steveloughran commented on a change in pull request #29471: URL: https://github.com/apache/spark/pull/29471#discussion_r478471138
########## File path: core/src/main/scala/org/apache/spark/util/HadoopFSUtils.scala ########## @@ -0,0 +1,360 @@ +/* + * 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.util + +import java.io.FileNotFoundException + +import scala.collection.mutable + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs._ +import org.apache.hadoop.fs.viewfs.ViewFileSystem +import org.apache.hadoop.hdfs.DistributedFileSystem + +import org.apache.spark._ +import org.apache.spark.annotation.Private +import org.apache.spark.internal.Logging +import org.apache.spark.metrics.source.HiveCatalogMetrics + +/** + * Utility functions to simplify and speed-up file listing. + */ +@Private +object HadoopFSUtils extends Logging { + /** + * Lists a collection of paths recursively. Picks the listing strategy adaptively depending + * on the number of paths to list. + * + * This may only be called on the driver. + * + * @param sc Spark context used to run parallel listing. + * @param paths Input paths to list + * @param hadoopConf Hadoop configuration + * @param filter Path filter used to exclude leaf files from result + * @param areSQLRootPaths Whether the input paths are SQL root paths + * @param ignoreMissingFiles Ignore missing files that happen during recursive listing + * (e.g., due to race conditions) + * @param ignoreLocality Whether to fetch data locality info when listing leaf files. If false, + * this will return `FileStatus` without `BlockLocation` info. + * @param parallelismThreshold The threshold to enable parallelism. If the number of input paths + * is smaller than this value, this will fallback to use + * sequential listing. + * @param parallelismMax The maximum parallelism for listing. If the number of input paths is + * larger than this value, parallelism will be throttled to this value + * to avoid generating too many tasks. + * @param filterFun Optional predicate on the leaf files. Files who failed the check will be + * excluded from the results + * @return for each input path, the set of discovered files for the path + */ + def parallelListLeafFiles( + sc: SparkContext, + paths: Seq[Path], + hadoopConf: Configuration, + filter: PathFilter, + areSQLRootPaths: Boolean, + ignoreMissingFiles: Boolean, + ignoreLocality: Boolean, + parallelismThreshold: Int, + parallelismMax: Int, + filterFun: Option[String => Boolean] = None): Seq[(Path, Seq[FileStatus])] = { + + // Short-circuits parallel listing when serial listing is likely to be faster. + if (paths.size <= parallelismThreshold) { + return paths.map { path => + val leafFiles = listLeafFiles( + path, + hadoopConf, + filter, + Some(sc), + ignoreMissingFiles = ignoreMissingFiles, + ignoreLocality = ignoreLocality, + isSQLRootPath = areSQLRootPaths, + parallelismThreshold = parallelismThreshold, + parallelismDefault = parallelismMax, + filterFun = filterFun) + (path, leafFiles) + } + } + + logInfo(s"Listing leaf files and directories in parallel under ${paths.length} paths." + + s" The first several paths are: ${paths.take(10).mkString(", ")}.") + HiveCatalogMetrics.incrementParallelListingJobCount(1) + + val serializableConfiguration = new SerializableConfiguration(hadoopConf) + val serializedPaths = paths.map(_.toString) + + // Set the number of parallelism to prevent following file listing from generating many tasks + // in case of large #defaultParallelism. + val numParallelism = Math.min(paths.size, parallelismMax) + + val previousJobDescription = sc.getLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION) + val statusMap = try { + val description = paths.size match { + case 0 => + "Listing leaf files and directories 0 paths" + case 1 => + s"Listing leaf files and directories for 1 path:<br/>${paths(0)}" + case s => + s"Listing leaf files and directories for $s paths:<br/>${paths(0)}, ..." + } + sc.setJobDescription(description) + sc + .parallelize(serializedPaths, numParallelism) + .mapPartitions { pathStrings => + val hadoopConf = serializableConfiguration.value + pathStrings.map(new Path(_)).toSeq.map { path => + val leafFiles = listLeafFiles( + path = path, + hadoopConf = hadoopConf, + filter = filter, + contextOpt = None, // Can't execute parallel scans on workers + ignoreMissingFiles = ignoreMissingFiles, + ignoreLocality = ignoreLocality, + isSQLRootPath = areSQLRootPaths, + filterFun = filterFun, + parallelismThreshold = Int.MaxValue, + parallelismDefault = 0) + (path, leafFiles) + }.iterator + }.map { case (path, statuses) => + val serializableStatuses = statuses.map { status => + // Turn FileStatus into SerializableFileStatus so we can send it back to the driver + val blockLocations = status match { + case f: LocatedFileStatus => + f.getBlockLocations.map { loc => + SerializableBlockLocation( + loc.getNames, + loc.getHosts, + loc.getOffset, + loc.getLength) + } + + case _ => + Array.empty[SerializableBlockLocation] + } + + SerializableFileStatus( + status.getPath.toString, + status.getLen, + status.isDirectory, + status.getReplication, + status.getBlockSize, + status.getModificationTime, + status.getAccessTime, + blockLocations) + } + (path.toString, serializableStatuses) + }.collect() + } finally { + sc.setJobDescription(previousJobDescription) + } + + // turn SerializableFileStatus back to Status + statusMap.map { case (path, serializableStatuses) => + val statuses = serializableStatuses.map { f => + val blockLocations = f.blockLocations.map { loc => + new BlockLocation(loc.names, loc.hosts, loc.offset, loc.length) + } + new LocatedFileStatus( + new FileStatus( + f.length, f.isDir, f.blockReplication, f.blockSize, f.modificationTime, + new Path(f.path)), + blockLocations) + } + (new Path(path), statuses) + } + } + + // scalastyle:off argcount + /** + * Lists a single filesystem path recursively. If a `SparkContext`` object is specified, this + * function may launch Spark jobs to parallelize listing based on parallelismThreshold. + * + * If sessionOpt is None, this may be called on executors. + * + * @return all children of path that match the specified filter. + */ + private def listLeafFiles( + path: Path, + hadoopConf: Configuration, + filter: PathFilter, + contextOpt: Option[SparkContext], + ignoreMissingFiles: Boolean, + ignoreLocality: Boolean, + isSQLRootPath: Boolean, + filterFun: Option[String => Boolean], + parallelismThreshold: Int, + parallelismDefault: Int): Seq[FileStatus] = { + + logTrace(s"Listing $path") + val fs = path.getFileSystem(hadoopConf) + + // Note that statuses only include FileStatus for the files and dirs directly under path, + // and does not include anything else recursively. + val statuses: Array[FileStatus] = try { + fs match { + // DistributedFileSystem overrides listLocatedStatus to make 1 single call to namenode + // to retrieve the file status with the file block location. The reason to still fallback + // to listStatus is because the default implementation would potentially throw a Review comment: w.r.t the faster (incremental) calls, yes, something to consider next. At the very least, you will be able to collect and report/aggregate stats. For example, here is me getting the stats on a large/deep list just by calling toString on the RemoteIterator after it's done its work ``` Listing statistics: counters=((object_list_request=1) (object_continue_list_request=40)); gauges=(); minimums=((object_list_request.min=1298) (object_continue_list_request.min=414)); maximums=((object_continue_list_request.max=746) (object_list_request.max=1298)); means=((object_continue_list_request.mean=(sum=18210, samples=39, mean=466.9231)) (object_list_request.mean=(sum=1298, samples=1, mean=1298.0000))); 2020-08-27 13:25:21,122 [main] INFO tools.MarkerTool (DurationInfo.java:close(98)) - marker scan s3a://landsat-pds/: duration 0:19.563s Listed 40000 objects under s3a://landsat-pds/ ``` Now, who wouldn't like to know things like that. And ideally, collect across threads and merge back in. As an aside, looked at {{org.apache.hadoop.mapred.LocatedFileStatusFetcher}}. This does multithreaded status fetching and collects those stats. Although it's tagged Private, I've noticed Parquet uses it so my next PR will convert to public/evolving and document the fact. If you could use that, we could look @ evolving it better, especially returning a RemoteIterator of results which we could incrementally fill in across threads rather than block for the final results. Anything which makes it possible for app code to process data (read footers, etc) while the listing goes on has significant benefit in the World of Object Stores ---------------------------------------------------------------- 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: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
