[SPARK-27190][SQL] add table capability for streaming

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.sql.kafka010
 
 import java.{util => ju}
-import java.util.{Collections, Locale, UUID}
+import java.util.{Locale, UUID}
 
 import scala.collection.JavaConverters._
 
@@ -29,9 +29,10 @@ import org.apache.kafka.common.serialization.{ByteArrayDeserializer, ByteArraySe
 import org.apache.spark.internal.Logging
 import org.apache.spark.kafka010.KafkaConfigUpdater
 import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SQLContext}
-import org.apache.spark.sql.execution.streaming.{Sink, Source}
+import org.apache.spark.sql.execution.streaming.{BaseStreamingSink, Sink, Source}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.sources.v2._
+import org.apache.spark.sql.sources.v2.TableCapability._
 import org.apache.spark.sql.sources.v2.reader.{Scan, ScanBuilder}
 import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousStream, MicroBatchStream}
 import org.apache.spark.sql.sources.v2.writer.WriteBuilder
@@ -353,13 +354,15 @@ private[kafka010] class KafkaSourceProvider extends DataSourceRegister
   }
 
   class KafkaTable(strategy: => ConsumerStrategy) extends Table
-    with SupportsMicroBatchRead with SupportsContinuousRead with SupportsStreamingWrite {
+    with SupportsRead with SupportsWrite with BaseStreamingSink {
 
     override def name(): String = s"Kafka $strategy"
 
     override def schema(): StructType = KafkaOffsetReader.kafkaSchema
 
-    override def capabilities(): ju.Set[TableCapability] = Collections.emptySet()
+    override def capabilities(): ju.Set[TableCapability] = {
+      Set(MICRO_BATCH_READ, CONTINUOUS_READ, STREAMING_WRITE).asJava
+    }
 
     override def newScanBuilder(options: CaseInsensitiveStringMap): ScanBuilder =
       () => new KafkaScan(options)

sql/core/src/main/java/org/apache/spark/sql/sources/v2/TableCapability.java

@@ -33,6 +33,16 @@ public enum TableCapability {
    */
   BATCH_READ,
 
+  /**
+   * Signals that the table supports reads in micro-batch streaming execution mode.
+   */
+  MICRO_BATCH_READ,
+
+  /**
+   * Signals that the table supports reads in continuous streaming execution mode.
+   */
+  CONTINUOUS_READ,
+
   /**
    * Signals that the table supports append writes in batch execution mode.
    * <p>
@@ -42,6 +52,15 @@ public enum TableCapability {
    */
   BATCH_WRITE,
 
+  /**
+   * Signals that the table supports append writes in streaming execution mode.
+   * <p>
+   * Tables that return this capability must support appending data and may also support additional
+   * write modes, like {@link #TRUNCATE}, {@link #OVERWRITE_BY_FILTER}, and
+   * {@link #OVERWRITE_DYNAMIC}.
+   */
+  STREAMING_WRITE,
+
   /**
    * Signals that the table can be truncated in a write operation.
    * <p>

sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/Scan.java

@@ -21,8 +21,6 @@
 import org.apache.spark.sql.sources.v2.reader.streaming.ContinuousStream;
 import org.apache.spark.sql.sources.v2.reader.streaming.MicroBatchStream;
 import org.apache.spark.sql.types.StructType;
-import org.apache.spark.sql.sources.v2.SupportsContinuousRead;
-import org.apache.spark.sql.sources.v2.SupportsMicroBatchRead;
 import org.apache.spark.sql.sources.v2.Table;
 import org.apache.spark.sql.sources.v2.TableCapability;
 
