The Table API and SQL are integrated in a joint API. The central concept of this API is a Table which serves as input and output of queries. This document shows the common structure of programs with Table API and SQL queries, how to register a Table, how to query a Table, and how to emit a Table.
All Table API and SQL programs for batch and streaming follow the same pattern. The following code example shows the common structure of Table API and SQL programs.
// create a TableEnvironment // for batch programs use BatchTableEnvironment instead of StreamTableEnvironment StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
// register a Table tableEnv.registerTable(“table1”, ...) // or tableEnv.registerTableSource(“table2”, ...); // or tableEnv.registerExternalCatalog(“extCat”, ...);
// create a Table from a Table API query Table tapiResult = tableEnv.scan(“table1”).select(...); // create a Table from a SQL query Table sqlResult = tableEnv.sqlQuery("SELECT ... FROM table2 ... ");
// emit a Table API result Table to a TableSink, same for SQL result tapiResult.writeToSink(...);
// execute env.execute();
{% endhighlight %}
// create a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env)
// register a Table tableEnv.registerTable(“table1”, ...) // or tableEnv.registerTableSource(“table2”, ...) // or tableEnv.registerExternalCatalog(“extCat”, ...)
// create a Table from a Table API query val tapiResult = tableEnv.scan(“table1”).select(...) // Create a Table from a SQL query val sqlResult = tableEnv.sqlQuery(“SELECT ... FROM table2 ...”)
// emit a Table API result Table to a TableSink, same for SQL result tapiResult.writeToSink(...)
// execute env.execute()
{% endhighlight %}
Note: Table API and SQL queries can be easily integrated with and embedded into DataStream or DataSet programs. Have a look at the Integration with DataStream and DataSet API section to learn how DataStreams and DataSets can be converted into Tables and vice versa.
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The TableEnvironment is a central concept of the Table API and SQL integration. It is responsible for:
Table in the internal catalogDataStream or DataSet into a TableExecutionEnvironment or StreamExecutionEnvironmentA Table is always bound to a specific TableEnvironment. It is not possible to combine 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).
// *********** // BATCH QUERY // *********** ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment(); // create a TableEnvironment for batch queries BatchTableEnvironment bTableEnv = TableEnvironment.getTableEnvironment(bEnv); {% endhighlight %}
// *********** // BATCH QUERY // *********** val bEnv = ExecutionEnvironment.getExecutionEnvironment // create a TableEnvironment for batch queries val bTableEnv = TableEnvironment.getTableEnvironment(bEnv) {% endhighlight %}
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A TableEnvironment maintains a catalog of tables which are registered by name. There are two types of tables, input tables and output tables. Input tables can be referenced in Table API and SQL queries and provide input data. Output tables can be used to emit the result of a Table API or SQL query to an external system.
An input table can be registered from various sources:
Table object, usually the result of a Table API or SQL query.TableSource, which accesses external data, such as a file, database, or messaging system.DataStream or DataSet from a DataStream or DataSet program. Registering a DataStream or DataSet is discussed in the Integration with DataStream and DataSet API section.An output table can be registered using a TableSink.
A Table is registered in a TableEnvironment as follows:
// Table is the result of a simple projection query Table projTable = tableEnv.scan(“X”).select(...);
// register the Table projTable as table “projectedX” tableEnv.registerTable(“projectedTable”, projTable); {% endhighlight %}
// Table is the result of a simple projection query val projTable: Table = tableEnv.scan(“X”).select(...)
// register the Table projTable as table “projectedX” tableEnv.registerTable(“projectedTable”, projTable) {% endhighlight %}
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.
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A TableSource provides access to external data which is stored in a storage system such as a database (MySQL, HBase, ...), a file with a 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 TableSource is registered in a TableEnvironment as follows:
// create a TableSource TableSource csvSource = new CsvTableSource(“/path/to/file”, ...);
// register the TableSource as table “CsvTable” tableEnv.registerTableSource(“CsvTable”, csvSource); {% endhighlight %}
// create a TableSource val csvSource: TableSource = new CsvTableSource(“/path/to/file”, ...)
// register the TableSource as table “CsvTable” tableEnv.registerTableSource(“CsvTable”, csvSource) {% endhighlight %}
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A registered TableSink can be used to emit the result of a Table API or SQL query to an external storage system, such as a database, key-value store, message queue, or file system (in different encodings, e.g., CSV, Apache [Parquet, Avro, ORC], ...).
