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/*
* 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 org.apache.spark.sql.kafka010
import java.{util => ju}
import java.util.{Locale, Optional, UUID}
import scala.collection.JavaConverters._
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.kafka.clients.producer.ProducerConfig
import org.apache.kafka.common.serialization.{ByteArrayDeserializer, ByteArraySerializer}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SparkSession, SQLContext}
import org.apache.spark.sql.execution.streaming.{Sink, Source}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, MicroBatchReadSupport, StreamWriteSupport}
import org.apache.spark.sql.sources.v2.reader.streaming.ContinuousInputPartitionReader
import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter
import org.apache.spark.sql.streaming.OutputMode
import org.apache.spark.sql.types.StructType
/**
* The provider class for all Kafka readers and writers. It is designed such that it throws
* IllegalArgumentException when the Kafka Dataset is created, so that it can catch
* missing options even before the query is started.
*/
private[kafka010] class KafkaSourceProvider extends DataSourceRegister
with StreamSourceProvider
with StreamSinkProvider
with RelationProvider
with CreatableRelationProvider
with StreamWriteSupport
with ContinuousReadSupport
with MicroBatchReadSupport
with Logging {
import KafkaSourceProvider._
override def shortName(): String = "kafka"
/**
* Returns the name and schema of the source. In addition, it also verifies whether the options
* are correct and sufficient to create the [[KafkaSource]] when the query is started.
*/
override def sourceSchema(
sqlContext: SQLContext,
schema: Option[StructType],
providerName: String,
parameters: Map[String, String]): (String, StructType) = {
validateStreamOptions(parameters)
require(schema.isEmpty, "Kafka source has a fixed schema and cannot be set with a custom one")
(shortName(), KafkaOffsetReader.kafkaSchema)
}
override def createSource(
sqlContext: SQLContext,
metadataPath: String,
schema: Option[StructType],
providerName: String,
parameters: Map[String, String]): Source = {
validateStreamOptions(parameters)
// Each running query should use its own group id. Otherwise, the query may be only assigned
// partial data since Kafka will assign partitions to multiple consumers having the same group
// id. Hence, we should generate a unique id for each query.
val uniqueGroupId = s"spark-kafka-source-${UUID.randomUUID}-${metadataPath.hashCode}"
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedKafkaParams =
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6).toString -> parameters(k) }
.toMap
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(caseInsensitiveParams,
STARTING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit)
val kafkaOffsetReader = new KafkaOffsetReader(
strategy(caseInsensitiveParams),
kafkaParamsForDriver(specifiedKafkaParams),
parameters,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaSource(
sqlContext,
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
parameters,
metadataPath,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveParams))
}
/**
* Creates a [[org.apache.spark.sql.sources.v2.reader.streaming.MicroBatchReader]] to read batches
* of Kafka data in a micro-batch streaming query.
*/
override def createMicroBatchReader(
schema: Optional[StructType],
metadataPath: String,
options: DataSourceOptions): KafkaMicroBatchReader = {
val parameters = options.asMap().asScala.toMap
validateStreamOptions(parameters)
// Each running query should use its own group id. Otherwise, the query may be only assigned
// partial data since Kafka will assign partitions to multiple consumers having the same group
// id. Hence, we should generate a unique id for each query.
val uniqueGroupId = s"spark-kafka-source-${UUID.randomUUID}-${metadataPath.hashCode}"
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedKafkaParams =
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6).toString -> parameters(k) }
.toMap
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(caseInsensitiveParams,
STARTING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit)
val kafkaOffsetReader = new KafkaOffsetReader(
strategy(caseInsensitiveParams),
kafkaParamsForDriver(specifiedKafkaParams),
parameters,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaMicroBatchReader(
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
options,
metadataPath,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveParams))
}
/**
* Creates a [[ContinuousInputPartitionReader]] to read
* Kafka data in a continuous streaming query.
