Filterer

About 3 min

The Filterer pattern aims to apply a series of filters to data objects, where each filter processes the data based on specific rules and criteria, and passes the data to the next filter in the sequence.

Explanation

Real world example

We are designing a threat (malware) detection software which can analyze target systems for threats that are present in it. In the design we have to take into consideration that new Threat types can be added later. Additionally, there is a requirement that the threat detection system can filter the detected threats based on different criteria (the target system acts as container-like object for threats).

In plain words

Filterer pattern is a design pattern that helps container-like objects return filtered versions of themselves.

Programmatic Example

To model the threat detection example presented above we introduce Threat and ThreatAwareSystem interfaces.

public interface Threat {
    String name();

    int id();

    ThreatType type();
}

public interface ThreatAwareSystem {
    String systemId();

    List<? extends Threat> threats();

    Filterer<? extends ThreatAwareSystem, ? extends Threat> filtered();
}

Notice the filtered method that returns instance of Filterer interface which is defined as:


@FunctionalInterface
public interface Filterer<G, E> {
    G by(Predicate<? super E> predicate);
}

It is used to fulfill the requirement for system to be able to filter itself based on threat properties. The container-like object (ThreatAwareSystem in our case) needs to have a method that returns an instance of Filterer. This helper interface gives ability to covariantly specify a lower bound of contravariant Predicate in the subinterfaces of interfaces representing the container-like objects.

In our example we will be able to pass a predicate that takes ? extends Threat object and return ? extends ThreatAwareSystem from Filtered::by method. A simple implementation of ThreatAwareSystem:

public class SimpleThreatAwareSystem implements ThreatAwareSystem {

    private final String systemId;
    private final ImmutableList<Threat> issues;

    public SimpleThreatAwareSystem(final String systemId, final List<Threat> issues) {
        this.systemId = systemId;
        this.issues = ImmutableList.copyOf(issues);
    }

    @Override
    public String systemId() {
        return systemId;
    }

    @Override
    public List<? extends Threat> threats() {
        return new ArrayList<>(issues);
    }

    @Override
    public Filterer<? extends ThreatAwareSystem, ? extends Threat> filtered() {
        return this::filteredGroup;
    }

    private ThreatAwareSystem filteredGroup(Predicate<? super Threat> predicate) {
        return new SimpleThreatAwareSystem(this.systemId, filteredItems(predicate));
    }

    private List<Threat> filteredItems(Predicate<? super Threat> predicate) {
        return this.issues.stream()
                .filter(predicate)
                .collect(Collectors.toList());
    }
}

The filtered method is overridden to filter the threats list by given predicate.

Now if we introduce a new subtype of Threat interface that adds probability with which given threat can appear:

public interface ProbableThreat extends Threat {
    double probability();
}

We can also introduce a new interface that represents a system that is aware of threats with their probabilities:

public interface ProbabilisticThreatAwareSystem extends ThreatAwareSystem {
    @Override
    List<? extends ProbableThreat> threats();

    @Override
    Filterer<? extends ProbabilisticThreatAwareSystem, ? extends ProbableThreat> filtered();
}

Notice how we override the filtered method in ProbabilisticThreatAwareSystem and specify different return covariant type by specifying different generic types. Our interfaces are clean and not cluttered by default implementations. We will be able to filter ProbabilisticThreatAwareSystem by ProbableThreat properties:

public class SimpleProbabilisticThreatAwareSystem implements ProbabilisticThreatAwareSystem {

    private final String systemId;
    private final ImmutableList<ProbableThreat> threats;

    public SimpleProbabilisticThreatAwareSystem(final String systemId, final List<ProbableThreat> threats) {
        this.systemId = systemId;
        this.threats = ImmutableList.copyOf(threats);
    }

    @Override
    public String systemId() {
        return systemId;
    }

    @Override
    public List<? extends ProbableThreat> threats() {
        return threats;
    }

    @Override
    public Filterer<? extends ProbabilisticThreatAwareSystem, ? extends ProbableThreat> filtered() {
        return this::filteredGroup;
    }

    private ProbabilisticThreatAwareSystem filteredGroup(final Predicate<? super ProbableThreat> predicate) {
        return new SimpleProbabilisticThreatAwareSystem(this.systemId, filteredItems(predicate));
    }

    private List<ProbableThreat> filteredItems(final Predicate<? super ProbableThreat> predicate) {
        return this.threats.stream()
                .filter(predicate)
                .collect(Collectors.toList());
    }
}

Now if we want filter ThreatAwareSystem by threat type we can do:

Threat rootkit=new SimpleThreat(ThreatType.ROOTKIT, 1, "Simple-Rootkit");
Threat trojan=new SimpleThreat(ThreatType.TROJAN, 2, "Simple-Trojan");
List<Threat> threats=List.of(rootkit, trojan);

ThreatAwareSystem threatAwareSystem=new SimpleThreatAwareSystem("System-1", threats);

ThreatAwareSystem rootkitThreatAwareSystem=threatAwareSystem.filtered().by(threat -> threat.type() == ThreatType.ROOTKIT);

Or if we want to filter ProbabilisticThreatAwareSystem:

ProbableThreat malwareTroyan=new SimpleProbableThreat("Troyan-ArcBomb", 1, ThreatType.TROJAN, 0.99);
ProbableThreat rootkit = new SimpleProbableThreat("Rootkit-System", 2, ThreatType.ROOTKIT, 0.8);
List<ProbableThreat> probableThreats = List.of(malwareTroyan, rootkit);

ProbabilisticThreatAwareSystem simpleProbabilisticThreatAwareSystem = new SimpleProbabilisticThreatAwareSystem("System-1", probableThreats);

ProbabilisticThreatAwareSystem filtered = simpleProbabilisticThreatAwareSystem.filtered().by(probableThreat -> Double.compare(probableThreat.probability(), 0.99) == 0);

Class diagram

Applicability

This pattern is useful in scenarios where data needs to be processed in discrete steps, and each step's output is the input for the next step. Common in stream processing, audio/video processing pipelines, or any data processing applications requiring staged transformations.

Tutorials

Known Uses

Stream processing libraries in Java, such as Apache Kafka Streams, utilize this pattern to build complex data processing pipelines.
Image processing software often uses filters to apply effects or transformations to images sequentially.

Consequences

Benefits:

Increases flexibility by allowing different filters to be added or reorganized without affecting other parts of the system.
Enhances testability, as filters can be tested independently.
Promotes loose coupling between the stages of data processing.

Trade-offs:

Potential performance overhead from continuous data passing between filters.
Complexity can increase with the number of filters, potentially affecting maintainability.

Chain of Responsibility: Filters can be seen as a specialized form of the Chain of Responsibility, where each filter decides if and how to process the input data and whether to pass it along the chain.
Decorator: Similar to Decorator in that both modify behavior dynamically; however, filters focus more on data transformation than on adding responsibilities.