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KAFKA-5358: Consumer perf tool should count rebalance time. #3188

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KAFKA-5358: Consumer perf tool should count rebalance time. #3188

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KAFKA-5358: Consumer perf tool should count rebalance time.
huxi-2b Jun 1, 2017
cbdf6c1
1. Refined the name to `total.rebalance.time`
huxi-2b Jun 2, 2017
7600e6b
Refined time counting for both fetcher and rebalance as per hachikuji…
huxi-2b Jun 6, 2017
08ff452
Correct the counting for total fetch time.
huxi-2b Jun 7, 2017
8c80f13
1. Split `MB.sec` into two parts: `total.MB.sec` and `fetch.MB.sec`
huxi-2b Jun 8, 2017
edf1d08
returned back to the original output format for new consumer
huxi-2b Jun 21, 2017
722e16d
As per hackikuji's comments, refined code to print out rebalance time…
huxi-2b Jun 27, 2017
57bd0e4
resovled conflicts with trunk
huxi-2b Aug 4, 2017
16cc2ca
Merge branch 'trunk' into KAKFA-5358
huxi-2b Aug 4, 2017
3798272
KAFKA-5358: Did not show newly-created headers if `--show-detailed-st…
huxi-2b Aug 4, 2017
bbba9b0
correct printHeader invoking to have testHeaderMatchBody passed
huxi-2b Aug 9, 2017
3a1ce0f
Added newly-created fields for detailed views.
huxi-2b Aug 17, 2017
c517815
1. Added parameter name when invoking testDetailedHeaderMatchBody; 2.…
huxi-2b Aug 21, 2017
9adda74
added a field that tracks periodic join time.
huxi-2b Sep 6, 2017
dfa9422
addressed Jason's last comments
huxi-2b Sep 18, 2017
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51 changes: 38 additions & 13 deletions core/src/main/scala/kafka/tools/ConsumerPerformance.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -54,17 +54,20 @@ object ConsumerPerformance {
val totalBytesRead = new AtomicLong(0)
val consumerTimeout = new AtomicBoolean(false)
var metrics: mutable.Map[MetricName, _ <: Metric] = null
val joinGroupTimeInMs = new AtomicLong(0)
val fetchTimeInMs = new AtomicLong(0)

@hachikuji hachikuji Aug 16, 2017

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There doesn't appear to be any reason for fetchTimeInMs to be an AtomicLong. We can use a regular long and just initialize it below.


if (!config.hideHeader) {
printHeader(!config.showDetailedStats)
printHeader(config.showDetailedStats, config.useOldConsumer)
}

var startMs, endMs = 0L
if (!config.useOldConsumer) {
val consumer = new KafkaConsumer[Array[Byte], Array[Byte]](config.props)
consumer.subscribe(Collections.singletonList(config.topic))
startMs = System.currentTimeMillis
consume(consumer, List(config.topic), config.numMessages, 1000, config, totalMessagesRead, totalBytesRead)
consume(consumer, List(config.topic), config.numMessages, 1000,
config, totalMessagesRead, totalBytesRead, joinGroupTimeInMs)
endMs = System.currentTimeMillis

if (config.printMetrics) {
Expand Down Expand Up @@ -95,10 +98,25 @@ object ConsumerPerformance {
consumerConnector.shutdown()
}
val elapsedSecs = (endMs - startMs) / 1000.0

@hachikuji hachikuji Jun 7, 2017 •
edited
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I think the main point of this patch is to compute the throughput statistics based only on the time spent fetching, so maybe we can move this below and just use fetchTimeInMs.get / 1000.0? We could also report MB.sec as two separate values: total.MB.sec which uses the total time, and fetch.MB.sec which uses only the fetch time. Similarly for nMsg.sec.

fetchTimeInMs.set((endMs - startMs) - joinGroupTimeInMs.get)
if (!config.showDetailedStats) {
val totalMBRead = (totalBytesRead.get * 1.0) / (1024 * 1024)
println("%s, %s, %.4f, %.4f, %d, %.4f".format(config.dateFormat.format(startMs), config.dateFormat.format(endMs),
totalMBRead, totalMBRead / elapsedSecs, totalMessagesRead.get, totalMessagesRead.get / elapsedSecs))
print("%s, %s, %.4f, %.4f, %d, %.4f".format(
config.dateFormat.format(startMs),
config.dateFormat.format(endMs),
totalMBRead,
totalMBRead / elapsedSecs,
totalMessagesRead.get,
totalMessagesRead.get / elapsedSecs
))
if (!config.useOldConsumer) {
print(", %d, %d, %.4f, %.4f".format(
joinGroupTimeInMs.get,
fetchTimeInMs.get,
totalMBRead / (fetchTimeInMs.get / 1000.0),
totalMessagesRead.get / (fetchTimeInMs.get / 1000.0)
))
}
}

if (metrics != null) {
Expand All @@ -107,11 +125,14 @@ object ConsumerPerformance {

