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https://issues.apache.org/jira/browse/FLINK-7062?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16654806#comment-16654806
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ASF GitHub Bot commented on FLINK-7062:
---------------------------------------
twalthr 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_r226190984
##########
File path:
flink-libraries/flink-table/src/test/scala/org/apache/flink/table/match/PatternTranslatorTestBase.scala
##########
@@ -0,0 +1,164 @@
+/*
+ * 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.`match`
+
+import com.google.common.collect.ImmutableList
+import org.apache.calcite.plan.hep.{HepMatchOrder, HepPlanner,
HepProgramBuilder}
+import org.apache.calcite.sql2rel.RelDecorrelator
+import org.apache.calcite.tools.{Programs, RelBuilder}
+import org.apache.flink.api.common.typeinfo.{BasicTypeInfo, TypeInformation}
+import org.apache.flink.api.java.typeutils.RowTypeInfo
+import org.apache.flink.cep.pattern.Pattern
+import org.apache.flink.streaming.api.datastream.{DataStream => JDataStream}
+import org.apache.flink.streaming.api.scala.{DataStream,
StreamExecutionEnvironment}
+import org.apache.flink.table.api.scala._
+import org.apache.flink.table.api.{TableConfig, TableEnvironment}
+import org.apache.flink.table.calcite.FlinkPlannerImpl
+import org.apache.flink.table.plan.nodes.FlinkConventions
+import org.apache.flink.table.plan.nodes.datastream.{DataStreamMatch,
DataStreamScan, PatternVisitor}
+import org.apache.flink.table.plan.rules.FlinkRuleSets
+import org.apache.flink.types.Row
+import org.apache.flink.util.TestLogger
+import org.junit.Assert._
+import org.junit.rules.ExpectedException
+import org.junit.{ComparisonFailure, Rule}
+import org.mockito.Mockito.{mock, when}
+
+class PatternTranslatorTestBase extends TestLogger{
+
+ private val typeInfo = new RowTypeInfo(BasicTypeInfo.INT_TYPE_INFO)
+
+ private val expectedException = ExpectedException.none()
+
+ @Rule
+ def thrown: ExpectedException = expectedException
+
+ // setup test utils
+ private val tableName = "testTable"
+ private val context = prepareContext(typeInfo)
+ private val planner = new FlinkPlannerImpl(
+ context._2.getFrameworkConfig,
+ context._2.getPlanner,
+ context._2.getTypeFactory)
+ private val logicalOptProgram =
Programs.ofRules(FlinkRuleSets.LOGICAL_OPT_RULES)
+ private val dataStreamOptProgram =
Programs.ofRules(FlinkRuleSets.DATASTREAM_OPT_RULES)
+
+ private def hepPlanner = {
+ val builder = new HepProgramBuilder
+ builder.addMatchOrder(HepMatchOrder.BOTTOM_UP)
+ val it = FlinkRuleSets.DATASTREAM_NORM_RULES.iterator()
+ while (it.hasNext) {
+ builder.addRuleInstance(it.next())
+ }
+ new HepPlanner(builder.build, context._2.getFrameworkConfig.getContext)
+ }
+
+ private def prepareContext(typeInfo: TypeInformation[Row])
+ : (RelBuilder, TableEnvironment, StreamExecutionEnvironment) = {
+ // create DataSetTable
+ val dataStreamMock = mock(classOf[DataStream[Row]])
+ val jDataStreamMock = mock(classOf[JDataStream[Row]])
+ when(dataStreamMock.javaStream).thenReturn(jDataStreamMock)
+ when(jDataStreamMock.getType).thenReturn(typeInfo)
+
+ val env = StreamExecutionEnvironment.getExecutionEnvironment
+ val tEnv = TableEnvironment.getTableEnvironment(env)
+ tEnv.registerDataStream(tableName, dataStreamMock, 'f0, 'proctime.proctime)
+
+ // prepare RelBuilder
+ val relBuilder = tEnv.getRelBuilder
+ relBuilder.scan(tableName)
+
+ (relBuilder, tEnv, env)
+ }
+
+ def verifyPattern(matchRecognize: String, expected: Pattern[Row, _ <: Row]):
Unit = {
+ // create RelNode from SQL expression
+ val parsed = planner.parse(
+ s"""
+ |SELECT *
+ |FROM $tableName
+ |$matchRecognize
+ |""".stripMargin)
+ val validated = planner.validate(parsed)
+ val converted = planner.rel(validated).rel
+
+ val decorPlan = RelDecorrelator.decorrelateQuery(converted)
+
+ // normalize
+ val normalizedPlan = if
(FlinkRuleSets.DATASET_NORM_RULES.iterator().hasNext) {
+ val planner = hepPlanner
+ planner.setRoot(decorPlan)
+ planner.findBestExp
+ } else {
+ decorPlan
+ }
+
+ // convert to logical plan
+ val logicalProps =
converted.getTraitSet.replace(FlinkConventions.LOGICAL).simplify()
+ val logicalCalc = logicalOptProgram.run(context._2.getPlanner,
normalizedPlan, logicalProps,
+ ImmutableList.of(), ImmutableList.of())
+
+ // convert to dataset plan
+ val physicalProps =
converted.getTraitSet.replace(FlinkConventions.DATASTREAM).simplify()
+ val dataStreamMatch = dataStreamOptProgram
+ .run(context._2.getPlanner, logicalCalc, physicalProps,
+ ImmutableList.of(), ImmutableList.of())
+
+ // throw exception if plan contains more than a calc
Review comment:
This line and the `fail` need an update.
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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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