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Posted to reviews@spark.apache.org by "zhenlineo (via GitHub)" <gi...@apache.org> on 2023/02/15 01:28:36 UTC
[GitHub] [spark] zhenlineo opened a new pull request, #40025: [CONNECT] Adding SparkSession#read
zhenlineo opened a new pull request, #40025:
URL: https://github.com/apache/spark/pull/40025
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[GitHub] [spark] amaliujia commented on a diff in pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1108011261
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
ah sure :)
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[GitHub] [spark] zhenlineo commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "zhenlineo (via GitHub)" <gi...@apache.org>.
zhenlineo commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107772179
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
The server test `ProtoToParsedPlanTestSuite` runs this test with data. Yet, it does not verify the result correctness.
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[GitHub] [spark] hvanhovell closed pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell closed pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
URL: https://github.com/apache/spark/pull/40025
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1106593785
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -177,6 +177,17 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
Review Comment:
Maybe add a few variations here? For example show the parquet path works, etc..
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107638645
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameReader.scala:
##########
@@ -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:
Alright, that works for me. Can you make sure we test this?
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1106592754
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/SparkSession.scala:
##########
@@ -62,6 +62,18 @@ class SparkSession(private val client: SparkConnectClient, private val cleaner:
builder.setSql(proto.SQL.newBuilder().setQuery(query))
}
+ /**
+ * Returns a [[DataFrameReader]] that can be used to read non-streaming data in as a
+ * `DataFrame`.
+ * {{{
+ * sparkSession.read.parquet("/path/to/file.parquet")
+ * sparkSession.read.schema(schema).json("/path/to/file.json")
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def read: DataFrameReader = new DataFrameReader(this)
Review Comment:
Can you also add `table()`? Can also be done in a follow-up.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1106592358
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameReader.scala:
##########
@@ -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:
You can also set path/paths in the options. How does this work? Does the server reconcile all these path options?
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[GitHub] [spark] zhenlineo commented on a diff in pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "zhenlineo (via GitHub)" <gi...@apache.org>.
zhenlineo commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107772179
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
The server test `ProtoToParsedPlanTestSuite` runs this test with data. Yet, it does not verify the read data result correctness. (e.g. the content of the csv is loaded correctly or not)
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[GitHub] [spark] amaliujia commented on a diff in pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107880879
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
I might be wrong but I am thinking if you offer a schema in the proto then the server side might not need to load the data: it loads the data as it needs to infer the scheme by reading the data directly when a schema is not set. I am not sure though besides this case if server side has to read the data directly.
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[GitHub] [spark] hvanhovell commented on pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on PR #40025:
URL: https://github.com/apache/spark/pull/40025#issuecomment-1430678848
Oh and can you add the ticket to the title?
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[GitHub] [spark] zhenlineo commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "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:
##########
@@ -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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[GitHub] [spark] amaliujia commented on pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on PR #40025:
URL: https://github.com/apache/spark/pull/40025#issuecomment-1432056014
So it is ok to not have e2e tests for read API (similarly for the write side)?
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[GitHub] [spark] amaliujia commented on a diff in pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107880879
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
I might be wrong but I am thinking if you offer a schema in the proto then the server side might not need to load the data: it loads the data as it needs to infer the scheme by reading the data directly when a schema is not set. I am not sure though except this case if server side still has to read the data directly.
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[GitHub] [spark] hvanhovell commented on pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on PR #40025:
URL: https://github.com/apache/spark/pull/40025#issuecomment-1430678447
@zhenlineo can you make the PR description a bit more descriptive?
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[GitHub] [spark] amaliujia commented on a diff in pull request #40025: [CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107754835
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
Why we need the physical data files checked in? If we only compare the plans then we only need a fake data path?
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[GitHub] [spark] zhenlineo commented on a diff in pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "zhenlineo (via GitHub)" <gi...@apache.org>.
zhenlineo commented on code in PR #40025:
URL: https://github.com/apache/spark/pull/40025#discussion_r1107951418
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala:
##########
@@ -184,6 +194,38 @@ class PlanGenerationTestSuite extends ConnectFunSuite with BeforeAndAfterAll wit
session.range(1, 10, 1, 2)
}
+ test("read") {
+ session.read.format("text")
+ .schema(StructType(StructField("name", StringType) :: StructField("age", IntegerType) :: Nil))
+ .option("op1", "op1")
+ .options(Map("op2" -> "op2"))
+ .load(testDataPath.resolve("people.txt").toString)
Review Comment:
Umm, tried with json file, but the schema seems not enough. :(
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[GitHub] [spark] amaliujia commented on pull request #40025: [SPARK-42457][CONNECT] Adding SparkSession#read
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on PR #40025:
URL: https://github.com/apache/spark/pull/40025#issuecomment-1432517546
LGTM
Looks like you only need `./build/mvn -Pscala-2.12 scalafmt:format -Dscalafmt.skip=false -Dscalafmt.validateOnly=false -Dscalafmt.changedOnly=false -pl connector/connect/common -pl connector/connect/server -pl connector/connect/client/jvm` to fix the style.
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