Concepts & Common API

// for batch programs use ExecutionEnvironment instead of StreamExecutionEnvironment
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

// create a TableEnvironment
// for batch programs use BatchTableEnvironment instead of StreamTableEnvironment
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// register a Table
tableEnv.registerTable("table1", ...)            // or
tableEnv.registerTableSource("table2", ...);
// register an output Table
tableEnv.registerTableSink("outputTable", ...);

// 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.insertInto("outputTable");

// execute
env.execute();

// for batch programs use ExecutionEnvironment instead of StreamExecutionEnvironment
val env = StreamExecutionEnvironment.getExecutionEnvironment

// create a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// register a Table
tableEnv.registerTable("table1", ...)           // or
tableEnv.registerTableSource("table2", ...)
// register an output Table
tableEnv.registerTableSink("outputTable", ...);

// 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.insertInto("outputTable")

// execute
env.execute()

# for batch programs use ExecutionEnvironment instead of StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

# create a TableEnvironment
table_env = StreamTableEnvironment.create(env)

# register a Table
table_env.register_table("table1", ...)           # or
table_env.register_table_source("table2", ...)

# register an output Table
table_env.register_table_sink("outputTable", ...);

# create a Table from a Table API query
tapi_result = table_env.scan("table1").select(...)
# create a Table from a SQL query
sql_result  = table_env.sql_query("SELECT ... FROM table2 ...")

# emit a Table API result Table to a TableSink, same for SQL result
tapi_result.insert_into("outputTable")

# execute
table_env.execute("python_job")

// ***************
// STREAMING QUERY
// ***************
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.java.StreamTableEnvironment;

StreamExecutionEnvironment sEnv = StreamExecutionEnvironment.getExecutionEnvironment();
// create a TableEnvironment for streaming queries
StreamTableEnvironment sTableEnv = StreamTableEnvironment.create(sEnv);

// ***********
// BATCH QUERY
// ***********
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.table.api.java.BatchTableEnvironment;

ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment();
// create a TableEnvironment for batch queries
BatchTableEnvironment bTableEnv = BatchTableEnvironment.create(bEnv);

// ***************
// STREAMING QUERY
// ***************
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment
import org.apache.flink.table.api.scala.StreamTableEnvironment

val sEnv = StreamExecutionEnvironment.getExecutionEnvironment
// create a TableEnvironment for streaming queries
val sTableEnv = StreamTableEnvironment.create(sEnv)

// ***********
// BATCH QUERY
// ***********
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.table.api.scala.BatchTableEnvironment

val bEnv = ExecutionEnvironment.getExecutionEnvironment
// create a TableEnvironment for batch queries
val bTableEnv = BatchTableEnvironment.create(bEnv)

# ***************
# STREAMING QUERY
# ***************
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

s_env = StreamExecutionEnvironment.get_execution_environment()
# create a TableEnvironment for streaming queries
s_table_env = StreamTableEnvironment.create(s_env)

# ***********
# BATCH QUERY
# ***********
from pyflink.dataset import ExecutionEnvironment
from pyflink.table import BatchTableEnvironment

b_env = ExecutionEnvironment.get_execution_environment()
# create a TableEnvironment for batch queries
b_table_env = BatchTableEnvironment.create(b_env)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// table is the result of a simple projection query 
Table projTable = tableEnv.scan("X").select(...);

// register the Table projTable as table "projectedTable"
tableEnv.registerTable("projectedTable", projTable);

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// table is the result of a simple projection query 
val projTable: Table = tableEnv.scan("X").select(...)

// register the Table projTable as table "projectedTable"
tableEnv.registerTable("projectedTable", projTable)

# get a TableEnvironment
table_env = StreamTableEnvironment.create(env)

# table is the result of a simple projection query 
proj_table = table_env.scan("X").select(...)

# register the Table projTable as table "projectedTable"
table_env.register_table("projectedTable", proj_table)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// create a TableSource
TableSource csvSource = new CsvTableSource("/path/to/file", ...);

// register the TableSource as table "CsvTable"
tableEnv.registerTableSource("CsvTable", csvSource);

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// create a TableSource
val csvSource: TableSource = new CsvTableSource("/path/to/file", ...)

// register the TableSource as table "CsvTable"
tableEnv.registerTableSource("CsvTable", csvSource)

# get a TableEnvironment
table_env = StreamTableEnvironment.create(env)

# create a TableSource
csv_source = CsvTableSource("/path/to/file", ...)

# register the TableSource as table "csvTable"
table_env.register_table_source("csvTable", csv_source)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// 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);

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// 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)

# get a TableEnvironment
table_env = StreamTableEnvironment.create(env)

# define the field names and types
field_names = ["a", "b", "c"]
field_types = [DataTypes.INT(), DataTypes.STRING(), DataTypes.BIGINT()]

# create a TableSink
csv_sink = CsvTableSink(field_names, field_types, "/path/to/file", ...)

# register the TableSink as table "CsvSinkTable"
table_env.register_table_sink("CsvSinkTable", csv_sink)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// 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

