[
https://issues.apache.org/jira/browse/FLINK-6746?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16031306#comment-16031306
]
ASF GitHub Bot commented on FLINK-6746:
---------------------------------------
Github user alpinegizmo commented on a diff in the pull request:
https://github.com/apache/flink/pull/4012#discussion_r119381768
--- Diff: docs/dev/table/common.md ---
@@ -98,374 +89,767 @@ env.execute("Your Query")
</div>
</div>
+**Note:** Table API and SQL queries can be easily integrated with and
embedded into DataStream or DataSet programs. Have a look a the [Integration
with DataStream and DataSet API](#integration-with-datastream-and-dataset-api)
section to learn how DataStreams and DataSets can be converted into Tables and
vice versa.
+
{% top %}
Create a TableEnvironment
-------------------------
-A `Table` is always bound to a specific `TableEnvironment`. It is not
possible to combine Tables of different TableEnvironments.
+The `TableEnvironment` is a central concept of the Table API and SQL
integration. It is responsible for:
+* Registering a `Table` in the internal catalog
+* Registering an external catalog
+* Executing SQL queries
+* Registering a user-defined (scalar, table, or aggregation) function
+* Converting a `DataStream` or `DataSet` into a `Table`
+* Holding a reference to an `ExecutionEnvironment` or
`StreamExecutionEnvironment`
+
+A `Table` is always bound to a specific `TableEnvironment`. It is not
process tables of different TableEnvironments in the same query, e.g., to join
or union them.
+
+A `TableEnvironment` is created by calling the static
`TableEnvironment.getTableEnvironment()` method with a
`StreamExecutionEnvironment` or an `ExecutionEnvironment` and an optional
`TableConfig`. The `TableConfig` can be used to configure the
`TableEnvironment` or to customize the query optimization and translation
process (see [Query Optimization](#query-optimization)).
-**TODO: Extend**
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// ***************
+// STREAMING QUERY
+// ***************
+StreamExecutionEnvironment sEnv =
StreamExecutionEnvironment.getExecutionEnvironment();
+// Create a TableEnvironment for streaming queries
+StreamTableEnvironment sTableEnv =
TableEnvironment.getTableEnvironment(sEnv);
+
+// ***********
+// BATCH QUERY
+// ***********
+ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment();
+// Create a TableEnvironment for batch queries
+BatchTableEnvironment bTableEnv =
TableEnvironment.getTableEnvironment(bEnv);
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// ***************
+// STREAMING QUERY
+// ***************
+val sEnv = StreamExecutionEnvironment.getExecutionEnvironment
+// Create a TableEnvironment for streaming queries
+val sTableEnv = TableEnvironment.getTableEnvironment(sEnv)
+
+// ***********
+// BATCH QUERY
+// ***********
+val bEnv = ExecutionEnvironment.getExecutionEnvironment
+// Create a TableEnvironment for batch queries
+val bTableEnv = TableEnvironment.getTableEnvironment(bEnv)
+{% endhighlight %}
+</div>
+</div>
{% top %}
Register a Table in the Catalog
-------------------------------
-`TableEnvironment`s have an internal table catalog to which tables can be
registered with a unique name. After registration, a table can be accessed from
the `TableEnvironment` by its name.
+A `TableEnvironment` has an internal catalog to register tables by name.
Table API or SQL queries can access tables, which are registered in the catalog
by referencing them with their name.
-*Note: `DataSet`s or `DataStream`s can be directly converted into `Table`s
without registering them in the `TableEnvironment`. See [Create a Table from a
DataStream or DataSet](#tbd) for details.
+A `TableEnvironment` allows to register a table from various sources:
+* an existing `Table` object, usually the result of a Table API or SQL
query.
+* a `TableSource`, which accesses external data, such as a file, database,
or messaging system.
+* a `DataStream` or `DataSet` from a DataStream or DataSet program.
+
+Registering a `DataStream` or `DataSet` as a table is discussed in the
[Integration with DataStream and DataSet
API](#integration-with-datastream-and-dataset-api) section.
