Compression Algorithm Selection and Optimization Service #

class CompressionProfile {
    private final DataCharacteristics characteristics;
    private final PerformanceRequirements requirements;
    private final HardwareCapabilities hardware;

    // Statistical properties
    private double entropy;
    private double repetitionRatio;
    private Map<String, Double> patternFrequencies;
}

class AlgorithmRegistry {
    private final Map<String, CompressionAlgorithm> algorithms;
    private final Map<String, PerformanceHistory> performanceHistory;
    private final ConcurrentSkipListMap<String, Double> successRates;
}

class OptimizationWorkspace {
    private final ParameterSpace parameterSpace;
    private final ObjectiveFunction objective;
    private final TerminationCriteria criteria;
    private volatile boolean converged;
}

import java.util.*;
import java.util.concurrent.*;
import java.nio.ByteBuffer;

public class AdaptiveCompressionService {
    private final DataProfiler profiler;
    private final AlgorithmRegistry registry;
    private final ParameterOptimizer optimizer;
    private final CompressionExecutor executor;
    private final PerformanceMonitor monitor;
    private final LearningEngine learner;

    // Configuration
    private final double samplingRatio;
    private final long maxOptimizationTimeMs;
    private final int workerThreads;

    public AdaptiveCompressionService() {
        this(0.05, 2000L, Runtime.getRuntime().availableProcessors());
    }

    public AdaptiveCompressionService(double samplingRatio, long maxOptimizationTimeMs, int workerThreads) {
        this.samplingRatio = samplingRatio;
        this.maxOptimizationTimeMs = maxOptimizationTimeMs;
        this.workerThreads = workerThreads;

        this.profiler = new DataProfiler(samplingRatio);
        this.registry = new AlgorithmRegistry();
        this.optimizer = new ParameterOptimizer(maxOptimizationTimeMs);
        this.executor = new CompressionExecutor(workerThreads);
        this.monitor = new PerformanceMonitor();
        this.learner = new LearningEngine();
    }

    public CompressionResult compress(byte[] input, CompressionRequirements requirements)
            throws CompressionException {
        try {
            // Step 1: Profile the data
            CompressionProfile profile = profiler.analyze(input, requirements);

            // Step 2: Select best algorithm
            List<CompressionAlgorithm> candidates = registry.getCandidates(profile);
            AlgorithmSelection bestSelection = selectOptimalAlgorithm(candidates, profile, requirements);

            // Step 3: Execute compression with parallel processing
            byte[] compressedData = executor.compress(bestSelection.algorithm,
                                                    bestSelection.parameters,
                                                    input,
                                                    requirements.priority);

            // Step 4: Measure and record performance
            PerformanceMetrics metrics = monitor.measure(input, compressedData,
                                                       bestSelection.algorithm,
                                                       bestSelection.parameters);

            // Step 5: Update learning models
            learner.updateModel(profile, bestSelection, metrics);

            return new CompressionResult(compressedData, bestSelection.algorithm, metrics);

        } catch (Exception e) {
            throw new CompressionException("Compression failed", e);
        }
    }

    private AlgorithmSelection selectOptimalAlgorithm(List<CompressionAlgorithm> candidates,
                                                   CompressionProfile profile,
                                                   CompressionRequirements requirements) {
        AlgorithmSelection best = null;
        double bestScore = Double.NEGATIVE_INFINITY;

        for (CompressionAlgorithm candidate : candidates) {
            // Get or tune parameters
            Map<String, Object> params = optimizer.optimize(candidate, profile, requirements);

            // Predict performance
            double score = registry.predictPerformance(candidate, params, profile, requirements);

            if (score > bestScore) {
                best = new AlgorithmSelection(candidate, params, score);
                bestScore = score;
            }
        }

        return best;
    }

    // Data profiling with entropy calculation
    static class DataProfiler {
        private final double samplingRatio;

        DataProfiler(double samplingRatio) {
            this.samplingRatio = samplingRatio;
        }

        public CompressionProfile analyze(byte[] data, CompressionRequirements requirements) {
            int sampleSize = (int) Math.max(1024, data.length * samplingRatio);
            byte[] sample = sampleData(data, sampleSize);

            double entropy = calculateEntropy(sample);
            double repetitionRatio = calculateRepetitionRatio(sample);
            Map<String, Double> patterns = identifyPatterns(sample);

            DataCharacteristics characteristics = new DataCharacteristics(entropy,
                                                                       repetitionRatio,
                                                                       patterns,
                                                                       data.length);

