我想以avro格式将dataframes流写入kafka;
我认为我应该将dataframe模式发布到schema registry作为avro模式，然后将 DF 流写入 Kafka，将该模式指定为选项。
所以我需要知道如何从dataframe推断avro模式，以便能够在schema registry中发布它。
如果有另一种方式，我将不胜感激
package com.databricks.spark.avro
import java.nio.ByteBuffer
import scala.collection.JavaConverters._
import org.apache.avro.generic.GenericData.Fixed
import org.apache.avro.generic.{GenericData, GenericRecord}
import org.apache.avro.{Schema, SchemaBuilder}
import org.apache.avro.SchemaBuilder._
import org.apache.avro.Schema.Type._
import org.apache.spark.sql.catalyst.expressions.GenericRow
import org.apache.spark.sql.types._
/**
* This object contains method that are used to convert sparkSQL schemas to avro schemas and vice
* versa.
*/
object SchemaConverters {
class IncompatibleSchemaException(msg: String, ex: Throwable = null) extends Exception(msg, ex)
case class SchemaType(dataType: DataType, nullable: Boolean)
/**
* This function takes an avro schema and returns a sql schema.
*/
def toSqlType(avroSchema: Schema): SchemaType = {
avroSchema.getType match {
case INT => SchemaType(IntegerType, nullable = false)
case STRING => SchemaType(StringType, nullable = false)
case BOOLEAN => SchemaType(BooleanType, nullable = false)
case BYTES => SchemaType(BinaryType, nullable = false)
case DOUBLE => SchemaType(DoubleType, nullable = false)
case FLOAT => SchemaType(FloatType, nullable = false)
case LONG => SchemaType(LongType, nullable = false)
case FIXED => SchemaType(BinaryType, nullable = false)
case ENUM => SchemaType(StringType, nullable = false)
case RECORD =>
val fields = avroSchema.getFields.asScala.map { f =>
val schemaType = toSqlType(f.schema())
StructField(f.name, schemaType.dataType, schemaType.nullable)
}
SchemaType(StructType(fields), nullable = false)
case ARRAY =>
val schemaType = toSqlType(avroSchema.getElementType)
SchemaType(
ArrayType(schemaType.dataType, containsNull = schemaType.nullable),
nullable = false)
case MAP =>
val schemaType = toSqlType(avroSchema.getValueType)
SchemaType(
MapType(StringType, schemaType.dataType, valueContainsNull = schemaType.nullable),
nullable = false)
case UNION =>
if (avroSchema.getTypes.asScala.exists(_.getType == NULL)) {
// In case of a union with null, eliminate it and make a recursive call
val remainingUnionTypes = avroSchema.getTypes.asScala.filterNot(_.getType == NULL)
if (remainingUnionTypes.size == 1) {
toSqlType(remainingUnionTypes.head).copy(nullable = true)
} else {
toSqlType(Schema.createUnion(remainingUnionTypes.asJava)).copy(nullable = true)
}
} else avroSchema.getTypes.asScala.map(_.getType) match {
case Seq(t1) =>
toSqlType(avroSchema.getTypes.get(0))
case Seq(t1, t2) if Set(t1, t2) == Set(INT, LONG) =>
SchemaType(LongType, nullable = false)
case Seq(t1, t2) if Set(t1, t2) == Set(FLOAT, DOUBLE) =>
SchemaType(DoubleType, nullable = false)
case _ =>
// Convert complex unions to struct types where field names are member0, member1, etc.
// This is consistent with the behavior when converting between Avro and Parquet.
val fields = avroSchema.getTypes.asScala.zipWithIndex.map {
case (s, i) =>
val schemaType = toSqlType(s)
// All fields are nullable because only one of them is set at a time
StructField(s"member$i", schemaType.dataType, nullable = true)
}
SchemaType(StructType(fields), nullable = false)
}
case other => throw new IncompatibleSchemaException(s"Unsupported type $other")
}
}
/**
* This function converts sparkSQL StructType into avro schema. This method uses two other
* converter methods in order to do the conversion.
*/
def convertStructToAvro[T](
structType: StructType,
schemaBuilder: RecordBuilder[T],
recordNamespace: String): T = {
val fieldsAssembler: FieldAssembler[T] = schemaBuilder.fields()
structType.fields.foreach { field =>
val newField = fieldsAssembler.name(field.name).`type`()
if (field.nullable) {
convertFieldTypeToAvro(field.dataType, newField.nullable(), field.name, recordNamespace)
.noDefault
} else {
convertFieldTypeToAvro(field.dataType, newField, field.name, recordNamespace)
.noDefault
}
}
fieldsAssembler.endRecord()
}
/**
* 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.
*/
private[avro] def createConverterToSQL(
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 scheman" +
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])
}
/**
* This function is used to convert some sparkSQL type to avro type. Note that this function won't
* be used to construct fields of avro record (convertFieldTypeToAvro is used for that).
