Utilities and Schema Conversion

/**
 * Converts between Flink and Parquet schemas
 */
public class ParquetSchemaConverter {
    
    // Schema naming constants
    static final String MAP_REPEATED_NAME = "key_value";
    static final String LIST_ELEMENT_NAME = "element";
    
    /**
     * Converts Flink RowType to Parquet MessageType schema
     * @param name Schema name for the MessageType
     * @param rowType Flink RowType to convert
     * @param conf Hadoop configuration for conversion settings
     * @return MessageType representing the Parquet schema
     */
    public static MessageType convertToParquetMessageType(
        String name, 
        RowType rowType, 
        Configuration conf
    );
    
    /**
     * Converts individual Flink LogicalType to Parquet Type
     * @param name Field name for the Type
     * @param logicalType Flink LogicalType to convert
     * @param conf Hadoop configuration for conversion settings
     * @return Parquet Type representation
     */
    public static Type convertToParquetType(
        String name, 
        LogicalType logicalType, 
        Configuration conf
    );
    
    /**
     * Computes minimum bytes required for decimal precision
     * @param precision Decimal precision (number of digits)
     * @return Minimum bytes needed to store the precision
     */
    public static int computeMinBytesForDecimalPrecision(int precision);
    
    /**
     * Checks if decimal precision fits in 32 bits (4 bytes)
     * @param precision Decimal precision to check
     * @return true if precision fits in 32 bits
     */
    public static boolean is32BitDecimal(int precision);
    
    /**
     * Checks if decimal precision fits in 64 bits (8 bytes)
     * @param precision Decimal precision to check
     * @return true if precision fits in 64 bits
     */
    public static boolean is64BitDecimal(int precision);
}

/**
 * Serializable wrapper for Hadoop Configuration
 */
public class SerializableConfiguration implements Serializable {
    
    /**
     * Creates a new SerializableConfiguration wrapping the provided Configuration
     * @param configuration Hadoop Configuration to wrap
     */
    public SerializableConfiguration(Configuration configuration);
    
    /**
     * Returns the wrapped Hadoop Configuration
     * @return Hadoop Configuration instance
     */
    public Configuration conf();
}

/**
 * Utilities for extracting and reporting Parquet file statistics
 */
public class ParquetFormatStatisticsReportUtil {
    
    /**
     * Extracts table statistics from Parquet file metadata
     * @param files List of Parquet files to analyze
     * @param producedDataType Expected output data type
     * @param conf Hadoop configuration
     * @param utcTimestamp Whether timestamps use UTC timezone
     * @return TableStats containing row counts and column statistics
     */
    public static TableStats getTableStatistics(
        List<Path> files, 
        DataType producedDataType, 
        Configuration conf, 
        boolean utcTimestamp
    );
    
    /**
     * Additional utility methods for statistics extraction
     */
    // ... other static methods for detailed statistics processing
}

/**
 * InputFile implementation for Parquet using Flink file system abstraction
 */
public class ParquetInputFile implements InputFile {
    
    /**
     * Creates a new ParquetInputFile
     * @param inputStream FSDataInputStream to read from
     * @param length Total length of the file
     */
    public ParquetInputFile(FSDataInputStream inputStream, long length);
    
    /**
     * Returns the length of the file
     * @return File length in bytes
     */
    public long getLength();
    
    /**
     * Creates a new SeekableInputStream for reading
     * @return SeekableInputStream for reading file data
     * @throws IOException if stream creation fails
     */
    public SeekableInputStream newStream() throws IOException;
}

/**
 * Utilities for handling nested data positions in vectorized reading
 */
public class NestedPositionUtil {
    
    /**
     * Calculates positions for nested array elements
     * @param definitionLevels Definition levels for null handling
     * @param repetitionLevels Repetition levels for nested structures
     * @param maxDefinitionLevel Maximum definition level
     * @param maxRepetitionLevel Maximum repetition level
     * @return Position information for nested elements
     */
    public static PositionInfo calculateNestedPositions(
        int[] definitionLevels,
        int[] repetitionLevels,
        int maxDefinitionLevel,
        int maxRepetitionLevel
    );
    
    /**
     * Additional utility methods for nested position calculations
     */
    // ... other static methods for position handling
}

/**
 * Utility for collecting statistics from Parquet files
 */
public class ParquetFormatStatisticsReportUtil {
    
    /**
     * Generates table statistics from list of Parquet files
     * @param files List of file paths to analyze
     * @param producedDataType Data type for the produced table
     * @param hadoopConf Hadoop configuration
     * @param isUtcTimestamp Whether to use UTC timezone for timestamps
     * @return TableStats containing collected statistics
     * @throws IOException if statistics collection fails
     */
    public static TableStats getTableStatistics(
        List<Path> files, 
        DataType producedDataType, 
        Configuration hadoopConf, 
        boolean isUtcTimestamp
    ) throws IOException;
}

