How do I calculate a moving average with Stream Gatherers in Java 24?

A moving average smooths out short-term changes in a series of readings. Instead of averaging all the readings once, you average the first few readings, move one position forward, and repeat.

In this example, you’ll calculate an average for every three consecutive temperature readings using Java’s Gatherers.windowSliding().

Stream Gatherers became a standard feature in Java 24, so no preview flags or external libraries are needed. See the Java 24 Gatherers API.

1. Group the readings into overlapping windows

Start with five readings: 18, 21, 24, 27, 23. A window size of three gives you [18, 21, 24] first. The next window drops 18 and adds 27, giving you [21, 24, 27].

The call .gather(Gatherers.windowSliding(3)) turns a stream of individual readings into a stream of these lists.

2. Keep complete windows and calculate each average

If the input has one or two readings, windowSliding(3) produces a single shorter window. The filter() below skips that window so every reported average uses exactly three readings. Empty input produces no windows.

For each remaining list, mapToInt() creates a stream of integers and average() computes the mean. The result is present because every window that reaches this step contains three values.

3. Compile and run the example

Save this file as MovingAverageExample.java:

import java.util.List;
import java.util.Locale;
import java.util.stream.Gatherers;

public class MovingAverageExample {
    public static void main(String[] args) {
        List<Integer> readings = List.of(18, 21, 24, 27, 23);
        int windowSize = 3;

        readings.stream()
                .gather(Gatherers.windowSliding(windowSize))
                .filter(window -> window.size() == windowSize)
                .forEachOrdered(window -> {
                    double average = window.stream()
                            .mapToInt(Integer::intValue)
                            .average()
                            .orElseThrow();

                    System.out.printf(Locale.ROOT, "%s -> %.2f%n",
                            window, average);
                });
    }
}

From the directory containing the file, run:

javac --release 24 MovingAverageExample.java
java MovingAverageExample

Output:

[18, 21, 24] -> 21.00
[21, 24, 27] -> 24.00
[24, 27, 23] -> 24.67

The last window averages 74 / 3. The %.2f format rounds the displayed value to two decimal places; it does not change the stored average.

Best practices

  • Put readings in chronological order before making windows. The gatherer follows the stream’s existing order.
  • Use a positive window size. If it comes from user input, validate it before creating the gatherer.
  • Decide what to do when there are too few readings. This example waits for a full window; if you need early results, define how partial averages should work.
  • A three-reading window counts samples. If your readings arrive at irregular intervals and you need a three-minute average, group them by timestamps instead.

How do I use Optional Stream with flatMap?

Using the Optional.stream() method with flatMap is a common scenario when you want to work with collections and operations involving Optional.

The Optional.stream() method converts an Optional value into a Stream, which will either contain the single value (if the Optional is present) or be empty (if the Optional is empty). This is particularly useful in combination with flatMap when working with streams.

Here’s how to use Optional.stream with flatMap in practice:

Example

Here’s an example demonstrating the usage of Optional.stream with flatMap:

package org.kodejava.util.stream;

import java.util.Optional;
import java.util.stream.Stream;

public class OptionalStreamExample {
    public static void main(String[] args) {
        Optional<String> optional1 = Optional.of("Hello");
        Optional<String> optional2 = Optional.of("World");

        // Combine optionals using flatMap and stream
        String result = Stream.of(optional1, optional2)
                .flatMap(Optional::stream)
                .reduce((s1, s2) -> s1 + " " + s2)
                .orElse("No Value");

        System.out.println(result); // Output: Hello World
    }
}

Explanation of the Code:

  1. Stream of Optionals:
    • Start with a Stream containing Optional objects (in this case, optional1 and optional2).
  2. FlatMap with Optional.stream:
    • Use flatMap(Optional::stream) to convert each Optional into a stream:
      • If the Optional contains a value, it will be represented as a Stream with a single element.
      • If the Optional is empty, it results in an empty Stream.
  3. Reduce the Result:
    • Use the reduce method on the resulting stream to combine the values.
    • In the example, s1 + " " + s2 concatenates the non-empty values together.
    • If the result is absent after combining, it defaults to "No Value" using orElse.