Flink aims to provide TableSinks for common data formats and storage systems. Please see the documentation about [Table Sources and Sinks]({{ site.baseurl }}/dev/table/sourceSinks.html) page for details about available sinks and instructions for how to implement a custom TableSink.
A TableSink is registered in a TableEnvironment as follows:
// create a TableSink TableSink csvSink = new CsvTableSink(“/path/to/file”, ...);
// define the field names and types String[] fieldNames = {“a”, “b”, “c”}; TypeInformation[] fieldTypes = {Types.INT, Types.STRING, Types.LONG};
// register the TableSink as table “CsvSinkTable” tableEnv.registerTableSink(“CsvSinkTable”, fieldNames, fieldTypes, csvSink); {% endhighlight %}
// create a TableSink val csvSink: TableSink = new CsvTableSink(“/path/to/file”, ...)
// define the field names and types val fieldNames: Array[String] = Array(“a”, “b”, “c”) val fieldTypes: Array[TypeInformation[_]] = Array(Types.INT, Types.STRING, Types.LONG)
// register the TableSink as table “CsvSinkTable” tableEnv.registerTableSink(“CsvSinkTable”, fieldNames, fieldTypes, csvSink) {% endhighlight %}
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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.
An external catalog can be created by implementing the ExternalCatalog interface and is registered in a TableEnvironment as follows:
// create an external catalog ExternalCatalog catalog = new InMemoryExternalCatalog();
// register the ExternalCatalog catalog tableEnv.registerExternalCatalog(“InMemCatalog”, catalog); {% endhighlight %}
// create an external catalog val catalog: ExternalCatalog = new InMemoryExternalCatalog
// register the ExternalCatalog catalog tableEnv.registerExternalCatalog(“InMemCatalog”, catalog) {% endhighlight %}
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 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.
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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.
The [Table API]({{ site.baseurl }}/dev/table/tableApi.html) document describes all Table API operations that are supported on streaming and batch tables.
The following example shows a simple Table API aggregation query:
// register Orders table
// 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 %}
// register Orders table
// scan registered Orders table val orders = tableEnv.scan(“Orders”) // compute revenue for all customers from France val 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. The Table API uses Scala implicits. Make sure to import org.apache.flink.api.scala._ and org.apache.flink.table.api.scala._ in order to use Scala implicit conversions.
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Flink's SQL integration is based on Apache Calcite, 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 a Table.
// register Orders table
// compute revenue for all customers from France Table revenue = tableEnv.sqlQuery( "SELECT cID, cName, SUM(revenue) AS revSum " + "FROM Orders " + "WHERE cCountry = ‘FRANCE’ " + “GROUP BY cID, cName” );
// emit or convert Table // execute query {% endhighlight %}
// register Orders table
// compute revenue for all customers from France val revenue = tableEnv.sqlQuery(""" |SELECT cID, cName, SUM(revenue) AS revSum |FROM Orders |WHERE cCountry = ‘FRANCE’ |GROUP BY cID, cName """.stripMargin)
// emit or convert Table // execute query {% endhighlight %}
The following example shows how to specify an update query that inserts its result into a registered table.
// register “Orders” table // register “RevenueFrance” output table
// compute revenue for all customers from France and emit to “RevenueFrance” tableEnv.sqlUpdate( "INSERT INTO RevenueFrance " + "SELECT cID, cName, SUM(revenue) AS revSum " + "FROM Orders " + "WHERE cCountry = ‘FRANCE’ " + “GROUP BY cID, cName” );
// execute query {% endhighlight %}
// register “Orders” table // register “RevenueFrance” output table
// compute revenue for all customers from France and emit to “RevenueFrance” tableEnv.sqlUpdate(""" |INSERT INTO RevenueFrance |SELECT cID, cName, SUM(revenue) AS revSum |FROM Orders |WHERE cCountry = ‘FRANCE’ |GROUP BY cID, cName """.stripMargin)
// execute query {% endhighlight %}
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Table API and SQL queries can be easily mixed because both return Table objects:
Table object returned by a SQL query.TableEnvironment and referencing it in the FROM clause of the SQL query.{% top %}
A Table is emitted by writing it 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).
A batch Table can only be written to a BatchTableSink, while a streaming Table requires either an AppendStreamTableSink, a RetractStreamTableSink, or an UpsertStreamTableSink.
