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https://issues.apache.org/jira/browse/FLINK-7062?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16655158#comment-16655158
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ASF GitHub Bot commented on FLINK-7062:
---------------------------------------
dawidwys 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_r226282704
##########
File path:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamMatch.scala
##########
@@ -0,0 +1,358 @@
+/*
+ * 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 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.")
+ }
+
+ val inputDS: DataStream[Row] = timestampedInput
+ .map(new ConvertToRow)
+ .setParallelism(timestampedInput.getParallelism)
+ .name("ConvertToRow")
+ .returns(inputTypeInfo)
+
+ val partitionKeys = logicalMatch.getPartitionKeys
+ val partitionedStream = applyPartitioning(partitionKeys, inputDS)
+
+ val patternStream: PatternStream[Row] = if (rowComparator.isDefined) {
+ CEP.pattern[Row](partitionedStream, cepPattern, new
EventRowComparator(rowComparator.get))
+ } else {
+ CEP.pattern[Row](partitionedStream, cepPattern)
+ }
+
+ val measures = logicalMatch.getMeasures
+ val outTypeInfo = CRowTypeInfo(schema.typeInfo)
+ if (logicalMatch.isAllRows) {
+ throw new TableException("All rows per match mode is not supported yet.")
+ } else {
+ val patternSelectFunction =
+ MatchUtil.generateOneRowPerMatchExpression(
+ config,
+ schema,
+ partitionKeys,
+ orderKeys,
+ measures,
+ inputTypeInfo)
+ patternStream.flatSelect[CRow](patternSelectFunction, outTypeInfo)
+ }
+ }
+}
+
+private[flink] class PatternVisitor(
+ config: TableConfig,
+ inputTypeInfo: TypeInformation[Row],
+ logicalMatch: FlinkLogicalMatch
+) extends RexDefaultVisitor[Pattern[Row, Row]] {
+
+ private var pattern: Pattern[Row, Row] = _
Review comment:
In general I agree, I never check against null explicitly (I go through
Option), but having Option as the field makes the code much more complex, as I
would need to change all method return types in this class. I used null here
intentionally to simplify it a little bit.
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> Support the basic functionality of MATCH_RECOGNIZE
> --------------------------------------------------
>
> Key: FLINK-7062
> URL: https://issues.apache.org/jira/browse/FLINK-7062
> Project: Flink
> Issue Type: Sub-task
> Components: CEP, Table API & SQL
> Reporter: Dian Fu
> Assignee: Dian Fu
> Priority: Major
> Labels: pull-request-available
>
> In this JIRA, we will support the basic functionality of {{MATCH_RECOGNIZE}}
> in Flink SQL API which includes the support of syntax {{MEASURES}},
> {{PATTERN}} and {{DEFINE}}. This would allow users write basic cep use cases
> with SQL like the following example:
> {code}
> SELECT T.aid, T.bid, T.cid
> FROM MyTable
> MATCH_RECOGNIZE (
> MEASURES
> A.id AS aid,
> B.id AS bid,
> C.id AS cid
> PATTERN (A B C)
> DEFINE
> A AS A.name = 'a',
> B AS B.name = 'b',
> C AS C.name = 'c'
> ) AS T
> {code}
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