[GitHub] [spark] zhenlineo commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read

["zhenlineo (via GitHub)" <gi...@apache.org>]

zhenlineo commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107634661


##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameReader.scala:
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@@ -0,0 +1,408 @@
+/*
+ * 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
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.annotation.Stable
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, CharVarcharUtils}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Interface used to load a [[Dataset]] from external storage systems (e.g. file systems,
+ * key-value stores, etc). Use `SparkSession.read` to access this.
+ *
+ * @since 3.4.0
+ */
+@Stable
+class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
+
+  /**
+   * Specifies the input data source format.
+   *
+   * @since 3.4.0
+   */
+  def format(source: String): DataFrameReader = {
+    this.source = source
+    this
+  }
+
+  /**
+   * Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema
+   * automatically from data. By specifying the schema here, the underlying data source can
+   * skip the schema inference step, and thus speed up data loading.
+   *
+   * @since 3.4.0
+   */
+  def schema(schema: StructType): DataFrameReader = {
+    if (schema != null) {
+      val replaced = CharVarcharUtils.failIfHasCharVarchar(schema).asInstanceOf[StructType]
+      this.userSpecifiedSchema = Option(replaced)
+    }
+    this
+  }
+
+  /**
+   * Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can
+   * infer the input schema automatically from data. By specifying the schema here, the underlying
+   * data source can skip the schema inference step, and thus speed up data loading.
+   *
+   * {{{
+   *   spark.read.schema("a INT, b STRING, c DOUBLE").csv("test.csv")
+   * }}}
+   *
+   * @since 3.4.0
+   */
+  def schema(schemaString: String): DataFrameReader = {
+    schema(StructType.fromDDL(schemaString))
+  }
+
+  /**
+   * Adds an input option for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def option(key: String, value: String): DataFrameReader = {
+    this.extraOptions = this.extraOptions + (key -> value)
+    this
+  }
+
+  /**
+   * Adds an input option for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def option(key: String, value: Boolean): DataFrameReader = option(key, value.toString)
+
+  /**
+   * Adds an input option for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def option(key: String, value: Long): DataFrameReader = option(key, value.toString)
+
+  /**
+   * Adds an input option for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def option(key: String, value: Double): DataFrameReader = option(key, value.toString)
+
+  /**
+   * (Scala-specific) Adds input options for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def options(options: scala.collection.Map[String, String]): DataFrameReader = {
+    this.extraOptions ++= options
+    this
+  }
+
+  /**
+   * Adds input options for the underlying data source.
+   *
+   * All options are maintained in a case-insensitive way in terms of key names.
+   * If a new option has the same key case-insensitively, it will override the existing option.
+   *
+   * @since 3.4.0
+   */
+  def options(options: java.util.Map[String, String]): DataFrameReader = {
+    this.options(options.asScala)
+    this
+  }
+
+  /**
+   * Loads input in as a `DataFrame`, for data sources that don't require a path (e.g. external
+   * key-value stores).
+   *
+   * @since 3.4.0
+   */
+  def load(): DataFrame = {
+    load(Seq.empty: _*) // force invocation of `load(...varargs...)`
+  }
+
+  /**
+   * Loads input in as a `DataFrame`, for data sources that require a path (e.g. data backed by
+   * a local or distributed file system).
+   *
+   * @since 3.4.0
+   */
+  def load(path: String): DataFrame = {
+    // force invocation of `load(...varargs...)`
+    load(Seq(path): _*)
+  }
+
+  /**
+   * Loads input in as a `DataFrame`, for data sources that support multiple paths.
+   * Only works if the source is a HadoopFsRelationProvider.
+   *
+   * @since 3.4.0
+   */
+  @scala.annotation.varargs
+  def load(paths: String*): DataFrame = {
+    sparkSession.newDataset { builder =>
+      val dataSourceBuilder = builder.getReadBuilder.getDataSourceBuilder
+      assertSourceFormatSpecified()
+      dataSourceBuilder.setFormat(source)
+      userSpecifiedSchema.foreach(schema => dataSourceBuilder.setSchema(schema.toDDL))
+      extraOptions.foreach {
+        case (k, v) => dataSourceBuilder.putOptions(k, v)
+      }
+      paths.foreach(path => dataSourceBuilder.addPaths(path))

Review Comment:
   @hvanhovell I do not think we can anything special here. The server planner will call the original SQL code and merge it based on the config settings. e.g.
   
   ```
   def load(paths: String*): DataFrame = {
   ....
       val legacyPathOptionBehavior = sparkSession.sessionState.conf.legacyPathOptionBehavior
       if (!legacyPathOptionBehavior &&
           (extraOptions.contains("path") || extraOptions.contains("paths")) && paths.nonEmpty) {
         throw QueryCompilationErrors.pathOptionNotSetCorrectlyWhenReadingError()
       }
   
       DataSource.lookupDataSourceV2(source, sparkSession.sessionState.conf).flatMap { provider =>
         DataSourceV2Utils.loadV2Source(sparkSession, provider, userSpecifiedSchema, extraOptions,
           source, paths: _*)
       }.getOrElse(loadV1Source(paths: _*))
   ```
   If we merge them on the client we by default assumed the config value. So the best is actually leave the result to server SQL API to handle the merging of paths.



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