/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.
 */

package com.uber.hoodie

import java.nio.ByteBuffer
import java.sql.{Date, Timestamp}
import java.util

import com.databricks.spark.avro.SchemaConverters
import com.databricks.spark.avro.SchemaConverters.IncompatibleSchemaException
import org.apache.avro.Schema.Type._
import org.apache.avro.generic.GenericData.{Fixed, Record}
import org.apache.avro.generic.{GenericData, GenericRecord}
import org.apache.avro.{Schema, SchemaBuilder}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.catalyst.expressions.GenericRow
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}

import scala.collection.JavaConverters._


object AvroConversionUtils {

  def createRdd(df: DataFrame, structName: String, recordNamespace: String): RDD[GenericRecord] = {
    val dataType = df.schema
    val encoder = RowEncoder.apply(dataType).resolveAndBind()
    df.queryExecution.toRdd.map(encoder.fromRow)
      .mapPartitions { records =>
      if (records.isEmpty) Iterator.empty
      else {
        val convertor = createConverterToAvro(dataType, structName, recordNamespace)
        records.map { x => convertor(x).asInstanceOf[GenericRecord] }
      }
    }
  }

  def createDataFrame(rdd: RDD[GenericRecord], schemaStr: String, ss : SparkSession): Dataset[Row] = {
    if (rdd.isEmpty()) {
      ss.emptyDataFrame
    } else {
      ss.createDataFrame(rdd.mapPartitions { records =>
        if (records.isEmpty) Iterator.empty
        else {
          val schema = Schema.parse(schemaStr)
          val dataType = convertAvroSchemaToStructType(schema)
          val convertor = createConverterToRow(schema, dataType)
          records.map { x => convertor(x).asInstanceOf[Row] }
        }
      }, convertAvroSchemaToStructType(Schema.parse(schemaStr))).asInstanceOf[Dataset[Row]]
    }
  }

  def getNewRecordNamespace(elementDataType: DataType,
                            currentRecordNamespace: String,
                            elementName: String): String = {

    elementDataType match {
      case StructType(_) => s"$currentRecordNamespace.$elementName"
      case _ => currentRecordNamespace
    }
  }

  /**
    * NOTE : This part of code is copied from com.databricks.spark.avro.SchemaConverters.scala (133:310) (spark-avro)
    *
    * Returns a converter function to convert row in avro format to GenericRow of catalyst.
    *
    * @param sourceAvroSchema Source schema before conversion inferred from avro file by passed in
    *                       by user.
    * @param targetSqlType Target catalyst sql type after the conversion.
    * @return returns a converter function to convert row in avro format to GenericRow of catalyst.
    */
  def createConverterToRow(sourceAvroSchema: Schema,
                           targetSqlType: DataType): AnyRef => AnyRef = {

    def createConverter(avroSchema: Schema,
                        sqlType: DataType, path: List[String]): AnyRef => AnyRef = {
      val avroType = avroSchema.getType
      (sqlType, avroType) match {
        // Avro strings are in Utf8, so we have to call toString on them
        case (StringType, STRING) | (StringType, ENUM) =>
          (item: AnyRef) => if (item == null) null else item.toString
        // Byte arrays are reused by avro, so we have to make a copy of them.
        case (IntegerType, INT) | (BooleanType, BOOLEAN) | (DoubleType, DOUBLE) |
             (FloatType, FLOAT) | (LongType, LONG) =>
          identity
        case (BinaryType, FIXED) =>
          (item: AnyRef) =>
            if (item == null) {
              null
            } else {
              item.asInstanceOf[Fixed].bytes().clone()
            }
        case (BinaryType, BYTES) =>
          (item: AnyRef) =>
            if (item == null) {
              null
            } else {
              val byteBuffer = item.asInstanceOf[ByteBuffer]
              val bytes = new Array[Byte](byteBuffer.remaining)
              byteBuffer.get(bytes)
              bytes
            }

        case (struct: StructType, RECORD) =>
          val length = struct.fields.length
          val converters = new Array[AnyRef => AnyRef](length)
          val avroFieldIndexes = new Array[Int](length)
          var i = 0
          while (i < length) {
            val sqlField = struct.fields(i)
            val avroField = avroSchema.getField(sqlField.name)
            if (avroField != null) {
              val converter = createConverter(avroField.schema(), sqlField.dataType,
                path :+ sqlField.name)
              converters(i) = converter
              avroFieldIndexes(i) = avroField.pos()
            } else if (!sqlField.nullable) {
              throw new IncompatibleSchemaException(
                s"Cannot find non-nullable field ${sqlField.name} at path ${path.mkString(".")} " +
                  "in Avro schema\n" +
                  s"Source Avro schema: $sourceAvroSchema.\n" +
                  s"Target Catalyst type: $targetSqlType")
            }
            i += 1
          }