*/
override def createContinuousReader(
schema: Optional[StructType],
metadataPath: String,
options: DataSourceOptions): KafkaContinuousReader = {
val parameters = options.asMap().asScala.toMap
validateStreamOptions(parameters)
// Each running query should use its own group id. Otherwise, the query may be only assigned
// partial data since Kafka will assign partitions to multiple consumers having the same group
// id. Hence, we should generate a unique id for each query.
val uniqueGroupId = s"spark-kafka-source-${UUID.randomUUID}-${metadataPath.hashCode}"
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedKafkaParams =
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6).toString -> parameters(k) }
.toMap
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(caseInsensitiveParams,
STARTING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit)
val kafkaOffsetReader = new KafkaOffsetReader(
strategy(caseInsensitiveParams),
kafkaParamsForDriver(specifiedKafkaParams),
parameters,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaContinuousReader(
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
parameters,
metadataPath,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveParams))
}
/**
* Returns a new base relation with the given parameters.
*
* @note The parameters' keywords are case insensitive and this insensitivity is enforced
* by the Map that is passed to the function.
*/
override def createRelation(
sqlContext: SQLContext,
parameters: Map[String, String]): BaseRelation = {
validateBatchOptions(parameters)
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedKafkaParams =
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6).toString -> parameters(k) }
.toMap
val startingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParams, STARTING_OFFSETS_OPTION_KEY, EarliestOffsetRangeLimit)
assert(startingRelationOffsets != LatestOffsetRangeLimit)
val endingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(caseInsensitiveParams,
ENDING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit)
assert(endingRelationOffsets != EarliestOffsetRangeLimit)
new KafkaRelation(
sqlContext,
strategy(caseInsensitiveParams),
sourceOptions = parameters,
specifiedKafkaParams = specifiedKafkaParams,
failOnDataLoss = failOnDataLoss(caseInsensitiveParams),
startingOffsets = startingRelationOffsets,
endingOffsets = endingRelationOffsets)
}
override def createSink(
sqlContext: SQLContext,
parameters: Map[String, String],
partitionColumns: Seq[String],
outputMode: OutputMode): Sink = {
val defaultTopic = parameters.get(TOPIC_OPTION_KEY).map(_.trim)
val specifiedKafkaParams = kafkaParamsForProducer(parameters)
new KafkaSink(sqlContext,
new ju.HashMap[String, Object](specifiedKafkaParams.asJava), defaultTopic)
}
override def createRelation(
outerSQLContext: SQLContext,
mode: SaveMode,
parameters: Map[String, String],
data: DataFrame): BaseRelation = {
mode match {
case SaveMode.Overwrite | SaveMode.Ignore =>
throw new AnalysisException(s"Save mode $mode not allowed for Kafka. " +
s"Allowed save modes are ${SaveMode.Append} and " +
s"${SaveMode.ErrorIfExists} (default).")
case _ => // good
}
val topic = parameters.get(TOPIC_OPTION_KEY).map(_.trim)
val specifiedKafkaParams = kafkaParamsForProducer(parameters)
KafkaWriter.write(outerSQLContext.sparkSession, data.queryExecution,
new ju.HashMap[String, Object](specifiedKafkaParams.asJava), topic)
/* This method is suppose to return a relation that reads the data that was written.
* We cannot support this for Kafka. Therefore, in order to make things consistent,
* we return an empty base relation.
*/
new BaseRelation {
override def sqlContext: SQLContext = unsupportedException
override def schema: StructType = unsupportedException
override def needConversion: Boolean = unsupportedException
override def sizeInBytes: Long = unsupportedException
override def unhandledFilters(filters: Array[Filter]): Array[Filter] = unsupportedException
private def unsupportedException =
throw new UnsupportedOperationException("BaseRelation from Kafka write " +
"operation is not usable.")
