[
https://issues.apache.org/jira/browse/FLINK-4469?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15671941#comment-15671941
]
ASF GitHub Bot commented on FLINK-4469:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/2653#discussion_r88342301
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/plan/rules/datastream/DataStreamCorrelateRule.scala
---
@@ -0,0 +1,89 @@
+/*
+ * 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.api.table.plan.rules.datastream
+
+import org.apache.calcite.plan.volcano.RelSubset
+import org.apache.calcite.plan.{Convention, RelOptRule, RelOptRuleCall,
RelTraitSet}
+import org.apache.calcite.rel.RelNode
+import org.apache.calcite.rel.convert.ConverterRule
+import org.apache.calcite.rel.logical.{LogicalFilter, LogicalCorrelate,
LogicalTableFunctionScan}
+import org.apache.calcite.rex.RexNode
+import
org.apache.flink.api.table.plan.nodes.datastream.{DataStreamCorrelate,
DataStreamConvention}
+
+/**
+ * parser cross/outer apply
+ */
+class DataStreamCorrelateRule
+ extends ConverterRule(
+ classOf[LogicalCorrelate],
+ Convention.NONE,
+ DataStreamConvention.INSTANCE,
+ "DataStreamCorrelateRule")
+{
+
+ override def matches(call: RelOptRuleCall): Boolean = {
+ val join: LogicalCorrelate = call.rel(0).asInstanceOf[LogicalCorrelate]
+ val right = join.getRight.asInstanceOf[RelSubset].getOriginal
+
+ right match {
+ // right node is a table function
+ case scan: LogicalTableFunctionScan => true
+ // a filter is pushed above the table function
+ case filter: LogicalFilter =>
+ filter.getInput.asInstanceOf[RelSubset].getOriginal
+ .isInstanceOf[LogicalTableFunctionScan]
+ case _ => false
+ }
+ }
+
+ override def convert(rel: RelNode): RelNode = {
+ val join: LogicalCorrelate = rel.asInstanceOf[LogicalCorrelate]
+ val traitSet: RelTraitSet =
rel.getTraitSet.replace(DataStreamConvention.INSTANCE)
+ val convInput: RelNode = RelOptRule.convert(join.getInput(0),
DataStreamConvention.INSTANCE)
+ val right: RelNode = join.getInput(1)
+
+ def convertToCorrelate(relNode: RelNode, condition: RexNode):
DataStreamCorrelate = {
--- End diff --
define `condition` as `Option[RexNode]` so we do not have to use `null`
> Add support for user defined table function in Table API & SQL
> --------------------------------------------------------------
>
> Key: FLINK-4469
> URL: https://issues.apache.org/jira/browse/FLINK-4469
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: Jark Wu
> Assignee: Jark Wu
>
> Normal user-defined functions, such as concat(), take in a single input row
> and output a single output row. In contrast, table-generating functions
> transform a single input row to multiple output rows. It is very useful in
> some cases, such as look up in HBase by rowkey and return one or more rows.
> Adding a user defined table function should:
> 1. inherit from UDTF class with specific generic type T
> 2. define one or more evel function.
> NOTE:
> 1. the eval method must be public and non-static.
> 2. the generic type T is the row type returned by table function. Because of
> Java type erasure, we can’t extract T from the Iterable.
> 3. use {{collect(T)}} to emit table row
> 4. eval method can be overload. Blink will choose the best match eval method
> to call according to parameter types and number.
> {code}
> public class Word {
> public String word;
> public Integer length;
> }
> public class SplitStringUDTF extends UDTF<Word> {
> public Iterable<Word> eval(String str) {
> if (str != null) {
> for (String s : str.split(",")) {
> collect(new Word(s, s.length()));
> }
> }
> }
> }
> // in SQL
> tableEnv.registerFunction("split", new SplitStringUDTF())
> tableEnv.sql("SELECT a, b, t.* FROM MyTable, LATERAL TABLE(split(c)) AS
> t(w,l)")
> // in Java Table API
> tableEnv.registerFunction("split", new SplitStringUDTF())
> // rename split table columns to “w” and “l”
> table.crossApply("split(c) as (w, l)")
> .select("a, b, w, l")
> // without renaming, we will use the origin field names in the POJO/case/...
> table.crossApply("split(c)")
> .select("a, b, word, length")
> // in Scala Table API
> val split = new SplitStringUDTF()
> table.crossApply(split('c) as ('w, 'l))
> .select('a, 'b, 'w, 'l)
> // outerApply for outer join to a UDTF
> table.outerApply(split('c))
> .select('a, 'b, 'word, 'length)
> {code}
> See [1] for more information about UDTF design.
> [1]
> https://docs.google.com/document/d/15iVc1781dxYWm3loVQlESYvMAxEzbbuVFPZWBYuY1Ek/edit#
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