Notes from Rich Hickey’s talk on simplicity at Strangeloop 2011.

The video is now available here.

Word origins:

• Simple  = sim – plex = one braid
• Easy = adjacent = lie near

• Near, at hand.
• Near to our understanding–being familiar.
• Near to our capabilities. We don’t like to talk about this because it tramples on our egos.
• We are far to interested in things being easy. This is to our detriment.
• Easy is relative (unlike “simple”). Music and German are hard from some and easy for others.

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My notes from a talk by Eric Brigham from TruCoin talking about BitCoin.

• BitCoin is “digital cash” — you can lose it just like you can lose a $100 bill and there isn’t a central source.
• Digicash (1990 – 1998) focused on security.
• Paypal (1998 – ) focused on convenience.
• Both required centralize servers to solve double spending problem.
• BitCoin solves the double spending problem without needing a centralized server. The transaction log is handled via P2P.
• BitCoin was created by Satoshi Nakamoto – it is unclear to who he actually is or even if he is a single person.
• A look at the code reveals that Satoshi is not a professional developer.
• BitCoin is the worlds first cryptographically secure distributed digital notary service.
• The number of BitCoins should level off to about 21,000,000 over the next 50 years.
• BitCoins are created via computational effort so they represent “computer work”.
• Cheating isn’t prevented, it is just made very expensive.
• A BitCoin address is a base 58 encoded public key.
• A transaction is simply a digitally signed message.
• It is theoretically possible to rewrite the transaction history, but it is very computationally expensive and is getting exponentially more expensive.
• If two separate people find the next coin at the same time, the chain that wins is the one that gets the next coin found on top of it.
• Most of the security issues have nothing to do with BitCoin, but they can be stolen in the same way that cash can be stolen.
• DOS attack are possible, but haven’t been much of an issue.
• Biggest known attack is if someone possesses >50% of the total network computational power you can double spend coins.
• It is very difficult to operate anonymously with BitCoin.
• People are working on a multi-signature transaction that would allow e-wallet type transactions similar to Paypal where a central site would keep part of your signature.

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You can use a testng.xml file to help control the tests and browser used by Selenium in Tapestry.  However, you have to tell Maven to look for the testng.xml file or it will ignore it.  You do this by putting the following in your pom.xml -> build -> plugins:

			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-surefire-plugin</artifactId>
				<version>2.6</version>
				<configuration>
					<suiteXmlFiles>
						<suiteXmlFile>src/test/conf/testng.xml</suiteXmlFile>
					</suiteXmlFiles>
				</configuration>
			</plugin>

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I ran into a bunch of problems getting Sonar installed in Tomcat 6 as a war file.  It turns out I just didn’t understand the installation process.  To install Sonar as a war, you have to download it, unzip it, build the war using a script, and then deploy the war file.  During the period where it builds the war file, it links the war back to the directory you are building from.  If you move the unzipped directory, the war won’t work.

So unzip the directory somewhere you can keep it, make it owned by Tomcat6, set your configuration file, then create the war and deploy it.  You can make configuration changes by coming back to the directory you unzipped, making a change and then restarting Tomcat.

I was deleting or moving the unzipped directory before deploying the war.  The errors were odd though because it kept saying that it couldn’t bind to the address because the part was already in use.  When trying to use Derby, it kept saying that something was already running on that port.  The same thing happened with MySQL.  Leaving the unzipped directory in place fixed the problem.

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I have a Nexus S from Sprint and set it up with Google Voice.  Unfortunately when I was getting Google Voice set up, I tried calling my number and leaving a message from another phone.  This voice mail got put on Sprint’s voice mail system.  When I switched over to Google Voice, my Sprint voice mail was deleted, but it continued to send me notifications of the voice mail.

So if you are looking at switching to Google Voice, be sure to make the switch when someone isn’t likely to leave you a voice mail.  Sprint tried all kinds of things and eventually said I needed to reset my Nexus S back to factory settings.  They said that the problem was on the phone because Sprint wasn’t sending out any type of voice mail notifications to me. I wiped it, turned it back on and immediately got the little tape recorder notification from Sprint voice mail.

There are things I like about Sprint, but sometimes it seems like their whole system is a piece of alien technology and works most of the time, but they have not idea what is going on or how it actually works.

Finally I went into www.google.com/voice and disabled the Sprint/Google Voice integration.  The Sprint Voicemail notification went away within a few minutes.

I had to wait 15 minutes to reactivate Google Voice again, but once that was done everything seemed to work fine.

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I was recently working on a simple application where the user will enter famous quotations.  Obviously we want to avoid duplicates so I needed a way to check for quotations that were substantially similar before a new quote was added to the database.

The idea was to show the top 5 most similar quotes before letting the user save the new quotation to the db. I used Lucene for this which allowed me to punt on the more difficult task of figuring out if two quotes were similar or not. I left that up to Lucene and only had to worry about how to get my information in and out of Lucene in a usable manner.