### Register a Table
-A `Table` that originates from a Table API operation or a SQL query is
registered in a `TableEnvironment` as follows:
+A `Table` is registered in a `TableEnvironment` as follows:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-// works for StreamExecutionEnvironment identically
-ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
-BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-// convert a DataSet into a Table
-Table custT = tableEnv
- .toTable(custDs, "name, zipcode")
- .where("zipcode = '12345'")
- .select("name");
+// Table is the result of a simple projection query
+Table projX = tableEnv.scan("X").project(...);
-// register the Table custT as table "custNames"
-tableEnv.registerTable("custNames", custT);
+// register the Table projX as table "projectedX"
+tableEnv.registerTable("projectedX", projX);
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-// works for StreamExecutionEnvironment identically
-val env = ExecutionEnvironment.getExecutionEnvironment
+// get a TableEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
-// convert a DataSet into a Table
-val custT = custDs
- .toTable(tableEnv, 'name, 'zipcode)
- .where('zipcode === "12345")
- .select('name)
+// Table is the result of a simple projection query
+val projX: Table = tableEnv.scan("X").project(...)
-// register the Table custT as table "custNames"
-tableEnv.registerTable("custNames", custT)
+// register the Table projX as table "projectedX"
+tableEnv.registerTable("projectedX", projX)
{% endhighlight %}
</div>
</div>
-A registered `Table` that originates from a Table API operation or SQL
query is treated similarly as a view as known from relational DBMS, i.e., it
can be inlined when optimizing the query.
+**Note:** A registered `Table` is treated similarly to a `VIEW` as known
from relational database systems, i.e., the query that defines the `Table` is
not optimized but will be inlined when another query references the registered
`Table`. If multiple queries reference the same registered `Table`, it will be
inlined for each referencing query and executed multiple times, i.e., the
result of the registered `Table` will *not* be shared.
{% top %}
-### Register a DataSet
+### Register a TableSource
+
+A `TableSource` provides access to external data which is stored in a
storage systems such as a database (MySQL, HBase, ...), a file with specific
encoding (CSV, Apache \[Parquet, Avro, ORC\], ...), or a messaging system
(Apache Kafka, RabbitMQ, ...).
+
+Flink aims to provide TableSources for common data formats and storage
systems. Please have a look at the [Table Sources and Sinks]({{ site.baseurl
}}/dev/table/sourceSinks.html) page for a list of supported TableSources and
instructions for how to build a custom `TableSource`.
-A `DataSet` is registered as a `Table` in a `BatchTableEnvironment` as
follows:
+A `TableSource` is registered in a `TableEnvironment` as follows:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
-BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-// register the DataSet cust as table "Customers" with fields derived from
the dataset
-tableEnv.registerDataSet("Customers", cust);
+// create a TableSource
+TableSource csvSource = new CsvTableSource("/path/to/file", ...);
-// register the DataSet ord as table "Orders" with fields user, product,
and amount
-tableEnv.registerDataSet("Orders", ord, "user, product, amount");
+// register the TableSource as table "CsvTable"
+tableEnv.registerTableSource("CsvTable", csvSource);
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-val env = ExecutionEnvironment.getExecutionEnvironment
+// get a TableEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
-// register the DataSet cust as table "Customers" with fields derived from
the dataset
-tableEnv.registerDataSet("Customers", cust)
+// create a TableSource
+val csvSource: TableSource = new CsvTableSource("/path/to/file", ...)
-// register the DataSet ord as table "Orders" with fields user, product,
and amount
-tableEnv.registerDataSet("Orders", ord, 'user, 'product, 'amount)
+// register the TableSource as table "CsvTable"
+tableEnv.registerTableSource("CsvTable", csvSource)
{% endhighlight %}
</div>
</div>
-*Note: The name of a `DataSet` `Table` must not match the
`^_DataSetTable_[0-9]+` pattern which is reserved for internal use only.*
-
{% top %}
-### Register a DataStream
+Register an External Catalog
+----------------------------
+
+An external catalog can provide information about external databases and
tables such as their name, schema, statistics, and information for how to
access data stored in an external database, table, or file.
