Archive for May, 2010

No SQL Taxonomy

May 13, 2010

In the last year or so there has been an incredible explosion of interest in the concept of No SQL. There are so many varying implementations that differ so wildly that it is often difficult to get a clear picture of what is what. Typically authors will either be intimately involved with one specific project or will give cursory overviews of a number of projects.

What is needed is some basic categorization and classification – in other words a taxonomy of the No SQL provider space. For example, what are the key criteria  used to classify implementations? There are disjoint subsets in the No SQL space, and comparison can be only made between subsets or between  implementations within a given subset. Its all about apple-to-apple comparison.

Here are a few links to shed light on the topic:

And of course let us not forget the contrarian view:

Twitter User Similarity and Collective Intelligence

May 13, 2010

This is the second part of a blog regarding a recent mini-project where I implemented a Twitter user similarity service.  The first part described my experience with the mechanics of the Twitter REST API – this part focuses more on the “collective intelligence” aspects.

The requirements were simple: define a concept of “similarity for two users” and implement a Twitter solution for it.

Resources used:

Being a bit rusty on basic CI concepts (my chagrin but in my defense there is so much computer stuff to know out there), I did a quick search for high quality links on the topic, drilled down a bit and read the high-value articles. I downloaded all free PDF chapters of the books and read relevant sections.  I went to my local Borders bookstore which was fortunately stocked with all the above books. I had already purchased Segaran’s book, so I used chapter 2 “Making Recommendations” which discusses the Euclidean distance Pearson correllation formula and this seemed to fit the bill. AIW also had an even more detailed discussion on the subject – too much to implement in the short time frame, but definitely a candidate for version two. I reviewed my statistics books, and lo and behold it turned out these were not exotic algorithms, but rather standard statistics data  comparison techniques. Too paraphrase an old sailing jingle: so much knowledge, so little time (so many boats, so little time).

I settled upon a defining the concept of similarity based on comparing word counts between two users for a set of Twitter status message for a given timeline. As usual I leveraged Spring for effortless configuration and bean wiring (thanks again Rod!). The basic logic was to issue calls to the Twitter API “method” user_timeline for each user. This returned a list of tweets for each user which I would iterate over and concatenate the Status text elements. I then computed a map of words and a count of all their occurences. This map was then  fed to the similarity scorer which would return a value between 0 and 1.

Last but not least was the WordCounter class. This object accepts raw text and returns a map of words and their counts. Of special interest is the lexical analyzer. For the first pass I used a simple String.split() and a list of stop words. But minimal analysis revealed a submerged world of complexity involving punctuations, stemming, etc. Whew! Ideally it too should be in interface.

Here’s a UML class diagram of the overall system:

The entry point is a service which returns a double value between 0 and 1 indicating user similarity.

    public interface SimilarityService {
        public double getSimilarityScore(String user1, String user2)

This service interface has four implementations: two real providers (Twitter4j and JTwitter) that issue actual calls to the Twitter API for two user timelines. The mock implementation operated on files containing the concatenated raw text. As an inspirational freebie, I threw in the RssSimilarity provider which performed the similarity scoring on RSS feeds. Its quite cool at how much can be done so easily and quickly when you’ve got the right abstractions and layering in place. Nothing excessively fancy here except solid engineering practices all wrapped in rocking Spring. The other extension point was the similary scorer which computed a similarity score for two word count maps.

    public interface SimilarityScorer {
         public double calculateSimilarityScore(Map wordCount1,
              Map wordCount2);

The two provided implementations are:

  • Euclidean Distance
  • Pearson Corellation

Other possible candidate solutions to be investigated are:

  • Manhattan (taxicab) distance
  • Jaccard distance

Overall, this was one of the more intellectually challenging projects in a while. On the “interest” scale it certainly compares with the NoSQL and eventual consistency stuff I’ve been recently doing. I certainly aim to pursue this topic more – hopefully in a remunerated capacity!

Twitter REST API

May 13, 2010

I recently finished a mini-project to calculate the similarity of two Twitter users. Being a REST fan and having implemented a substantial REST service, I’m always eager for an excuse to get my hands dirty with a bit o’ REST. I find it fascinating that no two REST APIs (web APIs) are the same, especially in terms of documentation.

