Vectorized Processing APIs

package org.apache.spark.sql.vectorized;

public abstract class ColumnVector implements AutoCloseable {
    /**
     * Data type of this column
     */
    public abstract DataType dataType();
    
    /**
     * Number of null values in this column
     */
    public abstract int numNulls();
    
    /**
     * Whether this column has any null values
     */
    public abstract boolean hasNull();
    
    /**
     * Check if value at given row is null
     */
    public abstract boolean isNullAt(int rowId);
    
    /**
     * Clean up resources
     */
    @Override
    public abstract void close();
    
    // Type-specific getters
    public boolean getBoolean(int rowId) { ... }
    public byte getByte(int rowId) { ... }
    public short getShort(int rowId) { ... }
    public int getInt(int rowId) { ... }
    public long getLong(int rowId) { ... }
    public float getFloat(int rowId) { ... }
    public double getDouble(int rowId) { ... }
    public UTF8String getUTF8String(int rowId) { ... }
    public byte[] getBinary(int rowId) { ... }
    public Decimal getDecimal(int rowId, int precision, int scale) { ... }
    
    // Complex type getters
    public ColumnVector getChild(int ordinal) { ... }
    public int getArrayLength(int rowId) { ... }
    public int getArrayOffset(int rowId) { ... }
}

public final class ColumnarBatch implements AutoCloseable {
    /**
     * Number of columns in this batch
     */
    public int numCols();
    
    /**
     * Number of rows in this batch
     */
    public int numRows();
    
    /**
     * Set number of rows (for dynamically sized batches)
     */
    public void setNumRows(int numRows);
    
    /**
     * Get column vector at given ordinal
     */
    public ColumnVector column(int ordinal);
    
    /**
     * Get row at given index
     */
    public ColumnarBatchRow getRow(int rowId);
    
    /**
     * Iterator over rows in this batch
     */
    public Iterator<InternalRow> rowIterator();
    
    @Override
    public void close();
}

public final class ArrowColumnVector extends ColumnVector {
    /**
     * Create ArrowColumnVector from Arrow ValueVector
     */
    public ArrowColumnVector(ValueVector vector);
    
    // Implements all ColumnVector methods with Arrow-optimized access
}

public class MyVectorizedPartitionReader implements PartitionReader<ColumnarBatch> {
    private final StructType schema;
    private final String dataPath;
    private final int batchSize;
    private ColumnVector[] columns;
    private int currentBatchRows;
    private boolean hasNextBatch = true;
    
    public MyVectorizedPartitionReader(StructType schema, String dataPath, int batchSize) {
        this.schema = schema;
        this.dataPath = dataPath;
        this.batchSize = batchSize;
        this.columns = createColumnVectors(schema, batchSize);
    }
    
    @Override
    public boolean next() throws IOException {
        if (!hasNextBatch) {
            return false;
        }
        
        // Load next batch of data into column vectors
        currentBatchRows = loadNextBatch();
        hasNextBatch = currentBatchRows > 0;
        
        return hasNextBatch;
    }
    
    @Override
    public ColumnarBatch get() {
        ColumnarBatch batch = new ColumnarBatch(columns, currentBatchRows);
        return batch;
    }
    
    @Override
    public void close() throws IOException {
        if (columns != null) {
            for (ColumnVector column : columns) {
                column.close();
            }
        }
    }
    
    private ColumnVector[] createColumnVectors(StructType schema, int capacity) {
        ColumnVector[] vectors = new ColumnVector[schema.length()];
        for (int i = 0; i < schema.length(); i++) {
            StructField field = schema.fields()[i];
            vectors[i] = createColumnVector(field.dataType(), capacity);
        }
        return vectors;
    }
    
    private ColumnVector createColumnVector(DataType dataType, int capacity) {
        if (dataType instanceof IntegerType) {
            return new OnHeapColumnVector(capacity, IntegerType);
        } else if (dataType instanceof LongType) {
            return new OnHeapColumnVector(capacity, LongType);
        } else if (dataType instanceof StringType) {
            return new OnHeapColumnVector(capacity, StringType);
        }
        // Handle other data types...
        throw new UnsupportedOperationException("Unsupported type: " + dataType);
    }
    
    private int loadNextBatch() throws IOException {
        // Implementation-specific batch loading
        // This would typically:
        // 1. Read data from external source
        // 2. Populate column vectors efficiently
        // 3. Return number of rows loaded
        return loadDataIntoVectors(columns);
    }
}

public class MyOnHeapColumnVector extends ColumnVector {
    private final DataType type;
    private final int capacity;
    private int numRows;
    
