quickly started to use these new language constructs.

Here is a quick demonstration of how the Maybe monad and the do keyword can simplify the chaining of methods. The following shows a succession of condition evaluations which must all succeed for the final function (process_json_entities) to be called:

For those of you who wonder, yes I know that WebMachine could do most of this stuff for me: using this awesome REST framework is not an option for my project.

Without the help of the maybe monad, I would have add to either nest case clauses (yuck!) or chain methods, each of them calling the next one in case of success of its condition.

There are two problems with the latter approach:

• the most obvious problem is that the overall sequence of calls would not be in one place but buried deeper and deeper in the succession of functions,
• the least obvious problem pertains to the classic issue of naming things: finding good names for a chain of methods, each of them testing a condition and calling the next one is very hard.

So what's inside each of these method? Let's look into the first, which is basically a blueprint for most of them:

Notice on line 4 and 7 how the return/1 and fail/1 method of the Maybe monad are used to influence the flow of the sequence in the encapsulating do. The values I'm passing to these two functions are purely arbitrary but it is not always the case. In the first example above, on line 5 the value passed to return/1 in extract_json_entities is assigned to JsonEntities, eventually passed to the actual processing function.

To make this work, besides a Rebar dependency, not much is needed besides the following directives:

Hopefully, this will whet your appetite and decide you to give Erlando a try!

JMX Monitoring for Scout

Scout is a very convenient monitoring platform in the cloud that I have started to use recently. I needed to monitor JMX data point, something that Scout doesn't do by default.

One of the many shiny things about Scout is its extensibility: it is super trivial to write a Ruby plug-in and have start use it to report custom data points.

Therefore, I've created a JMX plug-in which, after some QA from the awesome team at Scout, just ended up in their repository of supported plug-ins.

Read more about this here.

Put a rabbit in your HTTP

I'm pleased to announce the release of http-safe, a store-and-forward HTTP gateway plugin for RabbitMQ.

Its goal is to simplify the integration and communication of services over HTTP by relieving systems from the chore of resending requests when something went wrong with "the other side".

http-safe goes beyond the fire and forget paradigm as it supports the notion of delivery callback in order to inform the originating system of the success or failure of its dispatch request.

Read more on GitHub: https://github.com/ddossot/rabbitmq-http-safe

Listen to Your Applications

Applications have lots to say. Here's how I've learned to listen to them.

I have recently been involved in the development of a highly distributed cloud application. We were a small team and wanted to remain agile all the way through. We had extensive testing and continuous integration in place from day one giving us plenty of feedback during development, a feedback that is essential for building the right thing and building it well.

But what about production time?

We wanted to get feedback from this part of the application life cycle too, therefore we've decided to build and configure many different feedback sources so our application could speak to us.

And speaking it did. It actually provided us precious feedback on three very different aspects of itself: user experience, design and implementation.

I have tried to represent the different feedback sources we've baked into our application and to what domain they belong to on this lovely triangular diagram:

Let me detail each feedback source:

• Activity Log - This is a detailed audit trail of each and every user action you can capture. It provides detailed feedback on your features and how you've made them usable or not. Storing this data in a PostgreSQL partitioned table did well for us. With higher volumes, you may want to go NoSQL.
• Error Log - An embarrassing stack festival that may or may not have direct impact on the end user. No need to mention that this log is best kept empty. A service like Hoptoad can help you with that by putting errors in your face until you resolve them.
• Trace Log - This is where you take the true measure of what your application is actually doing, which is less than obvious in highly distributed applications. Logging correlation IDs and aggregating logs in a central place via syslog or Scribe is a good approach. You'll need searching capacities in these logs: think Clarity or Splunk, depending on your constraints and budget.
• Response Time - This is an obvious metric that will shed some light on your design and implementation. Just be sure you're logging it and paying attention to it.
• DB TPS - Though outside of the pure realm of your application feedback loop, this metric gives you a good measure on how DB intensive is your application and if it needs some redesign, like for example some low hanging fruits where caching could help.
• Cache Hit/Miss - Caching brings as much problems as it solves: a cache-happy application doesn't come for free, especially if it is distributed. Measuring the hit/miss ratio on each cache can help validate their usefulness or lack thereof.
• MQ Throughput - Monitoring of queues for high watermark thresholds is commonly done outside of the application's realm. An interesting MQ-related data an application can log is the time a message has been in-flight, including, or not, the processing time of the message after it's been consumed.
• Activity Intensity - This last one is a fun one: by representing the number of active application sessions and the current database activity, you can get a great idea of how active (or bored) are your users.

Let me mention a single benefit of this approach: thanks to the detailed activity log, we've been able to spot design issues that were preventing users to make full use of some features. And we've been able to fix these issues not based on assumptions or wild guesses but on measured data.

Your applications want to talk to you: do you listen to them? How do you do it?

Shamelessly Plugged: cferl & Mule Erlang Transport

Vancouver Erlang Meetup cferl & Mule Transport Plug

View more presentations from David Dossot.

DevOps: Time for Agile Operations!

I've made a little xtranormal movie to introduce DevOps on the blog of AgilePartner.


Go check it out!

Erlang + Cloud Files = cferl

To celebrate the return of CloudCamp in Vancouver, I'm happy to announce the very first release of cferl, the Erlang API for Rackspace Cloud Files.

cferl fully implements the current version of the Cloud Files API. With it, you can very easily create and manage storage containers and the data objects they contain. You also have full control over the publication of your data objects on Rackspace's CDN.

Here is a short example that demonstrates a few operations using cferl:

That's all it takes to create a container, add an XML document into it and make it available to the rest of the world over CDN!

To probe further, you can:

• Browse the readme document that contains many more syntax examples.
• Download cferl 1.0.
• Fork the project or report issues on github.

Enjoy cferl!