}

private[tools] def printHeader(showDetailedStats: Boolean): Unit = {
if (showDetailedStats)
println("start.time, end.time, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec")
else
println("time, threadId, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec")
private[tools] def printHeader(showDetailedStats: Boolean, useOldConsumer: Boolean): Unit = {
if (!showDetailedStats) {
println("start.time, end.time, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec" +
s"${if (!useOldConsumer) ", rebalance.time.ms, fetch.time.ms, fetch.MB.sec, fetch.nMsg.sec" else ""}"
)
} else {
println("time, threadId, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec")

@hachikuji hachikuji Aug 10, 2017

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Is there any reason we don't include the new fields in the "detailed" view?

@huxihx huxihx Aug 14, 2017

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In the detailed view, can we safely think fetch.time.ms is always equal to elapsedMs in printProgressMessage? If yes, then the new fields fetch.MB.sec and fetch.nMsg.sec will always be equal to the existing ones MB.sec and nMsg.sec respectively, so that's why I did not have those new fields included in the detailed view. Does it make any sense?

@hachikuji hachikuji Aug 16, 2017

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I'm not sure I follow. The fetch time is always equal to the total time minus the join group time. Can we not compute that periodically?

}
}

def consume(consumer: KafkaConsumer[Array[Byte], Array[Byte]],
Expand All @@ -120,25 +141,29 @@ object ConsumerPerformance {
timeout: Long,
config: ConsumerPerfConfig,
totalMessagesRead: AtomicLong,
totalBytesRead: AtomicLong) {
totalBytesRead: AtomicLong,
joinTime: AtomicLong) {
var bytesRead = 0L
var messagesRead = 0L
var lastBytesRead = 0L
var lastMessagesRead = 0L
var joinStart = 0L

// Wait for group join, metadata fetch, etc
val joinTimeout = 10000
val isAssigned = new AtomicBoolean(false)
consumer.subscribe(topics.asJava, new ConsumerRebalanceListener {
def onPartitionsAssigned(partitions: util.Collection[TopicPartition]) {
isAssigned.set(true)
joinTime.addAndGet(System.currentTimeMillis - joinStart)
}
def onPartitionsRevoked(partitions: util.Collection[TopicPartition]) {
isAssigned.set(false)
joinStart = System.currentTimeMillis
}})
val joinStart = System.currentTimeMillis()
val joinStartForFirstTime = System.currentTimeMillis()

@hachikuji hachikuji Sep 8, 2017

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Since we are now reporting the join time separately, I don't think we need the loop below anymore. It was initially intended to discount the rebalance overhead, but now we account for that transparently.

while (!isAssigned.get()) {
if (System.currentTimeMillis() - joinStart >= joinTimeout) {
if (System.currentTimeMillis() - joinStartForFirstTime >= joinTimeout) {
throw new Exception("Timed out waiting for initial group join.")
}
consumer.poll(100)
Expand Down Expand Up @@ -189,7 +214,7 @@ object ConsumerPerformance {
val totalMBRead = (bytesRead * 1.0) / (1024 * 1024)
val mbRead = ((bytesRead - lastBytesRead) * 1.0) / (1024 * 1024)
println("%s, %d, %.4f, %.4f, %d, %.4f".format(dateFormat.format(endMs), id, totalMBRead,
1000.0 * (mbRead / elapsedMs), messagesRead, ((messagesRead - lastMessagesRead) / elapsedMs) * 1000.0))
1000.0 * (mbRead / elapsedMs), messagesRead, ((messagesRead - lastMessagesRead) / elapsedMs) * 1000.0))
}

class ConsumerPerfConfig(args: Array[String]) extends PerfConfig(args) {
Expand Down
2 changes: 1 addition & 1 deletion core/src/test/scala/unit/kafka/tools/ConsumerPerformanceTest.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ class ConsumerPerformanceTest {
@Test
def testHeaderMatchBody(): Unit = {
Console.withOut(outContent) {
ConsumerPerformance.printHeader(true)
ConsumerPerformance.printHeader(true, false)

@hachikuji hachikuji Aug 10, 2017

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Please provide parameter names. Also, let's add additional test cases so that we are covering all of the variations: detailed=true/false, consumer=old/new.

@hachikuji hachikuji Aug 16, 2017

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Please address this comment.

ConsumerPerformance.printProgressMessage(1, 1024 * 1024, 0, 1, 0, 0, 1,
new SimpleDateFormat("yyyy-MM-dd HH:mm:ss:SSS")
)
Expand Down