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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/12/19 22:14:01 UTC
[GitHub] [spark] grundprinzip commented on a diff in pull request #39078: [SPARK-41534][CONNECT][SQL] Setup initial client module for Spark Connect
grundprinzip commented on code in PR #39078:
URL: https://github.com/apache/spark/pull/39078#discussion_r1052679741
##########
connector/connect/client/src/main/scala/org/apache/spark/sql/connect/client/SparkSession.scala:
##########
@@ -0,0 +1,133 @@
+/*
+ * 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.connect.client
+
+import scala.language.existentials
+
+import io.grpc.{ManagedChannel, ManagedChannelBuilder}
+import org.apache.arrow.memory.RootAllocator
+
+import org.apache.spark.SPARK_VERSION
+import org.apache.spark.connect.proto
+
+
+class SparkSession(
Review Comment:
From our work in the Python client it turned out that it's actually better to keep the GRPC client out of the `SparkSession` and have a separate `SparkConnectClient` class for that. That gives you as well better testability.
##########
connector/connect/client/src/main/scala/org/apache/spark/sql/connect/client/SparkSession.scala:
##########
@@ -0,0 +1,133 @@
+/*
+ * 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.connect.client
+
+import scala.language.existentials
+
+import io.grpc.{ManagedChannel, ManagedChannelBuilder}
+import org.apache.arrow.memory.RootAllocator
+
+import org.apache.spark.SPARK_VERSION
+import org.apache.spark.connect.proto
+
+
+class SparkSession(
+ private val userContext: proto.UserContext,
+ private val channel: ManagedChannel)
+ extends AutoCloseable {
+ private[this] val stub = proto.SparkConnectServiceGrpc.newBlockingStub(channel)
+
+ private[this] val allocator = new RootAllocator()
+
+ /**
+ * The version of Spark on which this application is running.
+ */
+ def version: String = SPARK_VERSION
+
+ /**
+ * Returns a `DataFrame` with no rows or columns.
+ *
+ * @since 3.4.0
+ */
+ @transient
+ lazy val emptyDataFrame: Dataset = newDataset { builder =>
+ builder.getLocalRelationBuilder
+ }
+
+ /**
+ * Creates a [[Dataset]] with a single `LongType` column named `id`, containing elements
+ * in a range from `start` to `end` (exclusive) with a step value, with partition number
+ * specified.
+ *
+ * @since 2.0.0
+ */
+ def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset = {
+ range(start, end, step, Option(numPartitions))
+ }
+
+ private def range(start: Long, end: Long, step: Long, numPartitions: Option[Int]): Dataset = {
+ newDataset { builder =>
+ val rangeBuilder = builder.getRangeBuilder
+ .setStart(start)
+ .setEnd(end)
+ .setStep(step)
+ numPartitions.foreach(rangeBuilder.setNumPartitions)
+ }
+ }
+
+ /**
+ * Executes a SQL query using Spark, returning the result as a `DataFrame`.
+ * This API eagerly runs DDL/DML commands, but not for SELECT queries.
+ *
+ * @since 2.0.0
+ */
+ def sql(query: String): Dataset = newDataset { builder =>
+ builder.setSql(proto.SQL.newBuilder().setQuery(query))
+ }
+
+ private[client] def newDataset(f: proto.Relation.Builder => Unit): Dataset = {
+ val builder = proto.Relation.newBuilder()
+ f(builder)
+ val plan = proto.Plan.newBuilder().setRoot(builder).build()
+ new Dataset(this, plan)
+ }
+
+ private[client] def analyze(plan: proto.Plan): proto.AnalyzePlanResponse = {
+ val request = proto.AnalyzePlanRequest.newBuilder()
+ .setPlan(plan)
+ .setUserContext(userContext)
+ .build()
+ stub.analyzePlan(request)
+ }
+
+ override def close(): Unit = {
+ channel.shutdownNow()
+ allocator.close()
+ }
+}
+
+object SparkSession {
+ def builder(): Builder = new Builder()
+
+ class Builder() {
Review Comment:
It would be good to use a similar `ChannelBuilder` construct as in the Python client to have the same behavior on both sides. I understand this is kind of painful to do for the first PR. Maybe the alternative is to make the first PR even smaller.
##########
connector/connect/client/src/main/scala/org/apache/spark/sql/connect/client/SparkSession.scala:
##########
@@ -0,0 +1,133 @@
+/*
+ * 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.connect.client
+
+import scala.language.existentials
+
+import io.grpc.{ManagedChannel, ManagedChannelBuilder}
+import org.apache.arrow.memory.RootAllocator
+
+import org.apache.spark.SPARK_VERSION
+import org.apache.spark.connect.proto
+
+
+class SparkSession(
+ private val userContext: proto.UserContext,
+ private val channel: ManagedChannel)
+ extends AutoCloseable {
+ private[this] val stub = proto.SparkConnectServiceGrpc.newBlockingStub(channel)
+
+ private[this] val allocator = new RootAllocator()
+
+ /**
+ * The version of Spark on which this application is running.
+ */
+ def version: String = SPARK_VERSION
+
+ /**
+ * Returns a `DataFrame` with no rows or columns.
+ *
+ * @since 3.4.0
+ */
+ @transient
+ lazy val emptyDataFrame: Dataset = newDataset { builder =>
+ builder.getLocalRelationBuilder
+ }
+
+ /**
+ * Creates a [[Dataset]] with a single `LongType` column named `id`, containing elements
+ * in a range from `start` to `end` (exclusive) with a step value, with partition number
+ * specified.
+ *
+ * @since 2.0.0
+ */
+ def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset = {
+ range(start, end, step, Option(numPartitions))
+ }
+
+ private def range(start: Long, end: Long, step: Long, numPartitions: Option[Int]): Dataset = {
+ newDataset { builder =>
+ val rangeBuilder = builder.getRangeBuilder
+ .setStart(start)
+ .setEnd(end)
+ .setStep(step)
+ numPartitions.foreach(rangeBuilder.setNumPartitions)
+ }
+ }
+
+ /**
+ * Executes a SQL query using Spark, returning the result as a `DataFrame`.
+ * This API eagerly runs DDL/DML commands, but not for SELECT queries.
+ *
+ * @since 2.0.0
Review Comment:
```suggestion
* @since 3.4.0
```
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