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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.streaming.ui
import scala.xml.Node
import org.apache.spark.ui.{UIUtils => SparkUIUtils}
private[ui] abstract class BatchTableBase(tableId: String, batchInterval: Long) {
protected def columns: Seq[Node] = {
<th>Batch Time</th>
<th>Input Size</th>
<th>Scheduling Delay
{SparkUIUtils.tooltip("Time taken by Streaming scheduler to submit jobs of a batch", "top")}
</th>
<th>Processing Time
{SparkUIUtils.tooltip("Time taken to process all jobs of a batch", "top")}</th>
}
/**
* Return the first failure reason if finding in the batches.
*/
protected def getFirstFailureReason(batches: Seq[BatchUIData]): Option[String] = {
batches.flatMap(_.outputOperations.flatMap(_._2.failureReason)).headOption
}
protected def getFirstFailureTableCell(batch: BatchUIData): Seq[Node] = {
val firstFailureReason = batch.outputOperations.flatMap(_._2.failureReason).headOption
firstFailureReason.map { failureReason =>
val failureReasonForUI = UIUtils.createOutputOperationFailureForUI(failureReason)
UIUtils.failureReasonCell(
failureReasonForUI, rowspan = 1, includeFirstLineInExpandDetails = false)
}.getOrElse(<td>-</td>)
}
protected def baseRow(batch: BatchUIData): Seq[Node] = {
val batchTime = batch.batchTime.milliseconds
val formattedBatchTime = UIUtils.formatBatchTime(batchTime, batchInterval)
val eventCount = batch.numRecords
val schedulingDelay = batch.schedulingDelay
val formattedSchedulingDelay = schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
val processingTime = batch.processingDelay
val formattedProcessingTime = processingTime.map(SparkUIUtils.formatDuration).getOrElse("-")
val batchTimeId = s"batch-$batchTime"
<td id={batchTimeId} sorttable_customkey={batchTime.toString}
isFailed={batch.isFailed.toString}>
<a href={s"batch?id=$batchTime"}>
{formattedBatchTime}
</a>
</td>
<td sorttable_customkey={eventCount.toString}>{eventCount.toString} events</td>
<td sorttable_customkey={schedulingDelay.getOrElse(Long.MaxValue).toString}>
{formattedSchedulingDelay}
</td>
<td sorttable_customkey={processingTime.getOrElse(Long.MaxValue).toString}>
{formattedProcessingTime}
</td>
}
private def batchTable: Seq[Node] = {
<table id={tableId} class="table table-bordered table-striped table-condensed sortable">
<thead>
{columns}
</thead>
<tbody>
{renderRows}
</tbody>
</table>
}
def toNodeSeq: Seq[Node] = {
batchTable
}
protected def createOutputOperationProgressBar(batch: BatchUIData): Seq[Node] = {
<td class="progress-cell">
{
SparkUIUtils.makeProgressBar(
started = batch.numActiveOutputOp,
completed = batch.numCompletedOutputOp,
failed = batch.numFailedOutputOp,
skipped = 0,
total = batch.outputOperations.size)
}
</td>
}
/**
* Return HTML for all rows of this table.
*/
protected def renderRows: Seq[Node]
}
private[ui] class ActiveBatchTable(
runningBatches: Seq[BatchUIData],
waitingBatches: Seq[BatchUIData],
batchInterval: Long) extends BatchTableBase("active-batches-table", batchInterval) {
private val firstFailureReason = getFirstFailureReason(runningBatches)
override protected def columns: Seq[Node] = super.columns ++ {
<th>Output Ops: Succeeded/Total</th>
<th>Status</th> ++ {
if (firstFailureReason.nonEmpty) {
<th>Error</th>
} else {
Nil
}
}
}
override protected def renderRows: Seq[Node] = {
// The "batchTime"s of "waitingBatches" must be greater than "runningBatches"'s, so display
// waiting batches before running batches
waitingBatches.flatMap(batch => <tr>{waitingBatchRow(batch)}</tr>) ++
runningBatches.flatMap(batch => <tr>{runningBatchRow(batch)}</tr>)
}
private def runningBatchRow(batch: BatchUIData): Seq[Node] = {
baseRow(batch) ++ createOutputOperationProgressBar(batch) ++ <td>processing</td> ++ {
if (firstFailureReason.nonEmpty) {
getFirstFailureTableCell(batch)
} else {
Nil
}
}
}
private def waitingBatchRow(batch: BatchUIData): Seq[Node] = {
baseRow(batch) ++ createOutputOperationProgressBar(batch) ++ <td>queued</td>++ {
if (firstFailureReason.nonEmpty) {
// Waiting batches have not run yet, so must have no failure reasons.
<td>-</td>
} else {
Nil
}
}
}
}
private[ui] class CompletedBatchTable(batches: Seq[BatchUIData], batchInterval: Long)
extends BatchTableBase("completed-batches-table", batchInterval) {
private val firstFailureReason = getFirstFailureReason(batches)
override protected def columns: Seq[Node] = super.columns ++ {
<th>Total Delay {SparkUIUtils.tooltip("Total time taken to handle a batch", "top")}</th>
<th>Output Ops: Succeeded/Total</th> ++ {
if (firstFailureReason.nonEmpty) {
<th>Error</th>
} else {
Nil
}
}
}
override protected def renderRows: Seq[Node] = {
batches.flatMap(batch => <tr>{completedBatchRow(batch)}</tr>)
}
private def completedBatchRow(batch: BatchUIData): Seq[Node] = {
val totalDelay = batch.totalDelay
val formattedTotalDelay = totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
baseRow(batch) ++ {
<td sorttable_customkey={totalDelay.getOrElse(Long.MaxValue).toString}>
{formattedTotalDelay}
</td>
} ++ createOutputOperationProgressBar(batch)++ {
if (firstFailureReason.nonEmpty) {
getFirstFailureTableCell(batch)
} else {
Nil
}
}
}
}
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