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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 org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord, KafkaConsumer}
import org.apache.kafka.common.TopicPartition
import org.apache.spark.{SparkEnv, SparkException, TaskContext}
import org.apache.spark.internal.Logging
/**
* Consumer of single topicpartition, intended for cached reuse.
* Underlying consumer is not threadsafe, so neither is this,
* but processing the same topicpartition and group id in multiple threads is usually bad anyway.
*/
private[kafka010] case class CachedKafkaConsumer private(
topicPartition: TopicPartition,
kafkaParams: ju.Map[String, Object]) extends Logging {
private val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String]
private val consumer = {
val c = new KafkaConsumer[Array[Byte], Array[Byte]](kafkaParams)
val tps = new ju.ArrayList[TopicPartition]()
tps.add(topicPartition)
c.assign(tps)
c
}
/** Iterator to the already fetch data */
private var fetchedData = ju.Collections.emptyIterator[ConsumerRecord[Array[Byte], Array[Byte]]]
private var nextOffsetInFetchedData = -2L
/**
* Get the record for the given offset, waiting up to timeout ms if IO is necessary.
* Sequential forward access will use buffers, but random access will be horribly inefficient.
*/
def get(offset: Long, pollTimeoutMs: Long): ConsumerRecord[Array[Byte], Array[Byte]] = {
logDebug(s"Get $groupId $topicPartition nextOffset $nextOffsetInFetchedData requested $offset")
if (offset != nextOffsetInFetchedData) {
logInfo(s"Initial fetch for $topicPartition $offset")
seek(offset)
poll(pollTimeoutMs)
}
if (!fetchedData.hasNext()) { poll(pollTimeoutMs) }
assert(fetchedData.hasNext(),
s"Failed to get records for $groupId $topicPartition $offset " +
s"after polling for $pollTimeoutMs")
var record = fetchedData.next()
if (record.offset != offset) {
logInfo(s"Buffer miss for $groupId $topicPartition $offset")
seek(offset)
poll(pollTimeoutMs)
assert(fetchedData.hasNext(),
s"Failed to get records for $groupId $topicPartition $offset " +
s"after polling for $pollTimeoutMs")
record = fetchedData.next()
assert(record.offset == offset,
s"Got wrong record for $groupId $topicPartition even after seeking to offset $offset")
}
nextOffsetInFetchedData = offset + 1
record
}
private def close(): Unit = consumer.close()
private def seek(offset: Long): Unit = {
logDebug(s"Seeking to $groupId $topicPartition $offset")
consumer.seek(topicPartition, offset)
}
private def poll(pollTimeoutMs: Long): Unit = {
val p = consumer.poll(pollTimeoutMs)
val r = p.records(topicPartition)
logDebug(s"Polled $groupId ${p.partitions()} ${r.size}")
fetchedData = r.iterator
}
}
private[kafka010] object CachedKafkaConsumer extends Logging {
private case class CacheKey(groupId: String, topicPartition: TopicPartition)
private lazy val cache = {
val conf = SparkEnv.get.conf
val capacity = conf.getInt("spark.sql.kafkaConsumerCache.capacity", 64)
new ju.LinkedHashMap[CacheKey, CachedKafkaConsumer](capacity, 0.75f, true) {
override def removeEldestEntry(
entry: ju.Map.Entry[CacheKey, CachedKafkaConsumer]): Boolean = {
if (this.size > capacity) {
logWarning(s"KafkaConsumer cache hitting max capacity of $capacity, " +
s"removing consumer for ${entry.getKey}")
try {
entry.getValue.close()
} catch {
case e: SparkException =>
logError(s"Error closing earliest Kafka consumer for ${entry.getKey}", e)
}
true
} else {
false
}
}
}
}
/**
* Get a cached consumer for groupId, assigned to topic and partition.
* If matching consumer doesn't already exist, will be created using kafkaParams.
*/
def getOrCreate(
topic: String,
partition: Int,
kafkaParams: ju.Map[String, Object]): CachedKafkaConsumer = synchronized {
val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String]
val topicPartition = new TopicPartition(topic, partition)
val key = CacheKey(groupId, topicPartition)
// If this is reattempt at running the task, then invalidate cache and start with
// a new consumer
if (TaskContext.get != null && TaskContext.get.attemptNumber > 1) {
cache.remove(key)
new CachedKafkaConsumer(topicPartition, kafkaParams)
} else {
if (!cache.containsKey(key)) {
cache.put(key, new CachedKafkaConsumer(topicPartition, kafkaParams))
}
cache.get(key)
}
}
}
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