-A `DataStream` is registered as a `Table` in a `StreamTableEnvironment` as
follows:
+An external catalog can be created by implementing the `ExternalCatalog`
interface and is registered in a `TableEnvironment` as follows:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-// register the DataStream cust as table "Customers" with fields derived
from the datastream
-tableEnv.registerDataStream("Customers", cust);
+// create an external catalog
+ExternalCatalog catalog = new InMemoryExternalCatalog();
-// register the DataStream ord as table "Orders" with fields user,
product, and amount
-tableEnv.registerDataStream("Orders", ord, "user, product, amount");
+// register the ExternalCatalog catalog
+tableEnv.registerExternalCatalog("InMemCatalog", catalog);
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-val env = StreamExecutionEnvironment.getExecutionEnvironment
+// get a TableEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
-// register the DataStream cust as table "Customers" with fields derived
from the datastream
-tableEnv.registerDataStream("Customers", cust)
+// create an external catalog
+val catalog: ExternalCatalog = new InMemoryExternalCatalog
-// register the DataStream ord as table "Orders" with fields user,
product, and amount
-tableEnv.registerDataStream("Orders", ord, 'user, 'product, 'amount)
+// register the ExternalCatalog catalog
+tableEnv.registerExternalCatalog("InMemCatalog", catalog)
{% endhighlight %}
</div>
</div>
-*Note: The name of a `DataStream` `Table` must not match the
`^_DataStreamTable_[0-9]+` pattern which is reserved for internal use only.*
+Once registered in a `TableEnvironment`, all tables defined in a
`ExternalCatalog` can be accessed from Table API or SQL queries by specifying
their full path, such as for example `catalog.database.table`.
+
+Currently, Flink provides an `InMemoryExternalCatalog` for demo and
testing purposes. However, the `ExternalCatalog` interface can also be used to
connect catalogs like HCatalog or Metastore to the Table API.
{% top %}
-### Register a TableSource
+Query a Table
+-------------
+
+### Table API
+
+The Table API is a language-integrated query API for Scala and Java. In
contrast to SQL, queries are not specified as Strings but are composed
step-by-step in the host language.
+
+The API is based on the `Table` class which represents a table (streaming
or batch) and offers methods to apply relational operations. These methods
return a new `Table` object, which represents the result of applying the
relational operation on the input `Table`. Some relational operations are
composed of multiple method calls such as `table.groupBy(...).select()`, where
`groupBy(...)` specifies a grouping of `table` and `select(...)` the projection
on the grouping of `table`.
-TableSources provided access to data stored in various storage systems
such as databases (MySQL, HBase, ...), file formats (CSV, Apache Parquet, Avro,
ORC, ...), or messaging systems (Apache Kafka, RabbitMQ, ...). Flink provides a
TableSources for common data formats and storage systems. Please have a look at
the [Table Sources and Sinks page]({{ site.baseurl
}}/dev/table/sourceSinks.html) for a list of provided TableSources and
documentation for how to built your own.
+The [Table API]({{ site.baseurl }}/dev/table/tableapi.html) document
describes all Table API operations that are supported on streaming and batch
tables.
-An external table is registered in a `TableEnvironment` using a
`TableSource` as follows:
+The following example shows a simple Table API aggregation query:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-// works for StreamExecutionEnvironment identically
-ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
-BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-TableSource custTS = new CsvTableSource("/path/to/file", ...);
+// register Orders table
-// register a `TableSource` as external table "Customers"
-tableEnv.registerTableSource("Customers", custTS);
+// scan registered Orders table
+Table orders = tableEnv.scan("Orders");
+// compute revenue for all customers from France
+Table revenue = orders
+ .filter("cCountry === 'FRANCE'")
+ .groupBy("cID, cName")
+ .select("cID, cName, revenue.sum AS revSum");
+
+// emit or convert Table
+// execute query
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-// works for StreamExecutionEnvironment identically
-val env = ExecutionEnvironment.getExecutionEnvironment
+// get a TableEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
-val custTS: TableSource = new CsvTableSource("/path/to/file", ...)
+// register Orders table
-// register a `TableSource` as external table "Customers"
-tableEnv.registerTableSource("Customers", custTS)
+// scan registered Orders table
+Table orders = tableEnv.scan("Orders")
+// compute revenue for all customers from France
+Table revenue = orders
+ .filter('cCountry === "FRANCE")
+ .groupBy('cID, 'cName)
+ .select('cID, 'cName, 'revenue.sum AS 'revSum)
+// emit or convert Table
+// execute query
{% endhighlight %}
+
+**Note:** The Scala Table API uses Scala Symbols, which start with a
single tick (`'`) to reference the attributes of a `Table`.
</div>
</div>
-A `TableSource` can provide access to data stored in various storage
systems such as databases (MySQL, HBase, ...), file formats (CSV, Apache
Parquet, Avro, ORC, ...), or messaging systems (Apache Kafka, RabbitMQ, ...).