          (item: AnyRef) => {
            if (item == null) {
              null
            } else {
              val record = item.asInstanceOf[GenericRecord]

              val result = new Array[Any](length)
              var i = 0
              while (i < converters.length) {
                if (converters(i) != null) {
                  val converter = converters(i)
                  result(i) = converter(record.get(avroFieldIndexes(i)))
                }
                i += 1
              }
              new GenericRow(result)
            }
          }
        case (arrayType: ArrayType, ARRAY) =>
          val elementConverter = createConverter(avroSchema.getElementType, arrayType.elementType,
            path)
          val allowsNull = arrayType.containsNull
          (item: AnyRef) => {
            if (item == null) {
              null
            } else {
              item.asInstanceOf[java.lang.Iterable[AnyRef]].asScala.map { element =>
                if (element == null && !allowsNull) {
                  throw new RuntimeException(s"Array value at path ${path.mkString(".")} is not " +
                    "allowed to be null")
                } else {
                  elementConverter(element)
                }
              }
            }
          }
        case (mapType: MapType, MAP) if mapType.keyType == StringType =>
          val valueConverter = createConverter(avroSchema.getValueType, mapType.valueType, path)
          val allowsNull = mapType.valueContainsNull
          (item: AnyRef) => {
            if (item == null) {
              null
            } else {
              item.asInstanceOf[java.util.Map[AnyRef, AnyRef]].asScala.map { x =>
                if (x._2 == null && !allowsNull) {
                  throw new RuntimeException(s"Map value at path ${path.mkString(".")} is not " +
                    "allowed to be null")
                } else {
                  (x._1.toString, valueConverter(x._2))
                }
              }.toMap
            }
          }
        case (sqlType, UNION) =>
          if (avroSchema.getTypes.asScala.exists(_.getType == NULL)) {
            val remainingUnionTypes = avroSchema.getTypes.asScala.filterNot(_.getType == NULL)
            if (remainingUnionTypes.size == 1) {
              createConverter(remainingUnionTypes.head, sqlType, path)
            } else {
              createConverter(Schema.createUnion(remainingUnionTypes.asJava), sqlType, path)
            }
          } else avroSchema.getTypes.asScala.map(_.getType) match {
            case Seq(t1) => createConverter(avroSchema.getTypes.get(0), sqlType, path)
            case Seq(a, b) if Set(a, b) == Set(INT, LONG) && sqlType == LongType =>
              (item: AnyRef) => {
                item match {
                  case null => null
                  case l: java.lang.Long => l
                  case i: java.lang.Integer => new java.lang.Long(i.longValue())
                }
              }
            case Seq(a, b) if Set(a, b) == Set(FLOAT, DOUBLE) && sqlType == DoubleType =>
              (item: AnyRef) => {
                item match {
                  case null => null
                  case d: java.lang.Double => d
                  case f: java.lang.Float => new java.lang.Double(f.doubleValue())
                }
              }
            case other =>
              sqlType match {
                case t: StructType if t.fields.length == avroSchema.getTypes.size =>
                  val fieldConverters = t.fields.zip(avroSchema.getTypes.asScala).map {
                    case (field, schema) =>
                      createConverter(schema, field.dataType, path :+ field.name)
                  }

