spark KafkaDataConsumer 源码
spark KafkaDataConsumer 代码
文件路径：/connector/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/consumer/KafkaDataConsumer.scala
/*
* 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.consumer
import java.{util => ju}
import java.io.Closeable
import java.time.Duration
import java.util.concurrent.TimeoutException
import scala.collection.JavaConverters._
import org.apache.kafka.clients.CommonClientConfigs
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord, KafkaConsumer, OffsetOutOfRangeException}
import org.apache.kafka.common.TopicPartition
import org.apache.spark.{SparkEnv, TaskContext}
import org.apache.spark.deploy.security.HadoopDelegationTokenManager
import org.apache.spark.internal.Logging
import org.apache.spark.kafka010.{KafkaConfigUpdater, KafkaTokenUtil}
import org.apache.spark.sql.kafka010.KafkaSourceProvider._
import org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.{AvailableOffsetRange, UNKNOWN_OFFSET}
import org.apache.spark.util.{ShutdownHookManager, UninterruptibleThread}
/**
* This class simplifies the usages of Kafka consumer in Spark SQL Kafka connector.
*
* NOTE: Like KafkaConsumer, this class is not thread-safe.
* NOTE for contributors: It is possible for the instance to be used from multiple callers,
* so all the methods should not rely on current cursor and use seek manually.
*/
private[kafka010] class InternalKafkaConsumer(
val topicPartition: TopicPartition,
val kafkaParams: ju.Map[String, Object]) extends Closeable with Logging {
val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String]
// Exposed for testing
private[consumer] val clusterConfig = KafkaTokenUtil.findMatchingTokenClusterConfig(
SparkEnv.get.conf, kafkaParams.get(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG)
.asInstanceOf[String])
// Kafka consumer is not able to give back the params instantiated with so we need to store it.
// It must be updated whenever a new consumer is created.
// Exposed for testing
private[consumer] var kafkaParamsWithSecurity: ju.Map[String, Object] = _
private val consumer = createConsumer()
/**
* Poll messages from Kafka starting from `offset` and returns a pair of "list of consumer record"
* and "offset after poll". The list of consumer record may be empty if the Kafka consumer fetches
* some messages but all of them are not visible messages (either transaction messages,
* or aborted messages when `isolation.level` is `read_committed`).
*
* @throws OffsetOutOfRangeException if `offset` is out of range.
* @throws TimeoutException if the consumer position is not changed after polling. It means the
* consumer polls nothing before timeout.
*/
def fetch(offset: Long, pollTimeoutMs: Long):
(ju.List[ConsumerRecord[Array[Byte], Array[Byte]]], Long, AvailableOffsetRange) = {
// Seek to the offset because we may call seekToBeginning or seekToEnd before this.
seek(offset)
val p = consumer.poll(Duration.ofMillis(pollTimeoutMs))
val r = p.records(topicPartition)
logDebug(s"Polled $groupId ${p.partitions()} ${r.size}")
val offsetAfterPoll = consumer.position(topicPartition)
logDebug(s"Offset changed from $offset to $offsetAfterPoll after polling")
val range = getAvailableOffsetRange()
val fetchedData = (r, offsetAfterPoll, range)
if (r.isEmpty) {
// We cannot fetch anything after `poll`. Two possible cases:
// - `offset` is out of range so that Kafka returns nothing. `OffsetOutOfRangeException` will
// be thrown.
// - Cannot fetch any data before timeout. `TimeoutException` will be thrown.
// - Fetched something but all of them are not invisible. This is a valid case and let the
// caller handles this.
if (offset < range.earliest || offset >= range.latest) {
throw new OffsetOutOfRangeException(
Map(topicPartition -> java.lang.Long.valueOf(offset)).asJava)
} else if (offset == offsetAfterPoll) {
throw new TimeoutException(
s"Cannot fetch record for offset $offset in $pollTimeoutMs milliseconds")
}
}
fetchedData
}
/**
* Return the available offset range of the current partition. It's a pair of the earliest offset
* and the latest offset.
