spark KafkaSourceProvider 源码
spark KafkaSourceProvider 代码
文件路径：/connector/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala
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* this work for additional information regarding copyright ownership.
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.sql.kafka010
import java.{util => ju}
import java.util.{Locale, 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.kafka010.KafkaConfigUpdater
import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SQLContext}
import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
import org.apache.spark.sql.connector.catalog.{SupportsRead, SupportsWrite, Table, TableCapability}
import org.apache.spark.sql.connector.metric.{CustomMetric, CustomSumMetric}
import org.apache.spark.sql.connector.read.{Batch, Scan, ScanBuilder}
import org.apache.spark.sql.connector.read.streaming.{ContinuousStream, MicroBatchStream}
import org.apache.spark.sql.connector.write.{LogicalWriteInfo, SupportsTruncate, Write, WriteBuilder}
import org.apache.spark.sql.execution.streaming.{Sink, Source}
import org.apache.spark.sql.internal.connector.{SimpleTableProvider, SupportsStreamingUpdateAsAppend}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.streaming.OutputMode
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.util.CaseInsensitiveStringMap
/**
* 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 SimpleTableProvider
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) = {
val caseInsensitiveParameters = CaseInsensitiveMap(parameters)
validateStreamOptions(caseInsensitiveParameters)
require(schema.isEmpty, "Kafka source has a fixed schema and cannot be set with a custom one")
val includeHeaders = caseInsensitiveParameters.getOrElse(INCLUDE_HEADERS, "false").toBoolean
(shortName(), KafkaRecordToRowConverter.kafkaSchema(includeHeaders))
}
override def createSource(
sqlContext: SQLContext,
metadataPath: String,
schema: Option[StructType],
providerName: String,
parameters: Map[String, String]): Source = {
val caseInsensitiveParameters = CaseInsensitiveMap(parameters)
validateStreamOptions(caseInsensitiveParameters)
// 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 = streamingUniqueGroupId(caseInsensitiveParameters, metadataPath)
val specifiedKafkaParams = convertToSpecifiedParams(caseInsensitiveParameters)
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParameters, STARTING_TIMESTAMP_OPTION_KEY,
STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_OPTION_KEY,
LatestOffsetRangeLimit)
val kafkaOffsetReader = KafkaOffsetReader.build(
strategy(caseInsensitiveParameters),
kafkaParamsForDriver(specifiedKafkaParams),
caseInsensitiveParameters,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaSource(
sqlContext,
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
caseInsensitiveParameters,
metadataPath,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveParameters))
}
override def getTable(options: CaseInsensitiveStringMap): KafkaTable = {
val includeHeaders = options.getBoolean(INCLUDE_HEADERS, false)
new KafkaTable(includeHeaders)
}
/**
* 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 = {
val caseInsensitiveParameters = CaseInsensitiveMap(parameters)
validateBatchOptions(caseInsensitiveParameters)
val specifiedKafkaParams = convertToSpecifiedParams(caseInsensitiveParameters)
val startingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParameters, STARTING_TIMESTAMP_OPTION_KEY,
STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_OPTION_KEY,
EarliestOffsetRangeLimit)
assert(startingRelationOffsets != LatestOffsetRangeLimit)
val endingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveParameters, ENDING_TIMESTAMP_OPTION_KEY,
ENDING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, ENDING_OFFSETS_OPTION_KEY,
LatestOffsetRangeLimit)
assert(endingRelationOffsets != EarliestOffsetRangeLimit)
val includeHeaders = caseInsensitiveParameters.getOrElse(INCLUDE_HEADERS, "false").toBoolean
new KafkaRelation(
sqlContext,
strategy(caseInsensitiveParameters),
sourceOptions = caseInsensitiveParameters,
specifiedKafkaParams = specifiedKafkaParams,
failOnDataLoss = failOnDataLoss(caseInsensitiveParameters),
includeHeaders = includeHeaders,
startingOffsets = startingRelationOffsets,
endingOffsets = endingRelationOffsets)
}
override def createSink(
sqlContext: SQLContext,
parameters: Map[String, String],
partitionColumns: Seq[String],
outputMode: OutputMode): Sink = {
val caseInsensitiveParameters = CaseInsensitiveMap(parameters)
val defaultTopic = caseInsensitiveParameters.get(TOPIC_OPTION_KEY).map(_.trim)
val specifiedKafkaParams = kafkaParamsForProducer(caseInsensitiveParameters)
new KafkaSink(sqlContext, specifiedKafkaParams, 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 caseInsensitiveParameters = CaseInsensitiveMap(parameters)
val topic = caseInsensitiveParameters.get(TOPIC_OPTION_KEY).map(_.trim)
val specifiedKafkaParams = kafkaParamsForProducer(caseInsensitiveParameters)
KafkaWriter.write(outerSQLContext.sparkSession, data.queryExecution, specifiedKafkaParams,
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.")
