[
https://issues.apache.org/jira/browse/SPARK-8367?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
SaintBacchus updated SPARK-8367:
--------------------------------
Description:
{code:title=BlockGenerator.scala|borderStyle=solid}
/** Change the buffer to which single records are added to. */
private def updateCurrentBuffer(time: Long): Unit = synchronized {
try {
val newBlockBuffer = currentBuffer
currentBuffer = new ArrayBuffer[Any]
if (newBlockBuffer.size > 0) {
val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
val newBlock = new Block(blockId, newBlockBuffer)
listener.onGenerateBlock(blockId)
blocksForPushing.put(newBlock) // put is blocking when queue is full
logDebug("Last element in " + blockId + " is " + newBlockBuffer.last)
}
} catch {
case ie: InterruptedException =>
logInfo("Block updating timer thread was interrupted")
case e: Exception =>
reportError("Error in block updating thread", e)
}
}
{code}
If *spark.streaming.blockInterval* was 0, the *blockId* in the code will always
be the same because of *time* was 0 and *blockIntervalMs* was 0 too.
{code:title=ReliableKafkaReceiver.scala|borderStyle=solid}
private def rememberBlockOffsets(blockId: StreamBlockId): Unit = {
// Get a snapshot of current offset map and store with related block id.
val offsetSnapshot = topicPartitionOffsetMap.toMap
blockOffsetMap.put(blockId, offsetSnapshot)
topicPartitionOffsetMap.clear()
}
{code}
If the *blockId* was the same, Streaming will put current data into later
*offset*.
So when exception occures, the *offset* had commit but the data will loss since
the data was in memory and not comsumed yet.
was:
{code:title=BlockGenerator.scala|borderStyle=solid}
/** Change the buffer to which single records are added to. */
private def updateCurrentBuffer(time: Long): Unit = synchronized {
try {
val newBlockBuffer = currentBuffer
currentBuffer = new ArrayBuffer[Any]
if (newBlockBuffer.size > 0) {
val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
val newBlock = new Block(blockId, newBlockBuffer)
listener.onGenerateBlock(blockId)
blocksForPushing.put(newBlock) // put is blocking when queue is full
logDebug("Last element in " + blockId + " is " + newBlockBuffer.last)
}
} catch {
case ie: InterruptedException =>
logInfo("Block updating timer thread was interrupted")
case e: Exception =>
reportError("Error in block updating thread", e)
}
}
{code}
If *spark.streaming.blockInterval* was 0, the *blockId* in the code will always
be the same because of *time* was 0 and *blockIntervalMs* was 0 too.
{code:title=ReliableKafkaReceiver.scala|borderStyle=solid}
private def rememberBlockOffsets(blockId: StreamBlockId): Unit = {
// Get a snapshot of current offset map and store with related block id.
val offsetSnapshot = topicPartitionOffsetMap.toMap
blockOffsetMap.put(blockId, offsetSnapshot)
topicPartitionOffsetMap.clear()
}
{code}
If the *blockId* was the same, Streaming will put current data into later
*offset*.
So when exception occures, the *offset* had commit but the data will loss
> ReliableKafka will loss data when `spark.streaming.blockInterval` was 0
> -----------------------------------------------------------------------
>
> Key: SPARK-8367
> URL: https://issues.apache.org/jira/browse/SPARK-8367
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.4.0
> Reporter: SaintBacchus
>
> {code:title=BlockGenerator.scala|borderStyle=solid}
> /** Change the buffer to which single records are added to. */
> private def updateCurrentBuffer(time: Long): Unit = synchronized {
> try {
> val newBlockBuffer = currentBuffer
> currentBuffer = new ArrayBuffer[Any]
> if (newBlockBuffer.size > 0) {
> val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
> val newBlock = new Block(blockId, newBlockBuffer)
> listener.onGenerateBlock(blockId)
> blocksForPushing.put(newBlock) // put is blocking when queue is full
> logDebug("Last element in " + blockId + " is " + newBlockBuffer.last)
> }
> } catch {
> case ie: InterruptedException =>
> logInfo("Block updating timer thread was interrupted")
> case e: Exception =>
> reportError("Error in block updating thread", e)
> }
> }
> {code}
> If *spark.streaming.blockInterval* was 0, the *blockId* in the code will
> always be the same because of *time* was 0 and *blockIntervalMs* was 0 too.
> {code:title=ReliableKafkaReceiver.scala|borderStyle=solid}
> private def rememberBlockOffsets(blockId: StreamBlockId): Unit = {
> // Get a snapshot of current offset map and store with related block id.
> val offsetSnapshot = topicPartitionOffsetMap.toMap
> blockOffsetMap.put(blockId, offsetSnapshot)
> topicPartitionOffsetMap.clear()
> }
> {code}
> If the *blockId* was the same, Streaming will put current data into later
> *offset*.
> So when exception occures, the *offset* had commit but the data will loss
> since the data was in memory and not comsumed yet.
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