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BatchTableSourceScan.scala
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BatchTableSourceScan.scala
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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.flink.table.plan.nodes.dataset
import org.apache.calcite.plan._
import org.apache.calcite.rel.RelNode
import org.apache.calcite.rel.`type`.RelDataType
import org.apache.calcite.rel.metadata.RelMetadataQuery
import org.apache.calcite.rex.RexNode
import org.apache.flink.api.java.DataSet
import org.apache.flink.table.api.{BatchTableEnvironment, TableException, Types}
import org.apache.flink.table.calcite.FlinkTypeFactory
import org.apache.flink.table.plan.nodes.PhysicalTableSourceScan
import org.apache.flink.table.plan.schema.RowSchema
import org.apache.flink.table.sources._
import org.apache.flink.types.Row
/** Flink RelNode to read data from an external source defined by a [[BatchTableSource]]. */
class BatchTableSourceScan(
cluster: RelOptCluster,
traitSet: RelTraitSet,
table: RelOptTable,
tableSource: BatchTableSource[_],
selectedFields: Option[Array[Int]])
extends PhysicalTableSourceScan(cluster, traitSet, table, tableSource, selectedFields)
with BatchScan {
override def deriveRowType(): RelDataType = {
val flinkTypeFactory = cluster.getTypeFactory.asInstanceOf[FlinkTypeFactory]
TableSourceUtil.getRelDataType(
tableSource,
selectedFields,
streaming = false,
flinkTypeFactory)
}
override def computeSelfCost (planner: RelOptPlanner, metadata: RelMetadataQuery): RelOptCost = {
val rowCnt = metadata.getRowCount(this)
planner.getCostFactory.makeCost(rowCnt, rowCnt, rowCnt * estimateRowSize(getRowType))
}
override def copy(traitSet: RelTraitSet, inputs: java.util.List[RelNode]): RelNode = {
new BatchTableSourceScan(
cluster,
traitSet,
getTable,
tableSource,
selectedFields
)
}
override def copy(
traitSet: RelTraitSet,
newTableSource: TableSource[_]): PhysicalTableSourceScan = {
new BatchTableSourceScan(
cluster,
traitSet,
getTable,
newTableSource.asInstanceOf[BatchTableSource[_]],
selectedFields
)
}
override def translateToPlan(tableEnv: BatchTableEnvironment): DataSet[Row] = {
val fieldIndexes = TableSourceUtil.computeIndexMapping(
tableSource,
isStreamTable = false,
selectedFields)
val config = tableEnv.getConfig
val inputDataSet = tableSource.getDataSet(tableEnv.execEnv).asInstanceOf[DataSet[Any]]
val outputSchema = new RowSchema(this.getRowType)
// check that declared and actual type of table source DataSet are identical
if (inputDataSet.getType != tableSource.getReturnType) {
throw new TableException(s"TableSource of type ${tableSource.getClass.getCanonicalName} " +
s"returned a DataSet of type ${inputDataSet.getType} that does not match with the " +
s"type ${tableSource.getReturnType} declared by the TableSource.getReturnType() method. " +
s"Please validate the implementation of the TableSource.")
}
// get expression to extract rowtime attribute
val rowtimeExpression: Option[RexNode] = TableSourceUtil.getRowtimeExtractionExpression(
tableEnv,
tableSource,
selectedFields,
cluster,
tableEnv.getRelBuilder,
Types.SQL_TIMESTAMP
)
// ingest table and convert and extract time attributes if necessary
convertToInternalRow(
outputSchema,
inputDataSet,
fieldIndexes,
config,
rowtimeExpression)
}
}