    // Storage arrays for different types
    private boolean[] booleanData;
    private int[] intData;
    private long[] longData;
    private double[] doubleData;
    private byte[][] binaryData;
    
    // Null tracking
    private boolean[] nulls;
    private int numNulls;
    
    public MyOnHeapColumnVector(int capacity, DataType type) {
        this.capacity = capacity;
        this.type = type;
        this.nulls = new boolean[capacity];
        
        // Allocate type-specific storage
        if (type instanceof IntegerType) {
            intData = new int[capacity];
        } else if (type instanceof LongType) {
            longData = new long[capacity];
        } else if (type instanceof DoubleType) {
            doubleData = new double[capacity];
        } else if (type instanceof BooleanType) {
            booleanData = new boolean[capacity];
        } else if (type instanceof StringType || type instanceof BinaryType) {
            binaryData = new byte[capacity][];
        }
    }
    
    @Override
    public DataType dataType() {
        return type;
    }
    
    @Override
    public int numNulls() {
        return numNulls;
    }
    
    @Override
    public boolean hasNull() {
        return numNulls > 0;
    }
    
    @Override
    public boolean isNullAt(int rowId) {
        return nulls[rowId];
    }
    
    @Override
    public int getInt(int rowId) {
        if (nulls[rowId]) return 0;
        return intData[rowId];
    }
    
    @Override
    public long getLong(int rowId) {
        if (nulls[rowId]) return 0L;
        return longData[rowId];
    }
    
    @Override
    public double getDouble(int rowId) {
        if (nulls[rowId]) return 0.0;
        return doubleData[rowId];
    }
    
    @Override
    public boolean getBoolean(int rowId) {
        if (nulls[rowId]) return false;
        return booleanData[rowId];
    }
    
    @Override
    public byte[] getBinary(int rowId) {
        if (nulls[rowId]) return null;
        return binaryData[rowId];
    }
    
    // Write methods for populating the vector
    public void putInt(int rowId, int value) {
        intData[rowId] = value;
        nulls[rowId] = false;
    }
    
    public void putLong(int rowId, long value) {
        longData[rowId] = value;
        nulls[rowId] = false;
    }
    
    public void putDouble(int rowId, double value) {
        doubleData[rowId] = value;
        nulls[rowId] = false;
    }
    
    public void putNull(int rowId) {
        nulls[rowId] = true;
        numNulls++;
    }
    
    public void setNumRows(int numRows) {
        this.numRows = numRows;
    }
    
    @Override
    public void close() {
        // Clean up arrays
        booleanData = null;
        intData = null;
        longData = null;
        doubleData = null;
        binaryData = null;
        nulls = null;
    }
}

public class ArrowVectorizedReader implements PartitionReader<ColumnarBatch> {
    private final VectorSchemaRoot root;
    private final ArrowFileReader arrowReader;
    private final ColumnVector[] columns;
    private boolean hasNext = true;
    
    public ArrowVectorizedReader(String arrowFilePath, StructType schema) 
            throws IOException {
        FileInputStream fis = new FileInputStream(arrowFilePath);
        this.arrowReader = new ArrowFileReader(
            new SeekableReadChannel(fis.getChannel()), 
            new RootAllocator()
        );
        
        this.root = arrowReader.getVectorSchemaRoot();
        this.columns = createArrowColumnVectors();
    }
    
    @Override
    public boolean next() throws IOException {
        if (!hasNext) {
            return false;
        }
        
        hasNext = arrowReader.loadNextBatch();
        return hasNext;
    }
    
    @Override
    public ColumnarBatch get() {
        int numRows = root.getRowCount();
        return new ColumnarBatch(columns, numRows);
    }
    