Why Use Optional.stream with flatMap?

  • Stream-Friendly Operations: It allows you to continue working seamlessly in the stream pipeline even if the values are wrapped in Optional.
  • Handling Empty Optionals: Automatically avoids null pointer exceptions or manual checks for empty Optional values.
  • Code Simplicity: Reduces boilerplate code by directly transforming Optional into a stream.

Another Example: Filtering and Transforming

Here’s another example where we filter and transform Optional values:

package org.kodejava.util.stream;

import java.util.Optional;
import java.util.stream.Stream;

public class OptionalStreamFilter {
    public static void main(String[] args) {
        Optional<Integer> optional1 = Optional.of(10);
        Optional<Integer> optional2 = Optional.of(20);

        // Sum values greater than 15
        int sum = Stream.of(optional1, optional2)
                .flatMap(Optional::stream)
                .filter(val -> val > 15)
                .mapToInt(Integer::intValue)
                .sum();

        System.out.println("Sum: " + sum); // Output: Sum: 20
    }
}

Key Points:

  • Optional.stream bridges the gap between Optional and Stream APIs.
  • Common use cases include combining multiple Optional values, filtering, transforming, or reducing them in a stream flow.

How do I use Map.Entry comparingByValue for sorting?

To use Map.Entry.comparingByValue for sorting a Map, you can leverage Java Streams, which provide an efficient way to process and sort collection data. Here’s how the process works:

  1. Retrieve the entrySet of the Map: This gives a set of Map.Entry objects that you can operate on with a stream.
  2. Sort using Map.Entry.comparingByValue: Use Stream.sorted() along with this comparator to sort the entries by their values.
  3. Collect the sorted entries into a LinkedHashMap: Preserve the sorted order by using a LinkedHashMap in combination with Collectors.toMap.

Here’s a step-by-step explanation in a generic template:

Code Example

Below is an example of sorting a Map<String, Integer> by its values using Map.Entry.comparingByValue:

package org.kodejava.util.stream;

import java.util.*;
import java.util.stream.*;

public class MapSortExample {
    public static void main(String[] args) {
        // Sample map
        Map<String, Integer> map = new HashMap<>();
        map.put("Apple", 10);
        map.put("Orange", 20);
        map.put("Banana", 5);

        // Sorting the map by value
        Map<String, Integer> sortedByValue = map.entrySet()
                .stream()
                .sorted(Map.Entry.comparingByValue()) // Default ascending order
                .collect(Collectors.toMap(
                        Map.Entry::getKey,        // Key mapper
                        Map.Entry::getValue,      // Value mapper
                        (oldValue, newValue) -> oldValue, // Merge function
                        LinkedHashMap::new        // Map type (preserves order)
                ));

        // Printing sorted map
        sortedByValue.forEach((key, value) ->
                System.out.println("Key: " + key + ", Value: " + value));
    }
}

Key Points

  1. Map.Entry.comparingByValue():
    • It returns a comparator that compares Map.Entry objects by their values in ascending order.
    • You can use .reversed() to reverse the order (for descending order).
  2. Preserve Order:
    • The LinkedHashMap is used when collecting to ensure the order of sorted entries is retained.
    • Other maps (e.g., HashMap) won’t maintain the sorted order.
  3. Custom Comparators:
    • If values in the map are objects other than Integer, you can provide a custom comparator to comparingByValue() for sorting purposes:
      Map.Entry.comparingByValue(Comparator.reverseOrder());
      
    • For ascending sorting, the default is enough.

  4. Streams:

    • The stream() method converts the entrySet of a map to a stream.
    • The sorted() operation applies the comparator to order the entries within the stream.
  5. Merging Duplicate Keys:
    • (oldValue, newValue) -> oldValue ensures no duplicate keys during the collection phase.

This approach is concise, leverages modern Java features, and ensures efficient sorting and processing.

How do I integrate Optional with Java Streams?

Integrating Optional with Java Streams can simplify many common scenarios when working with potentially absent values. Here are different techniques depending on your specific use case:

1. Use Optional in Stream Pipelines

When you have an Optional and you want to integrate it into a Stream pipeline, you can use stream() from Java 9 onward. The stream() method will return a single-element stream if a value is present, or an empty stream otherwise.