-
{% top %}
-Register an External Catalog
-----------------------------
+### SQL
-An external catalog is defined by the `ExternalCatalog` interface and
provides information about databases and tables such as their name, schema,
statistics, and access information. An `ExternalCatalog` is registered in a
`TableEnvironment` as follows:
+Flink's SQL integration is based on [Apache
Calcite](https://calcite.apache.org) which implements the SQL standard. SQL
queries are specified as regular Strings.
+
+The [SQL]({{ site.baseurl }}/dev/table/sql.html) document describes
Flink's SQL support for streaming and batch tables.
+
+The following example shows how to specify a query and return the result
as Table.
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-// works for StreamExecutionEnvironment identically
-ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
-BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+// register Orders table
-ExternalCatalog customerCatalog = new InMemoryExternalCatalog();
+// compute revenue for all customers from France
+Table revenue = tableEnv.sql(
+ "SELECT cID, cName, SUM(revenue) AS revSum " +
+ "FROM Orders " +
+ "WHERE cCountry = 'FRANCE' " +
+ "GROUP BY cID, cName"
+ );
-// register the ExternalCatalog customerCatalog
-tableEnv.registerExternalCatalog("Customers", customerCatalog);
+// emit or convert Table
+// execute query
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-// works for StreamExecutionEnvironment identically
-val env = ExecutionEnvironment.getExecutionEnvironment
+// get a TableEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
-val customerCatalog: ExternalCatalog = new InMemoryExternalCatalog
+// register Orders table
-// register the ExternalCatalog customerCatalog
-tableEnv.registerExternalCatalog("Customers", customerCatalog)
+// compute revenue for all customers from France
+Table revenue = tableEnv.sql("""
+ |SELECT cID, cName, SUM(revenue) AS revSum
+ |FROM Orders
+ |WHERE cCountry = 'FRANCE'
+ |GROUP BY cID, cName
+ """.stripMargin)
+// emit or convert Table
+// execute query
{% endhighlight %}
+
</div>
</div>
-Once registered in a `TableEnvironment`, all tables defined in a
`ExternalCatalog` can be accessed from Table API or SQL queries by specifying
their full path (`catalog`.`database`.`table`).
+{% top %}
+
+### Mixing Table API and SQL
-Currently, Flink provides an `InMemoryExternalCatalog` for demo and
testing purposes. However, the `ExternalCatalog` interface can also be used to
connect catalogs like HCatalog or Metastore to the Table API.
+Table API and SQL queries can be easily mixed because both return `Table`
objects:
+
+* A Table API query can be defined on the `Table` object returned by a SQL
query.
+* A SQL query can be defined on the result of a Table API query by
[registering the resulting Table](#register-a-table) in the `TableEnvironment`
and referencing it in the `FROM` clause of the SQL query.
{% top %}
-Create a Table from a DataStream or DataSet
--------------------------------------------
+Emit a Table
+------------
-Besides registering a Table in a catalog, it is also possible to directly
create a `Table` from a `DataStream` or `DataSet`.
+In order to emit a `Table`, it can be written to a `TableSink`. A
`TableSink` is a generic interface to support a wide variety of file formats
(e.g. CSV, Apache Parquet, Apache Avro), storage systems (e.g., JDBC, Apache
HBase, Apache Cassandra, Elasticsearch), or messaging systems (e.g., Apache
Kafka, RabbitMQ).
-### Create a Table from a DataStream
+A batch `Table` can only be written to a `BatchTableSink`, a streaming
table requires either an `AppendStreamTableSink`, a `RetractStreamTableSink`,
or an `UpsertStreamTableSink`.
-**TODO**
+Please see the documentation about [Table Sources & Sinks]({{ site.baseurl
}}/dev/table/sourceSinks.html) for details about available sinks and
instructions for how to implement a custom `TableSink`.
-{% top %}
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+// compute a result Table using Table API operators and/or SQL queries
+Table result = ...
+
+// create a TableSink
+TableSink sink = new CsvTableSink("/path/to/file", fieldDelim = "|");
+
+// write the result Table to the TableSink
+result.writeToSink(sink);
+
+// execute the program
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get a TableEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
-### Create a Table from a DataSet
+// compute a result Table using Table API operators and/or SQL queries
+val result: Table = ...