Below is the interesting method that uses Lucene to build an index of all the quotes in the system and then returns the five quotes that are most similar to the new quote text.  Obviously creating a new index each time a quote is added isn’t particularly efficient, but makes it easier to demonstrate how it works and processor efficiency isn’t much of an issue with this particular task.

    public List<Quote> getSimilarQuotes() throws CorruptIndexException, IOException {

        String quoteText = quote.getText();
        logger.info("creating RAMDirectory");
        RAMDirectory idx = new RAMDirectory();
        IndexWriterConfig indexWriterConfig = new IndexWriterConfig(Version.LUCENE_31, new StandardAnalyzer(Version.LUCENE_31));
        IndexWriter writer = new IndexWriter(idx, indexWriterConfig);

        List<Quote> quotes =  session.createCriteria(Quote.class).list();

        //Create a Lucene document for each quote and add them to the
        //RAMDirectory Index.  We include the db id so we can retrive the
        //similar quotes before returning them to the client.
        for (Quote quote : quotes) {
            Document doc = new Document();
            doc.add(new Field("contents", quote.getText(),Field.Store.YES, Field.Index.ANALYZED));
            doc.add(new Field("id", quote.getId().toString() ,Field.Store.YES, Field.Index.ANALYZED));
            writer.addDocument(doc);
        }

        //We are done writing documents to the index at this point
        writer.close();

        //Open the index
        IndexReader ir = IndexReader.open(idx);
        logger.info("ir has " + ir.numDocs() + " docs in it");
        IndexSearcher is = new IndexSearcher(idx, true);

        MoreLikeThis mlt = new MoreLikeThis(ir);

 		//lower some settings to MoreLikeThis will work with very short
        //quotations
        mlt.setMinTermFreq(1);
        mlt.setMinDocFreq(1);

		//We need a Reader to create the Query so we'll create one
        //using the string quoteText.
        Reader reader = new StringReader(quoteText);

        //Create the query that we can then use to search the index
        Query query = mlt.like( reader);

        //Search the index using the query and get the top 5 results
        TopDocs topDocs = is.search(query,5);
        logger.info("found " + topDocs.totalHits + " topDocs");

        //Create an array to hold the quotes we are going to
        //pass back to the client
        List<Quote> foundQuotes = new ArrayList<Quote>();
        for ( ScoreDoc scoreDoc : topDocs.scoreDocs ) {
            //This retrieves the actual Document from the index using
            //the document number. (scoreDoc.doc is an int that is the
            //doc's id
            Document doc = is.doc( scoreDoc.doc );

            //Get the id that we previously stored in the document from
            //hibernate and parse it back to a long.
            String idField =  doc.get("id");
            long id = Long.parseLong(idField);

            //retrieve the quote from Hibernate so we can pass
            //back an Array of actual Quote objects.
            Quote thisQuote = (Quote)session.get(Quote.class, id);

            //Add the quote to the array we'll pass back to the client
            foundQuotes.add(thisQuote);
        }

        return foundQuotes;
    }

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I recently switched phones to the Nexus S 4G. (Take a look at my detailed Nexus S Review.) I’ve been pretty impressed with how well the Nexus S integrates with Google Apps.  I suppose this is expected because it is a Google phone, but experience tells me that just because a company makes a device doesn’t mean it will work well with their software and services.

Google apps has really gown into a nice business package. Here are a few things that impressed me when I was setting up the Nexus S 4G.

• You can define security policies for Android devices so they can be managed centrally.  For example, you can make the Nexus S require a password or not.
• You can remotely ring your Nexus S 4G at full volume using the web interface–regardless of what the volume is set on.
• You can find the device on a map.
• You can lock the Nexus S 4G remotely.  For example, if you lose it, you can lock it so it will require a pin to log back in.
• The Nexus S 4G can be wiped remotely.  So if you lose your phone, you can delete everything on it by logging into Google Apps
• It appears that most of the Nexus S 4G settings are backed up to the Google App servers, so if you do need to wipe your device, you can easily reinstall and set everything back up again once you get it back again.
• Giving the Nexus S 4G your Google App account will let you connect to most Google services without needing to log back in again.
• The Nexus S 4G appears to support multiple Google accounts so you can use your business account and a gmail account if you like.

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I recently bought a Nexus S 4G and I’m trying to use the Sprint/Google Voice integration, but the following error keeps popping up.

We have a problem
Sorry, there was a problem determining your eligibility for a Sprint integrated account. Please try again at a later time.

It looks like this might mean several different things. Google lists these possible reasons:

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This is a demonstration of about 10 minutes of programming in Tapestry 5 creating a sample application. The app is pretty basic and just lets you add URLs to a list and then vote on them-similar to the idea behind Digg or Reddit. I didn’t really explain things in great detail so it is more of a demonstration of some of the things you can do than it is a step by step tutorial.


