[GitHub] dianfu commented on a change in pull request #6815: [FLINK-7062][cep][table] Added basic support for MATCH_RECOGNIZE

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dianfu commented on a change in pull request #6815:  [FLINK-7062][cep][table] Added basic support for MATCH_RECOGNIZE
URL: https://github.com/apache/flink/pull/6815#discussion_r225402418
 
 

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 File path: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamMatch.scala
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 @@ -0,0 +1,359 @@
+/*
+ * 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.datastream
+
+import java.util
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.RelFieldCollation.Direction
+import org.apache.calcite.rel._
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rex._
+import org.apache.calcite.sql.SqlKind
+import org.apache.calcite.sql.SqlMatchRecognize.AfterOption
+import org.apache.calcite.sql.fun.SqlStdOperatorTable._
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.common.typeutils.TypeComparator
+import org.apache.flink.cep.nfa.aftermatch.AfterMatchSkipStrategy
+import org.apache.flink.cep.nfa.compiler.NFACompiler
+import org.apache.flink.cep.pattern.Pattern
+import org.apache.flink.cep.pattern.Quantifier.QuantifierProperty
+import org.apache.flink.cep.pattern.conditions.BooleanConditions
+import org.apache.flink.cep.{CEP, EventComparator, PatternStream}
+import org.apache.flink.streaming.api.datastream.DataStream
+import org.apache.flink.table.api.{StreamQueryConfig, StreamTableEnvironment, TableConfig,
+  TableException}
+import org.apache.flink.table.calcite.FlinkTypeFactory
+import org.apache.flink.table.plan.nodes.MatchRecognize
+import org.apache.flink.table.plan.nodes.logical.FlinkLogicalMatch
+import org.apache.flink.table.plan.schema.RowSchema
+import org.apache.flink.table.plan.util.RexDefaultVisitor
+import org.apache.flink.table.runtime.`match`._
+import org.apache.flink.table.runtime.aggregate.SortUtil
+import org.apache.flink.table.runtime.types.{CRow, CRowTypeInfo}
+import org.apache.flink.table.runtime.{RowKeySelector, RowtimeProcessFunction}
+import org.apache.flink.types.Row
+import org.apache.flink.util.MathUtils
+
+import _root_.scala.collection.JavaConverters._
+
+/**
+  * Flink RelNode which matches along with LogicalMatch.
+  */
+class DataStreamMatch(
+  cluster: RelOptCluster,
+  traitSet: RelTraitSet,
+  input: RelNode,
+  logicalMatch: FlinkLogicalMatch,
+  schema: RowSchema,
+  inputSchema: RowSchema)
+  extends SingleRel(cluster, traitSet, input)
+    with MatchRecognize
+    with DataStreamRel {
+
+  private[flink] def getLogicalMatch = logicalMatch
+
+  override def deriveRowType(): RelDataType = schema.relDataType
+
+  override def copy(traitSet: RelTraitSet, inputs: util.List[RelNode]): RelNode = {
+    new DataStreamMatch(
+      cluster,
+      traitSet,
+      inputs.get(0),
+      logicalMatch,
+      schema,
+      inputSchema)
+  }
+
+  override def toString: String = {
+    matchToString(logicalMatch, inputSchema, getExpressionString)
+  }
+
+  override def explainTerms(pw: RelWriter): RelWriter = {
+    explainMatch(super.explainTerms(pw), logicalMatch, inputSchema, getExpressionString)
+  }
+
+  private def translateOrder(
+    tableEnv: StreamTableEnvironment,
+    crowInput: DataStream[CRow],
+    orderKeys: RelCollation) = {
+
+    if (orderKeys.getFieldCollations.size() == 0) {
+      throw new TableException("You must specify either rowtime or proctime for order by.")
+    }
+
+    // need to identify time between others order fields. Time needs to be first sort element
+    val timeOrderField = SortUtil.getFirstSortField(orderKeys, inputSchema.relDataType)
+
+    if (!FlinkTypeFactory.isTimeIndicatorType(timeOrderField.getType)) {
+      throw new TableException(
+        "You must specify either rowtime or proctime for order by as the first one.")
+    }
+
+    // time ordering needs to be ascending
+    if (SortUtil.getFirstSortDirection(orderKeys) != Direction.ASCENDING) {
+      throw new TableException("Primary sort order of a streaming table must be ascending on time.")
+    }
+
+    val rowComparator = if (orderKeys.getFieldCollations.size() > 1) {
+      Some(SortUtil
+        .createRowComparator(inputSchema.relDataType,
+          orderKeys.getFieldCollations.asScala.tail,
+          tableEnv.execEnv.getConfig))
+    } else {
+      None
+    }
+
+    timeOrderField.getType match {
+      case _ if FlinkTypeFactory.isRowtimeIndicatorType(timeOrderField.getType) =>
+        (crowInput.process(
+          new RowtimeProcessFunction(timeOrderField.getIndex, CRowTypeInfo(inputSchema.typeInfo))
+        ).setParallelism(crowInput.getParallelism),
+          rowComparator)
+      case _ =>
+        (crowInput, rowComparator)
+    }
+  }
+
+  private def applyPartitioning(partitionKeys: util.List[RexNode], inputDs: DataStream[Row]) = {
+    if (partitionKeys.size() > 0) {
+      val keys = partitionKeys.asScala.map {
+        case ref: RexInputRef => ref.getIndex
+      }.toArray
+      val keySelector = new RowKeySelector(keys, inputSchema.projectedTypeInfo(keys))
+      inputDs.keyBy(keySelector)
+    } else {
+      inputDs
+    }
+  }
+
+  override def translateToPlan(
+    tableEnv: StreamTableEnvironment,
+    queryConfig: StreamQueryConfig): DataStream[CRow] = {
+
+    val config = tableEnv.config
+    val inputTypeInfo = inputSchema.typeInfo
+
+    val crowInput: DataStream[CRow] = getInput
+      .asInstanceOf[DataStreamRel]
+      .translateToPlan(tableEnv, queryConfig)
+
+    val orderKeys = logicalMatch.getOrderKeys.getFieldCollations
+    val (timestampedInput, rowComparator) = translateOrder(tableEnv,
+      crowInput,
+      logicalMatch.getOrderKeys)
+
+    val cepPattern = logicalMatch.getPattern
+      .accept(new PatternVisitor(config, inputTypeInfo, logicalMatch))
+
+    //TODO remove this once it is supported in CEP library
+    if (NFACompiler.canProduceEmptyMatches(cepPattern)) {
+      throw new TableException(
+        "Patterns that can produce empty matches are not supported. There must be at least one " +
+          "non-optional state.")
+    }
+
+    //TODO remove this once it is supported in CEP library
+    if (cepPattern.getQuantifier.hasProperty(QuantifierProperty.GREEDY)) {
+      throw new TableException(
+        "Greedy quantifiers are not allowed as the last element of a Pattern yet. Finish your " +
+          "pattern with either a simple variable or reluctant quantifier.")
+    }
+
+    if (logicalMatch.getInterval != null) {
+      throw new TableException(
+        "WITHIN clause is not part of the SQL Standard, thus it is not supported.")
 
 Review comment:
   I think we should discuss whether to support WITHIN clause. IMO, we should support it as many use cases require a time constraint. Without it, the supported use cases will be very limited.

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