    @Override
    public void close() throws IOException {
        root.close();
        arrowReader.close();
    }
    
    private ColumnVector[] createArrowColumnVectors() {
        List<FieldVector> vectors = root.getFieldVectors();
        ColumnVector[] columnVectors = new ColumnVector[vectors.size()];
        
        for (int i = 0; i < vectors.size(); i++) {
            columnVectors[i] = new ArrowColumnVector(vectors.get(i));
        }
        
        return columnVectors;
    }
}

public class ArrowDataConverter {
    private final BufferAllocator allocator;
    
    public ArrowDataConverter() {
        this.allocator = new RootAllocator();
    }
    
    public VectorSchemaRoot convertToArrow(ColumnarBatch batch, StructType schema) {
        List<Field> fields = new ArrayList<>();
        List<FieldVector> vectors = new ArrayList<>();
        
        for (int i = 0; i < schema.length(); i++) {
            StructField field = schema.fields()[i];
            ColumnVector sparkVector = batch.column(i);
            
            Field arrowField = convertField(field);
            FieldVector arrowVector = convertVector(sparkVector, arrowField);
            
            fields.add(arrowField);
            vectors.add(arrowVector);
        }
        
        Schema arrowSchema = new Schema(fields);
        VectorSchemaRoot root = new VectorSchemaRoot(arrowSchema, vectors);
        root.setRowCount(batch.numRows());
        
        return root;
    }
    
    private Field convertField(StructField sparkField) {
        ArrowType arrowType = convertDataType(sparkField.dataType());
        return new Field(sparkField.name(), 
                        new FieldType(sparkField.nullable(), arrowType, null), 
                        null);
    }
    
    private ArrowType convertDataType(DataType sparkType) {
        if (sparkType instanceof IntegerType) {
            return new ArrowType.Int(32, true);
        } else if (sparkType instanceof LongType) {
            return new ArrowType.Int(64, true);
        } else if (sparkType instanceof DoubleType) {
            return new ArrowType.FloatingPoint(FloatingPointPrecision.DOUBLE);
        } else if (sparkType instanceof StringType) {
            return new ArrowType.Utf8();
        }
        throw new UnsupportedOperationException("Unsupported type: " + sparkType);
    }
    
    private FieldVector convertVector(ColumnVector sparkVector, Field field) {
        FieldVector arrowVector = field.createVector(allocator);
        arrowVector.allocateNew();
        
        int numRows = sparkVector.numRows();
        
        if (sparkVector.dataType() instanceof IntegerType) {
            IntVector intVector = (IntVector) arrowVector;
            for (int i = 0; i < numRows; i++) {
                if (sparkVector.isNullAt(i)) {
                    intVector.setNull(i);
                } else {
                    intVector.set(i, sparkVector.getInt(i));
                }
            }
        }
        // Handle other types similarly...
        
        arrowVector.setValueCount(numRows);
        return arrowVector;
    }
}

public class VectorizedAggregateFunction {
    
    public static long sum(ColumnVector vector) {
        long result = 0;
        int numRows = vector.numRows();
        
        if (vector.dataType() instanceof IntegerType) {
            for (int i = 0; i < numRows; i++) {
                if (!vector.isNullAt(i)) {
                    result += vector.getInt(i);
                }
            }
        } else if (vector.dataType() instanceof LongType) {
            for (int i = 0; i < numRows; i++) {
                if (!vector.isNullAt(i)) {
                    result += vector.getLong(i);
                }
            }
        }
        
        return result;
    }
    
    public static double average(ColumnVector vector) {
        long sum = 0;
        int count = 0;
        int numRows = vector.numRows();
        
        for (int i = 0; i < numRows; i++) {
            if (!vector.isNullAt(i)) {
                if (vector.dataType() instanceof IntegerType) {
                    sum += vector.getInt(i);
                } else if (vector.dataType() instanceof LongType) {
                    sum += vector.getLong(i);
                } else if (vector.dataType() instanceof DoubleType) {
                    sum += vector.getDouble(i);
                }
                count++;
            }
        }
        
        return count > 0 ? (double) sum / count : 0.0;
    }
    
    public static Object min(ColumnVector vector) {
        Object min = null;
        int numRows = vector.numRows();
        
