Paul King

Presentations

Functional Groovy

Groovy doesn't claim to be a fully-fledged functional programming language but it does provide the Java or Groovy developer with a whole toolbox of features for doing functional style programs. This talk looks at the key Groovy features which support a functional style. Topics covered include using closures, currying and partial evaluation, closure composition, useful functional-centric AST macros, useful functional-centric runtime meta-programming tricks, trampolining, using Java functional libraries, immutable data structures, lazy and infinite lists, using Groovy 2's static typing and approaches for moving beyond Java's type system.

There are many advantages to using a functional style in your programs. Learn what can be done to leverage functional style while retaining many of the productivity gains of the Groovy programming language.

Working with Databases and Groovy

This talk reviews the features in Groovy which make it easy to work with databases. It reviews the features of Groovy SQL including Groovy's LINQ-like lazy evaluation technology called datasets. In addition, it looks at working with a couple of NoSQL databases: MongoDB (using GMongo) and Neo4J (using it's Java api and via Gremlin support).

This talk looks at accessing relational databases using raw JDBC, Groovy's lazy LINQ-like datasets and briefly using hibernate, caching, and spring-data technologies. It also looks at the approaches that can be used to access NOSQL databases.

The talk covers Groovy-SQL in some depth and then briefly examines the other technologies mentioned to give you a feel for your options. For Groovy SQL, we look at how to do the basic CRUD operations and then examine a few more advanced features like working with transactions, batches, chunking, stored procedures and database metadata. Example code will be provided on github (under development) so that attendees can dive deeper into the examples if they wish.

Creating Groovy DSLs that Developers can Actually Use

In this presentation, Guillaume, Paul, and Andrew will show you how to leverage Groovy to build a Domain-Specific Language (DSL) used to control a rover on Mars! Various metaprogramming techniques and integration mechanisms will be demonstrated. But the language itself is only the first part of the story. Developers cannot be expected to properly use a DSL without first-class IDE support and documentation.

The presentation will start by building the DSL from scratch, using the power of Groovy to create a concise and readable mini-language, and showing how to secure its integration. The second part of the presentation will demonstrate how to integrate the DSL into Groovy-Eclipse with custom content assist, navigation, searching, and inline documentation.

Leveraging Groovy for Capturing Business Rules

Groovy has excellent support for the creation of Domain Specific Languages (DSLs). Such DSLs can be particularly useful when writing business rules. Rules can be written in English-like phrases which are straight-forward to read or write (by non-developers) yet can be fully executable code corresponding to a layer over the top of a traditional logic solving API. This talk illustrates various DSLs, highlights several logic solving APIs and looks at the pros and cons of the various approaches (including tool support, flexibility, lock-in).

Whilst Groovy is the language of choice for this talk, the techniques and principles are not specific to Groovy and apply readily to your favourite modern scripting language. The "logic solving" APIs being highlighted are primarily Choco, Drools Expert and Drools Planner but again these are just illustrative of the logic APIs that you can use when writing a DSL layer. We look at the benefits and costs when writing such DSL layers, numerous real-world examples and the all-important aspects of tooling; covering what non-developer, developer and cloud tooling is available with this kind of approach.

To give a flavour of the talk, here is a snippet from one of the code examples (Einstein’s riddle):

the Briton has a red house the owner of the green house drinks coffee the owner of the yellow house plays baseball the person known to play football keeps birds the man known to play tennis drinks beer the green house is on the left side of the white house the man known to play volleyball lives next to the one who keeps cats the Norwegian lives next to the blue house

When discussing this example, we look at how you create and debug such code, illustrate how several APIs can be used underneath this DSL layer, discuss the costs involved in creating the above DSL in its basic form and in more complex forms that allow type checking, code completion etc. and options for parallelism and cloud deployment.