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.
There are two ways to emit a table:
Table.writeToSink(TableSink sink) method emits the table using the provided TableSink and automatically configures the sink with the schema of the table to emit.Table.insertInto(String sinkTable) method looks up a TableSink that was registered with a specific schema under the provided name in the TableEnvironment's catalog. The schema of the table to emit is validated against the schema of the registered TableSink.The following examples shows how to emit a Table:
// 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 = “|”);
// METHOD 1: // Emit the result Table to the TableSink via the writeToSink() method result.writeToSink(sink);
// METHOD 2: // Register the TableSink with a specific schema String[] fieldNames = {“a”, “b”, “c”}; TypeInformation[] fieldTypes = {Types.INT, Types.STRING, Types.LONG}; tableEnv.registerTableSink(“CsvSinkTable”, fieldNames, fieldTypes, sink); // Emit the result Table to the registered TableSink via the insertInto() method result.insertInto(“CsvSinkTable”);
// execute the program {% endhighlight %}
// compute a result Table using Table API operators and/or SQL queries val result: Table = ...
// create a TableSink val sink: TableSink = new CsvTableSink(“/path/to/file”, fieldDelim = “|”)
// METHOD 1: // Emit the result Table to the TableSink via the writeToSink() method result.writeToSink(sink)
// METHOD 2: // Register the TableSink with a specific schema val fieldNames: Array[String] = Array(“a”, “b”, “c”) val fieldTypes: Array[TypeInformation] = Array(Types.INT, Types.STRING, Types.LONG) tableEnv.registerTableSink(“CsvSinkTable”, fieldNames, fieldTypes, sink) // Emit the result Table to the registered TableSink via the insertInto() method result.insertInto(“CsvSinkTable”)
// execute the program {% endhighlight %}
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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:
A Table API or SQL query is translated when:
Table is emitted to a TableSink, i.e., when Table.writeToSink() or Table.insertInto() is called.TableEnvironment.sqlUpdate() is called.Table is converted into a DataStream or DataSet (see 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.
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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, and then further process the data with either the DataStream or DataSet API (and any of the libraries built 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.
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._ in addition to org.apache.flink.api.scala._ for the Scala DataStream API.
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 for details.
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 %}
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 %}
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.
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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.
DataStream<Tuple2<Long, String>> stream = ...
// Convert the DataStream into a Table with default fields “f0”, “f1” Table table1 = tableEnv.fromDataStream(stream);
// Convert the DataStream into a Table with fields “myLong”, “myString” Table table2 = tableEnv.fromDataStream(stream, “myLong, myString”); {% endhighlight %}
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 %}
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A Table can be converted into a DataStream or DataSet. In this way, custom DataStream or DataSet programs can be run 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:
null values, no type-safe access.Table fields), arbitrary number of fields, support for null values, type-safe access.null values, type-safe access.null values, type-safe access.Table must have a single field, no support for null values, type-safe access.A Table that is the result of a streaming query will be updated dynamically, i.e., it is changing as new records arrive on the query's input streams. 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:
Table is only modified by INSERT changes, i.e, it is append-only and previously emitted results are never updated.INSERT and DELETE changes with a boolean flag.// Table with two fields (String name, Integer age) Table table = ...
// convert the Table into an append DataStream of Row by specifying the class DataStream 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 %}
// 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.toAppendStreamRow
// convert the Table into an append DataStream of Tuple2[String, Int] val dsTuple: DataStream[(String, Int)] dsTuple = tableEnv.toAppendStream(String, Int)
// 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.toRetractStreamRow {% endhighlight %}
Note: A detailed discussion about dynamic tables and their properties is given in the [Streaming Queries]({{ site.baseurl }}/dev/table/streaming.html) document.
A Table is converted into a DataSet as follows:
// Table with two fields (String name, Integer age) Table table = ...
// convert the Table into a DataSet of Row by specifying a class DataSet dsRow = tableEnv.toDataSet(table, Row.class);
// 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 %}
// Table with two fields (String name, Integer age) val table: Table = ...
// convert the Table into a DataSet of Row val dsRow: DataSet[Row] = tableEnv.toDataSetRow
// convert the Table into a DataSet of Tuple2[String, Int] val dsTuple: DataSet[(String, Int)] = tableEnv.toDataSet(String, Int) {% endhighlight %}
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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.
