June, 2008


21
Jun 08

Telling semantic lies

Inspired by conversations with some smart people at a recent Semantic Web Austin event, I’ve undertaken to restart my education on semantic web technologies like RDF, RDFa, Microformats, etc. When I wear my web developer hat, I’m definitely an advocate of clean semantic markup that correctly describes the structure of the data on the page. These technologies take that approach further (in some cases much, much further). In general, that seems like an unquestionably good idea. More semantic structure means more data portability and data discovery and therefore a more powerful web. It’s probably even a necessary step towards a WebOS.

However, in my limited research to this point, it seems there’s an elephant in the room in all this advocacy. Inevitably discussions of semantic technologies include “better search” as a chief raison d’etre for their use. We’ll have search engines that “understand” the machine readable data on our pages or RDF descriptions which can then draw logical inferences from the relationships among the universe of web resources. But, what if the semantic data is incorrect or just downright dishonest? Over-reliance on easily spammed meta tags gave us garbage in and garbage out in Altavista and Excite back in the 90s. It would be trivial to take my RDFa structured blog post, move it to a spam blog, find the semantically marked-up creator element, change it to someone else and republish. Poof! My finely crafted blog post on the semantic web is now selling ads for herbal remedies to unsuspecting web users with poor search skills. Of course, it’s also easy to just out and out lie when describing content. Maybe I’m not really Angelina Jolie‘s spouse or Bill Gates‘ neighbor even though I swear I am in my XFN standard rel attributes.

I would imagine that one thing that sets these approaches apart from 90s meta tags is the fact that many of these are used to specify relationships between resources which must be symmetric. Angelina’s resource dereferenced from her URI must indicate that I’m her spouse as well for that XFN relationship to be “believed” by a semantic web search that understands XFN. (How Angelina or any of us feel about being boiled down to an authoritative web resource identified by a URI is another issue.) Of course some people will try to game any system but I’m sure the vast majority of web users (or publishing tools) will include this structured data for legitimate purposes. But all this does make me wonder how much search engines will ultimately be able to rely on semantic data for drawing the intelligent inferences we hope to see from them. Can any of you out there that know more about these technologies help me better understand how we can ensure semantic data isn’t telling lies? If so, leave a comment; I’d love to know more.


14
Jun 08

Running Hadoop on Windows

What is Hadoop?

Hadoop is a an open source Apache project written in Java and designed to provide users with two things: a distributed file system (HDFS) and a method for distributed computation. It’s based on Google’s published Google File System and MapReduce concept which discuss how to build a framework capable of executing intensive computations across tons of computers. Something that might, you know, be helpful in building a giant search index. Read the Hadoop project description and wiki for more information and background on Hadoop.

What’s the big deal about running it on Windows?

Looking for Linux? If you’re looking for a comprehensive guide to getting Hadoop running on Linux, please check out Michael Noll’s excellent guides: Running Hadoop on Ubuntu Linux (Single Node Cluster) and Running Hadoop on Ubuntu Linux (Multi-Node Cluster). This post was inspired by these very informative articles.

Hadoop’s key design goal is to provide storage and computation on lots of homogenous “commodity” machines; usually a fairly beefy machine running Linux. With that goal in mind, the Hadoop team has logically focused on Linux platforms in their development and documentation. Their Quickstart even includes the caveat that “Win32 is supported as a development platform. Distributed operation has not been well tested on Win32, so this is not a production platform.” If you want to use Windows to run Hadoop in pseudo-distributed or distributed mode (more on these modes in a moment), you’re pretty much left on your own. Now, most people will still probably not run Hadoop in production on Windows machines, but the ability to deploy on the most widely used platform in the world is still probably a good idea for allowing Hadoop to be used by many of the developers out there that use Windows on a daily basis.

Caveat Emptor

I’m one of the few that has invested the time to setup an actual distributed Hadoop installation on Windows. I’ve used it for some successful development tests. I have not used this in production. Also, although I can get around in a Linux/Unix environment, I’m no expert so some of the advice below may not be the correct way to configure things. I’m also no security expert. If any of you out there have corrections or advice for me, please let me know in a comment and I’ll get it fixed.