            HardwareCapabilities hardware = detectHardwareCapabilities();

            return new CompressionProfile(characteristics, requirements, hardware);
        }

        private double calculateEntropy(byte[] data) {
            int[] frequencies = new int[256];
            for (byte b : data) {
                frequencies[b & 0xFF]++;
            }

            double entropy = 0.0;
            int total = data.length;
            for (int freq : frequencies) {
                if (freq > 0) {
                    double p = (double) freq / total;
                    entropy -= p * (Math.log(p) / Math.log(2));
                }
            }
            return entropy;
        }

        private double calculateRepetitionRatio(byte[] data) {
            Set<String> uniqueSequences = new HashSet<>();
            int sequenceLength = 4; // bytes

            for (int i = 0; i <= data.length - sequenceLength; i += sequenceLength) {
                byte[] sequence = Arrays.copyOfRange(data, i, i + sequenceLength);
                uniqueSequences.add(Arrays.toString(sequence));
            }

            int possibleSequences = data.length / sequenceLength;
            return 1.0 - ((double) uniqueSequences.size() / possibleSequences);
        }

        private Map<String, Double> identifyPatterns(byte[] data) {
            Map<String, Double> patterns = new HashMap<>();

            // Detect common patterns
            if (isTextData(data)) patterns.put("text", 1.0);
            if (isImageData(data)) patterns.put("image", 1.0);
            if (isStructuredData(data)) patterns.put("structured", 1.0);
            if (isSparseData(data)) patterns.put("sparse", 1.0);

            return patterns;
        }

        private boolean isTextData(byte[] data) {
            int printable = 0;
            for (byte b : data) {
                if ((b >= 32 && b <= 126) || b == '\n' || b == '\r' || b == '\t') {
                    printable++;
                }
            }
            return (double) printable / data.length > 0.8;
        }

        private boolean isImageData(byte[] data) {
            if (data.length < 8) return false;
            // Check for common image headers
            return (data[0] == (byte) 0xFF && data[1] == (byte) 0xD8 && data[2] == (byte) 0xFF) || // JPEG
                   (data[0] == (byte) 0x89 && data[1] == (byte) 0x50) || // PNG
                   (data[0] == (byte) 0x42 && data[1] == (byte) 0x4D); // BMP
        }

        private boolean isStructuredData(byte[] data) {
            // Basic JSON/XML detection
            String sample = new String(Arrays.copyOf(data, Math.min(100, data.length)));
            return sample.contains("{") || sample.contains("<");
        }

        private boolean isSparseData(byte[] data) {
            int zeros = 0;
            for (byte b : data) {
                if (b == 0) zeros++;
            }
            return (double) zeros / data.length > 0.5;
        }

        private byte[] sampleData(byte[] data, int sampleSize) {
            if (data.length <= sampleSize) return data;

            Random random = new Random(data.length); // Deterministic sampling
            byte[] sample = new byte[sampleSize];

            for (int i = 0; i < sampleSize; i++) {
                int index = random.nextInt(data.length);
                sample[i] = data[index];
            }

            return sample;
        }

        private HardwareCapabilities detectHardwareCapabilities() {
            return new HardwareCapabilities(
                Runtime.getRuntime().availableProcessors(),
                true, // assume AVX support
                true  // assume snappy hardware acceleration
            );
        }
    }

    // Algorithm registry with performance history
    static class AlgorithmRegistry {
        private final Map<String, CompressionAlgorithm> algorithms = new ConcurrentHashMap<>();
        private final Map<String, PerformanceHistory> performanceHistory = new ConcurrentHashMap<>();

        public AlgorithmRegistry() {
            // Register common algorithms
            registerAlgorithm(new LZ4Algorithm());
            registerAlgorithm(new ZstdAlgorithm());
            registerAlgorithm(new BrotliAlgorithm());
            registerAlgorithm(new DeflateAlgorithm());
        }

        private void registerAlgorithm(CompressionAlgorithm algorithm) {
            algorithms.put(algorithm.getName(), algorithm);
            performanceHistory.put(algorithm.getName(), new PerformanceHistory());
        }

        public List<CompressionAlgorithm> getCandidates(CompressionProfile profile) {
            return new ArrayList<>(algorithms.values());
        }

        public double predictPerformance(CompressionAlgorithm algorithm, Map<String, Object> params,
                                       CompressionProfile profile, CompressionRequirements requirements) {
            // Simple heuristic-based prediction
            String key = algorithm.getName();

            // Factor in data characteristics
            double entropyFactor = 1.0 - (profile.characteristics.entropy / 8.0); // 8 bits max entropy
            double repetitionFactor = profile.characteristics.repetitionRatio;

            // Algorithm-specific scoring
            double baseScore = getBaseScore(algorithm, profile.characteristics);

