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/**
 * SparkMLLibSparkSink composes a final, optional stage of a Batch ETL Pipeline. In addition to configuring the Batch run, it
 * can also perform RDD operations on the key value pairs provided by the Batch run.
 *
 * {@link SparkMLLib#runSparkSink#run} method is called inside the Batch Run while {@link SparkMLLib#prepareRunSparkSink#prepareRun} and
 * {@link SparkMLLib#onRunFinishSparkSink#onRunFinish} methods are called on the client side, which launches the Batch run, before the
 * Batch run starts and after it finishes respectively.
 *
 * @param <IN> The type of input record to the SparkMLLibSparkSink.
 */
@Beta
public abstract class SparkMLLib<IN>SparkSink<IN> extends BatchConfigurable<SparkMLLibContext>BatchConfigurable<SparkPluginContext> implements Serializable {

  public static final String PLUGIN_TYPE = "sparkmllibsparksink";

  private static final long serialVersionUID = -8600555200583639593L;

  /**
   * User Spark job which will be executed
   *
   * @param context {@link SparkMLLibContextSparkPluginContext} for this job
   * @param input the input from previous stages of the Batch run.
   */
  public abstract void run(SparkMLLibContextSparkPluginContext context, JavaRDD<IN> input) throws Exception;

}

/**
 * Context passed to spark SparkMLLibplugins.
 */
public interface SparkMLLibContextSparkPluginContext extends BatchContext {

  /**
   * Returns the logical start time of this Spark job. Logical start time is the time when this Spark
   * job is supposed to start if this job is started by the scheduler. Otherwise it would be the current time when the
   * job runs.
   *
   * @return Time in milliseconds since epoch time (00:00:00 January 1, 1970 UTC).
   */
  long getLogicalStartTime();

  /**
   * Create a Spark RDD that uses {@link Dataset} as input source
   *
   * @param datasetName the name of the {@link Dataset} to be read as an RDD
   * @param kClass      the key class
   * @param vClass      the value class
   * @param <T>         type of RDD
   * @return the RDD created from Dataset
   * @throws UnsupportedOperationException if the SparkContext is not yet initialized
   */
  <T> T readFromDataset(String datasetName, Class<?> kClass, Class<?> vClass);

  /**
   * Create a Spark RDD that uses {@link Dataset} instantiated using the provided arguments as an input source.
   *
   * @param datasetName the name of the {@link Dataset} to be read as an RDD
   * @param kClass      the key class
   * @param vClass      the value class
   * @param datasetArgs arguments for the dataset
   * @param <T>         type of RDD
   * @return the RDD created from Dataset
   * @throws UnsupportedOperationException if the SparkContext is not yet initialized
   */
  <T> T readFromDataset(String datasetName, Class<?> kClass, Class<?> vClass, Map<String, String> datasetArgs);

  /**
   * Writes a Spark RDD to {@link Dataset}
   *
   * @param rdd         the rdd to be stored
   * @param datasetName the name of the {@link Dataset} where the RDD should be stored
   * @param kClass      the key class
   * @param vClass      the value class
   * @param <T>         type of RDD
   * @throws UnsupportedOperationException if the SparkContext is not yet initialized
   */
  <T> void writeToDataset(T rdd, String datasetName, Class<?> kClass, Class<?> vClass);

  /**
   * Writes a Spark RDD to {@link Dataset} instantiated using the provided arguments.
   *
   * @param rdd         the rdd to be stored
   * @param datasetName the name of the {@link Dataset} where the RDD should be stored
   * @param kClass      the key class
   * @param vClass      the value class
   * @param datasetArgs arguments for the dataset
   * @param <T>         type of RDD
   * @throws UnsupportedOperationException if the SparkContext is not yet initialized
   */
  <T> void writeToDataset(T rdd, String datasetName, Class<?> kClass, Class<?> vClass, Map<String, String> datasetArgs);

  /**
   * Create a Spark RDD that uses complete {@link Stream} as input source
   *
   * @param streamName the name of the {@link Stream} to be read as an RDD
   * @param vClass     the value class
   * @param <T>        type of RDD
   * @return the RDD created from {@link Stream}
   */
  <T> T readFromStream(String streamName, Class<?> vClass);

  /**
   * Create a Spark RDD that uses {@link Stream} as input source
   *
   * @param streamName the name of the {@link Stream} to be read as an RDD
   * @param vClass     the value class
   * @param startTime  the starting time of the stream to be read in milliseconds. To read from the starting of the
   *                   stream set this to 0
   * @param endTime    the ending time of the streams to be read in milliseconds. To read up to the end of the stream
   *                   set this to Long.MAX_VALUE
   * @param <T>        type of RDD
   * @return the RDD created from {@link Stream}
   */
  <T> T readFromStream(String streamName, Class<?> vClass, long startTime, long endTime);

  /**
   * Create a Spark RDD that uses {@link Stream} as input source according to the given {@link StreamEventDecoder}
   *
   * @param streamName  the name of the {@link Stream} to be read as an RDD
   * @param vClass      the value class
   * @param startTime   the starting time of the stream to be read in milliseconds. To read from the starting of the
   *                    stream set this to 0
   * @param endTime     the ending time of the streams to be read in milliseconds. To read up to the end of the stream
   *                    set this to Long.MAX_VALUE
   * @param decoderType the decoder to use while reading streams
   * @param <T>         type of RDD
   * @return the RDD created from {@link Stream}
   */
  <T> T readFromStream(String streamName, Class<?> vClass, long startTime, long endTime,
                       Class<? extends StreamEventDecoder> decoderType);

  /**
   * Create a Spark RDD that uses {@link Stream} as input source according to the given {@link StreamBatchReadable}.
   *
   * @param stream a {@link StreamBatchReadable} containing information on the stream to read from
   * @param vClass the value class
   * @return the RDD created from {@link Stream}
   */
  <T> T readFromStream(StreamBatchReadable stream, Class<?> vClass);

  /**
   * Returns
   * <a href="http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.api.java.JavaSparkContext">
   * JavaSparkContext</a> or
   * <a href="http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.SparkContext">SparkContext</a>
   * depending on user's job type.
   *
   * @param <T> the type of Spark Context
   * @return the Spark Context
   */
  <T> T getOriginalSparkContext();

  /**
   * Returns a {@link Serializable} {@link PluginContext} which can be used to request for plugins instances. The
   * instance returned can also be used in Spark program's closures.
   *
   * @return A {@link Serializable} {@link PluginContext}.
   */
  PluginContext getPluginContext();

  /**
   * Sets a
   * <a href="http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.SparkConf">SparkConf</a>
   * to be used for the Spark execution. Only configurations set inside the
   * {@link SparkMLLib#prepareRunSparkSink#prepareRun} call will affect the Spark execution.
   *
   * @param <T> the SparkConf type
   */
  <T> void setSparkConf(T sparkConf);

}