This guide uses Hadoop v0.17 and assumes that you don’t have any previous Hadoop installation. I’ve also done my primary work with Hadoop on Windows XP. Where I’m aware of differences between XP and Vista, I’ve tried to note them. Please comment if something I’ve written is not appropriate for Vista.

Bottom line: your mileage may vary, but this guide should get you started running Hadoop on Windows.

A quick note on distributed Hadoop

Hadoop runs in one of three modes:

  • Standalone: All Hadoop functionality runs in one Java process. This works “out of the box” and is trivial to use on any platform, Windows included.
  • Pseudo-Distributed: Hadoop functionality all runs on the local machine but the various components will run as separate processes. This is much more like “real” Hadoop and does require some configuration as well as SSH. It does not, however, permit distributed storage or processing across multiple machines.
  • Fully Distributed: Hadoop functionality is distributed across a “cluster” of machines. Each machine participates in somewhat different (and occasionally overlapping) roles. This allows multiple machines to contribute processing power and storage to the cluster.

The Hadoop Quickstart can get you started on Standalone mode and Psuedo-Distributed (to some degree). Take a look at that if you’re not ready for Fully Distributed. This guide focuses on the Fully Distributed mode of Hadoop. After all, it’s the most interesting where you’re actually doing real distributed computing.

Pre-Requisites

Java

I’m assuming if you’re interested in running Hadoop that you’re familiar with Java programming and have Java installed on all the machines on which you want to run Hadoop. The Hadoop docs recommend Java 6 and require at least Java 5. Whichever you choose, you need to make sure that you have the same major Java version (5 or 6) installed on each machine. Also, any code you write for running using Hadoop’s MapReduce must be compiled with the version you choose. If you don’t have Java installed, go get it from Sun and install it. I will assume you’re using Java 6 in the rest of this guide.

Cygwin

As I said in the introduction, Hadoop assumes Linux (or a Unix flavor OS) is being used to run Hadoop. This assumption is buried pretty deeply. Various parts of Hadoop are executed using shell scripts that will only work on a Linux shell. It also uses passwordless secure shell (SSH) to communicate between computers in the Hadoop cluster. The best way to do these things on Windows is to make Windows act more like Linux. You can do this using Cygwin, which provides a “Linux-like environment for Windows” that allows you to use Linux-style command line utilities as well as run really useful Linux-centric software like OpenSSH. Go download the latest version of Cygwin. Don’t install it yet. I’ll describe how you need to install it below.

Hadoop

Go download Hadoop core. I’m writing this guide for version 0.17 and I will assume that’s what you’re using.

More than one Windows PC on a LAN

It should probably go without saying that to follow this guide, you’ll need to have more than one PC. I’m going to assume you have two computers and that they’re both on your LAN. Go ahead and designate one to be the Master and one to be the Slave. These machines together will be your “cluster”. The Master will be responsible for ensuring the Slaves have work to do (such as storing data or running MapReduce jobs). The Master can also do its share of this work as well. If you have more than two PCs, you can always setup Slave2, Slave3 and so on. Some of the steps below will need to be performed on all your cluster machines, some on just Master or Slaves. I’ll note which apply for each step.

Step 1: Configure your hosts file (All machines)

This step isn’t strictly necessary but it will make your life easier down the road if your computers change IPs. It’ll also help you keep things straight in your head as you edit configuration files. Open your Windows hosts file located at c:\windows\system32\drivers\etc\hosts (the file is named “hosts” with no extension) in a text editor and add the following lines (replacing the NNNs with the IP addresses of both master and slave):

master NNN.NNN.NNN.NNN
slave NNN.NNN.NNN.NNN

Save the file.

Step 2: Install Cygwin and Configure OpenSSH sshd (All machines)

Cygwin has a bit of an odd installation process because it lets you pick and choose which libraries of useful Linux-y programs and utilities you want to install. In this case, we’re really installing Cygwin to be able to run shell scripts and OpenSSH. OpenSSH is an implementation of a secure shell (SSH) server (sshd) and client (ssh). If you’re not familiar with SSH, you can think of it as a secure version of telnet. With the ssh command, you can login to another computer running sshd and work with it from the command line. Instead of reinventing the wheel, I’m going to tell you to go here for step-by-step instructions on how to install Cygwin on Windows and get OpenSSH’s sshd server running. You can stop after instruction 6. Like the linked instructions, I’ll assume you’ve installed Cygwin to c:\cygwin though you can install it elsewhere.