            // Hardware acceleration bonus
            double hardwareBonus = algorithm.supportsHardwareAccel() &&
                                 profile.hardware.hasAcceleration ? 0.2 : 0.0;

            // Requirement alignment
            double speedWeight = requirements.priority == Priority.SPEED ? 0.7 :
                               requirements.priority == Priority.BALANCED ? 0.5 : 0.3;
            double ratioWeight = 1.0 - speedWeight;

            return baseScore * entropyFactor * (1 + repetitionFactor) * (1 + hardwareBonus);
        }

        private double getBaseScore(CompressionAlgorithm algorithm, DataCharacteristics characteristics) {
            switch (algorithm.getName()) {
                case "LZ4": return characteristics.repetitionRatio > 0.3 ? 0.8 : 0.6;
                case "Zstd": return 0.9; // Good general purpose
                case "Brotli": return characteristics.patterns.containsKey("text") ? 0.95 : 0.7;
                case "Deflate": return characteristics.patterns.containsKey("text") ? 0.85 : 0.75;
                default: return 0.5;
            }
        }
    }

    // Parameter optimization engine
    static class ParameterOptimizer {
        private final long maxTimeMs;

        ParameterOptimizer(long maxTimeMs) {
            this.maxTimeMs = maxTimeMs;
        }

        public Map<String, Object> optimize(CompressionAlgorithm algorithm, CompressionProfile profile,
                                          CompressionRequirements requirements) {
            long startTime = System.currentTimeMillis();
            Map<String, Object> bestParams = new HashMap<>();
            double bestScore = Double.NEGATIVE_INFINITY;

            // Simple grid search for demonstration
            for (int level : algorithm.getSupportedLevels()) {
                if (System.currentTimeMillis() - startTime > maxTimeMs) break;

                Map<String, Object> params = Map.of("level", level);
                double score = evaluateParameters(algorithm, params, profile, requirements);

                if (score > bestScore) {
                    bestScore = score;
                    bestParams = new HashMap<>(params);
                }
            }

            return bestParams;
        }

        private double evaluateParameters(CompressionAlgorithm algorithm, Map<String, Object> params,
                                        CompressionProfile profile, CompressionRequirements requirements) {
            // Estimate based on algorithm characteristics
            int level = (Integer) params.getOrDefault("level", 1);

            // Higher levels generally better compression but slower
            double compressionBonus = Math.log(level + 1) * 0.1;
            double speedPenalty = Math.log(level + 1) * 0.05;

            double speedWeight = requirements.priority == Priority.SPEED ? 0.8 :
                               requirements.priority == Priority.BALANCED ? 0.5 : 0.2;

            return (0.5 + compressionBonus) * (1 - speedWeight) +
                   (0.5 - speedPenalty) * speedWeight;
        }
    }

    // Parallel compression executor
    static class CompressionExecutor {
        private final ExecutorService executor;

        CompressionExecutor(int threads) {
            this.executor = Executors.newFixedThreadPool(threads);
        }

        public byte[] compress(CompressionAlgorithm algorithm, Map<String, Object> params,
                              byte[] input, Priority priority) throws Exception {

            if (input.length < 1024 * 1024) { // 1MB
                // Small data: single-threaded
                return algorithm.compress(input, params);
            } else {
                // Large data: parallel chunked compression
                return compressParallel(algorithm, params, input, priority);
            }
        }

        private byte[] compressParallel(CompressionAlgorithm algorithm, Map<String, Object> params,
                                      byte[] input, Priority priority) throws Exception {
            int chunkSize = 64 * 1024; // 64KB chunks
            int numChunks = (int) Math.ceil((double) input.length / chunkSize);

            List<Future<byte[]>> futures = new ArrayList<>();

            // Submit compression tasks
            for (int i = 0; i < numChunks; i++) {
                int start = i * chunkSize;
                int end = Math.min(start + chunkSize, input.length);
                byte[] chunk = Arrays.copyOfRange(input, start, end);

                Future<byte[]> future = executor.submit(() -> algorithm.compress(chunk, params));
                futures.add(future);
            }

            // Collect compressed chunks
            ByteArrayOutputStream output = new ByteArrayOutputStream();
            for (Future<byte[]> future : futures) {
                byte[] compressedChunk = future.get();
                // Write chunk size followed by compressed data
                writeInt(output, compressedChunk.length);
                output.write(compressedChunk);
            }

            return output.toByteArray();
        }

        private void writeInt(ByteArrayOutputStream out, int value) {
            out.write((value >>> 24) & 0xFF);
            out.write((value >>> 16) & 0xFF);
            out.write((value >>> 8) & 0xFF);
            out.write(value & 0xFF);
        }
    }