*/
private def convertTypeToAvro[T](
dataType: DataType,
schemaBuilder: BaseTypeBuilder[T],
structName: String,
recordNamespace: String): T = {
dataType match {
case ByteType => schemaBuilder.intType()
case ShortType => schemaBuilder.intType()
case IntegerType => schemaBuilder.intType()
case LongType => schemaBuilder.longType()
case FloatType => schemaBuilder.floatType()
case DoubleType => schemaBuilder.doubleType()
case _: DecimalType => schemaBuilder.stringType()
case StringType => schemaBuilder.stringType()
case BinaryType => schemaBuilder.bytesType()
case BooleanType => schemaBuilder.booleanType()
case TimestampType => schemaBuilder.longType()
case DateType => schemaBuilder.longType()
case ArrayType(elementType, _) =>
val builder = getSchemaBuilder(dataType.asInstanceOf[ArrayType].containsNull)
val elementSchema = convertTypeToAvro(elementType, builder, structName, recordNamespace)
schemaBuilder.array().items(elementSchema)
case MapType(StringType, valueType, _) =>
val builder = getSchemaBuilder(dataType.asInstanceOf[MapType].valueContainsNull)
val valueSchema = convertTypeToAvro(valueType, builder, structName, recordNamespace)
schemaBuilder.map().values(valueSchema)
case structType: StructType =>
convertStructToAvro(
structType,
schemaBuilder.record(structName).namespace(recordNamespace),
recordNamespace)
case other => throw new IncompatibleSchemaException(s"Unexpected type $dataType.")
}
}
/**
* This function is used to construct fields of the avro record, where schema of the field is
* specified by avro representation of dataType. Since builders for record fields are different
* from those for everything else, we have to use a separate method.
*/
private def convertFieldTypeToAvro[T](
dataType: DataType,
newFieldBuilder: BaseFieldTypeBuilder[T],
structName: String,
recordNamespace: String): FieldDefault[T, _] = {
dataType match {
case ByteType => newFieldBuilder.intType()
case ShortType => newFieldBuilder.intType()
case IntegerType => newFieldBuilder.intType()
case LongType => newFieldBuilder.longType()
case FloatType => newFieldBuilder.floatType()
case DoubleType => newFieldBuilder.doubleType()
case _: DecimalType => newFieldBuilder.stringType()
case StringType => newFieldBuilder.stringType()
case BinaryType => newFieldBuilder.bytesType()
case BooleanType => newFieldBuilder.booleanType()
case TimestampType => newFieldBuilder.longType()
case DateType => newFieldBuilder.longType()
case ArrayType(elementType, _) =>
val builder = getSchemaBuilder(dataType.asInstanceOf[ArrayType].containsNull)
val elementSchema = convertTypeToAvro(elementType, builder, structName, recordNamespace)
newFieldBuilder.array().items(elementSchema)
case MapType(StringType, valueType, _) =>
val builder = getSchemaBuilder(dataType.asInstanceOf[MapType].valueContainsNull)
val valueSchema = convertTypeToAvro(valueType, builder, structName, recordNamespace)
newFieldBuilder.map().values(valueSchema)
case structType: StructType =>
convertStructToAvro(
structType,
newFieldBuilder.record(structName).namespace(recordNamespace),
recordNamespace)
case other => throw new IncompatibleSchemaException(s"Unexpected type $dataType.")
}
}
private def getSchemaBuilder(isNullable: Boolean): BaseTypeBuilder[Schema] = {
if (isNullable) {
SchemaBuilder.builder().nullable()
} else {
SchemaBuilder.builder()
}
}
}
- https://github.com/databricks/spark-avro/blob/branch-3.2/src/main/scala/com/databricks/spark/avro/SchemaConverters.scala
- https://github.com/databricks/spark-avro
我认为事情是这样的：
我应该从数据帧准备 avro 模式并将该模式发布到模式注册表中，以便消费者可以正确阅读消息。
然后以 avro 格式对数据帧进行编码。 最后像任何类型的其他消息一样编写 DF 流(无需将架构指定为选项(。
现在我正在实现这个逻辑，还没有完成它。
谢谢
有几点需要先澄清：
-
您不必发送 Avro 架构。如果你使用Kafka-Spark Integration，它会为你做。您需要编写一些配置，例如：
props.put("value.deserializer"， "io.confluent.kafka.serializers.KafkaAvroDeserializer"(; props.put("key.deserializer"， "org.apache.kafka.common.serialization.StringDeserializer"(; props.put("schema.registry.url"， "http://localhost:8081"(;
-
您甚至可以通过应用于数据框的查询将 DF 直接写入 Kafka。可以查看有关为流式处理查询创建 Kafka 接收器的文档中显示的示例。
val jsonFormatAvroSchema = SchemaConverters.toAvroType(myDf.schema(.toString