}
}
override def createStreamWriter(
queryId: String,
schema: StructType,
mode: OutputMode,
options: DataSourceOptions): StreamWriter = {
import scala.collection.JavaConverters._
val spark = SparkSession.getActiveSession.get
val topic = Option(options.get(TOPIC_OPTION_KEY).orElse(null)).map(_.trim)
// We convert the options argument from V2 -> Java map -> scala mutable -> scala immutable.
val producerParams = kafkaParamsForProducer(options.asMap.asScala.toMap)
KafkaWriter.validateQuery(
schema.toAttributes, new java.util.HashMap[String, Object](producerParams.asJava), topic)
new KafkaStreamWriter(topic, producerParams, schema)
}
private def strategy(caseInsensitiveParams: Map[String, String]) =
caseInsensitiveParams.find(x => STRATEGY_OPTION_KEYS.contains(x._1)).get match {
case ("assign", value) =>
AssignStrategy(JsonUtils.partitions(value))
case ("subscribe", value) =>
SubscribeStrategy(value.split(",").map(_.trim()).filter(_.nonEmpty))
case ("subscribepattern", value) =>
SubscribePatternStrategy(value.trim())
case _ =>
// Should never reach here as we are already matching on
// matched strategy names
throw new IllegalArgumentException("Unknown option")
}
private def failOnDataLoss(caseInsensitiveParams: Map[String, String]) =
caseInsensitiveParams.getOrElse(FAIL_ON_DATA_LOSS_OPTION_KEY, "true").toBoolean
private def validateGeneralOptions(parameters: Map[String, String]): Unit = {
// Validate source options
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedStrategies =
caseInsensitiveParams.filter { case (k, _) => STRATEGY_OPTION_KEYS.contains(k) }.toSeq
if (specifiedStrategies.isEmpty) {
throw new IllegalArgumentException(
"One of the following options must be specified for Kafka source: "
+ STRATEGY_OPTION_KEYS.mkString(", ") + ". See the docs for more details.")
} else if (specifiedStrategies.size > 1) {
throw new IllegalArgumentException(
"Only one of the following options can be specified for Kafka source: "
+ STRATEGY_OPTION_KEYS.mkString(", ") + ". See the docs for more details.")
}
val strategy = caseInsensitiveParams.find(x => STRATEGY_OPTION_KEYS.contains(x._1)).get match {
case ("assign", value) =>
if (!value.trim.startsWith("{")) {
throw new IllegalArgumentException(
"No topicpartitions to assign as specified value for option " +
s"'assign' is '$value'")
}
case ("subscribe", value) =>
val topics = value.split(",").map(_.trim).filter(_.nonEmpty)
if (topics.isEmpty) {
throw new IllegalArgumentException(
"No topics to subscribe to as specified value for option " +
s"'subscribe' is '$value'")
}
case ("subscribepattern", value) =>
val pattern = caseInsensitiveParams("subscribepattern").trim()
if (pattern.isEmpty) {
throw new IllegalArgumentException(
"Pattern to subscribe is empty as specified value for option " +
s"'subscribePattern' is '$value'")
}
case _ =>
// Should never reach here as we are already matching on
// matched strategy names
throw new IllegalArgumentException("Unknown option")
}
// Validate minPartitions value if present
if (caseInsensitiveParams.contains(MIN_PARTITIONS_OPTION_KEY)) {
val p = caseInsensitiveParams(MIN_PARTITIONS_OPTION_KEY).toInt
if (p <= 0) throw new IllegalArgumentException("minPartitions must be positive")
}
// Validate user-specified Kafka options
if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.GROUP_ID_CONFIG}")) {
throw new IllegalArgumentException(
s"Kafka option '${ConsumerConfig.GROUP_ID_CONFIG}' is not supported as " +
s"user-specified consumer groups are not used to track offsets.")
}
if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG}")) {
throw new IllegalArgumentException(
s"""
|Kafka option '${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG}' is not supported.
|Instead set the source option '$STARTING_OFFSETS_OPTION_KEY' to 'earliest' or 'latest'
|to specify where to start. Structured Streaming manages which offsets are consumed
|internally, rather than relying on the kafkaConsumer to do it. This will ensure that no
|data is missed when new topics/partitions are dynamically subscribed. Note that
|'$STARTING_OFFSETS_OPTION_KEY' only applies when a new Streaming query is started, and
|that resuming will always pick up from where the query left off. See the docs for more
|details.