As we all know the term “REST” refers to a style and not a standard, so it is not surprising that actual implementation vary quite a bit. This lack of  specificity is most pronounced on the client side. With SOAP, client access is almost always mediated by platform-specific client stubs automatically generated from the WSDL contract. With REST there is no such standard contract (despite WADL which has not become a dominant force), and therefore no fully automated way to build client stubs. You can partially automate the process if you define your payload as an XML schema, but this still leaves the other vital part of specifying the resource model. Section 1.3 of the JAX-RS specification explicitly states: The specification will not define client-side APIsSee my comments on the current non-standard situation of client stubs in some JAX-RS providers.

API Data Formats

In the absence of standard client stub generation mechanisms, documentation plays an increasingly important role. The fidelity to genuine REST precepts and the terminology used to describe resources and their HTTP methods becomes of prime importance to effective client usage.

How do we unambiguously describe  the different resources and methods? The number and types of payload formats influence the decision. Do we support only one format, JSON or XML? If XML, do we have a schema? If so, what schema do we use? XSD or RelaxNG? Multiple XML formats such as Atom, RSS and/or proprietary XML? By the way, the former two do not have a defined schema. Do we support multiple formats? If so, do we use prescribed REST content negotiation?

Considering the strong presence of the Twitter REST API and my short albeit intense usage of it, I am a bit reluctant to “criticize”. So upfront I issue a disclaimer that my knowledge is partial and subject to change. One very interesting fact I recently read in the book Building Social Web Applications is that over 80% of Twitter’s usage come from its API and not from its web site! Caramba, that’s quite an ecosystem that has evolved around Twitter! All the more reason to invest in API contract specification and  documentation.

General API Documentation

Professional  high quality API documentation is obviously a vital need especially as API usage increases. With an internet consumer-facing API, clients can access resources using any language of choice, so it is important to be as precise as possible. Having worked with many different APIs and services, I have come to appreciate the importance of good documentation. I regard documentation not as separate add-on to the executable code, but rather as an integral part of the experience. It is a first-order concern.

The metaphor I would suggest is DDD – Documentation Driven Development. In fact, on my last big REST project where I took on the responsibility of documenting the API, I soon found it more efficient to update the documentation as soon as any API change was made. This was especially true when data formats were modified! The document format was Atlassian Wiki which unfortunately didn’t allow for global cross-page changes, so I had to keep code and its corresponding documentation closely synchronized; otherwise the documentation would’ve quickly diverged and become unmanageable.

Deductive and Inductive Documentation

In general, documentation can be divided into deductive and inductive. Deductive documentation is based on the top-down approach. If you have an XML schema all the better – you can use this as your basic axiom, and derive all further documentation fragments in progressive refinement steps. Even in the absence of a schema, you can still leverage this principle.

Inductive documentation is solely based on examples – there is no general definition, and it is up to the client to infer commonalities. You practically have to do parallel diffs on many different XML examples to separate the common from the specific.

Theorem: all good documentation must have examples but it cannot rely only on examples! In other words, examples are necessary but not sufficient.

Google API as a Model

Google has done a great job in extracting a common subset of its many public APIs into Google Data Protocol. All Google APIs share this definition: common data formats, common errors mechanisms, header specification, collection counts, etc. Google has standardized on AtomPub and JSON as its two primary data formats (with some RSS too). It does an excellent job on having an unambiguous and clear specification of its entire protocol across all its API instances.

Take the YouTube API for example. Although neither Google nor Atom use an XML XSD schema, the precise details of the format are clearly described. Atom leverages the concept of extensions where you can insert external namespaces (vocabularies) into the base Atom XML. Google Atom does not have to reinvent the wheel for cross-cutting extensions, and can reuse common XML vocabularies in a standard way. See the Data API Protocol Page – XML element definitions page for details. Some namespaces are openSearch (Open Search Schema) for collection counts and paging, media for MRSS (yes, you can insert RSS into Atom – cool!).

