Data Processing and SQL Integration

/**
 * Iterator for scanning data from CDAP datasets in Spark applications
 * Provides efficient, type-safe access to dataset records
 * @tparam T Type of data items being scanned
 */
class DatumScannerIterator[T](scanner: Scanner[T]) extends Iterator[T] with Closeable {
    /**
     * Checks if there are more items to scan
     * @return true if more items are available
     */
    def hasNext: Boolean
    
    /**
     * Gets the next item from the scanner
     * @return Next data item of type T
     * @throws NoSuchElementException if no more items available
     */
    def next(): T
    
    /**
     * Closes the underlying scanner and releases resources
     */
    def close(): Unit
}

/**
 * Serializable wrapper for stream events in Spark processing
 * Enables efficient distribution of stream data across Spark executors
 */
public class SerializableStreamEvent implements Serializable {
    /**
     * Gets the underlying stream event
     * @return StreamEvent containing the actual event data
     */
    public StreamEvent getStreamEvent();
    
    /**
     * Gets the timestamp of the stream event
     * @return Event timestamp in milliseconds since epoch
     */
    public long getTimestamp();
    
    /**
     * Gets the event headers
     * @return Map of header key-value pairs
     */
    public Map<String, String> getHeaders();
    
    /**
     * Gets the event body
     * @return ByteBuffer containing the event body data
     */
    public ByteBuffer getBody();
    
    /**
     * Gets the event body as a byte array
     * @return Byte array containing the event body
     */
    public byte[] getBodyBytes();
    
    /**
     * Gets the event body as a UTF-8 string
     * @return String representation of the event body
     */
    public String getBodyAsString();
}

/**
 * Spark SQL data source provider for CDAP datasets
 * Enables SQL queries and DataFrame operations on CDAP datasets
 */
object DatasetRelationProvider extends RelationProvider with SchemaRelationProvider {
    /**
     * Gets the short name for this data source
     * @return "dataset" as the data source identifier
     */
    def shortName(): String
    
    /**
     * Creates a relation for the specified dataset
     * @param sqlContext Spark SQL context
     * @param parameters Data source parameters including dataset name and namespace
     * @return BaseRelation for querying the dataset
     */
    def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation
    
    /**
     * Creates a relation with a specified schema
     * @param sqlContext Spark SQL context
     * @param parameters Data source parameters
     * @param schema Expected schema for the dataset
     * @return BaseRelation with the specified schema
     */
    def createRelation(sqlContext: SQLContext, 
                      parameters: Map[String, String], 
                      schema: StructType): BaseRelation
}

/**
 * Spark SQL data source provider for CDAP streams
 * Enables SQL queries and DataFrame operations on CDAP streams
 */
object StreamRelationProvider extends RelationProvider with SchemaRelationProvider {
    /**
     * Gets the short name for this data source
     * @return "stream" as the data source identifier
     */
    def shortName(): String
    
    /**
     * Creates a relation for the specified stream
     * @param sqlContext Spark SQL context
     * @param parameters Data source parameters including stream name and time range
     * @return BaseRelation for querying the stream
     */
    def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation
    
    /**
     * Creates a relation with a specified schema
     * @param sqlContext Spark SQL context
     * @param parameters Data source parameters
     * @param schema Expected schema for the stream data
     * @return BaseRelation with the specified schema
     */
    def createRelation(sqlContext: SQLContext, 
                      parameters: Map[String, String], 
                      schema: StructType): BaseRelation
}

/**
 * Stream-based relation for Spark SQL queries
 * Provides efficient access to CDAP stream data through DataFrame operations
 */
class StreamRelation(parameters: Map[String, String], 
                    userSchema: Option[StructType])
                   (implicit sqlContext: SQLContext) extends BaseRelation with TableScan with PrunedScan {
    
    /**
     * Gets the schema for this relation
     * @return StructType defining the stream data schema
     */
    def schema: StructType
    
    /**
     * Builds a scan over the entire stream
     * @return RDD[Row] containing all stream events
     */
    def buildScan(): RDD[Row]
    
    /**
     * Builds a scan with column pruning
     * @param requiredColumns Array of column names to include in the scan
     * @return RDD[Row] containing only the required columns
     */
    def buildScan(requiredColumns: Array[String]): RDD[Row]
    
    /**
     * Gets the SQL context
     * @return SQLContext used for this relation
     */
    def sqlContext: SQLContext
}

import co.cask.cdap.app.runtime.spark.data.DatumScannerIterator
import co.cask.cdap.api.dataset.Dataset

// Create a scanner for a dataset
val dataset: Dataset = // ... obtain dataset instance
val scanner = dataset.scan()
val iterator = new DatumScannerIterator(scanner)