""".stripMargin)
}
if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG}")) {
throw new IllegalArgumentException(
s"Kafka option '${ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG}' is not supported as keys "
+ "are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame operations "
+ "to explicitly deserialize the keys.")
}
if (caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG}"))
{
throw new IllegalArgumentException(
s"Kafka option '${ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG}' is not supported as "
+ "values are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame "
+ "operations to explicitly deserialize the values.")
}
val otherUnsupportedConfigs = Seq(
ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, // committing correctly requires new APIs in Source
ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG) // interceptors can modify payload, so not safe
otherUnsupportedConfigs.foreach { c =>
if (caseInsensitiveParams.contains(s"kafka.$c")) {
throw new IllegalArgumentException(s"Kafka option '$c' is not supported")
}
}
if (!caseInsensitiveParams.contains(s"kafka.${ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG}")) {
throw new IllegalArgumentException(
s"Option 'kafka.${ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG}' must be specified for " +
s"configuring Kafka consumer")
}
}
private def validateStreamOptions(caseInsensitiveParams: Map[String, String]) = {
// Stream specific options
caseInsensitiveParams.get(ENDING_OFFSETS_OPTION_KEY).map(_ =>
throw new IllegalArgumentException("ending offset not valid in streaming queries"))
validateGeneralOptions(caseInsensitiveParams)
}
private def validateBatchOptions(caseInsensitiveParams: Map[String, String]) = {
// Batch specific options
KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParams, STARTING_OFFSETS_OPTION_KEY, EarliestOffsetRangeLimit) match {
case EarliestOffsetRangeLimit => // good to go
case LatestOffsetRangeLimit =>
throw new IllegalArgumentException("starting offset can't be latest " +
"for batch queries on Kafka")
case SpecificOffsetRangeLimit(partitionOffsets) =>
partitionOffsets.foreach {
case (tp, off) if off == KafkaOffsetRangeLimit.LATEST =>
throw new IllegalArgumentException(s"startingOffsets for $tp can't " +
"be latest for batch queries on Kafka")
case _ => // ignore
}
}
KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParams, ENDING_OFFSETS_OPTION_KEY, LatestOffsetRangeLimit) match {
case EarliestOffsetRangeLimit =>
throw new IllegalArgumentException("ending offset can't be earliest " +
"for batch queries on Kafka")
case LatestOffsetRangeLimit => // good to go
case SpecificOffsetRangeLimit(partitionOffsets) =>
partitionOffsets.foreach {
case (tp, off) if off == KafkaOffsetRangeLimit.EARLIEST =>
throw new IllegalArgumentException(s"ending offset for $tp can't be " +
"earliest for batch queries on Kafka")
case _ => // ignore
}
}
validateGeneralOptions(caseInsensitiveParams)
// Don't want to throw an error, but at least log a warning.
if (caseInsensitiveParams.get("maxoffsetspertrigger").isDefined) {
logWarning("maxOffsetsPerTrigger option ignored in batch queries")
}
}
}
private[kafka010] object KafkaSourceProvider extends Logging {
private val STRATEGY_OPTION_KEYS = Set("subscribe", "subscribepattern", "assign")
private[kafka010] val STARTING_OFFSETS_OPTION_KEY = "startingoffsets"
private[kafka010] val ENDING_OFFSETS_OPTION_KEY = "endingoffsets"
private val FAIL_ON_DATA_LOSS_OPTION_KEY = "failondataloss"
private val MIN_PARTITIONS_OPTION_KEY = "minpartitions"
val TOPIC_OPTION_KEY = "topic"
val INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_FALSE =
"""
|Some data may have been lost because they are not available in Kafka any more; either the
| data was aged out by Kafka or the topic may have been deleted before all the data in the
| topic was processed. If you want your streaming query to fail on such cases, set the source
| option "failOnDataLoss" to "true".