If you’re running a firewall on your machine, you’ll need to make sure port 22 is open for incoming SSH connections. As always with firewalls, open your machine up as little as possible. If you’re using Windows firewall, make sure the open port is scoped to your LAN. Microsoft has documentation for how to do all this with Windows Firewall (scroll down to the section titled “Configure Exceptions for Ports”).

Step 3: Configure SSH (All Machines)

Hadoop uses SSH to allow the master computer(s) in a cluster to start and stop processes on the slave computers. One of the nice things about SSH is it supports several modes of secure authentication: you can use passwords or you can use public/private keys to connect without passwords (“passwordless”). Hadoop requires that you setup SSH to do the latter. I’m not going to go into great detail on how this all works, but suffice it to say that you’re going to do the following:

  1. Generate a public-private key pair for your user on each cluster machine.
  2. Exchange each machine user’s public key with each other machine user in the cluster.

Generate public/private key pairs

To generate a key pair, open Cygwin and issue the following commands ($> is the command prompt):
$> ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$> cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

Now, you should be able to SSH into your local machine using the following command:
$> ssh localhost

When prompted for your password, enter it. You’ll see something like the following in your Cygwin terminal.

hayes@localhost's password:
Last login: Sun Jun 8 19:47:14 2008 from localhost

hayes@calculon ~
$>

To quit the SSH session and go back to your regular terminal, use:
$> exit

Make sure to do this on all computers in your cluster.

Exchange public keys

Now that you have public and private key pairs on each machine in your cluster, you need to share your public keys around to permit passwordless login from one machine to the other. Once a machine has a public key, it can safely authenticate a request from a remote machine that is encrypted using the private key that matches that public key.

On the master issue the following command in cygwin (where “<slaveusername>” is the username you use to login to Windows on the slave computer):

$> scp ~/.ssh/id_dsa.pub <slaveusername>@slave:~/.ssh/master-key.pub

Enter your password when prompted. This will copy your public key file in use on the master to the slave.

On the slave, issue the following command in cygwin:

$> cat ~/.ssh/master-key.pub >> ~/.ssh/authorized_keys

This will append your public key to the set of authorized keys the slave accepts for authentication purposes.

Back on the master, test this out by issuing the following command in cygwin:

$> ssh <slaveusername>@slave

If all is well, you should be logged into the slave computer with no password required.

Repeat this process in reverse, copying the slave’s public key to the master. Also, make sure to exchange public keys between the master and any other slaves that may be in your cluster.

Configure SSH to use default usernames (optional)

If all of your cluster machines are using the same username, you can safely skip this step. If not, read on.

Most Hadoop tutorials suggest that you setup a user specific to Hadoop. If you want to do that, you certainly can. Why setup a specific user for Hadoop? Well, in addition to being more secure from a file permissions and security perspective, when Hadoop uses SSH to issue commands from one machine to another it will automatically try to login to the remote machine using the same user as the current machine. If you have different users on different machines, the SSH login performed by Hadoop will fail. However, most of us on Windows typically use our machines with a single user and would probably prefer not to have to setup a new user on each machine just for Hadoop.

The way to allow Hadoop to work with multiple users is by configuring SSH to automatically select the appropriate user when Hadoop issues its SSH command. (You’ll also need to edit the hadoop-env.sh config file, but that comes later in this guide.) You can do this by editing the file named “config” (no extension) located in the same “.ssh” directory where you stored your public and private keys for authentication. Cygwin stores this directory under “c:\cygwin\home\<windowsusername>\.ssh”.

On the master, create a file called config and add the following lines (replacing “<slaveusername>” with the username you’re using on the Slave machine:

Host slave
User <slaveusername>

If you have more slaves in your cluster, add Host and User lines for those as well.