*/
def getAvailableOffsetRange(): AvailableOffsetRange = {
consumer.seekToBeginning(Set(topicPartition).asJava)
val earliestOffset = consumer.position(topicPartition)
consumer.seekToEnd(Set(topicPartition).asJava)
val latestOffset = consumer.position(topicPartition)
AvailableOffsetRange(earliestOffset, latestOffset)
}
override def close(): Unit = {
consumer.close()
}
/** Create a KafkaConsumer to fetch records for `topicPartition` */
private def createConsumer(): KafkaConsumer[Array[Byte], Array[Byte]] = {
kafkaParamsWithSecurity = KafkaConfigUpdater("executor", kafkaParams.asScala.toMap)
.setAuthenticationConfigIfNeeded(clusterConfig)
.build()
val c = new KafkaConsumer[Array[Byte], Array[Byte]](kafkaParamsWithSecurity)
val tps = new ju.ArrayList[TopicPartition]()
tps.add(topicPartition)
c.assign(tps)
c
}
private def seek(offset: Long): Unit = {
logDebug(s"Seeking to $groupId $topicPartition $offset")
consumer.seek(topicPartition, offset)
}
}
/**
* The internal object to store the fetched data from Kafka consumer and the next offset to poll.
*
* @param _records the pre-fetched Kafka records.
* @param _nextOffsetInFetchedData the next offset in `records`. We use this to verify if we
* should check if the pre-fetched data is still valid.
* @param _offsetAfterPoll the Kafka offset after calling `poll`. We will use this offset to
* poll when `records` is drained.
* @param _availableOffsetRange the available offset range in Kafka when polling the records.
*/
private[consumer] case class FetchedData(
private var _records: ju.ListIterator[ConsumerRecord[Array[Byte], Array[Byte]]],
private var _nextOffsetInFetchedData: Long,
private var _offsetAfterPoll: Long,
private var _availableOffsetRange: AvailableOffsetRange) {
def withNewPoll(
records: ju.ListIterator[ConsumerRecord[Array[Byte], Array[Byte]]],
offsetAfterPoll: Long,
availableOffsetRange: AvailableOffsetRange): FetchedData = {
this._records = records
this._nextOffsetInFetchedData = UNKNOWN_OFFSET
this._offsetAfterPoll = offsetAfterPoll
this._availableOffsetRange = availableOffsetRange
this
}
/** Whether there are more elements */
def hasNext: Boolean = _records.hasNext
/** Move `records` forward and return the next record. */
def next(): ConsumerRecord[Array[Byte], Array[Byte]] = {
val record = _records.next()
_nextOffsetInFetchedData = record.offset + 1
record
}
/** Move `records` backward and return the previous record. */
def previous(): ConsumerRecord[Array[Byte], Array[Byte]] = {
assert(_records.hasPrevious, "fetchedData cannot move back")
val record = _records.previous()
_nextOffsetInFetchedData = record.offset
record
}
/** Reset the internal pre-fetched data. */
def reset(): Unit = {
_records = ju.Collections.emptyListIterator()
_nextOffsetInFetchedData = UNKNOWN_OFFSET
_offsetAfterPoll = UNKNOWN_OFFSET
_availableOffsetRange = AvailableOffsetRange(UNKNOWN_OFFSET, UNKNOWN_OFFSET)
}
/**
* Returns the next offset in `records`. We use this to verify if we should check if the
* pre-fetched data is still valid.
*/
def nextOffsetInFetchedData: Long = _nextOffsetInFetchedData
/**
* Returns the next offset to poll after draining the pre-fetched records.
*/
def offsetAfterPoll: Long = _offsetAfterPoll
/**
* Returns the tuple of earliest and latest offsets that is the available offset range when
* polling the records.
*/
def availableOffsetRange: (Long, Long) =
(_availableOffsetRange.earliest, _availableOffsetRange.latest)
}
/**
* The internal object returned by the `fetchRecord` method. If `record` is empty, it means it is
* invisible (either a transaction message, or an aborted message when the consumer's
* `isolation.level` is `read_committed`), and the caller should use `nextOffsetToFetch` to fetch
* instead.
*/
private[consumer] case class FetchedRecord(
var record: ConsumerRecord[Array[Byte], Array[Byte]],
var nextOffsetToFetch: Long) {
def withRecord(
record: ConsumerRecord[Array[Byte], Array[Byte]],
nextOffsetToFetch: Long): FetchedRecord = {
this.record = record
this.nextOffsetToFetch = nextOffsetToFetch
this
}
}
/**
* This class helps caller to read from Kafka leveraging consumer pool as well as fetched data pool.
* This class throws error when data loss is detected while reading from Kafka.
*
* NOTE for contributors: we need to ensure all the public methods to initialize necessary resources
* via calling `getOrRetrieveConsumer` and `getOrRetrieveFetchedData`.
