Constraint Streams – Modern Java constraints without the Drools Rule Language

Traditionally, to scale out with OptaPlanner,
you had to learn DRL. No more.
With the new Constraints Streams API, inspired by Java 8 Streams and SQL,
you can now write your constraints in Java (or Kotlin or Scala)
and still benefit from incremental calculation.

Underneath, Constraints Streams (CS) still use the powerful Drools engine.
We also still fully support score DRLs too. They are not deprecated.

Let’s start with an example.
In nurse rostering, to avoid assigning shifts to employee Ann,
you would write this constraint in DRL:

rule "Don't assign Ann"
    when
        Shift(getEmployee().getName() == "Ann")
    then
        scoreHolder.addSoftConstraintMatch(kcontext, -1);
end

This is the same constraint in Java using Constraint Streams:

Constraint constraint = constraintFactory
        .from(Shift.class)
        .filter(shift -> shift.getEmployee().getName().equals("Ann"))
        .penalize("Don't assign Ann", HardSoftScore.ONE_SOFT);

If you’re familiar with SQL or Java 8 streams, this should look familiar.
Given a potential solution with four shifts (two of which are assigned to Ann),
those shifts flow through the Constraint Stream like this:

constraintStreamFilter part

This new approach to writing constraints has several benefits:

Incremental calculation

First off, unlike an EasyScoreCalculator,
Constraint Streams still apply incremental score calculation to scale out, just like DRL.
For example, when a move swaps the employee of two shifts, only the delta is calculated.
That’s a huge scalability gain:

constraintStreamIncrementalCalculation

Indexing

When joining multiple types, just like an SQL JOIN operator,
Constraint Streams apply hash lookups on indexes to scale better:

constraintStreamJoinWithJoiners

IDE support

Because ConstraintsStreams are written in the Java language,
they piggy-back on very strong tooling support.

Code highlighting, code completion and debugging just work:

Code highlighting

DRL code in IntelliJ IDEA Ultimate:

codeHighlightingDRL

Java code using Constraint Streams in IntelliJ IDEA Ultimate, for the same constraints:

codeHighlightingConstraintStreams

Code completion

Code completion for Constraint Streams:

codeCompletionConstraintStreams

Of course, all API methods have Javadocs.

Debugging

Add a breakpoint in ConstraintStream’s filter():

codeDebuggingConstraintStreams1

To diagnose issues while debugging:

codeDebuggingConstraintStreams2

Java syntax

Constraints written in Java with Constraint Streams follow the Java Language Specification (JLS), for good or bad.
Similar logic applies when using Constraint Streams from Kotlin or Scala.

When migrating between DRL and Constraint Streams, be aware of some differences between DRL and Java:

  • A DRL’s == operator translates to equals() in Java.

  • Besides getters, DRL also allows MVEL expressions that translate into getters in Java.

For example, this DRL has name and ==:

rule "Don't assign Ann"
    when
        Employee(name == "Ann")
    then ...
end

But the Java variant for the exact same constraint has getName() and equals() instead:

constraintFactory.from(Employee.class)
        .filter(employee -> employee.getName().equals("Ann"))
        .penalize("Don't assign Ann", ...);

Advanced functions

The Constraint Streams API allows us to add syntactic sugar
and powerful new concepts, specifically tailored to help you build complex constraints.

Just to highlight one of these, let’s take a look at the powerful groupBy method:

constraintStreamGroupBy

Similar to an SQL GROUP BY operator or a Java 8 Stream Collector,
it support sum(), count(), countDistinct(), min(), max(), toList()
and even custom functions, again without loss of incremental score calculation.

Future work for Constraint Streams

First off, a big thanks to Lukáš Petrovický
for all his work on Constraints Streams!

But this is just the beginning.
We envision more advanced functions,
such as load balancing/fairness methods
to make such constraints easier to implement.

Right now, our first priority is to make it easier to unit test constraints in isolation.
Think Test Driven Design. Stay tuned!

This post was original published on here.
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