/**
 * Position tracking for row data in vectorized reading
 */
public class RowPosition {
    
    /**
     * Creates a new RowPosition
     * @param currentPosition Current position in the row
     */
    public RowPosition(int currentPosition);
    
    /**
     * Updates the current position
     * @param newPosition New position value
     */
    public void updatePosition(int newPosition);
    
    /**
     * Gets the current position
     * @return Current position value
     */
    public int getCurrentPosition();
}

/**
 * Position tracking for collection data (arrays, maps) in vectorized reading
 */
public class CollectionPosition {
    
    /**
     * Creates a new CollectionPosition
     * @param startPosition Start position of the collection
     * @param length Length of the collection
     */
    public CollectionPosition(int startPosition, int length);
    
    /**
     * Gets the start position of the collection
     * @return Start position
     */
    public int getStartPosition();
    
    /**
     * Gets the length of the collection
     * @return Collection length
     */
    public int getLength();
    
    /**
     * Checks if the collection is empty
     * @return true if collection is empty
     */
    public boolean isEmpty();
}

/**
 * Base class for Parquet field representations
 */
public abstract class ParquetField {
    
    /**
     * Gets the field name
     * @return Field name
     */
    public abstract String getName();
    
    /**
     * Gets the field type
     * @return Parquet Type for this field
     */
    public abstract Type getType();
    
    /**
     * Checks if this field is repeated (array)
     * @return true if field is repeated
     */
    public abstract boolean isRepeated();
}

/**
 * Primitive field implementation
 */
public class ParquetPrimitiveField extends ParquetField {
    
    /**
     * Creates a new ParquetPrimitiveField
     * @param name Field name
     * @param primitiveType Parquet primitive type
     * @param repetition Repetition type (required, optional, repeated)
     */
    public ParquetPrimitiveField(String name, PrimitiveType primitiveType, Type.Repetition repetition);
    
    /**
     * Gets the primitive type
     * @return PrimitiveType for this field
     */
    public PrimitiveType getPrimitiveType();
}

/**
 * Group field implementation for nested structures
 */
public class ParquetGroupField extends ParquetField {
    
    /**
     * Creates a new ParquetGroupField
     * @param name Field name
     * @param groupType Parquet group type
     * @param children Child fields in the group
     */
    public ParquetGroupField(String name, GroupType groupType, List<ParquetField> children);
    
    /**
     * Gets the child fields
     * @return List of child ParquetField instances
     */
    public List<ParquetField> getChildren();
    
    /**
     * Gets a child field by name
     * @param name Child field name
     * @return ParquetField instance or null if not found
     */
    public ParquetField getChild(String name);
}

import org.apache.flink.formats.parquet.utils.ParquetSchemaConverter;
import org.apache.flink.table.types.logical.RowType;
import org.apache.parquet.schema.MessageType;

// Define Flink schema
RowType flinkSchema = RowType.of(
    new LogicalType[] {
        DataTypes.BIGINT().getLogicalType(),
        DataTypes.STRING().getLogicalType(),
        DataTypes.DECIMAL(10, 2).getLogicalType(),
        DataTypes.TIMESTAMP(3).getLogicalType(),
        ArrayType.newBuilder()
            .elementType(DataTypes.STRING().getLogicalType())
            .build()
    },
    new String[] {"id", "name", "price", "created_at", "tags"}
);

// Convert to Parquet schema
Configuration conf = new Configuration();
MessageType parquetSchema = ParquetSchemaConverter.convertToParquetMessageType(
    "MyRecord", 
    flinkSchema, 
    conf
);

System.out.println(parquetSchema);
// Output: Parquet schema with proper type mappings

import org.apache.flink.formats.parquet.utils.SerializableConfiguration;

// Create Hadoop configuration with Parquet settings
Configuration hadoopConf = new Configuration();
hadoopConf.set("parquet.compression", "SNAPPY");
hadoopConf.set("parquet.page.size", "1048576");
hadoopConf.set("parquet.block.size", "134217728");
hadoopConf.setBoolean("parquet.enable.dictionary", true);

// Wrap for serialization in Flink jobs
SerializableConfiguration serializableConf = new SerializableConfiguration(hadoopConf);

// Use in distributed operations
DataStream<MyData> processedStream = dataStream
    .map(new RichMapFunction<MyData, ProcessedData>() {
        private transient Configuration conf;
        
        @Override
        public void open(Configuration parameters) {
            // Access configuration in distributed context
            this.conf = serializableConf.conf();
        }
        