*/
private[kafka010] class KafkaDataConsumer(
topicPartition: TopicPartition,
kafkaParams: ju.Map[String, Object],
consumerPool: InternalKafkaConsumerPool,
fetchedDataPool: FetchedDataPool) extends Logging {
import KafkaDataConsumer._
private var offsetOutOfRange = 0L
private var dataLoss = 0L
private val isTokenProviderEnabled =
HadoopDelegationTokenManager.isServiceEnabled(SparkEnv.get.conf, "kafka")
// Exposed for testing
@volatile private[consumer] var _consumer: Option[InternalKafkaConsumer] = None
@volatile private var _fetchedData: Option[FetchedData] = None
private val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String]
private val cacheKey = CacheKey(groupId, topicPartition)
/**
* The fetched record returned from the `fetchRecord` method. This is a reusable private object to
* avoid memory allocation.
*/
private val fetchedRecord: FetchedRecord = FetchedRecord(null, UNKNOWN_OFFSET)
/**
* Get the record for the given offset if available.
*
* If the record is invisible (either a
* transaction message, or an aborted message when the consumer's `isolation.level` is
* `read_committed`), it will be skipped and this method will try to fetch next available record
* within [offset, untilOffset).
*
* This method also will try its best to detect data loss. If `failOnDataLoss` is `true`, it will
* throw an exception when it detects an unavailable offset. If `failOnDataLoss` is `false`, this
* method will try to fetch next available record within [offset, untilOffset). When this method
* reaches `untilOffset` and still can't find an available record, it will return `null`.
*
* @param offset the offset to fetch.
* @param untilOffset the max offset to fetch. Exclusive.
* @param pollTimeoutMs timeout in milliseconds to poll data from Kafka.
* @param failOnDataLoss When `failOnDataLoss` is `true`, this method will either return record at
* offset if available, or throw an exception. When `failOnDataLoss` is
* `false`, this method will return record at offset if available, or return
* the record at the next earliest available offset that is less than
* untilOffset, otherwise null.
*/
def get(
offset: Long,
untilOffset: Long,
pollTimeoutMs: Long,
failOnDataLoss: Boolean):
ConsumerRecord[Array[Byte], Array[Byte]] = runUninterruptiblyIfPossible {
require(offset < untilOffset,
s"offset must always be less than untilOffset [offset: $offset, untilOffset: $untilOffset]")
val consumer = getOrRetrieveConsumer()
val fetchedData = getOrRetrieveFetchedData(offset)
logDebug(s"Get $groupId $topicPartition nextOffset ${fetchedData.nextOffsetInFetchedData} " +
s"requested $offset")
// The following loop is basically for `failOnDataLoss = false`. When `failOnDataLoss` is
// `false`, we will try to fetch the record at `offset`, if the record does not exist, we will
// try to fetch next available record within [offset, untilOffset).
// If `failOnDataLoss` is `true`, the loop body will be executed only once, either return the
// record at `offset` or throw an exception when the record does not exist.
var toFetchOffset = offset
var fetchedRecord: FetchedRecord = null
// We want to break out of the while loop on a successful fetch to avoid using "return"
// which may cause a NonLocalReturnControl exception when this method is used as a function.
var isFetchComplete = false
while (toFetchOffset != UNKNOWN_OFFSET && !isFetchComplete) {
try {
fetchedRecord = fetchRecord(consumer, fetchedData, toFetchOffset, untilOffset,
pollTimeoutMs, failOnDataLoss)
if (fetchedRecord.record != null) {
isFetchComplete = true
} else {
toFetchOffset = fetchedRecord.nextOffsetToFetch
if (toFetchOffset >= untilOffset) {
fetchedData.reset()
toFetchOffset = UNKNOWN_OFFSET
} else {
logDebug(s"Skipped offsets [$offset, $toFetchOffset]")
}
}
} catch {
case e: OffsetOutOfRangeException =>
// When there is some error thrown, it's better to use a new consumer to drop all cached
// states in the old consumer. We don't need to worry about the performance because this
// is not a common path.
releaseConsumer()
fetchedData.reset()
reportDataLoss(topicPartition, groupId, failOnDataLoss,
s"Cannot fetch offset $toFetchOffset", e)
val oldToFetchOffsetd = toFetchOffset
toFetchOffset = getEarliestAvailableOffsetBetween(consumer, toFetchOffset, untilOffset)
if (toFetchOffset == UNKNOWN_OFFSET) {
offsetOutOfRange += (untilOffset - oldToFetchOffsetd)
} else {
offsetOutOfRange += (toFetchOffset - oldToFetchOffsetd)
}
}
}
if (isFetchComplete) {
fetchedRecord.record
} else {
fetchedData.reset()
null
}
}
/**
* Return the available offset range of the current partition. It's a pair of the earliest offset
* and the latest offset.