General steps in making the application:

• Create a Quickstart application from the Maven archetype.
• Add the dependencies and configs for Hibernate.
• Create an Item entity to store our url, title, and number of votes.
• Use a BeanEditForm and onSuccess method to create Items.
• Use a Loop to show the items in the database
• Use an ActionLink and onActionFromVote method to count the votes
• Add onValidate method to do validate the URLs.
• Stick the loop in a Zone and update it with the ActionLink

The app uses the Quickstart archetype from 5.3 so it uses the new green theme.

Errata

• I used @Component on the BeanEditForm, but I should have used @InjectComponent. As you can see in the comments, Howard isn’t really sure why this worked. So use @InjectComponent instead.
• The zone should have gone around the ul elements instead of within it. The zone renders as a div and we shouldn’t have a div inside of the open/close ul.

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Computer science is what enables programming, but it is possible to do a lot of programming without understanding the computer science concepts underlying the process of computation. This isn’t always a bad thing.  When we program we work at a much higher level of abstraction.  When we drive a car, we only concern ourselves with two or three pedals, a gearshift and a steering wheel.  You can safely operate a car without having any clear idea of how it works. However, if you want to operate a car at the very limits of its capabilities, you need to know a lot more about automobiles than just the three pedals, gearshift and steering wheel.

The same is true of programming.  Much mundane everyday work that can be accomplished with little or no understanding of computer science. You don’t need to understand computational theory to build a “contact us” form in PHP.  However, if you plan to write code that requires serious computation, you are going to need to understand a bit more about how computation works under the hood.

The purpose of this article is to provide some fundamental background for computation. If there is interest I may follow up with some more advanced topics, but right now I want to look at the logic behind one of the simplest abstract computational devices–a finite state machine.

Finite State Machine

A finite state machine is a mathematical abstraction used to design algorithms. In simple terms, a state machine will read a series of inputs.  When it reads an input it will switch to a different state.  Each state specifies which state to switch for a given input.  This sounds complicated but it is really quite simple.

Imagine a device that reads a long piece of paper.  For every inch of paper there is a single letter printed on it–either the letter a or the letter b.

As the state machine reads each letter it changes state.  Here is a very simple state machine.

The circles are “states” that the machine can be in.  The arrows are the transitions.  So if you are in state s and read an a you’ll transition to state q.  If you read a b, you’ll stay in state s.

So if we start on s and read the paper tape above from left to right, we will read the a and move to state q.  Then we’ll read a b and move back. Another b will keep us on s followed by an a which moves us back to the q state. Simple enough, but whats the point?

Well it turns out that you can run a tape through the state machine and once it is done tell something about the sequence of letters by examining the state you end up on.  In our simple state machine above, if we end on s, the tape ends with the letter b, if we end on q, the tape ends with the letter a.

This may sound pointless, but there are an awful lot of problems that can be solved with this type of approach. A very simple example would be to determine if a page of HTML contains these tags in this order:

<html>
	<head>
	</head>
	<body>
	</body>
</html>

The state machine can move to state that shows it has read the html tag, loop until it gets to the head tag, loop until it gets to the head close tag, etc. If it successfully makes it to the final state, then you have those particular tags in the right order.

Finite state machines can also be used to represent the mechanics of a parking meter, pop machine, automated gas pump and all kinds of other things.

Deterministic Finite State Machine

The state machines we’ve looked at so far are all deterministic state machines.  From any state there is only one transition for any allowed input.  In other words there aren’t two paths leading out of a state when you read the letter a. At first this sounds silly to even make this distinction.

What good is a set of decisions if the same input can result in moving to more than one state?  You can’t tell a computer, if x==true then execute doSomethingBig or execute doSomethingSmall, can you?

Well you kind of can with a state machine. The output of a state machine is its final state.  It goes through all its processing and then the final state is read and then an action is taken. A state machine doesn’t do anything as it moves from state to state.  It processes and then when it gets to the end, the state is read and something external triggers the desired action (dispenses a soda can, etc.).  This is an important concept when it comes to non-deterministic finite state machines.

Non-deterministic Finite State Machine

Non-deterministic finite state machines are finite state machines where a given input from a particular state can lead to more than one different state.  For example, lets say we want to build a finite state machine that can recognize strings of letters that start with a and are then followed by zero or more occurrences of the letter b or zero or more occurrences of the letter c terminated by the next letter of the alphabet. Valid strings would be:

• abbbbbbbbbc
• abbbc
• acccd
• acccccd
• ac (zero occurrences of b)
• ad (zero occurrences of c)

So it will recognize the letter a followed by zero or more of the same letter of b or c followed by the next letter of the alphabet. A very simple way to represent this is with a state machine that looks like the one below, where a final state of t means that the string was accepted and matches the pattern.

