Efficient Storage of Sparse Data in Java

How can I efficiently store sparse data in Java?

Map<Integer, Map<Integer, Double>> sparseData = new HashMap<>(); // Where the outer key is row index, inner key is column index.

Sparse data refers to datasets where a majority of the elements are zero or undefined, making traditional storage methods inefficient in terms of memory usage. In Java, various techniques can be employed to manage sparse data effectively, conserving space and optimizing performance.

class SparseMatrix {
    private Map<Integer, Map<Integer, Double>> matrix;

    public SparseMatrix() {
        matrix = new HashMap<>();
    }

    public void set(int row, int col, double value) {
        if (value != 0) {
            matrix.computeIfAbsent(row, k -> new HashMap<>()).put(col, value);
        } else {
            remove(row, col);
        }
    }

    public Double get(int row, int col) {
        return matrix.getOrDefault(row, Collections.emptyMap()).get(col);
    }

    public void remove(int row, int col) {
        if (matrix.containsKey(row)) {
            matrix.get(row).remove(col);
            if (matrix.get(row).isEmpty()) {
                matrix.remove(row);
            }
        }
    }
}

Causes

• Large datasets contain a high proportion of zero or non-value entries.
• Using standard arrays or lists for sparse data leads to inefficient memory usage.

Solutions

• Use a HashMap to map only non-zero entries to reduce memory consumption.
• Implement a sparse matrix class that only stores non-zero elements along with their indices.
• Consider third-party libraries designed for sparse data storage, such as Apache Commons Math or EJML.

Mistake: Using fixed-size arrays for sparse data.

Solution: Switch to a dynamic data structure like HashMap to only store non-null entries.

Mistake: Not removing zero values leads to unnecessary memory consumption.

Solution: Implement checks to remove zero or null entries from your storage structure.