        @Override
        public ProcessedData map(MyData value) throws Exception {
            // Use configuration for processing
            String compression = conf.get("parquet.compression");
            return processWithCompression(value, compression);
        }
    });

import org.apache.flink.formats.parquet.utils.ParquetFormatStatisticsReportUtil;
import org.apache.flink.table.plan.stats.TableStats;

// Extract statistics from Parquet files
List<Path> parquetFiles = Arrays.asList(
    new Path("/data/part-00000.parquet"),
    new Path("/data/part-00001.parquet"),
    new Path("/data/part-00002.parquet")
);

DataType outputType = DataTypes.ROW(
    DataTypes.FIELD("id", DataTypes.BIGINT()),
    DataTypes.FIELD("name", DataTypes.STRING()),
    DataTypes.FIELD("amount", DataTypes.DECIMAL(10, 2))
);

TableStats stats = ParquetFormatStatisticsReportUtil.getTableStatistics(
    parquetFiles,
    outputType,
    hadoopConf,
    true  // UTC timestamps
);

System.out.println("Row count: " + stats.getRowCount());
System.out.println("Column stats: " + stats.getColumnStats());

import org.apache.flink.formats.parquet.ParquetInputFile;
import org.apache.flink.core.fs.FileSystem;
import org.apache.flink.core.fs.Path;

// Create custom input file for Parquet library
Path filePath = new Path("hdfs://cluster/data/file.parquet");
FileSystem fileSystem = filePath.getFileSystem();

try (FSDataInputStream inputStream = fileSystem.open(filePath)) {
    long fileLength = fileSystem.getFileStatus(filePath).getLen();
    
    // Create ParquetInputFile for use with Parquet library
    ParquetInputFile inputFile = new ParquetInputFile(inputStream, fileLength);
    
    // Use with Parquet readers
    ParquetFileReader reader = ParquetFileReader.open(inputFile);
    ParquetMetadata metadata = reader.getFooter();
    
    System.out.println("Schema: " + metadata.getFileMetaData().getSchema());
    System.out.println("Row groups: " + metadata.getBlocks().size());
}

import org.apache.flink.formats.parquet.utils.ParquetSchemaConverter;

// Check decimal precision requirements
int[] precisions = {5, 10, 15, 20, 25, 30, 35};

for (int precision : precisions) {
    int minBytes = ParquetSchemaConverter.computeMinBytesForDecimalPrecision(precision);
    boolean is32Bit = ParquetSchemaConverter.is32BitDecimal(precision);
    boolean is64Bit = ParquetSchemaConverter.is64BitDecimal(precision);
    
    System.out.printf("Precision %d: %d bytes, 32-bit: %b, 64-bit: %b%n", 
                     precision, minBytes, is32Bit, is64Bit);
}

// Output:
// Precision 5: 3 bytes, 32-bit: true, 64-bit: true
// Precision 10: 5 bytes, 32-bit: false, 64-bit: true
// Precision 15: 7 bytes, 32-bit: false, 64-bit: true
// Precision 20: 9 bytes, 32-bit: false, 64-bit: false

import org.apache.flink.formats.parquet.utils.NestedPositionUtil;

// Handle nested array positions
int[] definitionLevels = {3, 3, 2, 3, 3, 3, 1, 3, 3};
int[] repetitionLevels = {0, 1, 0, 0, 1, 1, 0, 0, 1};

PositionInfo positions = NestedPositionUtil.calculateNestedPositions(
    definitionLevels,
    repetitionLevels,
    3,  // max definition level
    1   // max repetition level
);

// Use positions for vectorized nested data processing
processNestedData(positions);

import org.apache.flink.formats.parquet.utils.*;

public class CustomParquetFormat implements BulkFormat<CustomRecord, FileSourceSplit> {
    
    private final SerializableConfiguration hadoopConf;
    private final RowType schema;
    
    public CustomParquetFormat(Configuration conf, RowType schema) {
        this.hadoopConf = new SerializableConfiguration(conf);
        this.schema = schema;
    }
    
    @Override
    public Reader<CustomRecord> createReader(Configuration config, FileSourceSplit split) {
        // Convert schema
        MessageType parquetSchema = ParquetSchemaConverter.convertToParquetMessageType(
            "CustomRecord", 
            schema, 
            hadoopConf.conf()
        );
        
        // Create input file
        ParquetInputFile inputFile = new ParquetInputFile(
            openInputStream(split.path()), 
            split.length()
        );
        
        return new CustomParquetReader(inputFile, parquetSchema);
    }
    
    @Override
    public Reader<CustomRecord> restoreReader(Configuration config, FileSourceSplit split) {
        return createReader(config, split);
    }
    
    @Override
    public boolean isSplittable() {
        return true;
    }
}