// Use iterator to process data
try {
    while (iterator.hasNext) {
        val record = iterator.next()
        // Process record
        println(s"Processing record: $record")
    }
} finally {
    iterator.close()
}

// Use with Spark RDD
val rdd = sparkContext.parallelize(Seq(iterator))
val processedRDD = rdd.flatMap(_.toSeq)

import co.cask.cdap.app.runtime.spark.SerializableStreamEvent;
import java.util.Map;

// Process serializable stream events in Spark
JavaRDD<SerializableStreamEvent> streamRDD = // ... obtain from stream source
JavaRDD<String> processedRDD = streamRDD.map(event -> {
    // Access event metadata
    long timestamp = event.getTimestamp();
    Map<String, String> headers = event.getHeaders();
    
    // Process event body
    String body = event.getBodyAsString();
    return "Processed at " + timestamp + ": " + body;
});

import org.apache.spark.sql.SQLContext

// Create SQL context
val sqlContext = new SQLContext(sparkContext)

// Read from CDAP dataset using SQL
val datasetDF = sqlContext.read
    .format("dataset")
    .option("dataset.name", "my-dataset")
    .option("dataset.namespace", "default")
    .load()

// Query the dataset
datasetDF.createOrReplaceTempView("my_table")
val results = sqlContext.sql("SELECT * FROM my_table WHERE age > 21")
results.show()

// Use DataFrame API
val filteredDF = datasetDF.filter($"age" > 21).select($"name", $"age")
filteredDF.collect().foreach(println)

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types._

// Define stream schema
val streamSchema = StructType(Seq(
    StructField("timestamp", LongType, nullable = false),
    StructField("headers", MapType(StringType, StringType), nullable = true),
    StructField("body", StringType, nullable = true)
))

// Read from CDAP stream
val streamDF = sqlContext.read
    .format("stream")
    .option("stream.name", "my-stream")
    .option("stream.namespace", "default")
    .option("stream.start.time", "2023-01-01T00:00:00Z")
    .option("stream.end.time", "2023-12-31T23:59:59Z")
    .schema(streamSchema)
    .load()

// Query stream data
streamDF.createOrReplaceTempView("stream_events")
val recentEvents = sqlContext.sql(
    "SELECT * FROM stream_events WHERE timestamp > unix_timestamp() - 3600"
)

/**
 * Interface for dataset scanners
 * @tparam T Type of data items being scanned
 */
trait Scanner[T] extends Closeable {
    /**
     * Checks if there are more items to scan
     * @return true if more items are available
     */
    def hasNext: Boolean
    
    /**
     * Gets the next item from the scanner
     * @return Next data item of type T
     */
    def next(): T
    
    /**
     * Closes the scanner and releases resources
     */
    def close(): Unit
}

/**
 * Base relation interface for Spark SQL data sources
 */
trait BaseRelation {
    /**
     * Gets the SQL context
     * @return SQLContext for this relation
     */
    def sqlContext: SQLContext
    
    /**
     * Gets the schema for this relation
     * @return StructType defining the data schema
     */
    def schema: StructType
}

/**
 * Interface for relations that support full table scans
 */
trait TableScan {
    /**
     * Builds a scan over the entire relation
     * @return RDD[Row] containing all data
     */
    def buildScan(): RDD[Row]
}

/**
 * Interface for relations that support column pruning
 */
trait PrunedScan {
    /**
     * Builds a scan with column pruning
     * @param requiredColumns Array of column names to include
     * @return RDD[Row] containing only the required columns
     */
    def buildScan(requiredColumns: Array[String]): RDD[Row]
}

/**
 * Interface for CDAP stream events
 */
public interface StreamEvent {
    /**
     * Gets the event timestamp
     * @return Timestamp in milliseconds since epoch
     */
    long getTimestamp();
    
    /**
     * Gets the event headers
     * @return Map of header key-value pairs
     */
    Map<String, String> getHeaders();
    
    /**
     * Gets the event body
     * @return ByteBuffer containing the event data
     */
    ByteBuffer getBody();
}

/**
 * Data source parameters for dataset access
 */
public class DatasetParameters {
    public static final String DATASET_NAME = "dataset.name";
    public static final String DATASET_NAMESPACE = "dataset.namespace";
    public static final String DATASET_ARGUMENTS = "dataset.arguments";
}

/**
 * Data source parameters for stream access
 */
public class StreamParameters {
    public static final String STREAM_NAME = "stream.name";
    public static final String STREAM_NAMESPACE = "stream.namespace";
    public static final String STREAM_START_TIME = "stream.start.time";
    public static final String STREAM_END_TIME = "stream.end.time";
    public static final String STREAM_BATCH_SIZE = "stream.batch.size";
}