On each slave, create a file called config and add the following lines (replacing “<masterusername>” with the username you’re using on the Master machine:

Host master
User <masterusername>

Now test this out. On the master, go to cygwin and issue the following command:

$> ssh slave

You should be automatically logged into the slave machine with no username and no password required. Make sure to exit out of your ssh session.

For more information on this configuration file’s format and what it does, go here or run man ssh_config in cygwin.

Step 4: Extract Hadoop (All Machines)

If you haven’t downloaded Hadoop 0.17, go do that now. The file will have a “.tar.gz” extension which is not natively understood by Windows. You’ll need something like WinRAR to extract it. (If anyone knows something easier than WinRAR for extracting tarred-gzipped files on Windows, please leave a comment.)

Once you’ve got an extraction utility, extract it directly into c:\cygwin\usr\local. (Assuming you installed Cygwin to c:\cygwin as described above.)

The extracted folder will be named hadoop-0.17.0. Rename it to hadoop. All further steps assume you’re in this hadoop directory and will use relative paths for configuration files and shell scripts.

Step 5: Configure hadoop-env.sh (All Machines)

The conf/hadoop-env.sh file is a shell script that sets up various environment variables that Hadoop needs to run. Open conf/hadoop-env.sh in a text editor. Look for the line that starts with “#export JAVA_HOME”. Change that line to something like the following:

export JAVA_HOME=c:\\Program\ Files\\Java\\jdk1.6.0_06

This should be the home directory of your Java installation. Note that you need to remove the leading “#” (comment) symbol and that you need to escape both backslashes and spaces with a backslash.

Next, locate the line that starts with “#export HADOOP_IDENT_STRING”. Change it to something like the following:

export HADOOP_IDENT_STRING=MYHADOOP

Where MYHADOOP can be anything you want to identify your Hadoop cluster with. Just make sure each machine in your cluster uses the same value.

To test these changes issue the following commands in cygwin:

$> cd /usr/local/hadoop
$> bin/hadoop version

You should see output similar to this:

Hadoop 0.17.0
Subversion http://svn.apache.org/repos/asf/hadoop/core/branches/branch-0.17 -r 656523
Compiled by hadoopqa on Thu May 15 07:22:55 UTC 2008

If you see output like this:

bin/hadoop: line 166: c:\Program Files\Java\jdk1.6.0_05/bin/java: No such file or directory
bin/hadoop: line 251: c:\Program Files\Java\jdk1.6.0_05/bin/java: No such file or directory
bin/hadoop: line 251: exec: c:\Program Files\Java\jdk1.6.0_05/bin/java: cannot execute: No such file or directory

This means that your Java home directory is wrong. Go back and make sure you specified the correct directory and used the appropriate escaping.

Step 6: Configure hadoop-site.xml (All Machines)

The conf/hadoop-site.xml file is basically a properties file that lets you configure all sorts of HDFS and MapReduce parameters on a per-machine basis. I’m not going to go into detail here about what each property does, but there are 3 that you need to configure on all machines: fs.default.name, mapred.job.tracker and dfs.replication. You can just copy the XML below into your conf/hadoop-site.xml file.

<?xml version=”1.0″?>
<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>
<!– Put site-specific property overrides in this file. –>
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://master:47110</value>
</property>
<property>
<name>mapred.job.tracker</name>
<value>master:47111</value>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
</configuration>

For more information about what these configuration properties (and others) do, see the Hadoop cluster setup docs and the hadoop-default.xml documentation.

Step 7: Configure slaves file (Master only)

The conf/slaves file tells the master where it can find slaves to do work. Open yours in a text editor. It will probably have one line which says “localhost”. Replace that with the following:

master
slave

Step 8: Firewall Configuration (All Machines)

If you’re using Windows Firewall, you will need to ensure that the appropriate ports are open so that the slaves can make HTTP requests for information from the master. (This is different from the port 22 needed for SSH.) The list of ports for which you should make exceptions are as follows: 47110, 47111, 50010, 50030, 50060, 50070, 50075, 50090. These should all be open on the master for request coming from your local network. For more information about these ports, see the Hadoop default configuration file documentation.

You should also make sure that Java applications are allowed by the firewall to connect to the network on all your machines including the slaves.