}
}
private def strategy(params: CaseInsensitiveMap[String]) = {
val lowercaseParams = params.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
lowercaseParams.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 (SUBSCRIBE_PATTERN, 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(params: CaseInsensitiveMap[String]) =
params.getOrElse(FAIL_ON_DATA_LOSS_OPTION_KEY, "true").toBoolean
private def validateGeneralOptions(params: CaseInsensitiveMap[String]): Unit = {
// Validate source options
val lowercaseParams = params.map { case (k, v) => (k.toLowerCase(Locale.ROOT), v) }
val specifiedStrategies =
lowercaseParams.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.")
}
lowercaseParams.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 (SUBSCRIBE_PATTERN, value) =>
val pattern = params(SUBSCRIBE_PATTERN).trim()
if (pattern.isEmpty) {
throw new IllegalArgumentException(
"Pattern to subscribe is empty as specified value for option " +
s"'subscribePattern' is '$value'")
}
case _ =>
// Should never reach here as we are already matching on
// matched strategy names
throw new IllegalArgumentException("Unknown option")
}
// Validate minPartitions value if present
if (params.contains(MIN_PARTITIONS_OPTION_KEY)) {
val p = params(MIN_PARTITIONS_OPTION_KEY).toInt
if (p <= 0) throw new IllegalArgumentException("minPartitions must be positive")
}
// Validate user-specified Kafka options
if (params.contains(s"kafka.${ConsumerConfig.GROUP_ID_CONFIG}")) {
logWarning(CUSTOM_GROUP_ID_ERROR_MESSAGE)
if (params.contains(GROUP_ID_PREFIX)) {
logWarning("Option 'groupIdPrefix' will be ignored as " +
s"option 'kafka.${ConsumerConfig.GROUP_ID_CONFIG}' has been set.")
}
}
if (params.contains(s"kafka.${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
|'$STARTING_OFFSETS_OPTION_KEY' only applies when a new Streaming query is started, and
|that resuming will always pick up from where the query left off. See the docs for more
|details.
""".stripMargin)
}
if (params.contains(s"kafka.${ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG}")) {
throw new IllegalArgumentException(
s"Kafka option '${ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG}' is not supported as keys "
+ "are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame operations "
+ "to explicitly deserialize the keys.")
}
if (params.contains(s"kafka.${ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG}"))
{
throw new IllegalArgumentException(
s"Kafka option '${ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG}' is not supported as "
+ "values are deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame "
+ "operations to explicitly deserialize the values.")
}
val otherUnsupportedConfigs = Seq(
ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, // committing correctly requires new APIs in Source
ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG) // interceptors can modify payload, so not safe
otherUnsupportedConfigs.foreach { c =>
if (params.contains(s"kafka.$c")) {
throw new IllegalArgumentException(s"Kafka option '$c' is not supported")
}
}
if (!params.contains(s"kafka.${ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG}")) {
throw new IllegalArgumentException(
s"Option 'kafka.${ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG}' must be specified for " +
s"configuring Kafka consumer")
}
if (params.contains(MIN_OFFSET_PER_TRIGGER) && params.contains(MAX_OFFSET_PER_TRIGGER)) {
val minOffsets = params.get(MIN_OFFSET_PER_TRIGGER).get.toLong
val maxOffsets = params.get(MAX_OFFSET_PER_TRIGGER).get.toLong
if (minOffsets > maxOffsets) {
throw new IllegalArgumentException(s"The value of minOffsetPerTrigger($minOffsets) is " +
s"higher than the maxOffsetsPerTrigger($maxOffsets).")