        for (int i = 0; i < numRows; i++) {
            if (!vector.isNullAt(i)) {
                Object value = getValue(vector, i);
                if (min == null || compareValues(value, min, vector.dataType()) < 0) {
                    min = value;
                }
            }
        }
        
        return min;
    }
    
    public static Object max(ColumnVector vector) {
        Object max = null;
        int numRows = vector.numRows();
        
        for (int i = 0; i < numRows; i++) {
            if (!vector.isNullAt(i)) {
                Object value = getValue(vector, i);
                if (max == null || compareValues(value, max, vector.dataType()) > 0) {
                    max = value;
                }
            }
        }
        
        return max;
    }
    
    private static Object getValue(ColumnVector vector, int rowId) {
        DataType type = vector.dataType();
        if (type instanceof IntegerType) {
            return vector.getInt(rowId);
        } else if (type instanceof LongType) {
            return vector.getLong(rowId);
        } else if (type instanceof DoubleType) {
            return vector.getDouble(rowId);
        } else if (type instanceof StringType) {
            return vector.getUTF8String(rowId);
        }
        return null;
    }
    
    @SuppressWarnings("unchecked")
    private static int compareValues(Object v1, Object v2, DataType dataType) {
        if (v1 instanceof Comparable && v2 instanceof Comparable) {
            return ((Comparable<Object>) v1).compareTo(v2);
        }
        return 0;
    }
}

public class VectorizedFilters {
    
    public static boolean[] equalTo(ColumnVector vector, Object value) {
        int numRows = vector.numRows();
        boolean[] result = new boolean[numRows];
        
        for (int i = 0; i < numRows; i++) {
            if (vector.isNullAt(i)) {
                result[i] = false;
            } else {
                Object vectorValue = getValue(vector, i);
                result[i] = Objects.equals(vectorValue, value);
            }
        }
        
        return result;
    }
    
    public static boolean[] greaterThan(ColumnVector vector, Object value) {
        int numRows = vector.numRows();
        boolean[] result = new boolean[numRows];
        
        for (int i = 0; i < numRows; i++) {
            if (vector.isNullAt(i)) {
                result[i] = false;
            } else {
                Object vectorValue = getValue(vector, i);
                result[i] = compareValues(vectorValue, value, vector.dataType()) > 0;
            }
        }
        
        return result;
    }
    
    public static boolean[] and(boolean[] left, boolean[] right) {
        boolean[] result = new boolean[left.length];
        for (int i = 0; i < left.length; i++) {
            result[i] = left[i] && right[i];
        }
        return result;
    }
    
    public static boolean[] or(boolean[] left, boolean[] right) {
        boolean[] result = new boolean[left.length];
        for (int i = 0; i < left.length; i++) {
            result[i] = left[i] || right[i];
        }
        return result;
    }
    
    public static ColumnarBatch filter(ColumnarBatch batch, boolean[] mask) {
        int selectedRows = 0;
        for (boolean selected : mask) {
            if (selected) selectedRows++;
        }
        
        if (selectedRows == 0) {
            return new ColumnarBatch(new ColumnVector[0], 0);
        }
        
        ColumnVector[] filteredColumns = new ColumnVector[batch.numCols()];
        for (int colIdx = 0; colIdx < batch.numCols(); colIdx++) {
            filteredColumns[colIdx] = filterColumn(batch.column(colIdx), mask, selectedRows);
        }
        
        return new ColumnarBatch(filteredColumns, selectedRows);
    }
    
    private static ColumnVector filterColumn(ColumnVector source, boolean[] mask, int resultRows) {
        DataType dataType = source.dataType();
        ColumnVector result = createColumnVector(dataType, resultRows);
        
        int destIdx = 0;
        for (int srcIdx = 0; srcIdx < mask.length; srcIdx++) {
            if (mask[srcIdx]) {
                copyValue(source, srcIdx, result, destIdx);
                destIdx++;
            }
        }
        