-**TODO**
+// create a TableSink
+val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|")
-### Scala Implicit Conversion
+// write the result Table to the TableSink
+result.writeToSink(sink)
-If you use the Scala API, A `DataSet` or `DataStream` can be implicitly
converted into a `Table`.
+// execute the program
+{% endhighlight %}
+</div>
+</div>
{% top %}
-Query a Table
--------------
-### Table API
+Translate and Execute a Query
+-----------------------------
+
+Table API and SQL queries are translated into [DataStream]({{ site.baseurl
}}/dev/datastream_api.html) or [DataSet]({{ site.baseurl }}/dev/batch) programs
depending on whether their input is a streaming or batch input. A query is
internally represented as a logical query plan and is translated in two phases:
-**TODO**
+1. optimization of the logical plan,
+2. translation into a DataStream or DataSet program.
+
+A Table API or SQL query is translated when:
+
+* the `Table` is emitted to a `TableSink`, i.e., when
`Table.writeToSink()` is called.
+* the `Table` is converted into a `DataStream` or `DataSet` (see
[Integration with DataStream and DataSet
API](#integration-with-dataStream-and-dataSet-api)).
+
+Once translated, a Table API or SQL query is handled like a regular
DataStream or DataSet program and is executed when
`StreamExecutionEnvironment.execute()` or `ExecutionEnvironment.execute()` is
called.
{% top %}
-### SQL
+Integration with DataStream and DataSet API
+-------------------------------------------
+
+Table API and SQL queries can be easily integrated with and embedded into
[DataStream]({{ site.baseurl }}/dev/datastream_api.html) and [DataSet]({{
site.baseurl }}/dev/batch) programs. For instance it is possible to query an
external table (for example from a RDBMS), do some pre-processing, such as
filtering, projecting, aggregating, or joining with meta data, before the data
is processed with a custom DataStream or DataSet program (including any of the
libraries on top of these APIs, such as CEP or Gelly). Inversely, a Table API
or SQL query can also be applied on the result of a DataStream or DataSet
program.
+
+This interaction can be achieved by converting a `DataStream` or `DataSet`
into a `Table` and vice versa. In this section, we describe how these
conversions are done.
+
+### Implicit Conversion for Scala
+
+The Scala Table API features implicit conversions for the `DataSet`,
`DataStream`, and `Table` classes. These conversions are enabled by importing
the package `org.apache.flink.table.api.scala._`
+
+### Register a DataStream or DataSet as Table
+
+A `DataStream` or `DataSet` can be registered in a `TableEnvironment` as a
Table. The schema of the resulting table depends on the data type of the
registered `DataStream` or `DataSet`. Please check the section about [mapping
of data types to table schema](#mapping-of-data-types-to-table-schema) for
details.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get StreamTableEnvironment.
+// registration of a DataSet in a BatchTableEnvironment is equivalent
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+DataStream<Tuple2<Long, String>> stream = ...
+
+// register the DataStream as Table "myTable" with fields "f0", "f1"
+tableEnv.registerDataStream("myTable", stream);
+
+// register the DataStream as table "myTable2" with fields "myLong",
"myString"
+tableEnv.registerDataStream("myTable2", stream, "myLong, myString");
+{% endhighlight %}
+</div>
-**TODO**
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get TableEnvironment.
+// registration of a DataSet is equivalent
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+val stream: DataStream[(Long, String)] = ...
+
+// register the DataStream as Table "myTable" with fields "f0", "f1"
+tableEnv.registerDataStream("myTable", stream)
+
+// register the DataStream as table "myTable2" with fields "myLong",
"myString"
+tableEnv.registerDataStream("myTable2", stream, 'myLong, 'myString)
+{% endhighlight %}
+</div>
+</div>
+
+**Note:** The name of a `DataStream` `Table` must not match the
`^_DataStreamTable_[0-9]+` pattern and the name of a `DataSet` `Table` must not
match the `^_DataSetTable_[0-9]+` pattern. These patterns are reserved for
internal use only.
{% top %}
-### Interoperability
+### Convert a DataStream or DataSet into a Table
+
+Instead of registering a `DataStream` or `DataSet` in a
`TableEnvironment`, it can also be directly converted into a `Table`. This is
convenient if you want to use the Table in a Table API query.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get StreamTableEnvironment.
