DDevelopment & Design

Hadoop for Enterprises

Hadoop’s usage as a big data processing framework gains a lot of attention lately. Now, not only big players see, that they can embrace the data their sites or products are generating and develop their businesses on it. For that to happen two things are needed: the data itself and means of processing really big amounts of it.

Gathering data is relatively easy. These are not necessarily structured data, you don’t need to plan their usage at first. Just start collecting them and than you may experiment with their potential usage. If they’ll come out as useless rubbish – deleting them won’t be hard But imagine the values it may contribute to your business:

  • faster services – working on optimized data
  • more clients – because of more relevant search results
  • happy clients – your service can “read their minds”
  • etc.

There are many companies that utilize Hadoop ecosystem for their own needs. You can read about some of them here: http://wiki.apache.org/hadoop/PoweredBy But since that page lacks insight into specific applications of Hadoop I’ve tried to delve into

details of how Hadoop helped tame some companies’ big data sets.

Facebook

Being a social network provider, a widely used one, they require no introduction. However if you’ve lived under a rock for last couple years just visit their website http://facebook.com

Their main usage is data warehousing. Since they require to be able to access the data fast and reliably they had a need for real-time querying of their huge, and always growing data set. Their switch from MySQL databases was required due to the increasing workloads they experienced with standard databases. What they got “out of the box” with Hadoop was all the benefits of distributed file system (HDFS features). They expanded the ideas behind that even further and implemented truly Highly Available file system without Single Point of Failure.

Facebook has 3 interesting usage scenarios in which Hadoop plays a major role:

  • Titan – is Facebook’s messaging system. It processes messages exchanged between users. Ensures that it happens fast and without glitches. Here Hadoop is used mainly as a huge, unlimited storage.
  • Puma – Facebook Insights – a tool providing page statistics for advanced Facebook users. Based on streams of data (clicks, likes, shares, comments and impressions) it graphs those data and makes it available near instantly.
  • ODS – Operational Data Store – which stores Facebook’s internal metrics – collections of OS and cluster health metrics. And it facilitates multiple accounting solutions.

Twitter

This popular micro-blogging platform, where you can register your account and follow friends and celebrities for their micro-messages does some pretty interesting things with their Hadoop cluster.

One of their motivations is to speed up their web-page’s functionality. That is why the compute users’ friendships in Twitter’s social graph with Hadoop. Using connections between users they calculate their relationship to each other and estimate groups of users.

Since this service’s users generate lots of content, the company conducts researches based on natural language processing. They probe what could be told about a user from his tweets. They use tweets’ contents for advertisement purpose, trends analysis and many more.

From tweets and user’s behaviours they characterise usage scenarios. Also, they gather usage statistics, like number of searches daily, number of tweets. Based on this seemingly irrelevant data they run comparisons of different types of users. Twitter analyzes data to determine whether mobile users, users who use third party clients or power users use Twitter differently from average users. Of course theses seem like really specific applications but nevertheless they are very original and base on the data that Twitter has been gathering for some time now.

EBay

Being the biggest auctioning site on the Internet, EBay uses Hadoop processing for increasing search relevance based on click-stream data, user data. This seems pretty obvious, considering their area of operation.

However the also have one other interesting thing – they try hard to automatically fill auctioned objects’ metadata, based on the descriptions and other data provided by users. They employ data mining approach for this tasks and judging from their constant growth it seems to work

LinkedIn

Social network for professionals, thou a lot smaller than Facebook. Based on click-streams they discover relations between users. All the data concerning latest visits on your profile or people you may know from other places – this comes from Hadoop based analysis of those clicks people make all the time on their sites.

Also a very neat feature, called InMaps (http://inmaps.linkedinlabs.com/) analyse declared schools and companies and generates data for graph with clustered friends of yours.

Last.fm

This on-line radio site, praised by many for its invaluable recommendations’ system seems like a rather small and simple service. But behind the facade of simple web page there are lots of data being processed, so that their services could match a certain level of perfection.

Such large volume of their data comes from scrobbles. Each users of their service listening to a song generates a note about this fact – called scrobble. Based on that and user profiles they calculate global band popularity charts, maps of bands’ popularity and many more usage statistics and timeline charts.

Conclusion

They just try to detect and trace new patterns in seemingly chaotic data sets. Perhaps you could also do the same? Analyze your data and expand your business value?

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Już w najbliższą sobotę Confitura 2012

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DDevelopment & Design

Super Confitura Man

How Super Confitura Man came to be :)

Recently at TouK we had a one-day hackathon. There was no main theme for it, you just could post a project idea, gather people around it and hack on that idea for a whole day - drinks and pizza included.

My main idea was to create something that could be fun to build and be useful somehow to others. I’d figured out that since Confitura was just around a corner I could make a game, that would be playable at TouK’s booth at the conference venue. This idea seemed good enough to attract Rafał Nowak @RNowak3 and Marcin Jasion @marcinjasion - two TouK employees, that with me formed a team for the hackathon.

Confitura 01

The initial plan was to develop a simple mario-style game, with preceduraly generated levels, random collectible items and enemies. One of the ideas was to introduce Confitura Man as the main character, but due to time constraints, this fall through. We’ve decided to just choose a random available sprite for a character - hence the onion man :)

Confitura 02

How the game is played?

Since we wanted to have a scoreboard and have unique users, we’ve printed out QR codes. A person that would like to play the game could pick up a QR code, show it against a camera attached to the play booth. The start page scanned the QR code and launched the game with username read from paper code.

The rest of the game was playable with gamepad or keyboard.