Flink treats primitives (Integer, Double, String) or generic types (types that 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 and the name of the attribute must be specified.
DataStream stream = ... // convert DataStream into Table with field “myLong” Table table = tableEnv.fromDataStream(stream, “myLong”); {% endhighlight %}
val stream: DataStream[Long] = ... // convert DataStream into Table with field 'myLong val table: Table = tableEnv.fromDataStream(stream, 'myLong) {% endhighlight %}
Flink supports Scala's built-in tuples and provides its 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.
DataStream<Tuple2<Long, String>> stream = ...
// convert DataStream into Table with field names “myLong”, “myString” Table table1 = tableEnv.fromDataStream(stream, “myLong, myString”);
// convert DataStream into Table with default field names “f0”, “f1” Table table2 = tableEnv.fromDataStream(stream); {% endhighlight %}
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)
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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 keyword AS because POJO fields have no inherent order. The name mapping requires the original names and cannot be done by positions.
// Person is a POJO with fields “name” and “age” DataStream 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 %}
// 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 %}
The Row data type supports an arbitrary number of fields and fields with null values. Field names can be specified via a RowTypeInfo or when converting a Row DataStream or DataSet into a Table (based on position).
// DataStream of Row with two fields “name” and “age” specified in RowTypeInfo DataStream 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, “myName, myAge”); {% endhighlight %}
// DataStream of Row with two fields “name” and “age” specified in RowTypeInfo val stream: DataStream[Row] = ...
// 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, 'myName, 'myAge) {% endhighlight %}
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Apache Flink leverages Apache Calcite to optimize and translate queries. The optimization currently performed include projection and filter push-down, subquery decorrelation, and other kinds of query rewriting. Flink does not yet optimize the order of joins, but executes them in the same order as defined in the query (order of Tables in the FROM clause and/or order of join predicates in the WHERE clause).
It is possible to tweak the set of optimization rules which are applied in different phases by providing a CalciteConfig object. This can be created via a builder by calling CalciteConfig.createBuilder()) and is provided to the TableEnvironment by calling tableEnv.getConfig.setCalciteConfig(calciteConfig).
The Table API provides a mechanism to explain the logical and optimized query plans to compute a Table. This is done through the TableEnvironment.explain(table) method. It returns a String describing three plans:
The following code shows an example and the corresponding output:
DataStream<Tuple2<Integer, String>> stream1 = env.fromElements(new Tuple2<>(1, “hello”)); DataStream<Tuple2<Integer, String>> stream2 = env.fromElements(new Tuple2<>(1, “hello”));
Table table1 = tEnv.fromDataStream(stream1, “count, word”); Table table2 = tEnv.fromDataStream(stream2, “count, word”); Table table = table1 .where(“LIKE(word, ‘F%’)”) .unionAll(table2);
String explanation = tEnv.explain(table); System.out.println(explanation); {% endhighlight %}
val table1 = env.fromElements((1, “hello”)).toTable(tEnv, 'count, 'word) val table2 = env.fromElements((1, “hello”)).toTable(tEnv, 'count, 'word) val table = table1 .where('word.like(“F%”)) .unionAll(table2)
val explanation: String = tEnv.explain(table) println(explanation) {% endhighlight %}
{% highlight text %} == Abstract Syntax Tree == LogicalUnion(all=[true]) LogicalFilter(condition=[LIKE($1, ‘F%’)]) LogicalTableScan(table=[[_DataStreamTable_0]]) LogicalTableScan(table=[[_DataStreamTable_1]])
== Optimized Logical Plan == DataStreamUnion(union=[count, word]) DataStreamCalc(select=[count, word], where=[LIKE(word, ‘F%’)]) DataStreamScan(table=[[_DataStreamTable_0]]) DataStreamScan(table=[[_DataStreamTable_1]])
== Physical Execution Plan == Stage 1 : Data Source content : collect elements with CollectionInputFormat
Stage 2 : Data Source content : collect elements with CollectionInputFormat
Stage 3 : Operator content : from: (count, word) ship_strategy : REBALANCE
Stage 4 : Operator
content : where: (LIKE(word, 'F%')), select: (count, word)
ship_strategy : FORWARD
Stage 5 : Operator
content : from: (count, word)
ship_strategy : REBALANCE
{% endhighlight %}
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