*/
def getAvailableOffsetRange(): AvailableOffsetRange = runUninterruptiblyIfPossible {
val consumer = getOrRetrieveConsumer()
consumer.getAvailableOffsetRange()
}
def getNumOffsetOutOfRange(): Long = offsetOutOfRange
def getNumDataLoss(): Long = dataLoss
/**
* Release borrowed objects in data reader to the pool. Once the instance is created, caller
* must call method after using the instance to make sure resources are not leaked.
*/
def release(): Unit = {
releaseConsumer()
releaseFetchedData()
}
private def releaseConsumer(): Unit = {
if (_consumer.isDefined) {
consumerPool.returnObject(_consumer.get)
_consumer = None
}
}
private def releaseFetchedData(): Unit = {
if (_fetchedData.isDefined) {
fetchedDataPool.release(cacheKey, _fetchedData.get)
_fetchedData = None
}
}
/**
* Return the next earliest available offset in [offset, untilOffset). If all offsets in
* [offset, untilOffset) are invalid (e.g., the topic is deleted and recreated), it will return
* `UNKNOWN_OFFSET`.
*/
private def getEarliestAvailableOffsetBetween(
consumer: InternalKafkaConsumer,
offset: Long,
untilOffset: Long): Long = {
val range = consumer.getAvailableOffsetRange()
logWarning(s"Some data may be lost. Recovering from the earliest offset: ${range.earliest}")
val topicPartition = consumer.topicPartition
val groupId = consumer.groupId
if (offset >= range.latest || range.earliest >= untilOffset) {
// [offset, untilOffset) and [earliestOffset, latestOffset) have no overlap,
// either
// --------------------------------------------------------
// ^ ^ ^ ^
// | | | |
// earliestOffset latestOffset offset untilOffset
//
// or
// --------------------------------------------------------
// ^ ^ ^ ^
// | | | |
// offset untilOffset earliestOffset latestOffset
val warningMessage =
s"""
|The current available offset range is $range.
| Offset $offset is out of range, and records in [$offset, $untilOffset) will be
| skipped ${additionalMessage(topicPartition, groupId, failOnDataLoss = false)}
""".stripMargin
logWarning(warningMessage)
UNKNOWN_OFFSET
} else if (offset >= range.earliest) {
// -----------------------------------------------------------------------------
// ^ ^ ^ ^
// | | | |
// earliestOffset offset min(untilOffset,latestOffset) max(untilOffset, latestOffset)
//
// This will happen when a topic is deleted and recreated, and new data are pushed very fast,
// then we will see `offset` disappears first then appears again. Although the parameters
// are same, the state in Kafka cluster is changed, so the outer loop won't be endless.
logWarning(s"Found a disappeared offset $offset. Some data may be lost " +
s"${additionalMessage(topicPartition, groupId, failOnDataLoss = false)}")
offset
} else {
// ------------------------------------------------------------------------------
// ^ ^ ^ ^
// | | | |
// offset earliestOffset min(untilOffset,latestOffset) max(untilOffset, latestOffset)
val warningMessage =
s"""
|The current available offset range is $range.
| Offset ${offset} is out of range, and records in [$offset, ${range.earliest}) will be
| skipped ${additionalMessage(topicPartition, groupId, failOnDataLoss = false)}
""".stripMargin
logWarning(warningMessage)
range.earliest
}
}
/**
* Get the fetched record for the given offset if available.
*
* If the record is invisible (either a transaction message, or an aborted message when the
* consumer's `isolation.level` is `read_committed`), it will return a `FetchedRecord` with the
* next offset to fetch.
*
* This method also will try the best to detect data loss. If `failOnDataLoss` is `true`, it will
* throw an exception when we detect an unavailable offset. If `failOnDataLoss` is `false`, this
* method will return `null` if the next available record is within [offset, untilOffset).
*
* @throws OffsetOutOfRangeException if `offset` is out of range
* @throws TimeoutException if cannot fetch the record in `pollTimeoutMs` milliseconds.
*/
private def fetchRecord(
consumer: InternalKafkaConsumer,
fetchedData: FetchedData,
offset: Long,
untilOffset: Long,
pollTimeoutMs: Long,
failOnDataLoss: Boolean): FetchedRecord = {
if (offset != fetchedData.nextOffsetInFetchedData) {
// This is the first fetch, or the fetched data has been reset.
