/*
 * Copyright (c) 2015 Couchbase, Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

// Putting this in this Spark package to be able to access internalCreateDataFrame
// See my (currently unanswered) SO post for context:
// https://stackoverflow.com/questions/56183811/how-to-create-a-custom-structured-streaming-source-for-apache-spark-2-3-0
package org.apache.spark.sql

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.types.StructType

/** Helpers to create streaming DataFrames.
  */
object DataFrameCreation {
  def createStreamingDataFrame(sqlContext: SQLContext,
                                rdd: RDD[Row],
                                schema: StructType): DataFrame = {
    // internalCreateDataFrame requires an RDD[InternalRow]
    val encoder = RowEncoder.apply(schema)
    val encoded: RDD[InternalRow] = rdd.map(row => {
      encoder.toRow(row)
    })

    sqlContext.internalCreateDataFrame(encoded, schema, isStreaming = true)
  }

  def createStreamingDataFrame(sqlContext: SQLContext,
                               df: DataFrame,
                               schema: StructType): DataFrame = {
    // internalCreateDataFrame requires an RDD[InternalRow]
    val encoder = RowEncoder.apply(schema)
    val encoded: RDD[InternalRow] = df.rdd.map(row => {
      encoder.toRow(row)
    })
    sqlContext.internalCreateDataFrame(encoded, schema, isStreaming = true)
  }
}