Twitter Data Format Documentation

The Twitter General API documentation page and the FAQ do not do a good job in giving a high-level description of the Twitter data formats for requests and responses. There is only a one basic mention of this on the Things Every Developer Should Know page:

The API presently supports the following data formats: XML, JSON, and the RSS and Atom syndication formats, with some methods only accepting a subset of these formats.

No clear indication is given as to which kinds of resources accept which formats. Common sense would lead us to believe that JSON and proprietary XML are  isomorphic and supported for both request and responses. Being feed formats, RSS and Atom would be supported only for responses.  It is unfortunate that this is not explicitly stated anywhere.

More disturbing is the lack of an XML schema or any attempt to formally define the XML vocabulary! It seems that only XML examples are provided for each resource. Googling for “Twitter API XSD” confirms my suspicion in that it returns many mentions of “inferring XML schemas” from instances – a scary proposition indeed! What is the cardinality of XML elements? Which ones are repeatable or not? What about the data types? The DRY (don’t repeat yourself) principle is violated since you have the same XML example redundantly repeated on many pages. You can maybe get away with this for a small API, but for a widely used API such as Twitter I would have thought Twitter would have invested more resources in API contract specification.

Twitter Content Negotiation

Another concern is the way Twitter handles content negotation. Instead of using the REST convention/standard of  the ACCEPT header or a content query parameter, Twitter appends the format type to the URL (.json, .xml) which in effect creates a new resource. For example Google GData uses a query parameter such as alt=json or alt=rss to indicate data format.

TWitter API Versioning

This lack of explicit contract specification leads to problems regarding versioning. Versioning is one of those very difficult API problems that has no ideal satisfactory answer. Instead, there are partial solutions depending on the use case. Without a contract, it is difficult to even know what new changes have been implemented.

Let’s say a change is made to an XML snippet that is shared across many resource representations. Twitter would have to make changes to each resource documentation page. Even worse, it has to then have some mechanism to inform clients as to the contract change. Having some schema or at least some common way of describing the format would be a much better idea.The Twitter FAQ weakly states that clients have to proactively monitor the following:

Google uses HTTP headers to indicate API versions as well as standard namespace naming conventions.

API Client Packages

Typically REST APIs will leave it to third parties to provide language-specific client stubs. This is understandable because of the large number of languages out there – it would be prohibitive for a small(er) company  to implement and test all these packages! However the downside is that these packages are by definition non-standard (caveat emptor), and it is an open question as to how reliably they implement the current service definition.

Documentation wildly varies.   Firstly, it is not always clear which API to use if several choices exists. Being mostly a Java guy, I focus here on Java clients. For example, Twitter has four Java clients. You most often find minimal Javadoc with no further explanation. API coverage is incomplete – features are missing . For example, for the user-timeline “method” (sidebar: misuse of REST term method!), Twitter4j supports the count query parameter whereas JTwitter apparently does not. The problem here is of client fidelity to the API. When the Twitter API changes, what is the guarantee that the “mom and pop” client will sync up?

Speaking of the devil, a rather interesting development happened just the other day with Amazon’s AWS Java client. On March 22, 2010 Amazon announced  rollout of a brand new AWS SDK for Java! Until then, they too had depended on third-parties – the venerable Jets3t (only supports S3 and CloudFront) and typica. Undoubtedly this was due to client pressure for precisely those reasons enumerated above! See Mr. Jets3t’s comment on the news.


One of the wonders of the human condition is how people manage to work around major obstacles when there is an overriding need. The plethora of Twitter clients in the real world is truly a testimony to the ubiquity of the Twitter API.

However, there is still considerable room for improvement to remove incidental accidental complexity and maximize client productivity. Twitter is still a young company and there is still an obvious maturation process ahead.

After all, Google has many more resources to fine tune the API experience. But if I was the chief Twitter API architect, I would certainly take a long and hard look at the strategic direction of the API. Obviously there is major momentum in this direction especially with the June deprecation of Basic Auth in favor of OAuth and the realignment of the REST and Search APIs. There is no reason to blindly mimic someone else’s API documentation style (think branding), but even less reason not to learn from others (prior knowledge) and to minimize client cognitive overhead.