Confitura game screen

Technicalities

Writing a game takes a lot of time and effort. We wanted to deliver, so we’ve decided to spend some time in the days before the hackathon just to bootstrap the technology stack of our enterprise.

We’ve decided that the game would be written in some Javascript based engine, with Google Chrome as a web platform. There are a lot of HTML5 game engines - list of html5 game engines and you could easily create a game with each and every of them. We’ve decided to use Phaser IO which handles a lot of difficult, game-related stuff on its own. So, we didn’t have to worry about physics, loading and storing assets, animations, object collisions, controls input/output. Go see for yourself, it is really nice and easy to use.

Scoreboard would be a rip-off from JIRA Survivor with stats being served from some web server app. To make things harder, the backend server was written in Clojure. With no experience in that language in the team, it was a bit risky, but the tasks of the server were trivial, so if all that clojure effort failed, it could be rewritten in something we know.

Statistics

During the whole Confitura day there were 69 unique players (69 QR codes were used), and 1237 games were played. The final score looked like this:

  1. Barister Lingerie 158 - 1450 points
  2. Boilerdang Custardbath 386 - 1060 points
  3. Benadryl Clarytin 306 - 870 points

And the obligatory scoreboard screenshot:

Confitura 03

Obstacles

The game, being created in just one day, had to have problems :) It wasn’t play tested enough, there were some rough edges. During the day we had to make a few fixes:

  • the server did not respect the highest score by specific user, it was just overwritting a user’s score with it’s latest one,
  • there was one feature not supported on keyboard, that was available on gamepad - turbo button
  • server was opening a database connection each time it got a request, so after around 5 minutes it would exhaust open file limit for MongoDB (backend database), this was easily fixed - thou the fix is a bit hackish :)

These were easily identified and fixed. Unfortunately there were issues that we were unable to fix while the event was on:

  • google chrome kept asking for the permission to use webcam - this was very annoying, and all the info found on the web did not work - StackOverflow thread
  • it was hard to start the game with QR code - either the codes were too small, or the lighting around that area was inappropriate - I think this issue could be fixed by printing larger codes,

Technology evaluation

All in all we were pretty happy with the chosen stack. Phaser was easy to use and left us with just the fun parts of the game creation process. Finding the right graphics with appropriate licensing was rather hard. We didn’t have enough time to polish all the visual aspects of the game before Confitura.

Writing a server in clojure was the most challenging part, with all the new syntax and new libraries. There were tasks, trivial in java/scala, but hard in Clojure - at least for a whimpy beginners :) Nevertheless Clojure seems like a really handy tool and I’d like to dive deeper into its ecosystem.

Source code

All of the sources for the game can be found here TouK/confitura-man.

The repository is split into two parts:

  • game - HTML5 game
  • server - clojure based backend server

To run the server you need to have a local MongoDB installation. Than in server’s directory run: $ lein ring server-headless This will start a server on http://localhost:3000

To run the game you need to install dependencies with bower and than run $ grunt from game’s directory.

To launch the QR reading part of the game, you enter http://localhost:9000/start.html. After scanning the code you’ll be redirected to http://localhost:9000/index.html - and the game starts.

Conclusion

Summing up, it was a great experience creating the game. It was fun to watch people playing the game. And even with all those glitches and stupid graphics, there were people vigorously playing it, which was awesome.

Thanks to Rafał and Michał for great coding experience, and thanks to all the players of our stupid little game. If you’d like to ask me about anything - feel free to contact me by mail or twitter @zygm0nt

Recently at TouK we had a one-day hackathon. There was no main theme for it, you just could post a project idea, gather people around it and hack on that idea for a whole day - drinks and pizza included.

My main idea was to create something that could be fun to build and be useful somehow to others. I’d figured out that since Confitura was just around a corner I could make a game, that would be playable at TouK’s booth at the conference venue. This idea seemed good enough to attract >Conclusion

DDevelopment & Design

Writing JAXB in Groovy

Suppose you want write a jaxb class in groovy. Why? Because you do not have to write these all getters, setters and other methods. You only have to write your fields down.@XmlRootElement@HashCodeAndEquals@ToStringclass Person { String firstName String ...
DDevelopment & Design

Grails with Spock unit test + IntelliJ IDEA = No thread-bound request found

During my work with Grails project using Spock test in IntelliJ IDEA I've encountered this error:

java.lang.IllegalStateException: No thread-bound request found: Are you referring to request attributes outside of an actual web request, or processing a request outside of the originally receiving thread? If you are actually operating within a web request and still receive this message, your code is probably running outside of DispatcherServlet/DispatcherPortlet: In this case, use RequestContextListener or RequestContextFilter to expose the current request.
 at org.springframework.web.context.request.RequestContextHolder.currentRequestAttributes(RequestContextHolder.java:131)
 at org.codehaus.groovy.grails.plugins.web.api.CommonWebApi.currentRequestAttributes(CommonWebApi.java:205)
 at org.codehaus.groovy.grails.plugins.web.api.CommonWebApi.getParams(CommonWebApi.java:65)
... // and few more lines of stacktrace ;)

It occurred when I tried to debug one of test from IDEA level. What is interesting, this error does not happen when I'm running all test using grails test-app for instance.

So what was the issue? With little of reading and tip from Tomek Kalkosiński (http://refaktor.blogspot.com/) it turned out that our test was missing @TestFor annotation and adding it solved all problems.

This annotation, according to Grails docs (link), indicates Spock what class is being tested and implicitly creates field with given type in test class. It is somehow strange as problematic test had explicitly and "manually" created field with proper controller type. Maybe there is a problem with mocking servlet requests?