+// registration of a DataSet in a BatchTableEnvironment is equivalent
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+DataStream<Tuple2<Long, String>> stream = ...
-**TODO**
+// Convert the DataStream into a Table with default fields "f0", "f1"
+Table table1 = tableEnv.fromDataStream(stream);
-* Mix SQL and Table as you like
-* Table API to SQL requires registered tables, register Table
-* SQL to Table API just use resulting table
+// Convert the DataStream into a Table with fields "myLong", "myString"
+Table table2 = tableEnv.fromDataStream(stream, "myLong, myString");
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get TableEnvironment.
+// registration of a DataSet is equivalent
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+val stream: DataStream[(Long, String)] = ...
+
+// convert the DataStream into a Table with default fields '_1, '_2
+val table1: Table = tableEnv.fromDataStream(stream)
+
+// convert the DataStream into a Table with fields 'myLong, 'myString
+val table2: Table = tableEnv.fromDataStream(stream, 'myLong, 'myString)
+{% endhighlight %}
+</div>
+</div>
{% top %}
-Emit a Table
-------------
+### Convert a Table into a DataStream or DataSet
+
+A `Table` can be converted into a `DataStream` or `DataSet`. This allows
to run custom DataStream or DataSet programs on the result of a Table API or
SQL query.
+
+When converting a `Table` into a `DataStream` or `DataSet`, you need to
specify the data type of the resulting `DataStream` or `DataSet`, i.e., the
data type into which the rows of the `Table` are to be converted. Often the
most convenient conversion type is `Row`. The following list gives an overview
of the features of the different options:
+
+- **Row**: fields are mapped by position, arbitrary number of fields,
support for `null` values, no type-safe access.
+- **POJO**: fields are mapped by name (POJO fields must be named as
`Table` fields), arbitrary number of fields, support for `null` values,
type-safe access.
+- **Case Class**: fields are mapped by position, no support for `null`
values, type-safe access.
+- **Tuple**: fields are mapped by position, limitation to 22 (Scala) or 25
(Java) fields, no support for `null` values, type-safe access.
+- **Atomic Type**: `Table` must have a single field, no support for `null`
values, type-safe access.
+
+#### Convert a Table into a DataStream
+
+A `Table` that is the result of a streaming query is dynamically changing,
i.e., it is updated depending on the input streams of the query. Hence, the
`DataStream` into which such a dynamic query is converted needs to encode the
updates of the table.
+
+There are two modes to convert a `Table` into a `DataStream`:
+
+1. **Append Mode**: This mode can only be used in the dynamic `Table` is
only modified by `INSERT` changes, i.e, it is append-only and previously
emitted results are never updated.
+2. **Retract Mode**: This mode can always be used. It encodes `INSERT` and
`DELETE` changes with a `boolean` flag.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get StreamTableEnvironment.
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+// Table with two fields (String name, Integer age)
+Table table = ...
+
+// convert the Table into an append DataStream of Row by specifying the
class
+DataStream<Row> dsRow = tableEnv.toAppendStream(table, Row.class);
+
+// convert the Table into an append DataStream of Tuple2<String, Integer>
+// via a TypeInformation
+TupleTypeInfo<Tuple2<String, Integer>> tupleType = new TupleTypeInfo<>(
+ Types.STRING(),
+ Types.INT());
+DataStream<Tuple2<String, Integer>> dsTuple =
+ tableEnv.toAppendStream(table, tupleType);
+
+// convert the Table into a retract DataStream of Row.
+// A retract stream of type X is a DataStream<Tuple2<Boolean, X>>.
+// The boolean field indicates the type of the change.
+// True is INSERT, false is DELETE.
+DataStream<Tuple2<Boolean, Row>> retractStream =
+ tableEnv.toRetractStream(table, Row.class);
+
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get TableEnvironment.
+// registration of a DataSet is equivalent
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+// Table with two fields (String name, Integer age)
+val table: Table = ...
+
+// convert the Table into an append DataStream of Row
+val dsRow: DataStream[Row] = tableEnv.toAppendStream[Row](table)
-### Emit to a TableSink
+// convert the Table into an append DataStream of Tuple2[String, Int]
+val dsTuple: DataStream[(String, Int)] dsTuple =
+ tableEnv.toAppendStream[(String, Int)](table)
+
+// convert the Table into a retract DataStream of Row.