Example:

package org.kodejava.util;

import java.util.Optional;
import java.util.stream.Stream;

public class OptionalWithStream {
    public static void main(String[] args) {
        Optional<String> optionalValue = Optional.of("Hello, Stream!");

        // Convert Optional to a Stream and process it
        optionalValue.stream()
                .map(String::toUpperCase)
                .forEach(System.out::println);
    }
}

Output:

HELLO, STREAM!

2. Use Streams to Produce Optionals

Stream operations often result in an Optional, such as methods like findFirst(), findAny(), and max().

Example:

package org.kodejava.util;

import java.util.Arrays;
import java.util.List;
import java.util.Optional;

public class StreamToOptional {
    public static void main(String[] args) {
        List<String> values = Arrays.asList("a", "b", "c", "d");

        // Find the first value that matches a condition
        Optional<String> result = values.stream()
                .filter(value -> value.equals("b"))
                .findFirst();

        result.ifPresent(System.out::println); // Output: b
    }
}

3. Flatten Optional<Optional<T>> in Stream Pipelines

If you end up with a nested Optional<Optional<T>>, you can use flatMap() to flatten it.

Example:

package org.kodejava.util;

import java.util.Optional;

public class NestedOptional {
    public static void main(String[] args) {
        Optional<Optional<String>> nestedOptional = Optional.of(Optional.of("Value"));

        // Flatten the nested Optional
        nestedOptional.flatMap(inner -> inner)
                .ifPresent(System.out::println); // Output: Value
    }
}

Similarly, if you’re working with streams, you can achieve something equivalent:

package org.kodejava.util;

import java.util.List;
import java.util.Optional;
import java.util.stream.Collectors;

public class OptionalWithStream {
    public static void main(String[] args) {
        List<Optional<String>> optionals = List.of(Optional.of("A"), Optional.empty(), Optional.of("B"));

        // Flatten the optional values into a single stream
        List<String> results = optionals.stream()
                .flatMap(Optional::stream)
                .collect(Collectors.toList());

        System.out.println(results); // Output: [A, B]
    }
}

4. Filter Optional Using Stream

If you want to filter the Optional based on some condition before further processing, using filter() is concise and effective.

Example:

package org.kodejava.util;

import java.util.Optional;

public class FilterOptionalWithStream {
    public static void main(String[] args) {
        Optional<String> optional = Optional.of("hello");

        // Filter and process the value if it passes the condition
        optional.filter(value -> value.length() > 4)
                .ifPresent(System.out::println); // Output: hello
    }
}

5. Handle Streams with Empty Optionals

If you have a situation where an Optional can be empty and you want to safely handle values, you can convert the Optional into a Stream and continue processing.

Example:

package org.kodejava.util;

import java.util.Optional;
import java.util.stream.Stream;

public class EmptyOptionalStream {
    public static void main(String[] args) {
        Optional<String> optional = Optional.empty();

        optional.stream()
                .map(String::toUpperCase)
                .forEach(System.out::println);
        // No output, as the Optional is empty
    }
}

6. Combine Optional and Stream Elements

You can also work with a mix of Stream elements and Optionals. This is especially useful for chaining or merging operations.

Example:

package org.kodejava.util;

import java.util.List;
import java.util.Optional;
import java.util.stream.Stream;

public class CombineOptionalWithStream {
    public static void main(String[] args) {
        List<String> list = List.of("foo", "bar");
        Optional<String> optionalValue = Optional.of("baz");

        Stream<String> combinedStream = Stream.concat(list.stream(), optionalValue.stream());

        // Output: foo, bar, baz
        combinedStream.forEach(System.out::println);
    }
}

Summary of Key Methods:

  • Convert Optional to Stream: Optional.stream() (Java 9+)
  • Flatten nested Optionals: flatMap(Optional::stream)
  • Handle presence or absence: filter() or orElse()/orElseGet()
  • Produce Optionals from Streams: Use stream terminal operations like findFirst(), findAny(), max(), and min()
  • Combine Streams and Optionals: Leverage Stream.concat() or Optional.stream()

By effectively combining Optional and Stream, you can avoid null checks and achieve a functional, clean approach to processing sequences in Java.