        return result;
    }
    
    private static void copyValue(ColumnVector source, int srcIdx, 
                                ColumnVector dest, int destIdx) {
        if (source.isNullAt(srcIdx)) {
            ((MyOnHeapColumnVector) dest).putNull(destIdx);
        } else {
            DataType type = source.dataType();
            if (type instanceof IntegerType) {
                ((MyOnHeapColumnVector) dest).putInt(destIdx, source.getInt(srcIdx));
            } else if (type instanceof LongType) {
                ((MyOnHeapColumnVector) dest).putLong(destIdx, source.getLong(srcIdx));
            } else if (type instanceof DoubleType) {
                ((MyOnHeapColumnVector) dest).putDouble(destIdx, source.getDouble(srcIdx));
            }
            // Handle other types...
        }
    }
}

public class OffHeapColumnVector extends ColumnVector {
    private final DataType dataType;
    private final long capacity;
    private final long dataAddress;
    private final long nullsAddress;
    private int numRows;
    
    public OffHeapColumnVector(DataType dataType, int capacity) {
        this.dataType = dataType;
        this.capacity = capacity;
        
        // Allocate off-heap memory
        int typeSize = getTypeSize(dataType);
        this.dataAddress = PlatformDependent.allocateMemory(capacity * typeSize);
        this.nullsAddress = PlatformDependent.allocateMemory(capacity); // 1 byte per null flag
        
        // Initialize memory
        PlatformDependent.setMemory(dataAddress, capacity * typeSize, (byte) 0);
        PlatformDependent.setMemory(nullsAddress, capacity, (byte) 0);
    }
    
    @Override
    public DataType dataType() {
        return dataType;
    }
    
    @Override
    public boolean isNullAt(int rowId) {
        return PlatformDependent.getByte(nullsAddress + rowId) == 1;
    }
    
    @Override
    public int getInt(int rowId) {
        if (isNullAt(rowId)) return 0;
        return PlatformDependent.getInt(dataAddress + rowId * 4);
    }
    
    @Override
    public long getLong(int rowId) {
        if (isNullAt(rowId)) return 0L;
        return PlatformDependent.getLong(dataAddress + rowId * 8);
    }
    
    public void putInt(int rowId, int value) {
        PlatformDependent.putInt(dataAddress + rowId * 4, value);
        PlatformDependent.putByte(nullsAddress + rowId, (byte) 0);
    }
    
    public void putLong(int rowId, long value) {
        PlatformDependent.putLong(dataAddress + rowId * 8, value);
        PlatformDependent.putByte(nullsAddress + rowId, (byte) 0);
    }
    
    public void putNull(int rowId) {
        PlatformDependent.putByte(nullsAddress + rowId, (byte) 1);
    }
    
    @Override
    public void close() {
        if (dataAddress != 0) {
            PlatformDependent.freeMemory(dataAddress);
        }
        if (nullsAddress != 0) {
            PlatformDependent.freeMemory(nullsAddress);
        }
    }
    
    private int getTypeSize(DataType dataType) {
        if (dataType instanceof IntegerType) return 4;
        if (dataType instanceof LongType) return 8;
        if (dataType instanceof DoubleType) return 8;
        if (dataType instanceof FloatType) return 4;
        return 8; // Default size
    }
}

public class BatchSizeOptimizer {
    private static final int MIN_BATCH_SIZE = 1024;
    private static final int MAX_BATCH_SIZE = 8192;
    private static final long TARGET_BATCH_MEMORY = 64 * 1024 * 1024; // 64MB
    
    public static int calculateOptimalBatchSize(StructType schema) {
        long rowSize = calculateRowSize(schema);
        int calculatedBatchSize = (int) (TARGET_BATCH_MEMORY / rowSize);
        
        // Clamp to reasonable bounds
        return Math.max(MIN_BATCH_SIZE, Math.min(MAX_BATCH_SIZE, calculatedBatchSize));
    }
    
    private static long calculateRowSize(StructType schema) {
        long totalSize = 0;
        for (StructField field : schema.fields()) {
            totalSize += getDataTypeSize(field.dataType());
        }
        return totalSize;
    }
    
    private static long getDataTypeSize(DataType dataType) {
        if (dataType instanceof BooleanType) return 1;
        if (dataType instanceof ByteType) return 1;
        if (dataType instanceof ShortType) return 2;
        if (dataType instanceof IntegerType) return 4;
        if (dataType instanceof LongType) return 8;
        if (dataType instanceof FloatType) return 4;
        if (dataType instanceof DoubleType) return 8;
        if (dataType instanceof StringType) return 20; // Average string size estimate
        if (dataType instanceof BinaryType) return 16; // Average binary size estimate
        return 8; // Default estimate
    }
}