// Fetch records from Kafka and update `fetchedData`.
fetchData(consumer, fetchedData, offset, pollTimeoutMs)
} else if (!fetchedData.hasNext) { // The last pre-fetched data has been drained.
if (offset < fetchedData.offsetAfterPoll) {
// Offsets in [offset, fetchedData.offsetAfterPoll) are invisible. Return a record to ask
// the next call to start from `fetchedData.offsetAfterPoll`.
val nextOffsetToFetch = fetchedData.offsetAfterPoll
fetchedData.reset()
return fetchedRecord.withRecord(null, nextOffsetToFetch)
} else {
// Fetch records from Kafka and update `fetchedData`.
fetchData(consumer, fetchedData, offset, pollTimeoutMs)
}
}
if (!fetchedData.hasNext) {
// When we reach here, we have already tried to poll from Kafka. As `fetchedData` is still
// empty, all messages in [offset, fetchedData.offsetAfterPoll) are invisible. Return a
// record to ask the next call to start from `fetchedData.offsetAfterPoll`.
assert(offset <= fetchedData.offsetAfterPoll,
s"seek to $offset and poll but the offset was reset to ${fetchedData.offsetAfterPoll}")
fetchedRecord.withRecord(null, fetchedData.offsetAfterPoll)
} else {
val record = fetchedData.next()
// In general, Kafka uses the specified offset as the start point, and tries to fetch the next
// available offset. Hence we need to handle offset mismatch.
if (record.offset > offset) {
val (earliestOffset, _) = fetchedData.availableOffsetRange
if (earliestOffset <= offset) {
// `offset` is still valid but the corresponding message is invisible. We should skip it
// and jump to `record.offset`. Here we move `fetchedData` back so that the next call of
// `fetchRecord` can just return `record` directly.
fetchedData.previous()
return fetchedRecord.withRecord(null, record.offset)
}
// This may happen when some records aged out but their offsets already got verified
if (failOnDataLoss) {
reportDataLoss(consumer.topicPartition, consumer.groupId, failOnDataLoss = true,
s"Cannot fetch records in [$offset, ${record.offset})")
// Never happen as "reportDataLoss" will throw an exception
throw new IllegalStateException(
"reportDataLoss didn't throw an exception when 'failOnDataLoss' is true")
} else if (record.offset >= untilOffset) {
reportDataLoss(consumer.topicPartition, consumer.groupId, failOnDataLoss = false,
s"Skip missing records in [$offset, $untilOffset)")
// Set `nextOffsetToFetch` to `untilOffset` to finish the current batch.
fetchedRecord.withRecord(null, untilOffset)
} else {
reportDataLoss(consumer.topicPartition, consumer.groupId, failOnDataLoss = false,
s"Skip missing records in [$offset, ${record.offset})")
fetchedRecord.withRecord(record, fetchedData.nextOffsetInFetchedData)
}
} else if (record.offset < offset) {
// This should not happen. If it does happen, then we probably misunderstand Kafka internal
// mechanism.
throw new IllegalStateException(
s"Tried to fetch $offset but the returned record offset was ${record.offset}")
} else {
fetchedRecord.withRecord(record, fetchedData.nextOffsetInFetchedData)
}
}
}
/**
* Poll messages from Kafka starting from `offset` and update `fetchedData`. `fetchedData` may be
* empty if the Kafka consumer fetches some messages but all of them are not visible messages
* (either transaction messages, or aborted messages when `isolation.level` is `read_committed`).
*
* @throws OffsetOutOfRangeException if `offset` is out of range.
* @throws TimeoutException if the consumer position is not changed after polling. It means the
* consumer polls nothing before timeout.
*/
private def fetchData(
consumer: InternalKafkaConsumer,
fetchedData: FetchedData,
offset: Long,
pollTimeoutMs: Long): Unit = {
val (records, offsetAfterPoll, range) = consumer.fetch(offset, pollTimeoutMs)
fetchedData.withNewPoll(records.listIterator, offsetAfterPoll, range)
}
private[kafka010] def getOrRetrieveConsumer(): InternalKafkaConsumer = {
if (!_consumer.isDefined) {
retrieveConsumer()
}
require(_consumer.isDefined, "Consumer must be defined")
if (isTokenProviderEnabled && KafkaTokenUtil.needTokenUpdate(
_consumer.get.kafkaParamsWithSecurity, _consumer.get.clusterConfig)) {
logDebug("Cached consumer uses an old delegation token, invalidating.")
releaseConsumer()
consumerPool.invalidateKey(cacheKey)
fetchedDataPool.invalidate(cacheKey)
retrieveConsumer()
}
_consumer.get
}
private def retrieveConsumer(): Unit = {
_consumer = Option(consumerPool.borrowObject(cacheKey, kafkaParams))
require(_consumer.isDefined, "borrowing consumer from pool must always succeed.")