Step 9: Starting your cluster (Master Only)

To start your cluster, make sure you’re in cygwin on the master and have changed to your hadoop installation directory. To fully start your cluster, you’ll need to start DFS first and then MapReduce.

Starting DFS

Issue the following command:

$> bin/start-dfs.sh

You should see output somewhat like the following (note that I have 2 slaves in my cluster which has a cluster ID of Appozite, your mileage will vary somewhat):

starting namenode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-namenode-calculon.out
master: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-calculon.out
slave: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-hayes-daviss-macbo
ok-pro.local.out
slave2: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-XTRAPUFFYJR.out
master: starting secondarynamenode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-secondarynamenode
-calculon.out

To see if your distributed file system is actually running across multiple machines, you can open the Hadoop DFS web interface which will be running on your master on port 50070. You can probably open it by clicking this link: http://localhost:50070. Below is a screenshot of my cluster. As you can see, there are 3 nodes with a total of 712.27 GB of space. (Click the image to see the larger version.)

Starting MapReduce

To start the MapReduce part of Hadoop, issue the following command:

$> bin/start-mapred.sh

You should see output similar to the following (again noting that I’ve got 3 nodes in my cluster):

starting jobtracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-jobtracker-calculon.out
master: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-calculon.ou
t
slave: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-hayes-daviss
-macbook-pro.local.out
slave2: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-XTRAPUFFYJR
.out

You can view your MapReduce setup using the MapReduce monitoring web app that comes with Hadoop which runs on port 50030 of your master node. You can probably open it by clicking this like: http://localhost:50030. Below is a screenshot from my browser. There’s not much exciting to see here until you have an actual MapReduce job running.

Testing it out

Now that you’ve got your Hadoop cluster up and running, executing MapReduce jobs or writing to and reading from DFS are no different on Windows than any other platform so long as you use cygwin to execute commands. At this point, I’ll refer you to Michael Noll’s Hadoop on Ubuntu Linux tutorial for an explanation on how to run a large enough MapReduce job to take advantage of your cluster. (Note that he’s using Hadoop 0.16.0 instead of 0.17.0, so you’ll replace “0.16.0″ with “0.17.0″ where applicable.) Follow his instructions and you should be good to go. The Hadoop site also offers a MapReduce tutorial to you can get started writing your own jobs in Java. If you’re interested in writing MapReduce jobs in other languages that take advantage of Hadoop, check out the Hadoop Streaming documentation.

How to stop your cluster

When you’re ready to stop your cluster, it’s simple. Just top MapReduce and then DFS.

To stop MapReduce, issue the following command on the master:

$>bin/stop-mapred.sh

You should see output similar to the following:

stopping jobtracker
slave: stopping tasktracker
master: stopping tasktracker
slave2: stopping tasktracker

To stop DFS, issue the following command on the master:

$> bin/stop-dfs.sh

You should see output similar to the following:

stopping namenode
master: stopping datanode
slave: stopping datanode
slave2: stopping datanode
master: stopping secondarynamenode

And that’s it

I hope this helps anyone out there trying to run Hadoop on Windows. If any of you have corrections, questions or suggestions please comment and let me know. Happy Hadooping!

Update 6/18/2008: Fixed link to Hadoop Admin screenshot. Thanks to Robert Towne for pointing out the bad link.


11
Jun 08

Most interesting sentence I’ve read today

From Free Exchange (the blog home of The Economist):

…by some measures—including those used by the Congressional Budget Office—the American economy has uninterruptedly performed below potential since the 2001 recession.

Even though we’re not technically in a recession (two quarters of negative GDP) and even though being technically correct is the best kind of correct, this would make things feel very recessionary to a lot of people.


5
Jun 08

Let’s toss those plastic discs

Jenn and I have been using Netflix‘s streaming service a lot recently to watch all sorts of great classic sci-fi on our TV but the whole s-video-from-the-laptop-to-the-TV-with-no-remote setup is wearing thin. Clunky setup aside, the actual streaming experience has been mostly flawless with only the occasional hiccup. That’s why we’re eagerly awaiting the arrival of our new Roku box which promises to be a much more TV-centric video streaming experience.

