We validate our approach with respect to two different criteria: performance and integration costs. Benchmark tests were executed with and without the use of HL7Middleware and with different network bandwidths. These results demonstrated that the performance of the interoperability bus is higher when compared to traditional database access for larger volumes of data and when the bandwidth of the user is considerably lower than the bandwidth of the connection between HL7Server and database.
The overall development and deployment cost was considered low and the reusability degree of wrapper code was considered high, thus suggesting a progressive reduction of the integration costs of additional services and subsystems of an organization. Related Articles:. Home References Article citations. The first two are simple parsers that move through the text and parse integer or spaces correspondingly.
And the last one, between, is a parser combinator. It takes two bracketing characters and pushes into its middle parser everything in between. If we look closer at the definition of the Parser type, it's just a function that takes a stream of characters and returns the result, failure or success, depending on the parsing outcome.
To build more intuition around what it looks like to do parsing with these primitives, we can look at this example:. We want to parse those two numbers into a tuple. To do so, we're using the library functions mentioned earlier and building a single parser by combining other, simpler parsers.
In the highlighted row, we're constructing the main part of the parser. On top of this, we also need to specify that the addition expression is enclosed inside parentheses. To do so, we're using between, which you may recall is a parser combinator that can return a parser for text enclosed inside starting and ending symbols.
The only thing unknown for us now is the combinators circled in yellow in the snippet. Those are special combinators that specify the order of parsing and the value we should keep or discard while progressing through the text. Here we can find all the familiar parsers and combinators, and many more. One of the most important things to note is that we don't just parse text into primitive types, but rather we build an abstract syntax tree on the fly. And as an end result, we're getting a clean final function that parses all the text into our bespoke domain model, or returns a nice error explaining why it can't do it.
A parsed rule is quite nice-looking by itself, but we can't do anything useful with it until we can properly interpret the encoded instructions. This can be done rather straightforwardly by recursively traversing the AST we built. Having implemented everything we discussed, the only thing left is to wrap it all up into a web application and conveniently host it somewhere. Where do you go when you want to spawn a small server for your small service with no configuration and scalability burdens?
As we're already running a lot of our services on Amazon, it was quite natural for us to look into AWS Lambda first. And surprisingly, AWS has everything needed for F functions to run: from the. NET runtime to tooling and scaffolding templates. AWS already has F templates based on the Giraffe web framework with all the necessary boilerplate for query parameters and other AWS service communication configurations built in.
So basically, it was just as simple as bundling up the core matching mechanism and deploying this code as a Lambda service. The nice things about Lambda are:. If it compiles, it has no bugs. The initial prototype was developed during the weekend while just exploring an interesting concept. Even though F might not top any charts of most-used programming languages, it has a quite nice vendor support, as we saw with AWS Lambda scaffolding.
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I plan on uploading a few more Minecraft related videos so if you have any requests post them in the comments below.
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