    // Algorithm interface
    interface CompressionAlgorithm {
        String getName();
        byte[] compress(byte[] input, Map<String, Object> params) throws CompressionException;
        byte[] decompress(byte[] input, Map<String, Object> params) throws CompressionException;
        int[] getSupportedLevels();
        boolean supportsHardwareAccel();
    }

    // Simple LZ4 implementation placeholder
    static class LZ4Algorithm implements CompressionAlgorithm {
        @Override
        public String getName() { return "LZ4"; }
        @Override
        public byte[] compress(byte[] input, Map<String, Object> params) {
            // Placeholder - would use actual LZ4 library
            return input; // Identity for demo
        }
        @Override
        public byte[] decompress(byte[] input, Map<String, Object> params) {
            return input;
        }
        @Override
        public int[] getSupportedLevels() { return new int[]{1, 2, 3, 4}; }
        @Override
        public boolean supportsHardwareAccel() { return true; }
    }

    // Data classes
    static class CompressionProfile {
        final DataCharacteristics characteristics;
        final CompressionRequirements requirements;
        final HardwareCapabilities hardware;

        CompressionProfile(DataCharacteristics characteristics, CompressionRequirements requirements,
                          HardwareCapabilities hardware) {
            this.characteristics = characteristics;
            this.requirements = requirements;
            this.hardware = hardware;
        }
    }

    static class DataCharacteristics {
        final double entropy;
        final double repetitionRatio;
        final Map<String, Double> patterns;
        final int originalSize;

        DataCharacteristics(double entropy, double repetitionRatio, Map<String, Double> patterns,
                           int originalSize) {
            this.entropy = entropy;
            this.repetitionRatio = repetitionRatio;
            this.patterns = patterns;
            this.originalSize = originalSize;
        }
    }

    static class HardwareCapabilities {
        final int cpuCores;
        final boolean hasAVX;
        final boolean hasSnappyAccel;

        HardwareCapabilities(int cpuCores, boolean hasAVX, boolean hasSnappyAccel) {
            this.cpuCores = cpuCores;
            this.hasAVX = hasAVX;
            this.hasSnappyAccel = hasSnappyAccel;
        }
    }

    enum Priority { SPEED, BALANCED, RATIO }

    static class CompressionRequirements {
        final Priority priority;
        final double minRatio;
        final long maxTimeMs;

        CompressionRequirements(Priority priority, double minRatio, long maxTimeMs) {
            this.priority = priority;
            this.minRatio = minRatio;
            this.maxTimeMs = maxTimeMs;
        }
    }

    static class PerformanceMetrics {
        final double compressionRatio;
        final long compressionTimeMs;
        final long compressedSize;

        PerformanceMetrics(double compressionRatio, long compressionTimeMs, long compressedSize) {
            this.compressionRatio = compressionRatio;
            this.compressionTimeMs = compressionTimeMs;
            this.compressedSize = compressedSize;
        }
    }

    static class AlgorithmSelection {
        final CompressionAlgorithm algorithm;
        final Map<String, Object> parameters;
        final double predictedScore;

        AlgorithmSelection(CompressionAlgorithm algorithm, Map<String, Object> parameters,
                          double predictedScore) {
            this.algorithm = algorithm;
            this.parameters = parameters;
            this.predictedScore = predictedScore;
        }
    }

    static class CompressionResult {
        final byte[] compressedData;
        final CompressionAlgorithm algorithm;
        final PerformanceMetrics metrics;

        CompressionResult(byte[] compressedData, CompressionAlgorithm algorithm,
                         PerformanceMetrics metrics) {
            this.compressedData = compressedData;
            this.algorithm = algorithm;
            this.metrics = metrics;
        }
    }

    // Learning engine for continuous improvement
    static class LearningEngine {
        public void updateModel(CompressionProfile profile, AlgorithmSelection selection,
                               PerformanceMetrics metrics) {
            // Update performance history
            // Retrain ML models
            // Adjust selection algorithms
        }
    }

    // Performance monitoring
    static class PerformanceMonitor {
        public PerformanceMetrics measure(byte[] original, byte[] compressed,
                                        CompressionAlgorithm algorithm,
                                        Map<String, Object> params) {
            long compressedSize = compressed.length;
            double ratio = (double) original.length / compressedSize;
            long timeMs = 1L; // Placeholder - would measure actual time

            return new PerformanceMetrics(ratio, timeMs, compressedSize);
        }
    }

    // Custom exceptions
    public static class CompressionException extends Exception {
        public CompressionException(String message) {
            super(message);
        }

        public CompressionException(String message, Throwable cause) {
            super(message, cause);
        }
    }
}