+// A retract stream of type X is a DataStream[(Boolean, X)].
+// The boolean field indicates the type of the change.
+// True is INSERT, false is DELETE.
+val retractStream: DataStream[(Boolean, Row)] =
tableEnv.toRetractStream[Row](table)
+{% endhighlight %}
+</div>
+</div>
-A `Table` can be written to a `TableSink`, which is a generic interface to
support a wide variety of file formats (e.g. CSV, Apache Parquet, Apache Avro),
storage systems (e.g., JDBC, Apache HBase, Apache Cassandra, Elasticsearch), or
messaging systems (e.g., Apache Kafka, RabbitMQ). A batch `Table` can only be
written to a `BatchTableSink`, a streaming table requires a `StreamTableSink`.
A `TableSink` can implement both interfaces at the same time.
+**Note:** A detailed discussion about dynamic tables and their properties
is given in the [Streaming Queries]({{ site.baseurl
}}/dev/table/streaming.html) document.
-Currently, Flink only provides a `CsvTableSink` that writes a batch or
streaming `Table` to CSV-formatted files. A custom `TableSink` can be defined
by implementing the `BatchTableSink` and/or `StreamTableSink` interface.
+#### Convert a Table into a DataSet
+
+A `Table` is converted into a `DataSet` as follows:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
-ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
+// get BatchTableEnvironment.
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
-// compute the result Table using Table API operators and/or SQL queries
-Table result = ...
+// Table with two fields (String name, Integer age)
+Table table = ...
-// create a TableSink
-TableSink sink = new CsvTableSink("/path/to/file", fieldDelim = "|");
-// write the result Table to the TableSink
-result.writeToSink(sink);
+// Convert the Table into a DataSet of Row by specifying a class
+DataSet<Row> dsRow = tableEnv.toDataSet(table, Row.class);
-// execute the program
-env.execute();
+// Convert the Table into a DataSet of Tuple2<String, Integer> via a
TypeInformation
+TupleTypeInfo<Tuple2<String, Integer>> tupleType = new TupleTypeInfo<>(
+ Types.STRING(),
+ Types.INT());
+DataStream<Tuple2<String, Integer>> dsTuple =
+ tableEnv.toAppendStream(table, tupleType);
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
-val env = ExecutionEnvironment.getExecutionEnvironment
+// get TableEnvironment.
+// registration of a DataSet is equivalent
val tableEnv = TableEnvironment.getTableEnvironment(env)
-// compute the result Table using Table API operators and/or SQL queries
-val result: Table = ...
+// Table with two fields (String name, Integer age)
+val table: Table = ...
-// create a TableSink
-val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|")
-// write the result Table to the TableSink
-result.writeToSink(sink)
+// Convert the Table into a DataSet of Row
+val dsRow: DataSet[Row] = tableEnv.toDataSet[Row](table)
-// execute the program
-env.execute()
+// Convert the Table into a DataSet of Tuple2[String, Int]
+val dsTuple: DataSet[(String, Int)] = tableEnv.toDataSet[(String,
Int)](table)
{% endhighlight %}
</div>
</div>
{% top %}
-### Convert to a DataStream
+### Mapping of Data Types to Table Schema
-**TODO**
+Flink's DataStream and DataSet APIs support very diverse types, such as
Tuples (built-in Scala and Flink Java tuples), POJOs, case classes, and atomic
types. In the following we describe how the Table API converts these types into
an internal row representation and show examples of converting a `DataStream`
into a `Table`.
-{% top %}
+#### Atomic Types
-### Convert to a DataSet
+Flink treats primitives (`Integer`, `Double`, `String`) or generic types
(types cannot be analyzed and decomposed) as atomic types. A `DataStream` or
`DataSet` of an atomic type is converted into a `Table` with a single
attribute. The type of the attribute is inferred from the atomic type, the name
of the attribute must be specified.
-**TODO**
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-### Scala Implicit Conversion
+DataStream<Long> stream = ...
+// convert DataStream into Table with field "myLong"
+Table table = tableEnv.fromDataStream(stream, "myLong");
+{% endhighlight %}
+</div>
-If you use the Scala API, A `Table` can be implicitly converted into a
`DataSet` or `DataStream`.
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get a TableEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
-{% top %}
+val stream: DataStream[Long] = ...