}
}
}
private def validateStreamOptions(params: CaseInsensitiveMap[String]) = {
// Stream specific options
params.get(ENDING_OFFSETS_OPTION_KEY).map(_ =>
throw new IllegalArgumentException("ending offset not valid in streaming queries"))
params.get(ENDING_OFFSETS_BY_TIMESTAMP_OPTION_KEY).map(_ =>
throw new IllegalArgumentException("ending timestamp not valid in streaming queries"))
validateGeneralOptions(params)
}
private def validateBatchOptions(params: CaseInsensitiveMap[String]) = {
// Batch specific options
KafkaSourceProvider.getKafkaOffsetRangeLimit(
params, STARTING_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY,
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
}
case _: SpecificTimestampRangeLimit => // good to go
case _: GlobalTimestampRangeLimit => // good to go
}
KafkaSourceProvider.getKafkaOffsetRangeLimit(
params, ENDING_TIMESTAMP_OPTION_KEY, ENDING_OFFSETS_BY_TIMESTAMP_OPTION_KEY,
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
}
case _: SpecificTimestampRangeLimit => // good to go
case _: GlobalTimestampRangeLimit => // good to go
}
validateGeneralOptions(params)
// Don't want to throw an error, but at least log a warning.
if (params.contains(MAX_OFFSET_PER_TRIGGER)) {
logWarning("maxOffsetsPerTrigger option ignored in batch queries")
}
if (params.contains(MIN_OFFSET_PER_TRIGGER)) {
logWarning("minOffsetsPerTrigger option ignored in batch queries")
}
if (params.contains(MAX_TRIGGER_DELAY)) {
logWarning("maxTriggerDelay option ignored in batch queries")
}
}
class KafkaTable(includeHeaders: Boolean) extends Table with SupportsRead with SupportsWrite {
override def name(): String = "KafkaTable"
override def schema(): StructType = KafkaRecordToRowConverter.kafkaSchema(includeHeaders)
override def capabilities(): ju.Set[TableCapability] = {
import TableCapability._
// ACCEPT_ANY_SCHEMA is needed because of the following reasons:
// * Kafka writer validates the schema instead of the SQL analyzer (the schema is fixed)
// * Read schema differs from write schema (please see Kafka integration guide)
ju.EnumSet.of(BATCH_READ, BATCH_WRITE, MICRO_BATCH_READ, CONTINUOUS_READ, STREAMING_WRITE,
ACCEPT_ANY_SCHEMA)
}
override def newScanBuilder(options: CaseInsensitiveStringMap): ScanBuilder =
() => new KafkaScan(options)
override def newWriteBuilder(info: LogicalWriteInfo): WriteBuilder = {
new WriteBuilder with SupportsTruncate with SupportsStreamingUpdateAsAppend {
private val options = info.options
private val inputSchema: StructType = info.schema()
private val topic = Option(options.get(TOPIC_OPTION_KEY)).map(_.trim)
private val producerParams =
kafkaParamsForProducer(CaseInsensitiveMap(options.asScala.toMap))
override def build(): Write = KafkaWrite(topic, producerParams, inputSchema)
override def truncate(): WriteBuilder = this
}
}
}
class KafkaScan(options: CaseInsensitiveStringMap) extends Scan {
val includeHeaders = options.getBoolean(INCLUDE_HEADERS, false)
override def readSchema(): StructType = {
KafkaRecordToRowConverter.kafkaSchema(includeHeaders)
}
override def toBatch(): Batch = {
val caseInsensitiveOptions = CaseInsensitiveMap(options.asScala.toMap)
validateBatchOptions(caseInsensitiveOptions)
val specifiedKafkaParams = convertToSpecifiedParams(caseInsensitiveOptions)
val startingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveOptions, STARTING_TIMESTAMP_OPTION_KEY,
STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_OPTION_KEY,
EarliestOffsetRangeLimit)
val endingRelationOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveOptions, ENDING_TIMESTAMP_OPTION_KEY,
ENDING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, ENDING_OFFSETS_OPTION_KEY,
LatestOffsetRangeLimit)
new KafkaBatch(
strategy(caseInsensitiveOptions),
caseInsensitiveOptions,
specifiedKafkaParams,
failOnDataLoss(caseInsensitiveOptions),
startingRelationOffsets,
endingRelationOffsets,
includeHeaders)
}
override def toMicroBatchStream(checkpointLocation: String): MicroBatchStream = {
