org.apache.hudi.DefaultSource.scala Maven / Gradle / Ivy

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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.hudi

import org.apache.hadoop.fs.Path
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.common.model.HoodieTableType
import org.apache.hudi.DataSourceWriteOptions.{BOOTSTRAP_OPERATION_OPT_VAL, OPERATION_OPT_KEY}
import org.apache.hudi.common.fs.FSUtils
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.hudi.exception.HoodieException
import org.apache.hudi.hadoop.HoodieROTablePathFilter
import org.apache.log4j.LogManager
import org.apache.spark.sql.execution.datasources.DataSource
import org.apache.spark.sql.execution.streaming.Sink
import org.apache.spark.sql.sources._
import org.apache.spark.sql.streaming.OutputMode
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}

/**
  * Hoodie Spark Datasource, for reading and writing hoodie tables
  *
  */
class DefaultSource extends RelationProvider
  with SchemaRelationProvider
  with CreatableRelationProvider
  with DataSourceRegister
  with StreamSinkProvider
  with Serializable {

  private val log = LogManager.getLogger(classOf[DefaultSource])

  override def createRelation(sqlContext: SQLContext,
                              parameters: Map[String, String]): BaseRelation = {
    createRelation(sqlContext, parameters, null)
  }

  override def createRelation(sqlContext: SQLContext,
                              optParams: Map[String, String],
                              schema: StructType): BaseRelation = {
    // Add default options for unspecified read options keys.
    val parameters = Map(QUERY_TYPE_OPT_KEY -> DEFAULT_QUERY_TYPE_OPT_VAL) ++ translateViewTypesToQueryTypes(optParams)

    val path = parameters.get("path")
    val readPathsStr = parameters.get(DataSourceReadOptions.READ_PATHS_OPT_KEY)
    if (path.isEmpty && readPathsStr.isEmpty) {
      throw new HoodieException(s"'path' or '$READ_PATHS_OPT_KEY' or both must be specified.")
    }

    val readPaths = readPathsStr.map(p => p.split(",").toSeq).getOrElse(Seq())
    val allPaths = path.map(p => Seq(p)).getOrElse(Seq()) ++ readPaths

    val fs = FSUtils.getFs(allPaths.head, sqlContext.sparkContext.hadoopConfiguration)
    val globPaths = HoodieSparkUtils.checkAndGlobPathIfNecessary(allPaths, fs)

    val tablePath = DataSourceUtils.getTablePath(fs, globPaths.toArray)
    log.info("Obtained hudi table path: " + tablePath)

    val metaClient = new HoodieTableMetaClient(fs.getConf, tablePath)
    val isBootstrappedTable = metaClient.getTableConfig.getBootstrapBasePath.isPresent
    log.info("Is bootstrapped table => " + isBootstrappedTable)

    if (parameters(QUERY_TYPE_OPT_KEY).equals(QUERY_TYPE_SNAPSHOT_OPT_VAL)) {
      if (metaClient.getTableType.equals(HoodieTableType.MERGE_ON_READ)) {
        if (isBootstrappedTable) {
          // Snapshot query is not supported for Bootstrapped MOR tables
          log.warn("Snapshot query is not supported for Bootstrapped Merge-on-Read tables." +
            " Falling back to Read Optimized query.")
          new HoodieBootstrapRelation(sqlContext, schema, globPaths, metaClient, optParams)
        } else {
          new MergeOnReadSnapshotRelation(sqlContext, optParams, schema, globPaths, metaClient)
        }
      } else {
        getBaseFileOnlyView(sqlContext, parameters, schema, readPaths, isBootstrappedTable, globPaths, metaClient)
      }
    } else if(parameters(QUERY_TYPE_OPT_KEY).equals(QUERY_TYPE_READ_OPTIMIZED_OPT_VAL)) {
      getBaseFileOnlyView(sqlContext, parameters, schema, readPaths, isBootstrappedTable, globPaths, metaClient)
    } else if (parameters(QUERY_TYPE_OPT_KEY).equals(QUERY_TYPE_INCREMENTAL_OPT_VAL)) {
      new IncrementalRelation(sqlContext, tablePath, optParams, schema)
    } else {
      throw new HoodieException("Invalid query type :" + parameters(QUERY_TYPE_OPT_KEY))
    }
  }

  /**
    * This DataSource API is used for writing the DataFrame at the destination. For now, we are returning a dummy
    * relation here because Spark does not really make use of the relation returned, and just returns an empty
    * dataset at [[org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run()]]. This saves us the cost
    * of creating and returning a parquet relation here.
    *
    * TODO: Revisit to return a concrete relation here when we support CREATE TABLE AS for Hudi with DataSource API.
    *       That is the only case where Spark seems to actually need a relation to be returned here
    *       [[DataSource.writeAndRead()]]
    *
    * @param sqlContext Spark SQL Context
    * @param mode Mode for saving the DataFrame at the destination
    * @param optParams Parameters passed as part of the DataFrame write operation
    * @param df Spark DataFrame to be written
    * @return Spark Relation
    */
  override def createRelation(sqlContext: SQLContext,
                              mode: SaveMode,
                              optParams: Map[String, String],
                              df: DataFrame): BaseRelation = {
    val parameters = HoodieWriterUtils.parametersWithWriteDefaults(optParams)
    if (parameters(OPERATION_OPT_KEY).equals(BOOTSTRAP_OPERATION_OPT_VAL)) {
      HoodieSparkSqlWriter.bootstrap(sqlContext, mode, parameters, df)
    } else {
      HoodieSparkSqlWriter.write(sqlContext, mode, parameters, df)
    }
    new HoodieEmptyRelation(sqlContext, df.schema)
  }

  override def createSink(sqlContext: SQLContext,
                          optParams: Map[String, String],
                          partitionColumns: Seq[String],
                          outputMode: OutputMode): Sink = {
    val parameters = HoodieWriterUtils.parametersWithWriteDefaults(optParams)
    new HoodieStreamingSink(
      sqlContext,
      parameters,
      partitionColumns,
      outputMode)
  }

  override def shortName(): String = "hudi"

  private def getBaseFileOnlyView(sqlContext: SQLContext,
                                  optParams: Map[String, String],
                                  schema: StructType,
                                  extraReadPaths: Seq[String],
                                  isBootstrappedTable: Boolean,
                                  globPaths: Seq[Path],
                                  metaClient: HoodieTableMetaClient): BaseRelation = {
    log.warn("Loading Base File Only View.")

    if (isBootstrappedTable) {
      // For bootstrapped tables, use our custom Spark relation for querying
      new HoodieBootstrapRelation(sqlContext, schema, globPaths, metaClient, optParams)
    } else {
      // this is just effectively RO view only, where `path` can contain a mix of
      // non-hoodie/hoodie path files. set the path filter up
      sqlContext.sparkContext.hadoopConfiguration.setClass(
        "mapreduce.input.pathFilter.class",
        classOf[HoodieROTablePathFilter],
        classOf[org.apache.hadoop.fs.PathFilter])

      log.info("Constructing hoodie (as parquet) data source with options :" + optParams)
      // simply return as a regular parquet relation
      DataSource.apply(
        sparkSession = sqlContext.sparkSession,
        paths = extraReadPaths,
        userSpecifiedSchema = Option(schema),
        className = "parquet",
        options = optParams)
        .resolveRelation()
    }
  }
}