Do you see the problem?  From starting point s, we don’t know which path to take.  If we read the letter a, we don’t know whether to go to the state q or r. There are a few ways to solve this problem.  One is by back tracking.  You simply take all the possible paths and ignore or back out of the ones where you get stuck.

This is basically how most of the chess playing computers work.  They look at all the possibilities and all the possibilities of those possibilities and choose the path that gives them the greatest number of advantages over their opponent.

The other option is to convert the non-deterministic machine into a deterministic machine. One of the interesting attributes of a non-deterministic machine is that there exists an algorithm to turn any non-deterministic machine into a deterministic one.  However, it is often much more complicated.  Fortunately for us, the example above is only slightly more complicated. In fact this one is simple enough we can transform it into a deterministic machine in our head without the aid of a formal algorithm.

The machine below is a deterministic version of the non-deterministic machine above.  In the machine below, a final state of t or v is reached by any string that is accepted by the machine.

The non-deterministic model has four states and six transitions.  The deterministic model has 6 states, 10 transitions and two possible final states. That isn’t that much more, but the complexity usually grows exponentially and a moderately sized non-deterministic machine can produce an absolutely huge deterministic machine.

Regular Expression

If you have done any type of programming, you’ve probably encountered regular expressions.  Regular expressions and finite state machines are functionally equivalent.  Anything you can accept or match with a regular expression can be accepted or matched with a state machine. For example the pattern above could be matched with:

a(b*c|c*d)

Regular expressions and finite state machines also have the same limitations.  In particular they both can only match or accept patterns that can be handled with finite memory.  So what type of patterns can’t they match?  Lets say you want to only match strings of a and b, where there are a number of a’s followed by an equal number of b’s. Or n a’s followed by n b’s where n is some number. Examples would be:

• ab
• aabb
• aaaaaabbbbbb
• aaaaaaaaaaaaaaaaaaaabbbbbbbbbbbbbbbbbbbb

At first this looks like an easy job for a finite state machine. The problem is that you’ll quickly run out of states or you’ll have to assume an infinite number of states–at which point it is no longer a finite state machine. Lets say you create a finite state machine that can accept up to 20 a’s followed by 20 b’s.  That works fine until you get a string of 21 a’s followed by 21 b’s at which point you either need to rewrite your machine to handle a longer string. For any string you can recognize, there is one just a little bit longer that your machine can’t recognize because it runs out of memory.

This is known as the Pumping Lemma which basically says, if your pattern has a section that can be repeated like the one above then the pattern is not regular.  In other words you can’t use a regular expression or finite state machine to recognize all the strings that do match the pattern.

If you look carefully, you’ll notice that this type of pattern where every a has a matching b, looks very similar to HTML where within any pair of tags you may have any number of other matching pairs of tags. So while you may be able to use a regular expression or finite state machine to recognize if a page of HTML has the html, head and body elements in the correct order, you can’t use a regular expression to tell if an entire HTML page is valid or not because HTML is not a regular pattern.

Turing Machines

So how do you recognize non-regular patterns?  There is a theoretical device that is similar to a state machine called a Turing Machine.  It is similar to a finite state machine that it has a paper strip that it reads, but a Turing Machine can erase and write on the paper tape. Explaining a Turing Machine will take more space that we have here, but there are a few important points relevant to our discussion of finite state machines and regular expressions.

Turing Machines are computationally complete and anything that can be computed can be computed on a Turing Machine. Since a Turing Machine can write as well as read from the paper tape, it is not limited to a finite number of states. The paper tape can be assumed to be infinite in length.  Obviously actual computers don’t have an infinite amount of memory, but they contain enough memory that you don’t hit the limit for the type of problems they process.

Turing Machines give us an imaginary mechanical device that lets us visualize and understand how the computational process works. It is particularly useful in understanding the limits of computation. If there is interest I’ll do another article on Turing Machines in the future.

Why does this matter?

So whats the point?  How is this going to help you create that next PHP form?  Regardless of their limitations state machines are a very central concept to computing.  In particular the recognition that for any non-deterministic state machine you can design, there exists a deterministic state machine that does the same thing.  This is a very key point because it means you can design your algorithm in which ever way is the easiest to think about.  Once you have a proper algorithm, you can convert it into whatever form is most efficient.

The understanding that finite state machines and regular expressions are functionally equivalent opens up some incredibly interesting uses for regular expression engines–particularly when it comes to creating business rules that can be changed without recompiling a system.

A foundation in computer science allows you to take a problem X that you don’t know how to solve and reason, “I don’t know how to solve X, but I do know how to solve Y and I know how to convert a solution for Y into a solution for X.  Therefore I now known how to solve X.”

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