val caseInsensitiveOptions = CaseInsensitiveMap(options.asScala.toMap)
validateStreamOptions(caseInsensitiveOptions)
// 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 = streamingUniqueGroupId(caseInsensitiveOptions, checkpointLocation)
val specifiedKafkaParams = convertToSpecifiedParams(caseInsensitiveOptions)
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveOptions, STARTING_TIMESTAMP_OPTION_KEY,
STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_OPTION_KEY,
LatestOffsetRangeLimit)
val kafkaOffsetReader = KafkaOffsetReader.build(
strategy(caseInsensitiveOptions),
kafkaParamsForDriver(specifiedKafkaParams),
caseInsensitiveOptions,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaMicroBatchStream(
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
options,
checkpointLocation,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveOptions))
}
override def toContinuousStream(checkpointLocation: String): ContinuousStream = {
val caseInsensitiveOptions = CaseInsensitiveMap(options.asScala.toMap)
validateStreamOptions(caseInsensitiveOptions)
// 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 = streamingUniqueGroupId(caseInsensitiveOptions, checkpointLocation)
val specifiedKafkaParams = convertToSpecifiedParams(caseInsensitiveOptions)
val startingStreamOffsets = KafkaSourceProvider.getKafkaOffsetRangeLimit(
caseInsensitiveOptions, STARTING_TIMESTAMP_OPTION_KEY,
STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY, STARTING_OFFSETS_OPTION_KEY,
LatestOffsetRangeLimit)
val kafkaOffsetReader = KafkaOffsetReader.build(
strategy(caseInsensitiveOptions),
kafkaParamsForDriver(specifiedKafkaParams),
caseInsensitiveOptions,
driverGroupIdPrefix = s"$uniqueGroupId-driver")
new KafkaContinuousStream(
kafkaOffsetReader,
kafkaParamsForExecutors(specifiedKafkaParams, uniqueGroupId),
options,
checkpointLocation,
startingStreamOffsets,
failOnDataLoss(caseInsensitiveOptions))
}
override def supportedCustomMetrics(): Array[CustomMetric] = {
Array(new OffsetOutOfRangeMetric, new DataLossMetric)
}
}
}
private[spark] class OffsetOutOfRangeMetric extends CustomSumMetric {
override def name(): String = "offsetOutOfRange"
override def description(): String = "estimated number of fetched offsets out of range"
}
private[spark] class DataLossMetric extends CustomSumMetric {
override def name(): String = "dataLoss"
override def description(): String = "number of data loss error"
}
private[kafka010] object KafkaSourceProvider extends Logging {
private val ASSIGN = "assign"
private val SUBSCRIBE_PATTERN = "subscribepattern"
private val SUBSCRIBE = "subscribe"
private val STRATEGY_OPTION_KEYS = Set(SUBSCRIBE, SUBSCRIBE_PATTERN, ASSIGN)
private[kafka010] val STARTING_OFFSETS_OPTION_KEY = "startingoffsets"
private[kafka010] val ENDING_OFFSETS_OPTION_KEY = "endingoffsets"
private[kafka010] val STARTING_OFFSETS_BY_TIMESTAMP_OPTION_KEY = "startingoffsetsbytimestamp"
private[kafka010] val ENDING_OFFSETS_BY_TIMESTAMP_OPTION_KEY = "endingoffsetsbytimestamp"
private[kafka010] val STARTING_TIMESTAMP_OPTION_KEY = "startingtimestamp"
private[kafka010] val ENDING_TIMESTAMP_OPTION_KEY = "endingtimestamp"
private val FAIL_ON_DATA_LOSS_OPTION_KEY = "failondataloss"
private[kafka010] val MIN_PARTITIONS_OPTION_KEY = "minpartitions"
private[kafka010] val MAX_OFFSET_PER_TRIGGER = "maxoffsetspertrigger"
private[kafka010] val MIN_OFFSET_PER_TRIGGER = "minoffsetspertrigger"
private[kafka010] val MAX_TRIGGER_DELAY = "maxtriggerdelay"
private[kafka010] val DEFAULT_MAX_TRIGGER_DELAY = "15m"
private[kafka010] val FETCH_OFFSET_NUM_RETRY = "fetchoffset.numretries"
private[kafka010] val FETCH_OFFSET_RETRY_INTERVAL_MS = "fetchoffset.retryintervalms"
private[kafka010] val CONSUMER_POLL_TIMEOUT = "kafkaconsumer.polltimeoutms"
private[kafka010] val STARTING_OFFSETS_BY_TIMESTAMP_STRATEGY_KEY =
"startingoffsetsbytimestampstrategy"
private val GROUP_ID_PREFIX = "groupidprefix"
private[kafka010] val INCLUDE_HEADERS = "includeheaders"
// This is only for internal testing and should not be used otherwise.
