File Format Support

import org.apache.spark.sql.execution.datasources.FileFormat
import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat
import org.apache.spark.sql.hive.orc.OrcFileOperator
import org.apache.spark.sql.hive.execution.HiveFileFormat
import org.apache.spark.sql.sources.DataSourceRegister

class OrcFileFormat extends FileFormat with DataSourceRegister {
  
  /**
   * Infer schema from ORC files
   * @param sparkSession Active Spark session
   * @param options Format-specific options
   * @param files Sequence of file statuses to analyze
   * @returns Inferred schema or None if cannot infer
   */
  def inferSchema(
    sparkSession: SparkSession,
    options: Map[String, String],
    files: Seq[FileStatus]
  ): Option[StructType]
  
  /**
   * Prepare write operations for ORC format
   * @param sparkSession Active Spark session
   * @param job Hadoop job configuration
   * @param options Write options and settings
   * @param dataSchema Schema of data to write
   * @returns OutputWriterFactory for creating writers
   */
  def prepareWrite(
    sparkSession: SparkSession,
    job: Job,
    options: Map[String, String],
    dataSchema: StructType
  ): OutputWriterFactory
  
  /**
   * Build reader for scanning ORC files
   * @param sparkSession Active Spark session
   * @param dataSchema Schema of data in files
   * @param partitionSchema Schema of partition columns
   * @param requiredSchema Schema of required columns
   * @param filters Push-down filters for optimization
   * @param options Read options and settings
   * @param hadoopConf Hadoop configuration
   * @returns Function to create PartitionedFile readers
   */
  def buildReader(
    sparkSession: SparkSession,
    dataSchema: StructType,
    partitionSchema: StructType,
    requiredSchema: StructType,
    filters: Seq[Filter],
    options: Map[String, String],
    hadoopConf: Configuration
  ): PartitionedFile => Iterator[InternalRow]
  
  /** Short name for this data source format */
  def shortName(): String = "orc"
}

object OrcFileOperator extends Logging {
  
  /**
   * Read schema from ORC file footer
   * @param file Path to ORC file
   * @param conf Hadoop configuration
   * @param ignoreCorruptFiles Whether to ignore corrupt files
   * @returns Tuple of (schema, user metadata)
   */
  def readSchema(
    file: Path,
    conf: Configuration,
    ignoreCorruptFiles: Boolean
  ): Option[(StructType, Map[String, String])]
  
  /**
   * Read ORC file metadata including statistics
   * @param files Sequence of ORC files to read
   * @param conf Hadoop configuration
   * @returns Aggregated file metadata
   */
  def readFileMetadata(
    files: Seq[Path],
    conf: Configuration
  ): Map[String, String]
}

private[hive] class HiveFileFormat(fileSinkConf: FileSinkDesc) extends FileFormat {
  
  /**
   * Prepare write using Hive OutputFormat
   * @param sparkSession Active Spark session
   * @param job Hadoop job for write operation
   * @param options Write configuration options
   * @param dataSchema Schema of data to be written
   * @returns OutputWriterFactory using Hive serialization
   */
  def prepareWrite(
    sparkSession: SparkSession,
    job: Job,
    options: Map[String, String],
    dataSchema: StructType
  ): OutputWriterFactory
  
  /**
   * Build reader using Hive InputFormat and SerDe
   * @param sparkSession Active Spark session
   * @param dataSchema Full schema of data files
   * @param partitionSchema Schema of partition columns
   * @param requiredSchema Schema of columns to read
   * @param filters Filters to push down to storage
   * @param options Read configuration options
   * @param hadoopConf Hadoop configuration
   * @returns Function to read PartitionedFile
   */
  def buildReader(
    sparkSession: SparkSession,
    dataSchema: StructType,
    partitionSchema: StructType,
    requiredSchema: StructType,
    filters: Seq[Filter],
    options: Map[String, String],
    hadoopConf: Configuration
  ): PartitionedFile => Iterator[InternalRow]
}

// Note: These are Java classes with Scala signatures for documentation

/**
 * Custom ORC record reader optimized for Spark
 */
class SparkOrcNewRecordReader extends RecordReader[NullWritable, VectorizedRowBatch] {
  