}
private def getOrRetrieveFetchedData(offset: Long): FetchedData = _fetchedData match {
case None =>
_fetchedData = Option(fetchedDataPool.acquire(cacheKey, offset))
require(_fetchedData.isDefined, "acquiring fetched data from cache must always succeed.")
_fetchedData.get
case Some(fetchedData) => fetchedData
}
/**
* Return an addition message including useful message and instruction.
*/
private def additionalMessage(
topicPartition: TopicPartition,
groupId: String,
failOnDataLoss: Boolean): String = {
if (failOnDataLoss) {
s"(GroupId: $groupId, TopicPartition: $topicPartition). " +
s"$INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_TRUE"
} else {
s"(GroupId: $groupId, TopicPartition: $topicPartition). " +
s"$INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_FALSE"
}
}
/**
* Throw an exception or log a warning as per `failOnDataLoss`.
*/
private def reportDataLoss(
topicPartition: TopicPartition,
groupId: String,
failOnDataLoss: Boolean,
message: String,
cause: Throwable = null): Unit = {
val finalMessage = s"$message ${additionalMessage(topicPartition, groupId, failOnDataLoss)}"
dataLoss += 1
reportDataLoss0(failOnDataLoss, finalMessage, cause)
}
private def runUninterruptiblyIfPossible[T](body: => T): T = Thread.currentThread match {
case ut: UninterruptibleThread =>
ut.runUninterruptibly(body)
case _ =>
logWarning("KafkaDataConsumer is not running in UninterruptibleThread. " +
"It may hang when KafkaDataConsumer's methods are interrupted because of KAFKA-1894")
body
}
}
private[kafka010] object KafkaDataConsumer extends Logging {
val UNKNOWN_OFFSET = -2L
case class AvailableOffsetRange(earliest: Long, latest: Long)
case class CacheKey(groupId: String, topicPartition: TopicPartition) {
def this(topicPartition: TopicPartition, kafkaParams: ju.Map[String, Object]) =
this(kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String], topicPartition)
}
private val sparkConf = SparkEnv.get.conf
private val consumerPool = new InternalKafkaConsumerPool(sparkConf)
private val fetchedDataPool = new FetchedDataPool(sparkConf)
ShutdownHookManager.addShutdownHook { () =>
try {
fetchedDataPool.shutdown()
consumerPool.close()
} catch {
case e: Throwable =>
logWarning("Ignoring Exception while shutting down pools from shutdown hook", e)
}
}
/**
* Get a data reader for groupId, assigned to topic and partition.
* If matching consumer doesn't already exist, will be created using kafkaParams.
* The returned data reader must be released explicitly.
*/
def acquire(
topicPartition: TopicPartition,
kafkaParams: ju.Map[String, Object]): KafkaDataConsumer = {
if (TaskContext.get != null && TaskContext.get.attemptNumber >= 1) {
val cacheKey = new CacheKey(topicPartition, kafkaParams)
// If this is reattempt at running the task, then invalidate cached consumer if any.
consumerPool.invalidateKey(cacheKey)
// invalidate all fetched data for the key as well
// sadly we can't pinpoint specific data and invalidate cause we don't have unique id
fetchedDataPool.invalidate(cacheKey)
}
new KafkaDataConsumer(topicPartition, kafkaParams, consumerPool, fetchedDataPool)
}
private def reportDataLoss0(
failOnDataLoss: Boolean,
finalMessage: String,
cause: Throwable = null): Unit = {
if (failOnDataLoss) {
if (cause != null) {
throw new IllegalStateException(finalMessage, cause)
} else {
throw new IllegalStateException(finalMessage)
}
} else {
if (cause != null) {
logWarning(finalMessage, cause)
} else {
logWarning(finalMessage)
}
}
}
}
相关信息
相关文章
0
赞
- 所属分类： 前端技术
- 本文标签：
热门推荐
-
2、 - 优质文章
-
3、 gate.io
-
8、 golang
-
9、 openharmony
-
10、 Vue中input框自动聚焦