Github user rmetzger commented on a diff in the pull request:
https://github.com/apache/flink/pull/503#discussion_r26974901
--- Diff: docs/linq.md ---
@@ -23,58 +23,91 @@ under the License.
* This will be replaced by the TOC
{:toc}
-**Language-Integrated Queries are an experimental feature and can
currently only be used with
-the Scala API**
+**Language-Integrated Queries are an experimental feature**
-Flink provides an API that allows specifying operations using SQL-like
expressions.
-This Expression API can be enabled by importing
-`org.apache.flink.api.scala.expressions._`. This enables implicit
conversions that allow
-converting a `DataSet` or `DataStream` to an `ExpressionOperation` on
which relational queries
-can be specified. This example shows how a `DataSet` can be converted, how
expression operations
-can be specified and how an expression operation can be converted back to
a `DataSet`:
+Flink provides an API that allows specifying operations using SQL-like
expressions. Instead of
+manipulating `DataSet` or `DataStream` you work with `Table` on which
relational operations can
+be performed.
+
+The following dependency must be added to your project when using the
Table API:
+
+{% highlight xml %}
+<dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-table</artifactId>
+ <version>{{site.FLINK_VERSION_SHORT }}</version>
+</dependency>
+{% endhighlight %}
+
+## Scala Table API
+
+The Table API can be enabled by importing
`org.apache.flink.api.scala.table._`. This enables
+implicit conversions that allow
+converting a DataSet or DataStream to a Table. This example shows how a
DataSet can
+be converted, how relationa queries can be specified and how a Table can be
+converted back to a DataSet`:
{% highlight scala %}
import org.apache.flink.api.scala._
-import org.apache.flink.api.scala.expressions._
+import org.apache.flink.api.scala.table._
case class WC(word: String, count: Int)
val input = env.fromElements(WC("hello", 1), WC("hello", 1), WC("ciao", 1))
-val expr = input.toExpression
-val result = expr.groupBy('word).select('word, 'count.sum).as[WC]
+val expr = input.toTable
+val result = expr.groupBy('word).select('word, 'count.sum).toSet[WC]
{% endhighlight %}
The expression DSL uses Scala symbols to refer to field names and we use
code generation to
transform expressions to efficient runtime code. Please not that the
conversion to and from
--- End diff --
"Please not" ... "Please note"
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