                  (item: AnyRef) => if (item == null) {
                    null
                  } else {
                    val i = GenericData.get().resolveUnion(avroSchema, item)
                    val converted = new Array[Any](fieldConverters.length)
                    converted(i) = fieldConverters(i)(item)
                    new GenericRow(converted)
                  }
                case _ => throw new IncompatibleSchemaException(
                  s"Cannot convert Avro schema to catalyst type because schema at path " +
                    s"${path.mkString(".")} is not compatible " +
                    s"(avroType = $other, sqlType = $sqlType). \n" +
                    s"Source Avro schema: $sourceAvroSchema.\n" +
                    s"Target Catalyst type: $targetSqlType")
              }
          }
        case (left, right) =>
          throw new IncompatibleSchemaException(
            s"Cannot convert Avro schema to catalyst type because schema at path " +
              s"${path.mkString(".")} is not compatible (avroType = $left, sqlType = $right). \n" +
              s"Source Avro schema: $sourceAvroSchema.\n" +
              s"Target Catalyst type: $targetSqlType")
      }
    }
    createConverter(sourceAvroSchema, targetSqlType, List.empty[String])
  }

  def createConverterToAvro(dataType: DataType,
                            structName: String,
                            recordNamespace: String): Any => Any = {
    dataType match {
      case BinaryType => (item: Any) =>
        item match {
          case null => null
          case bytes: Array[Byte] => ByteBuffer.wrap(bytes)
        }
      case IntegerType | LongType |
           FloatType | DoubleType | StringType | BooleanType => identity
      case ByteType => (item: Any) =>
        if (item == null) null else item.asInstanceOf[Byte].intValue
      case ShortType => (item: Any) =>
        if (item == null) null else item.asInstanceOf[Short].intValue
      case _: DecimalType => (item: Any) => if (item == null) null else item.toString
      case TimestampType => (item: Any) =>
        if (item == null) null else item.asInstanceOf[Timestamp].getTime
      case DateType => (item: Any) =>
        if (item == null) null else item.asInstanceOf[Date].getTime
      case ArrayType(elementType, _) =>
        val elementConverter = createConverterToAvro(
          elementType,
          structName,
          getNewRecordNamespace(elementType, recordNamespace, structName))
        (item: Any) => {
          if (item == null) {
            null
          } else {
            val sourceArray = item.asInstanceOf[Seq[Any]]
            val sourceArraySize = sourceArray.size
            val targetList = new util.ArrayList[Any](sourceArraySize)
            var idx = 0
            while (idx < sourceArraySize) {
              targetList.add(elementConverter(sourceArray(idx)))
              idx += 1
            }
            targetList
          }
        }
      case MapType(StringType, valueType, _) =>
        val valueConverter = createConverterToAvro(
          valueType,
          structName,
          getNewRecordNamespace(valueType, recordNamespace, structName))
        (item: Any) => {
          if (item == null) {
            null
          } else {
            val javaMap = new util.HashMap[String, Any]()
            item.asInstanceOf[Map[String, Any]].foreach { case (key, value) =>
              javaMap.put(key, valueConverter(value))
            }
            javaMap
          }
        }
      case structType: StructType =>
        val builder = SchemaBuilder.record(structName).namespace(recordNamespace)
        val schema: Schema = SchemaConverters.convertStructToAvro(
          structType, builder, recordNamespace)
        val fieldConverters = structType.fields.map(field =>
          createConverterToAvro(
            field.dataType,
            field.name,
            getNewRecordNamespace(field.dataType, recordNamespace, field.name)))
        (item: Any) => {
          if (item == null) {
            null
          } else {
            val record = new Record(schema)
            val convertersIterator = fieldConverters.iterator
            val fieldNamesIterator = dataType.asInstanceOf[StructType].fieldNames.iterator
            val rowIterator = item.asInstanceOf[Row].toSeq.iterator

            while (convertersIterator.hasNext) {
              val converter = convertersIterator.next()
              record.put(fieldNamesIterator.next(), converter(rowIterator.next()))
            }
            record
          }
        }
    }
  }

  def convertStructTypeToAvroSchema(structType: StructType,
                                    structName: String,
                                    recordNamespace: String): Schema = {
    val builder = SchemaBuilder.record(structName).namespace(recordNamespace)
    SchemaConverters.convertStructToAvro(structType, builder, recordNamespace)
  }

  def convertAvroSchemaToStructType(avroSchema: Schema): StructType = {
    SchemaConverters.toSqlType(avroSchema).dataType.asInstanceOf[StructType];
  }
}