+// convert DataStream into Table with field 'myLong
+val table: Table = tableEnv.fromDataStream(stream, 'myLong)
+{% endhighlight %}
+</div>
+</div>
-Execute a Query
----------------
+#### Tuples (Scala and Java) and Case Classes (Scala only)
-**TODO**
+Flink supports Scala's built-in tuples and provides own tuple classes for
Java. DataStreams and DataSets of both kinds of tuples can be converted into
tables. Fields can be renamed by providing names for all fields (mapping based
on position). If no field names are specified, the default field names are used.
-{% top %}
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
-Mappings Types to Table Schema
-------------------------------
+DataStream<Tuple2<Long, String>> stream = ...
-* Explain how types are mapped to table schema
- * Atomic Types
- * Row
- * Tuples (Java / Scala)
- * Pojos
- * Case Classes
+// convert DataStream into Table with field names "myLong", "myString"
+Table table1 = tableEnv.fromDataStream(stream, "myLong, myString");
-**TODO**
+// convert DataStream into Table with default field names "f0", "f1"
+Table table2 = tableEnv.fromDataStream(stream);
+{% endhighlight %}
+</div>
-{% top %}
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get a TableEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
-Integration with DataSet and DataStream API
--------------------------------------------
+val stream: DataStream[(Long, String)] = ...
+
+// convert DataStream into Table with field names 'myLong, 'myString
+val table1: Table = tableEnv.fromDataStream(stream, 'myLong, 'myString)
+
+// convert DataStream into Table with default field names '_1, '_2
+val table2: Table = tableEnv.fromDataStream(stream)
+
+// define case class
+case class Person(name: String, age: Int)
+val streamCC: DataStream[Person] = ...
+
+// convert DataStream into Table with default field names 'name, 'age
+val tableCC1 = tableEnv.fromDataStream(streamCC)
+
+// convert DataStream into Table with field names 'myName, 'myAge
+val tableCC1 = tableEnv.fromDataStream(streamCC, 'myName, 'myAge)
+
+{% endhighlight %}
+</div>
+</div>
+
+#### POJO (Java and Scala)
+
+Flink supports POJOs as composite types. The rules for what determines a
POJO are documented [here]({{ site.baseurl }}/dev/api_concepts.html#pojos).
+
+When converting a POJO `DataStream` or `DataSet` into a `Table` without
specifying field names, the names of the original POJO fields are used.
Renaming the original POJO fields requires the keywords `as` because POJO
fields have no inherent order such that the name mapping requires the original
names and cannot be done by positions.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+// get a StreamTableEnvironment, works for BatchTableEnvironment
equivalently
+StreamTableEnvironment tableEnv =
TableEnvironment.getTableEnvironment(env);
+
+// Person is a POJO with fields "name" and "age"
+DataStream<Person> stream = ...
+
+// convert DataStream into Table with field names "name", "age"
+Table table1 = tableEnv.fromDataStream(stream);
+
+// convert DataStream into Table with field names "myName", "myAge"
+Table table2 = tableEnv.fromDataStream(stream, "name as myName, age as
myAge");
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+// get a TableEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+// Person is a POJO with field names "name" and "age"
+val stream: DataStream[Person] = ...
+
+// convert DataStream into Table with field names 'name, 'age
+val table1: Table = tableEnv.fromDataStream(stream)
+
+// convert DataStream into Table with field names 'myName, 'myAge
+val table2: Table = tableEnv.fromDataStream(stream, 'name as 'myName, 'age
as 'myAge)
+{% endhighlight %}
+</div>
+</div>
+
+#### Row
+
+The Row data type supports an arbitrary number of fields and fields with
`null` values. Field names of rows can be specified via a `RowTypeInfo` or when
converting a `Row` `DataStream` or `DataSet` into a `Table` (based on position).
--- End diff --
Field names of rows can be => Field names can be
> Table API / SQL Docs: Common Page
> ---------------------------------
>
> Key: FLINK-6746
> URL: https://issues.apache.org/jira/browse/FLINK-6746
> Project: Flink
> Issue Type: Sub-task
> Components: Documentation, Table API & SQL
> Affects Versions: 1.3.0
> Reporter: Fabian Hueske
> Assignee: Fabian Hueske
>
> Update and refine ./docs/dev/table/common.md in feature branch
> https://github.com/apache/flink/tree/tableDocs
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