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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.streaming.kafka010
import java.{ util => ju }
import org.apache.kafka.clients.consumer._
import org.apache.kafka.common.TopicPartition
import org.apache.spark.SparkContext
import org.apache.spark.annotation.Experimental
import org.apache.spark.api.java.{ JavaRDD, JavaSparkContext }
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.api.java.{ JavaInputDStream, JavaStreamingContext }
import org.apache.spark.streaming.dstream._
/**
* :: Experimental ::
* object for constructing Kafka streams and RDDs
*/
@Experimental
object KafkaUtils extends Logging {
/**
* :: Experimental ::
* Scala constructor for a batch-oriented interface for consuming from Kafka.
* Starting and ending offsets are specified in advance,
* so that you can control exactly-once semantics.
* @param kafkaParams Kafka
* <a href="http://kafka.apache.org/documentation.html#newconsumerconfigs">
* configuration parameters</a>. Requires "bootstrap.servers" to be set
* with Kafka broker(s) specified in host1:port1,host2:port2 form.
* @param offsetRanges offset ranges that define the Kafka data belonging to this RDD
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createRDD[K, V](
sc: SparkContext,
kafkaParams: ju.Map[String, Object],
offsetRanges: Array[OffsetRange],
locationStrategy: LocationStrategy
): RDD[ConsumerRecord[K, V]] = {
val preferredHosts = locationStrategy match {
case PreferBrokers =>
throw new AssertionError(
"If you want to prefer brokers, you must provide a mapping using PreferFixed " +
"A single KafkaRDD does not have a driver consumer and cannot look up brokers for you.")
case PreferConsistent => ju.Collections.emptyMap[TopicPartition, String]()
case PreferFixed(hostMap) => hostMap
}
val kp = new ju.HashMap[String, Object](kafkaParams)
fixKafkaParams(kp)
val osr = offsetRanges.clone()
new KafkaRDD[K, V](sc, kp, osr, preferredHosts, true)
}
/**
* :: Experimental ::
* Java constructor for a batch-oriented interface for consuming from Kafka.
* Starting and ending offsets are specified in advance,
* so that you can control exactly-once semantics.
* @param kafkaParams Kafka
* <a href="http://kafka.apache.org/documentation.html#newconsumerconfigs">
* configuration parameters</a>. Requires "bootstrap.servers" to be set
* with Kafka broker(s) specified in host1:port1,host2:port2 form.
* @param offsetRanges offset ranges that define the Kafka data belonging to this RDD
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createRDD[K, V](
jsc: JavaSparkContext,
kafkaParams: ju.Map[String, Object],
offsetRanges: Array[OffsetRange],
locationStrategy: LocationStrategy
): JavaRDD[ConsumerRecord[K, V]] = {
new JavaRDD(createRDD[K, V](jsc.sc, kafkaParams, offsetRanges, locationStrategy))
}
/**
* :: Experimental ::
* Scala constructor for a DStream where
* each given Kafka topic/partition corresponds to an RDD partition.
* The spark configuration spark.streaming.kafka.maxRatePerPartition gives the maximum number
* of messages
* per second that each '''partition''' will accept.
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @param consumerStrategy In most cases, pass in [[ConsumerStrategies.Subscribe]],
* see [[ConsumerStrategies]] for more details
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createDirectStream[K, V](
ssc: StreamingContext,
locationStrategy: LocationStrategy,
consumerStrategy: ConsumerStrategy[K, V]
): InputDStream[ConsumerRecord[K, V]] = {
val ppc = new DefaultPerPartitionConfig(ssc.sparkContext.getConf)
createDirectStream[K, V](ssc, locationStrategy, consumerStrategy, ppc)
}
/**
* :: Experimental ::
* Scala constructor for a DStream where
* each given Kafka topic/partition corresponds to an RDD partition.
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @param consumerStrategy In most cases, pass in [[ConsumerStrategies.Subscribe]],
* see [[ConsumerStrategies]] for more details.
* @param perPartitionConfig configuration of settings such as max rate on a per-partition basis.
* see [[PerPartitionConfig]] for more details.
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createDirectStream[K, V](
ssc: StreamingContext,
locationStrategy: LocationStrategy,
consumerStrategy: ConsumerStrategy[K, V],
perPartitionConfig: PerPartitionConfig
): InputDStream[ConsumerRecord[K, V]] = {
new DirectKafkaInputDStream[K, V](ssc, locationStrategy, consumerStrategy, perPartitionConfig)
}
/**
* :: Experimental ::
* Java constructor for a DStream where
* each given Kafka topic/partition corresponds to an RDD partition.
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @param consumerStrategy In most cases, pass in [[ConsumerStrategies.Subscribe]],
* see [[ConsumerStrategies]] for more details
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createDirectStream[K, V](
jssc: JavaStreamingContext,
locationStrategy: LocationStrategy,
consumerStrategy: ConsumerStrategy[K, V]
): JavaInputDStream[ConsumerRecord[K, V]] = {
new JavaInputDStream(
createDirectStream[K, V](
jssc.ssc, locationStrategy, consumerStrategy))
}
/**
* :: Experimental ::
* Java constructor for a DStream where
* each given Kafka topic/partition corresponds to an RDD partition.
* @param locationStrategy In most cases, pass in [[LocationStrategies.PreferConsistent]],
* see [[LocationStrategies]] for more details.
* @param consumerStrategy In most cases, pass in [[ConsumerStrategies.Subscribe]],
* see [[ConsumerStrategies]] for more details
* @param perPartitionConfig configuration of settings such as max rate on a per-partition basis.
* see [[PerPartitionConfig]] for more details.
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
*/
@Experimental
def createDirectStream[K, V](
jssc: JavaStreamingContext,
locationStrategy: LocationStrategy,
consumerStrategy: ConsumerStrategy[K, V],
perPartitionConfig: PerPartitionConfig
): JavaInputDStream[ConsumerRecord[K, V]] = {
new JavaInputDStream(
createDirectStream[K, V](
jssc.ssc, locationStrategy, consumerStrategy, perPartitionConfig))
}
/**
* Tweak kafka params to prevent issues on executors
*/
private[kafka010] def fixKafkaParams(kafkaParams: ju.HashMap[String, Object]): Unit = {
logWarning(s"overriding ${ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG} to false for executor")
kafkaParams.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false: java.lang.Boolean)
logWarning(s"overriding ${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG} to none for executor")
kafkaParams.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "none")
// driver and executor should be in different consumer groups
val originalGroupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG)
if (null == originalGroupId) {
logError(s"${ConsumerConfig.GROUP_ID_CONFIG} is null, you should probably set it")
}
val groupId = "spark-executor-" + originalGroupId
logWarning(s"overriding executor ${ConsumerConfig.GROUP_ID_CONFIG} to ${groupId}")
kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId)
// possible workaround for KAFKA-3135
val rbb = kafkaParams.get(ConsumerConfig.RECEIVE_BUFFER_CONFIG)
if (null == rbb || rbb.asInstanceOf[java.lang.Integer] < 65536) {
logWarning(s"overriding ${ConsumerConfig.RECEIVE_BUFFER_CONFIG} to 65536 see KAFKA-3135")
kafkaParams.put(ConsumerConfig.RECEIVE_BUFFER_CONFIG, 65536: java.lang.Integer)
}
}
}
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