@@ -74,8 +72,8 @@ default Batch toBatch() {
   /**
    * Returns the physical representation of this scan for streaming query with micro-batch mode. By
    * default this method throws exception, data sources must overwrite this method to provide an
-   * implementation, if the {@link Table} that creates this scan implements
-   * {@link SupportsMicroBatchRead}.
+   * implementation, if the {@link Table} that creates this scan returns
+   * {@link TableCapability#MICRO_BATCH_READ} support in its {@link Table#capabilities()}.
    *
    * @param checkpointLocation a path to Hadoop FS scratch space that can be used for failure
    *                           recovery. Data streams for the same logical source in the same query
@@ -90,8 +88,8 @@ default MicroBatchStream toMicroBatchStream(String checkpointLocation) {
   /**
    * Returns the physical representation of this scan for streaming query with continuous mode. By
    * default this method throws exception, data sources must overwrite this method to provide an
-   * implementation, if the {@link Table} that creates this scan implements
-   * {@link SupportsContinuousRead}.
+   * implementation, if the {@link Table} that creates this scan returns
+   * {@link TableCapability#CONTINUOUS_READ} support in its {@link Table#capabilities()}.
    *
    * @param checkpointLocation a path to Hadoop FS scratch space that can be used for failure
    *                           recovery. Data streams for the same logical source in the same query

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/noop/NoopDataSource.scala

@@ -23,6 +23,7 @@ import scala.collection.JavaConverters._
 
 import org.apache.spark.sql.SaveMode
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.streaming.BaseStreamingSink
 import org.apache.spark.sql.sources.DataSourceRegister
 import org.apache.spark.sql.sources.v2._
 import org.apache.spark.sql.sources.v2.writer._
@@ -39,11 +40,13 @@ class NoopDataSource extends TableProvider with DataSourceRegister {
   override def getTable(options: CaseInsensitiveStringMap): Table = NoopTable
 }
 
-private[noop] object NoopTable extends Table with SupportsWrite with SupportsStreamingWrite {
+private[noop] object NoopTable extends Table with SupportsWrite with BaseStreamingSink {
   override def newWriteBuilder(options: CaseInsensitiveStringMap): WriteBuilder = NoopWriteBuilder
   override def name(): String = "noop-table"
   override def schema(): StructType = new StructType()
-  override def capabilities(): util.Set[TableCapability] = Set(TableCapability.BATCH_WRITE).asJava
+  override def capabilities(): util.Set[TableCapability] = {
+    Set(TableCapability.BATCH_WRITE, TableCapability.STREAMING_WRITE).asJava
+  }
 }
 
 private[noop] object NoopWriteBuilder extends WriteBuilder

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2StreamingScanSupportCheck.scala

@@ -0,0 +1,64 @@
+/*
+ * 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.datasources.v2
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.streaming.{StreamingRelation, StreamingRelationV2}
+import org.apache.spark.sql.sources.v2.TableCapability.{CONTINUOUS_READ, MICRO_BATCH_READ}
+
+/**
+ * This rules adds some basic table capability check for streaming scan, without knowing the actual
+ * streaming execution mode.
+ */
+object V2StreamingScanSupportCheck extends (LogicalPlan => Unit) {
+  import DataSourceV2Implicits._
+
+  override def apply(plan: LogicalPlan): Unit = {
+    plan.foreach {
+      case r: StreamingRelationV2 if !r.table.supportsAny(MICRO_BATCH_READ, CONTINUOUS_READ) =>
+        throw new AnalysisException(
+          s"Table ${r.table.name()} does not support either micro-batch or continuous scan.")
+      case _ =>
+    }
+
+    val streamingSources = plan.collect {
+      case r: StreamingRelationV2 => r.table
+    }
+    val v1StreamingRelations = plan.collect {
+      case r: StreamingRelation => r
+    }
+
+    if (streamingSources.length + v1StreamingRelations.length > 1) {
+      val allSupportsMicroBatch = streamingSources.forall(_.supports(MICRO_BATCH_READ))
+      // v1 streaming data source only supports micro-batch.
+      val allSupportsContinuous = streamingSources.forall(_.supports(CONTINUOUS_READ)) &&
+        v1StreamingRelations.isEmpty
+      if (!allSupportsMicroBatch && !allSupportsContinuous) {
+        val microBatchSources =
+          streamingSources.filter(_.supports(MICRO_BATCH_READ)).map(_.name()) ++
+            v1StreamingRelations.map(_.sourceName)
+        val continuousSources = streamingSources.filter(_.supports(CONTINUOUS_READ)).map(_.name())
+        throw new AnalysisException(
+          "The streaming sources in a query do not have a common supported execution mode.\n" +
+            "Sources support micro-batch: " + microBatchSources.mkString(", ") + "\n" +
+            "Sources support continuous: " + continuousSources.mkString(", "))
+      }
+    }
+  }
+}