spark FileStreamSink 源码

spark FileStreamSink 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSink.scala

/*
 * 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.execution.streaming

import scala.util.control.NonFatal

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileSystem, Path}

import org.apache.spark.SparkException
import org.apache.spark.internal.Logging
import org.apache.spark.internal.io.FileCommitProtocol
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.errors.QueryExecutionErrors
import org.apache.spark.sql.execution.datasources.{BasicWriteJobStatsTracker, FileFormat, FileFormatWriter}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.util.{SerializableConfiguration, Utils}

object FileStreamSink extends Logging {
  // The name of the subdirectory that is used to store metadata about which files are valid.
  val metadataDir = "_spark_metadata"

  /**
   * Returns true if there is a single path that has a metadata log indicating which files should
   * be read.
   */
  def hasMetadata(path: Seq[String], hadoopConf: Configuration, sqlConf: SQLConf): Boolean = {
    // User explicitly configs to ignore sink metadata.
    if (sqlConf.fileStreamSinkMetadataIgnored) {
      return false
    }

    path match {
      case Seq(singlePath) =>
        val hdfsPath = new Path(singlePath)
        try {
          val fs = hdfsPath.getFileSystem(hadoopConf)
          if (fs.isDirectory(hdfsPath)) {
            val metadataPath = getMetadataLogPath(fs, hdfsPath, sqlConf)
            fs.exists(metadataPath)
          } else {
            false
          }
        } catch {
          case e: SparkException => throw e
          case NonFatal(e) =>
            logWarning(s"Assume no metadata directory. Error while looking for " +
              s"metadata directory in the path: $singlePath.", e)
            false
        }
      case _ => false
    }
  }

  def getMetadataLogPath(fs: FileSystem, path: Path, sqlConf: SQLConf): Path = {
    val metadataDir = new Path(path, FileStreamSink.metadataDir)
    FileStreamSink.checkEscapedMetadataPath(fs, metadataDir, sqlConf)
    metadataDir
  }

  def checkEscapedMetadataPath(fs: FileSystem, metadataPath: Path, sqlConf: SQLConf): Unit = {
    if (sqlConf.getConf(SQLConf.STREAMING_CHECKPOINT_ESCAPED_PATH_CHECK_ENABLED)
        && StreamExecution.containsSpecialCharsInPath(metadataPath)) {
      val legacyMetadataPath = new Path(metadataPath.toUri.toString)
      val legacyMetadataPathExists =
        try {
          fs.exists(legacyMetadataPath)
        } catch {
          case NonFatal(e) =>
            // We may not have access to this directory. Don't fail the query if that happens.
            logWarning(e.getMessage, e)
            false
        }
      if (legacyMetadataPathExists) {
        throw QueryExecutionErrors.legacyMetadataPathExistsError(metadataPath, legacyMetadataPath)
      }
    }
  }

  /**
   * Returns true if the path is the metadata dir or its ancestor is the metadata dir.
   * E.g.:
   *  - ancestorIsMetadataDirectory(/.../_spark_metadata) => true
   *  - ancestorIsMetadataDirectory(/.../_spark_metadata/0) => true
   *  - ancestorIsMetadataDirectory(/a/b/c) => false
   */
  def ancestorIsMetadataDirectory(path: Path, hadoopConf: Configuration): Boolean = {
    val fs = path.getFileSystem(hadoopConf)
    var currentPath = path.makeQualified(fs.getUri, fs.getWorkingDirectory)
    while (currentPath != null) {
      if (currentPath.getName == FileStreamSink.metadataDir) {
        return true
      } else {
        currentPath = currentPath.getParent
      }
    }
    false
  }
}

/**
 * A sink that writes out results to parquet files.  Each batch is written out to a unique
 * directory. After all of the files in a batch have been successfully written, the list of
 * file paths is appended to the log atomically. In the case of partial failures, some duplicate
 * data may be present in the target directory, but only one copy of each file will be present
 * in the log.
 */
class FileStreamSink(
    sparkSession: SparkSession,
    path: String,
    fileFormat: FileFormat,
    partitionColumnNames: Seq[String],
    options: Map[String, String]) extends Sink with Logging {

  import FileStreamSink._

  private val hadoopConf = sparkSession.sessionState.newHadoopConf()
  private val basePath = new Path(path)
  private val logPath = getMetadataLogPath(basePath.getFileSystem(hadoopConf), basePath,
    sparkSession.sessionState.conf)
  private val retention = options.get("retention").map(Utils.timeStringAsMs)
  private val fileLog = new FileStreamSinkLog(FileStreamSinkLog.VERSION, sparkSession,
    logPath.toString, retention)

  private def basicWriteJobStatsTracker: BasicWriteJobStatsTracker = {
    val serializableHadoopConf = new SerializableConfiguration(hadoopConf)
    new BasicWriteJobStatsTracker(serializableHadoopConf, BasicWriteJobStatsTracker.metrics)
  }

  override def addBatch(batchId: Long, data: DataFrame): Unit = {
    if (batchId <= fileLog.getLatestBatchId().getOrElse(-1L)) {
      logInfo(s"Skipping already committed batch $batchId")
    } else {
      val committer = FileCommitProtocol.instantiate(
        className = sparkSession.sessionState.conf.streamingFileCommitProtocolClass,
        jobId = batchId.toString,
        outputPath = path)

      committer match {
        case manifestCommitter: ManifestFileCommitProtocol =>
          manifestCommitter.setupManifestOptions(fileLog, batchId)
        case _ =>  // Do nothing
      }

      // Get the actual partition columns as attributes after matching them by name with
      // the given columns names.
      val partitionColumns: Seq[Attribute] = partitionColumnNames.map { col =>
        val nameEquality = data.sparkSession.sessionState.conf.resolver
        data.logicalPlan.output.find(f => nameEquality(f.name, col)).getOrElse {
          throw QueryExecutionErrors.partitionColumnNotFoundInSchemaError(col, data.schema)
        }
      }
      val qe = data.queryExecution

      FileFormatWriter.write(
        sparkSession = sparkSession,
        plan = qe.executedPlan,
        fileFormat = fileFormat,
        committer = committer,
        outputSpec = FileFormatWriter.OutputSpec(path, Map.empty, qe.analyzed.output),
        hadoopConf = hadoopConf,
        partitionColumns = partitionColumns,
        bucketSpec = None,
        statsTrackers = Seq(basicWriteJobStatsTracker),
        options = options)
    }
  }

  override def toString: String = s"FileSink[$path]"
}

相关信息

spark 源码目录

相关文章

spark AvailableNowDataStreamWrapper 源码

spark AvailableNowMicroBatchStreamWrapper 源码

spark AvailableNowSourceWrapper 源码

spark CheckpointFileManager 源码

spark CommitLog 源码

spark CompactibleFileStreamLog 源码

spark ContinuousRecordEndpoint 源码

spark EventTimeWatermarkExec 源码

spark FileStreamOptions 源码

spark FileStreamSinkLog 源码