  /**
   * Initialize the record reader
   * @param inputSplit Input split to read
   * @param context Task attempt context
   */
  def initialize(inputSplit: InputSplit, context: TaskAttemptContext): Unit
  
  /**
   * Read next batch of records
   * @returns true if more records available
   */
  def nextKeyValue(): Boolean
  
  /**
   * Get current key (always null for ORC)
   * @returns NullWritable key
   */
  def getCurrentKey(): NullWritable
  
  /**
   * Get current batch of records
   * @returns VectorizedRowBatch containing records
   */
  def getCurrentValue(): VectorizedRowBatch
  
  /**
   * Get reading progress as percentage
   * @returns Progress between 0.0 and 1.0
   */
  def getProgress(): Float
  
  /** Close the record reader */
  def close(): Unit
}

/**
 * Input format for handling symlinked text files
 */
class DelegateSymlinkTextInputFormat extends TextInputFormat {
  
  /**
   * Get input splits for symlinked files
   * @param job Job configuration
   * @returns Array of input splits
   */
  def getSplits(job: JobContext): java.util.List[InputSplit]
}

import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .enableHiveSupport()
  .getOrCreate()

// Read ORC files directly
val orcDF = spark.read
  .format("orc")
  .option("mergeSchema", "true")
  .load("/path/to/orc/files")

orcDF.printSchema()
orcDF.show()

// Read Hive ORC table
val hiveOrcTable = spark.sql("SELECT * FROM hive_orc_table")
hiveOrcTable.explain(true)

import org.apache.spark.sql.SaveMode

// Create sample data
val data = Seq(
  (1, "Alice", 25),
  (2, "Bob", 30),
  (3, "Charlie", 35)
).toDF("id", "name", "age")

// Write as ORC with Hive compatibility
data.write
  .mode(SaveMode.Overwrite)
  .option("compression", "snappy")
  .format("orc")
  .save("/path/to/output/orc")

// Write to Hive table using ORC format
data.write
  .mode(SaveMode.Overwrite)
  .saveAsTable("my_database.orc_table")

// Enable schema merging for ORC files
val mergedDF = spark.read
  .format("orc")
  .option("mergeSchema", "true")
  .load("/path/to/orc/files/*")

// Handle schema evolution gracefully
val evolvedDF = spark.read
  .format("orc")
  .option("recursiveFileLookup", "true")
  .load("/path/to/evolved/schema/files")

// Check for schema differences
mergedDF.printSchema()
evolvedDF.printSchema()

// Read options
val orcOptions = Map(
  "mergeSchema" -> "true",           // Merge schemas from multiple files
  "recursiveFileLookup" -> "true",   // Recursively look for files
  "ignoreCorruptFiles" -> "false",   // Fail on corrupt files
  "compression" -> "snappy"          // Compression codec
)

// Write options  
val writeOptions = Map(
  "compression" -> "zlib",           // zlib, snappy, lzo, lz4, none
  "orc.compress" -> "SNAPPY",        // ORC compression
  "orc.stripe.size" -> "67108864",   // 64MB stripe size
  "orc.block.size" -> "268435456"    // 256MB block size
)

// Configure ORC conversion from Hive metastore
spark.conf.set("spark.sql.hive.convertMetastoreOrc", "true")
spark.conf.set("spark.sql.orc.impl", "native")
spark.conf.set("spark.sql.orc.enableVectorizedReader", "true")

import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat

try {
  val df = spark.read.format("orc").load("/path/to/corrupt/files")
  df.count()
} catch {
  case e: java.io.IOException if e.getMessage.contains("Malformed ORC file") =>
    println("ORC file is corrupted")
  case e: AnalysisException if e.getMessage.contains("Unable to infer schema") =>
    println("Cannot determine schema from ORC files")
}

case class OrcOptions(
  parameters: Map[String, String]
) {
  def mergeSchema: Boolean
  def ignoreCorruptFiles: Boolean  
  def recursiveFileLookup: Boolean
  def compression: String
}

```scala { .api }
trait FileFormat {
  def inferSchema(
    sparkSession: SparkSession,
    options: Map[String, String], 
    files: Seq[FileStatus]
  ): Option[StructType]
  
  def prepareWrite(
    sparkSession: SparkSession,
    job: Job,
    options: Map[String, String],
    dataSchema: StructType
  ): OutputWriterFactory
}