docs/libs/table.md

---
title: "Table API - Relational Queries"
is_beta: true
---
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**The Table API an experimental feature**

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.version }}</version>
</dependency>
{% endhighlight %}

Note that the Table API is currently not part of the binary distribution. See linking with it for cluster execution [here]({{ site.baseurl }}/apis/cluster_execution.html#linking-with-modules-not-contained-in-the-binary-distribution).

## 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 relational 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.table._ 

case class WC(word: String, count: Int)
val input = env.fromElements(WC("hello", 1), WC("hello", 1), WC("ciao", 1))
val expr = input.toTable
val result = expr.groupBy('word).select('word, 'count.sum as 'count).toDataSet[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 note that the conversion to and from
Tables only works when using Scala case classes or Flink POJOs. Please check out
the [programming guide]({{ site.baseurl }}/apis/programming_guide.html) to learn the requirements for a class to be 
considered a POJO.
 
This is another example that shows how you
can join to Tables:

{% highlight scala %}
case class MyResult(a: String, d: Int)

val input1 = env.fromElements(...).toTable('a, 'b)
val input2 = env.fromElements(...).toTable('c, 'd)
val joined = input1.join(input2).where("b = a && d > 42").select("a, d").toDataSet[MyResult]
{% endhighlight %}

Notice, how a DataSet can be converted to a Table by using `as` and specifying new
names for the fields. This can also be used to disambiguate fields before a join operation. Also,
in this example we see that you can also use Strings to specify relational expressions.

Please refer to the Scaladoc (and Javadoc) for a full list of supported operations and a
description of the expression syntax. 

## Java Table API

When using Java, Tables can be converted to and from DataSet and DataStream using `TableEnvironment`.
This example is equivalent to the above Scala Example:

{% highlight java %}

public class WC {

  public WC(String word, int count) {
    this.word = word; this.count = count;
  }

  public WC() {} // empty constructor to satisfy POJO requirements

  public String word;
  public int count;
}

...

ExecutionEnvironment env = ExecutionEnvironment.createCollectionsEnvironment();
TableEnvironment tableEnv = new TableEnvironment();

DataSet<WC> input = env.fromElements(
        new WC("Hello", 1),
        new WC("Ciao", 1),
        new WC("Hello", 1));

Table table = tableEnv.fromDataSet(input);

Table filtered = table
        .groupBy("word")
        .select("word.count as count, word")
        .filter("count = 2");

DataSet<WC> result = tableEnv.toDataSet(filtered, WC.class);
{% endhighlight %}

When using Java, the embedded DSL for specifying expressions cannot be used. Only String expressions
are supported. They support exactly the same feature set as the expression DSL.

## Expression Syntax

A `Table` supports to following operations: `select`, `where`, `groupBy`, `join` (Plus `filter` as
an alias for `where`.). These are also documented in the [Javadoc](http://flink.apache.org/docs/latest/api/java/org/apache/flink/api/table/Table.html) 
of Table.

Some of these expect an expression. These can either be specified using an embedded Scala DSL or
a String expression. Please refer to the examples above to learn how expressions can be
formulated.

This is the complete EBNF grammar for expressions:

{% highlight ebnf %}

expression = single expression , { "," , single expression } ;

single expression = alias | logic ;

alias = logic | logic , "AS" , field reference ;

logic = comparison , [ ( "&&" | "||" ) , comparison ] ;

comparison = term , [ ( "=" | "!=" | ">" | ">=" | "<" | "<=" ) , term ] ;

term = product , [ ( "+" | "-" ) , product ] ;

product = binary bitwise , [ ( "*" | "/" | "%" ) , binary bitwise ] ;

binary bitwise = unary , [ ( "&" | "!" | "^" ) , unary ] ;

unary = [ "!" | "-" | "~" ] , suffix ;

suffix = atom | aggregation | cast | as | substring ;

aggregation = atom , [ ".sum" | ".min" | ".max" | ".count" | "avg" ] ;

cast = atom , ".cast(" , data type , ")" ;

data type = "BYTE" | "SHORT" | "INT" | "LONG" | "FLOAT" | "DOUBLE" | "BOOL" | "BOOLEAN" | "STRING" ;

as = atom , ".as(" , field reference , ")" ;

substring = atom , ".substring(" , substring start , ["," substring end] , ")" ;

substring start = single expression ;

substring end = single expression ;

atom = ( "(" , single expression , ")" ) | literal | field reference ;

{% endhighlight %}

Here, `literal` is a valid Java literal and `field reference` specifies a column in the data. The
column names follow Java identifier syntax.