""".stripMargin
val INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_TRUE =
"""
|Some data may have been lost because they are not available in Kafka any more; either the
| data was aged out by Kafka or the topic may have been deleted before all the data in the
| topic was processed. If you don't want your streaming query to fail on such cases, set the
| source option "failOnDataLoss" to "false".
""".stripMargin
private val deserClassName = classOf[ByteArrayDeserializer].getName
def getKafkaOffsetRangeLimit(
params: Map[String, String],
offsetOptionKey: String,
defaultOffsets: KafkaOffsetRangeLimit): KafkaOffsetRangeLimit = {
params.get(offsetOptionKey).map(_.trim) match {
case Some(offset) if offset.toLowerCase(Locale.ROOT) == "latest" =>
LatestOffsetRangeLimit
case Some(offset) if offset.toLowerCase(Locale.ROOT) == "earliest" =>
EarliestOffsetRangeLimit
case Some(json) => SpecificOffsetRangeLimit(JsonUtils.partitionOffsets(json))
case None => defaultOffsets
}
}
def kafkaParamsForDriver(specifiedKafkaParams: Map[String, String]): ju.Map[String, Object] =
ConfigUpdater("source", specifiedKafkaParams)
.set(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, deserClassName)
.set(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, deserClassName)
// Set to "earliest" to avoid exceptions. However, KafkaSource will fetch the initial
// offsets by itself instead of counting on KafkaConsumer.
.set(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest")
// So that consumers in the driver does not commit offsets unnecessarily
.set(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false")
// So that the driver does not pull too much data
.set(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, new java.lang.Integer(1))
// If buffer config is not set, set it to reasonable value to work around
// buffer issues (see KAFKA-3135)
.setIfUnset(ConsumerConfig.RECEIVE_BUFFER_CONFIG, 65536: java.lang.Integer)
.build()
def kafkaParamsForExecutors(
specifiedKafkaParams: Map[String, String],
uniqueGroupId: String): ju.Map[String, Object] =
ConfigUpdater("executor", specifiedKafkaParams)
.set(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, deserClassName)
.set(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, deserClassName)
// Make sure executors do only what the driver tells them.
.set(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "none")
// So that consumers in executors do not mess with any existing group id
.set(ConsumerConfig.GROUP_ID_CONFIG, s"$uniqueGroupId-executor")
// So that consumers in executors does not commit offsets unnecessarily
.set(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false")
// If buffer config is not set, set it to reasonable value to work around
// buffer issues (see KAFKA-3135)
.setIfUnset(ConsumerConfig.RECEIVE_BUFFER_CONFIG, 65536: java.lang.Integer)
.build()
/** Class to conveniently update Kafka config params, while logging the changes */
private case class ConfigUpdater(module: String, kafkaParams: Map[String, String]) {
private val map = new ju.HashMap[String, Object](kafkaParams.asJava)
def set(key: String, value: Object): this.type = {
map.put(key, value)
logDebug(s"$module: Set $key to $value, earlier value: ${kafkaParams.getOrElse(key, "")}")
this
}
def setIfUnset(key: String, value: Object): ConfigUpdater = {
if (!map.containsKey(key)) {
map.put(key, value)
logDebug(s"$module: Set $key to $value")
}
this
}
def build(): ju.Map[String, Object] = map
}
private[kafka010] def kafkaParamsForProducer(
parameters: Map[String, String]): Map[String, String] = {
val caseInsensitiveParams = parameters.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}")) {
throw new IllegalArgumentException(
s"Kafka option '${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}' is not supported as keys "
+ "are serialized with ByteArraySerializer.")
}
if (caseInsensitiveParams.contains(s"kafka.${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}"))
{
throw new IllegalArgumentException(
s"Kafka option '${ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG}' is not supported as "
+ "value are serialized with ByteArraySerializer.")
}
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6).toString -> parameters(k) }
.toMap + (ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName,
ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG -> classOf[ByteArraySerializer].getName)
}
}
