How to build a language binding for a web API

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Recently I’ve worked with a web API, the Cometdocs API, in order to use their converter of documents, particularly for automating some conversions from PDF documents to Excel spreadsheets for data extraction.

I wanted to use this API from my two favorite development platforms: Java and .Net/C#, so I needed to build what is called a language binding, i.e. a small library that acts as a proxy between the application code and the web API.

The development of these two bindings was really interesting from a technical point of view, and I’ve learned a bunch of things during the process.

I’d like to share the interesting stuff with you, it should be of interest even if you don’t have any plan for interacting with a web API because all the technologies and techniques I’ve used (the HTTP protocol, JSON data binding, SSL/TLS…) are applicable to other types of developments.

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JSON vs XML: some hard numbers about verbosity


More and more JSON is becoming the data interchange format of the web and even starts to leak outside of this world, replacing XML wherever it can, and there is really good reasons for that.
But often people are driven towards JSON for other reasons, not necessarily bad reasons, but based on more anecdotal facts, like the so-called verbosity of XML.
Indeed this is the argument you’ll hear the most often, e.g. just have a look at this nice comparison of the two formats: the first cons is of course “verbosity“.

And it’s a factual argument: the size gains can be important if your values are small, typically for representing business objects like customers because the markup overhead (all the closing tags) will become important relatively to the carried information (e.g. the names and zip codes of your customers).

But you rarely send big chunk of data in a raw text format as XML or JSON, because nowadays servers and clients (e.g. web browsers) supports live gzipping of the workloads, and use it transparently.
So the size advantage of JSON over XML should reduce because GZIP knows how to factorize redundant information like markups.

At least this seems a reasonable speculation, but while intuition is good hard numbers are better to be definitely convinced and to have a numerical idea of the impact.
So I’ve written a small Java benchmark that I’ll present, along with its results, in this article.

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