How do I combine multiple Optionals in functional-style code?

Combining multiple Optional objects in Java in a functional style is a common need, especially when working with potentially nullable values without resorting to null checks. Here are examples of some approaches you can use based on the scenario:


1. Combining If All Optionals Are Present

If you want to combine values only when all Optionals are non-empty, you can use flatMap() and map() to transform and combine their values.

Example:

package org.kodejava.util;

import java.util.Optional;

public class OptionalCombination {
    public static void main(String[] args) {
        Optional<String> optional1 = Optional.of("Hello");
        Optional<String> optional2 = Optional.of("World");

        Optional<String> combined = optional1.flatMap(val1 ->
                optional2.map(val2 -> val1 + " " + val2)
        );

        // Output: Hello World
        combined.ifPresent(System.out::println); 
    }
}

Here:

  • flatMap is used on the first Optional.
  • map is applied on the second Optional inside the flatMap block.
  • This ensures the operation occurs only if both Optionals are present.

2. Using Multiple Optionals Dynamically with Streams

If you have multiple Optional objects, a dynamic approach using streams may be more suitable.

Example:

package org.kodejava.util;

import java.util.Optional;
import java.util.stream.Stream;

public class OptionalCombinationWithStreams {
    public static void main(String[] args) {
        Optional<String> optional1 = Optional.of("Hello");
        Optional<String> optional2 = Optional.of("Functional");
        Optional<String> optional3 = Optional.of("Java");

        String result = Stream.of(optional1, optional2, optional3)
                .flatMap(Optional::stream)
                .reduce((s1, s2) -> s1 + " " + s2)
                .orElse("No values");

        // Output: Hello Functional Java
        System.out.println(result);
    }
}

Steps in this approach:

  1. Use Stream.of() to collect your Optional objects.
  2. Extract their values using flatMap(Optional::stream).
  3. Combine the values with reduce.

3. Getting the First Non-Empty Optional

Sometimes, you’re only interested in the first non-empty Optional. For this, you can use Optional.or(), which was introduced in Java 9.

Example:

package org.kodejava.util;

import java.util.Optional;

public class FirstNonEmptyOptional {
    public static void main(String[] args) {
        Optional<String> optional1 = Optional.empty();
        Optional<String> optional2 = Optional.of("Hello");
        Optional<String> optional3 = Optional.empty();

        Optional<String> firstPresent = optional1
                .or(() -> optional2)
                .or(() -> optional3);

        // Output: Hello
        firstPresent.ifPresent(System.out::println);
    }
}

4. Handling Custom Logic with Optionals

You can define custom logic to process multiple Optionals and combine them using a utility function when needed.

Example:

package org.kodejava.util;

import java.util.Optional;
import java.util.stream.Stream;
import java.util.stream.Collectors;

public class OptionalCustomCombination {
    public static void main(String[] args) {
        Optional<Integer> optional1 = Optional.of(10);
        Optional<Integer> optional2 = Optional.of(20);
        Optional<Integer> optional3 = Optional.empty();

        Optional<Integer> combined = combineOptionals(optional1, optional2, optional3);
        combined.ifPresent(System.out::println); // Output: 30
    }

    @SafeVarargs
    public static Optional<Integer> combineOptionals(Optional<Integer>... optionals) {
        return Stream.of(optionals)
                .flatMap(Optional::stream)
                .collect(Collectors.reducing(Integer::sum));
    }
}

In this example:

  • The combineOptionals method dynamically handles any number of Optional<Integer>.
  • Non-empty values are summed using Collectors.reducing().

Which Pattern Should You Use?

  • Combine Only When All Optionals Are Present: Use flatMap and map chaining.
  • Combine Dynamically with Multiple Optionals: Use a Stream.
  • Use First Non-Empty Optional: Use Optional.or().
  • Custom Processing Logic: Create a reusable utility method.

This way, you can handle Optional objects cleanly and avoid verbose null checks.