sarutak commented on a change in pull request #26201: [SPARK-29543][SS][UI] Init structured streaming ui URL: https://github.com/apache/spark/pull/26201#discussion_r344676885
########## File path: sql/core/src/main/scala/org/apache/spark/sql/streaming/ui/StreamingQueryStatisticsPage.scala ########## @@ -0,0 +1,296 @@ +/* + * 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.streaming.ui + +import java.{util => ju} +import java.lang.{Long => JLong} +import java.text.SimpleDateFormat +import java.util.UUID +import javax.servlet.http.HttpServletRequest + +import scala.collection.JavaConverters._ +import scala.xml.{Node, Unparsed} + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.catalyst.util.DateTimeUtils.getTimeZone +import org.apache.spark.sql.execution.streaming.{QuerySummary, StreamQueryStore} +import org.apache.spark.sql.execution.ui.SQLTab +import org.apache.spark.sql.streaming.StreamingQuery +import org.apache.spark.sql.streaming.ui.UIUtils._ +import org.apache.spark.ui.{GraphUIData, JsCollector, UIUtils => SparkUIUtils, WebUIPage} + +class StreamingQueryStatisticsPage( + parent: SQLTab, + store: Option[StreamQueryStore]) + extends WebUIPage("streaming/statistics") with Logging { + val df = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'Z'") + df.setTimeZone(getTimeZone("UTC")) + + def generateLoadResources(request: HttpServletRequest): Seq[Node] = { + // scalastyle:off + <script src={SparkUIUtils.prependBaseUri(request, "/static/d3.min.js")}></script> + <link rel="stylesheet" href={SparkUIUtils.prependBaseUri(request, "/static/streaming-page.css")} type="text/css"/> + <script src={SparkUIUtils.prependBaseUri(request, "/static/streaming-page.js")}></script> + // scalastyle:on + } + + override def render(request: HttpServletRequest): Seq[Node] = { + val parameterId = request.getParameter("id") + require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") + + val (query, timeSinceStart) = if (store.nonEmpty) { + store.get.allStreamQueries.find { case (query, _) => + query.runId.equals(UUID.fromString(parameterId)) + }.getOrElse(throw new Exception(s"Can not find streaming query $parameterId")) + } else { + throw new Exception(s"Can not find streaming query $parameterId") + } + + val resources = generateLoadResources(request) + val basicInfo = generateBasicInfo(query, timeSinceStart) + val content = + store.synchronized { // make sure all parts in this page are consistent + resources ++ + basicInfo ++ + generateStatTable(query) + } + SparkUIUtils.headerSparkPage(request, "Streaming Query Statistics", content, parent) + } + + def generateTimeMap(times: Seq[Long]): Seq[Node] = { + val js = "var timeFormat = {};\n" + times.map { time => + val formattedTime = + SparkUIUtils.formatBatchTime(time, 1, showYYYYMMSS = false) + s"timeFormat[$time] = '$formattedTime';" + }.mkString("\n") + + <script>{Unparsed(js)}</script> + } + + def generateVar(values: Array[(Long, ju.Map[String, JLong])]): Seq[Node] = { + val js = "var timeToValues = {};\n" + values.map { case (x, y) => + val s = y.asScala.toSeq.sortBy(_._1).map(e => s""""${e._2.toDouble}"""") + .mkString("[", ",", "]") + s"""timeToValues["${SparkUIUtils.formatBatchTime(x, 1, showYYYYMMSS = false)}"] = $s;""" + }.mkString("\n") + + <script>{Unparsed(js)}</script> + } + + def generateBasicInfo(query: StreamingQuery, timeSinceStart: Long): Seq[Node] = { + val duration = if (query.isActive) { + SparkUIUtils.formatDurationVerbose(System.currentTimeMillis() - timeSinceStart) + } else { + withNoProgress(query, { + val end = query.lastProgress.timestamp + val start = query.recentProgress.head.timestamp + SparkUIUtils.formatDurationVerbose( + df.parse(end).getTime - df.parse(start).getTime) + }, "-") + } + + val name = if (query.name == null || query.name.isEmpty) { + "null" + } else { + query.name + } + + val numBatches = withNoProgress(query, { query.lastProgress.batchId + 1L }, 0) + val totalRecords = query.getQuerySummary.getMetric(QuerySummary.TOTAL_INPUT_RECORDS, 0L) + <div>Running batches for + <strong> + {duration} + </strong> + since + <strong> + {SparkUIUtils.formatDate(timeSinceStart)} + </strong> + (<strong>{numBatches}</strong> completed batches, <strong>{totalRecords}</strong> records) + </div> + <br /> + <div> + [name = <strong>{name}</strong>, + id = <strong>{query.id}</strong>, + runId = <strong>{query.runId}</strong>] + </div> + <br /> + } + + def generateStatTable(query: StreamingQuery): Seq[Node] = { + val batchTimes = withNoProgress(query, + query.recentProgress.map(p => df.parse(p.timestamp).getTime), Array.empty[Long]) + val minBatchTime = + withNoProgress(query, df.parse(query.recentProgress.head.timestamp).getTime, 0L) + val maxBatchTime = + withNoProgress(query, df.parse(query.lastProgress.timestamp).getTime, 0L) + val maxRecordRate = + withNoProgress(query, query.recentProgress.map(_.inputRowsPerSecond).max, 0L) + val minRecordRate = 0L + val maxProcessRate = + withNoProgress(query, query.recentProgress.map(_.processedRowsPerSecond).max, 0L) + val minProcessRate = 0L + val maxRows = withNoProgress(query, query.recentProgress.map(_.numInputRows).max, 0L) + val minRows = 0L + val maxBatchDuration = withNoProgress(query, query.recentProgress.map(_.batchDuration).max, 0L) + val minBatchDuration = 0L + + val inputRateData = withNoProgress(query, query.recentProgress.map(p => + (df.parse(p.timestamp).getTime, withNumberInvalid { p.inputRowsPerSecond })), + Array.empty[(Long, Double)]) + val processRateData = withNoProgress(query, query.recentProgress.map(p => + (df.parse(p.timestamp).getTime, withNumberInvalid { p.processedRowsPerSecond })), + Array.empty[(Long, Double)]) + val inputRowsData = withNoProgress(query, query.recentProgress.map(p => + (df.parse(p.timestamp).getTime, withNumberInvalid { p.numInputRows })), + Array.empty[(Long, Double)]) + val batchDurations = withNoProgress(query, query.recentProgress.map(p => + (df.parse(p.timestamp).getTime, withNumberInvalid { p.batchDuration })), + Array.empty[(Long, Double)]) + val operationDurationData = withNoProgress(query, query.recentProgress.map { case p => Review comment: I think we don't need `case` here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
