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https://issues.apache.org/jira/browse/FLINK-947?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14325036#comment-14325036
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ASF GitHub Bot commented on FLINK-947:
--------------------------------------
Github user rmetzger commented on a diff in the pull request:
https://github.com/apache/flink/pull/405#discussion_r24862039
--- Diff:
flink-staging/flink-linq/src/main/scala/org/apache/flink/api/scala/expressions/package.scala
---
@@ -0,0 +1,104 @@
+/*
+ * 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.scala
+
+import com.google.common.base.Preconditions
+import org.apache.flink.api.expressions.{Row, ExpressionOperation}
+import org.apache.flink.api.common.typeutils.CompositeType
+import org.apache.flink.streaming.api.scala.DataStream
+
+import scala.language.implicitConversions
+
+/**
+ * == Language Integrated Queries (aka Expression Operations) ==
+ *
+ * Importing this package with:
+ *
+ * {{{
+ * import org.apache.flink.api.scala.expressions._
+ * }}}
+ *
+ * imports implicit conversions for converting a [[DataSet]] or
[[DataStream]] to an
+ * [[ExpressionOperation]]. This can be used to perform SQL-like queries
on data. Please have
+ * a look at [[ExpressionOperation]] to see which operations are supported
and
+ * [[org.apache.flink.api.scala.expressions.ImplicitExpressionOperations]]
to see how an
+ * expression can be specified.
+ *
+ * Inside an expression operation you can use Scala Symbols to refer to
field names. One would
+ * refer to field `a` by writing `'a`. Sometimes it is necessary to
manually confert a
+ * Scala literal to an Expression Literal, in those cases use `Literal`,
as in `Literal(3)`.
+ *
+ * Example:
+ *
+ * {{{
+ * import org.apache.flink.api.scala._
+ * import org.apache.flink.api.scala.expressions._
+ *
+ * val env = ExecutionEnvironment.getExecutionEnvironment
+ * val input = env.fromElements(("Hello", 2), ("Hello", 5), ("Ciao", 3))
+ * val result = input.as('word, 'count).groupBy('word).select('word,
'count.avg)
+ * result.print()
+ *
+ * env.execute()
+ * }}}
+ *
+ * The result of an [[ExpressionOperation]] can be converted back to the
underlying API
+ * representation using `as`:
+ *
+ * {{{
+ * case class Word(word: String, count: Int)
+ *
+ * val result = in.select(...).as('word, 'count)
+ * val set = result.as[Word]
+ * }}}
+ */
+package object expressions extends ImplicitExpressionConversions {
+
+ implicit def dataSet2DataSetConversions[T](set: DataSet[T]):
DataSetConversions[T] = {
+ Preconditions.checkArgument(set.getType.isInstanceOf[CompositeType[T]])
--- End diff --
I think we need a good error message here, telling the user why the type
cannot be used with the expr lang.
> Add support for "Named Datasets"
> --------------------------------
>
> Key: FLINK-947
> URL: https://issues.apache.org/jira/browse/FLINK-947
> Project: Flink
> Issue Type: New Feature
> Components: Java API
> Reporter: Aljoscha Krettek
> Assignee: Aljoscha Krettek
> Priority: Minor
>
> This would create an API that is a mix between SQL like declarativity and the
> power of user defined functions. Example user code could look like this:
> {code:Java}
> NamedDataSet one = ...
> NamedDataSet two = ...
> NamedDataSet result = one.join(two).where("key").equalTo("otherKey")
> .project("a", "b", "c")
> .map( (UserTypeIn in) -> return new UserTypeOut(...) )
> .print();
> {code}
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