public class VectorizedExpressionEvaluator {
    
    public static ColumnVector evaluateExpression(Expression expr, ColumnarBatch input) {
        if (expr instanceof Literal) {
            return evaluateLiteral((Literal) expr, input.numRows());
        } else if (expr instanceof NamedReference) {
            return evaluateColumnReference((NamedReference) expr, input);
        } else if (expr instanceof BinaryExpression) {
            return evaluateBinaryExpression((BinaryExpression) expr, input);
        }
        
        throw new UnsupportedOperationException("Unsupported expression: " + expr);
    }
    
    private static ColumnVector evaluateLiteral(Literal literal, int numRows) {
        Object value = literal.value();
        DataType dataType = inferDataType(value);
        ColumnVector result = createColumnVector(dataType, numRows);
        
        // Fill all rows with the literal value
        for (int i = 0; i < numRows; i++) {
            setColumnValue(result, i, value);
        }
        
        return result;
    }
    
    private static ColumnVector evaluateColumnReference(NamedReference ref, ColumnarBatch input) {
        String[] fieldNames = ref.fieldNames();
        String columnName = fieldNames[0]; // Simplified - assume single-level reference
        
        // Find column index by name
        // This would require schema information in a real implementation
        int columnIndex = findColumnIndex(columnName, input);
        return input.column(columnIndex);
    }
    
    private static ColumnVector evaluateBinaryExpression(BinaryExpression expr, ColumnarBatch input) {
        ColumnVector left = evaluateExpression(expr.left(), input);
        ColumnVector right = evaluateExpression(expr.right(), input);
        
        if (expr instanceof Add) {
            return vectorizedAdd(left, right);
        } else if (expr instanceof Subtract) {
            return vectorizedSubtract(left, right);
        } else if (expr instanceof Multiply) {
            return vectorizedMultiply(left, right);
        }
        
        throw new UnsupportedOperationException("Unsupported binary expression: " + expr);
    }
    
    private static ColumnVector vectorizedAdd(ColumnVector left, ColumnVector right) {
        int numRows = Math.min(left.numRows(), right.numRows());
        DataType resultType = promoteTypes(left.dataType(), right.dataType());
        ColumnVector result = createColumnVector(resultType, numRows);
        
        for (int i = 0; i < numRows; i++) {
            if (left.isNullAt(i) || right.isNullAt(i)) {
                ((MyOnHeapColumnVector) result).putNull(i);
            } else {
                if (resultType instanceof IntegerType) {
                    int sum = left.getInt(i) + right.getInt(i);
                    ((MyOnHeapColumnVector) result).putInt(i, sum);
                } else if (resultType instanceof LongType) {
                    long sum = left.getLong(i) + right.getLong(i);
                    ((MyOnHeapColumnVector) result).putLong(i, sum);
                } else if (resultType instanceof DoubleType) {
                    double sum = left.getDouble(i) + right.getDouble(i);
                    ((MyOnHeapColumnVector) result).putDouble(i, sum);
                }
            }
        }
        
        return result;
    }
}

public class VectorizedDataSource implements Table, SupportsRead {
    
    @Override
    public ScanBuilder newScanBuilder(CaseInsensitiveStringMap options) {
        return new VectorizedScanBuilder(schema, options);
    }
    
    @Override
    public Set<TableCapability> capabilities() {
        return Set.of(
            TableCapability.BATCH_READ,
            TableCapability.ACCEPT_ANY_SCHEMA
        );
    }
}

public class VectorizedScan implements Scan, SupportsReportStatistics {
    
    @Override
    public Batch toBatch() {
        return new VectorizedBatch(schema, paths);
    }
    
    @Override
    public Statistics estimateStatistics() {
        // Provide statistics for query optimization
        return new Statistics() {
            @Override
            public OptionalLong sizeInBytes() {
                return OptionalLong.of(calculateDataSize());
            }
            
            @Override
            public OptionalLong numRows() {
                return OptionalLong.of(estimateRowCount());
            }
        };
    }
}