private[kafka010] val MOCK_SYSTEM_TIME = "_mockSystemTime"
private[kafka010] object StrategyOnNoMatchStartingOffset extends Enumeration {
val ERROR, LATEST = Value
}
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
val CUSTOM_GROUP_ID_ERROR_MESSAGE =
s"""Kafka option 'kafka.${ConsumerConfig.GROUP_ID_CONFIG}' has been set on this query, it is
| not recommended to set this option. This option is unsafe to use since multiple concurrent
| queries or sources using the same group id will interfere with each other as they are part
| of the same consumer group. Restarted queries may also suffer interference from the
| previous run having the same group id. The user should have only one query per group id,
| and/or set the option 'kafka.session.timeout.ms' to be very small so that the Kafka
| consumers from the previous query are marked dead by the Kafka group coordinator before the
| restarted query starts running.
""".stripMargin
private val serClassName = classOf[ByteArraySerializer].getName
private val deserClassName = classOf[ByteArrayDeserializer].getName
def getKafkaOffsetRangeLimit(
params: CaseInsensitiveMap[String],
globalOffsetTimestampOptionKey: String,
offsetByTimestampOptionKey: String,
offsetOptionKey: String,
defaultOffsets: KafkaOffsetRangeLimit): KafkaOffsetRangeLimit = {
// The order below represents "preferences"
val strategyOnNoMatchStartingOffset = params.get(STARTING_OFFSETS_BY_TIMESTAMP_STRATEGY_KEY)
.map(v => StrategyOnNoMatchStartingOffset.withName(v.toUpperCase(Locale.ROOT)))
.getOrElse(StrategyOnNoMatchStartingOffset.ERROR)
if (params.contains(globalOffsetTimestampOptionKey)) {
// 1. global timestamp
val tsStr = params(globalOffsetTimestampOptionKey).trim
try {
val ts = tsStr.toLong
GlobalTimestampRangeLimit(ts, strategyOnNoMatchStartingOffset)
} catch {
case _: NumberFormatException =>
throw new IllegalArgumentException(s"Expected a single long value, got $tsStr")
}
} else if (params.contains(offsetByTimestampOptionKey)) {
// 2. timestamp per topic partition
val json = params(offsetByTimestampOptionKey).trim
SpecificTimestampRangeLimit(JsonUtils.partitionTimestamps(json),
strategyOnNoMatchStartingOffset)
} else {
// 3. latest/earliest/offset
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] =
KafkaConfigUpdater("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, java.lang.Integer.valueOf(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] =
KafkaConfigUpdater("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
.setIfUnset(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()
/**
* Returns a unique batch consumer group (group.id), allowing the user to set the prefix of
* the consumer group
*/
private[kafka010] def batchUniqueGroupId(params: CaseInsensitiveMap[String]): String = {
val groupIdPrefix = params.getOrElse(GROUP_ID_PREFIX, "spark-kafka-relation")
s"${groupIdPrefix}-${UUID.randomUUID}"
}
/**
* Returns a unique streaming consumer group (group.id), allowing the user to set the prefix of
* the consumer group
*/
private def streamingUniqueGroupId(
params: CaseInsensitiveMap[String],
metadataPath: String): String = {
val groupIdPrefix = params.getOrElse(GROUP_ID_PREFIX, "spark-kafka-source")
s"${groupIdPrefix}-${UUID.randomUUID}-${metadataPath.hashCode}"
}
private[kafka010] def kafkaParamsForProducer(
params: CaseInsensitiveMap[String]): ju.Map[String, Object] = {
if (params.contains(s"kafka.${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}")) {
throw new IllegalArgumentException(
s"Kafka option '${ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG}' is not supported as keys "
+ "are serialized with ByteArraySerializer.")
}
if (params.contains(s"kafka.${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.")
}
val specifiedKafkaParams = convertToSpecifiedParams(params)
KafkaConfigUpdater("executor", specifiedKafkaParams)
.set(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, serClassName)
.set(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, serClassName)
.build()
}
private def convertToSpecifiedParams(parameters: Map[String, String]): Map[String, String] = {
parameters
.keySet
.filter(_.toLowerCase(Locale.ROOT).startsWith("kafka."))
.map { k => k.drop(6) -> parameters(k) }
.toMap
}
}
相关信息
相关文章
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spark KafkaMicroBatchStream 源码
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