Anyway, the imminent arrival of our Roku box got me thinking about the death of physical media for storing and transporting movies. Gizmodo had a couple of posts this week along these lines. One noted that less than 50% of HDTV owners don’t even know what blu-ray is! These are HDTV owners who are usually the ones who know what’s up. Forget the rest of the tv-watching public squinting at their standard def 28 inchers, these are the guys with the 48″ LCDs sitting back and counting pores on John Madden’s face in glorious 1080p. Doesn’t say much for the next generation plastic disc. Minimal awareness of a new technology is one thing, but the second post is about Staples’ eye-stabbingly ridiculous plan to sell self destructing DVDs for $4.99. Can you say EPIC FAIL?

I, for one, welcome our new movie streaming overlords. Stay tuned and I’ll let you know how it goes with the Roku.


1
Jun 08

Best geeky sentence I’ve read today

With apologies to Tyler Cohen

Normalization is a kind of ethical system for data.

This is from a great post on the always fascinating High Scalability about how you sometimes just have to let go and de-normalize.


1
Jun 08

Facebook chat uses Erlang to scale

I started playing with Erlang last year. Mostly that meant reading the Joe Armstrong book, looking at ejabberd and writing a little code. Sadly, I’ve not had the chance to go much beyond the “playing” stage. Anyway, I’ve got a soft spot for functional languages like Erlang since my Programming Language Theory class in undergrad where we used ML. I especially like the way Joe and the rest of the people behind Erlang have built it for concurrency via tiny processes that share nothing and have provided a framework for building apps that know how to operate correctly in soft-realtime. It’s a very different way of thinking about building systems and seems to be remarkably effective.

The Erlang guys have to be feeling pretty good to hear that Facebook has used Erlang as a core component of their new chat service. (High Scalability also has a good writeup.) As Facebook engineer Eugene Letuchy describes it, their implementation uses XHR long polling which means tons of open HTTP connections. Spread this out over 70 million potential users and it’s not hard to see that Apache would break down pretty quickly. Basically it sounds like they have tons of Erlang processes servicing these connections and holding messages and presence events for users in memory if there’s not an open connection to the client.

Eugene mentions the challenge of delivering presence information as being more difficult than real-time messaging. (Something I thought a lot about when building Effusia.) He lays out the issues inherent in broadcasting presence on every state change in the form of a nasty worst-case asymptotic complexity:

The naive implementation of sending a notification to all friends whenever a user comes online or goes offline has a worst case cost of O(average friendlist size * peak users * churn rate) messages/second, where churn rate is the frequency with which users come online and go offline, in events/second.

However, he doesn’t really go into any detail on how they solved this problem. I can only assume they used some form of periodic polling on a need-to-know basis and/or coalescing friend presence updates in such a way that they’re only occasionally sent to a user.

A few other interesting notes… Apparently they used C++ to do the chat logs as Erlang is not that great at raw I/O. They also apparently use Thrift to glue everything together. (Reminds me I need to look into Thrift in more detail.)


1
Jun 08

Appozite starts to take shape

I’m currently in the process of getting my new company, Appozite, off the ground. We’re still being a little intentionally vague about what exactly we’re doing but suffice it to say that I’m excited enough about it to leave my job at Kadro Solutions to get Appozite off the ground. We’ll start to fill everyone in on the details in time. Just know that we’re doing some very exciting stuff coming up with better ways to connect people with products that delight them.

This last week we launched two Appozite blogs: Appozitegeist and dev.appozite. There’s not much there yet, but there will definitely be a lot more to come.

Appozitegeist is my co-founder Jenn’s space to talk about the intersection of social software, shopping and style. She’ll give you her take on how people are using all their shiny social software to keep up with all the shiny new styles.

dev.appozite is where I’m going to geek out (much like I’ve been doing here) and talk about what we’re up to from a technical perspective. I’m passionate about web standards, building scalable systems, programming languages and algorithms. I’ll talk about all those things and whatever else comes to mind on dev.appozite.

Anyway, we’re really psyched about what we’re doing at Appozite so check out the blogs and follow along because we’re going to be changing the way you shop.