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// 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

# get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
table_env = StreamTableEnvironment.create(env)

# register Orders table

# scan registered Orders table
orders = table_env.scan("Orders")
# compute revenue for all customers from France
revenue = orders \
    .filter("cCountry === 'FRANCE'") \
    .group_by("cID, cName") \
    .select("cID, cName, revenue.sum AS revSum")

# emit or convert Table
# execute query

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// 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

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// 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

# get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
table_env = StreamTableEnvironment.create(env)

# register Orders table

# compute revenue for all customers from France
revenue = table_env.sql_query(
    "SELECT cID, cName, SUM(revenue) AS revSum "
    "FROM Orders "
    "WHERE cCountry = 'FRANCE' "
    "GROUP BY cID, cName"
)

# emit or convert Table
# execute query

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// 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

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// 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

# get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
table_env = StreamTableEnvironment.create(env)

# register "Orders" table
# register "RevenueFrance" output table

# compute revenue for all customers from France and emit to "RevenueFrance"
table_env.sql_update(
    "INSERT INTO RevenueFrance "
    "SELECT cID, cName, SUM(revenue) AS revSum "
    "FROM Orders "
    "WHERE cCountry = 'FRANCE' "
    "GROUP BY cID, cName"
)

# execute query

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// create a TableSink
TableSink sink = new CsvTableSink("/path/to/file", fieldDelim = "|");

// 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);

// compute a result Table using Table API operators and/or SQL queries
Table result = ...
// emit the result Table to the registered TableSink
result.insertInto("CsvSinkTable");

// execute the program

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// create a TableSink
val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|")

// 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)

// compute a result Table using Table API operators and/or SQL queries
val result: Table = ...

// emit the result Table to the registered TableSink
result.insertInto("CsvSinkTable")

// execute the program

# get a TableEnvironment
table_env = StreamTableEnvironment.create(env)

field_names = ["a", "b", "c"]
field_types = [DataTypes.INT(), DataTypes.STRING(), DataTypes.BIGINT()]

# create a TableSink
sink = CsvTableSink(field_names, field_types, "/path/to/file", "|")

table_env.register_table_sink("CsvSinkTable", sink)

# compute a result Table using Table API operators and/or SQL queries
result = ...

# emit the result Table to the registered TableSink
result.insert_into("CsvSinkTable")

# execute the program

// get StreamTableEnvironment
// registration of a DataSet in a BatchTableEnvironment is equivalent
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(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");

// get TableEnvironment 
// registration of a DataSet is equivalent
val tableEnv = StreamTableEnvironment.create(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)

// get StreamTableEnvironment
// registration of a DataSet in a BatchTableEnvironment is equivalent
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

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");

// get TableEnvironment
// registration of a DataSet is equivalent
val tableEnv = StreamTableEnvironment.create(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)

// get StreamTableEnvironment. 
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(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);

// get TableEnvironment. 
// registration of a DataSet is equivalent
val tableEnv = StreamTableEnvironment.create(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)

// 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)

// get BatchTableEnvironment
BatchTableEnvironment tableEnv = BatchTableEnvironment.create(env);

// Table with two fields (String name, Integer age)
Table table = ...

// convert the Table into a DataSet of Row by specifying a class
DataSet<Row> 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());
DataSet<Tuple2<String, Integer>> dsTuple = 
  tableEnv.toDataSet(table, tupleType);

// get TableEnvironment 
// registration of a DataSet is equivalent
val tableEnv = BatchTableEnvironment.create(env)

// Table with two fields (String name, Integer age)
val table: Table = ...

// convert the Table into a DataSet of Row
val dsRow: DataSet[Row] = tableEnv.toDataSet[Row](table)

// convert the Table into a DataSet of Tuple2[String, Int]
val dsTuple: DataSet[(String, Int)] = tableEnv.toDataSet[(String, Int)](table)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

DataStream<Tuple2<Long, Integer>> stream = ...

// convert DataStream into Table with default field names "f0" and "f1"
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with field "myLong" only
Table table = tableEnv.fromDataStream(stream, "myLong");

// convert DataStream into Table with field names "myLong" and "myInt"
Table table = tableEnv.fromDataStream(stream, "myLong, myInt");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val stream: DataStream[(Long, Int)] = ...

// convert DataStream into Table with default field names "_1" and "_2"
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with field "myLong" only
val table: Table = tableEnv.fromDataStream(stream, 'myLong)

// convert DataStream into Table with field names "myLong" and "myInt"
val table: Table = tableEnv.fromDataStream(stream, 'myLong, 'myInt)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

DataStream<Tuple2<Long, Integer>> stream = ...

// convert DataStream into Table with default field names "f0" and "f1"
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with field "f1" only
Table table = tableEnv.fromDataStream(stream, "f1");

// convert DataStream into Table with swapped fields
Table table = tableEnv.fromDataStream(stream, "f1, f0");

// convert DataStream into Table with swapped fields and field names "myInt" and "myLong"
Table table = tableEnv.fromDataStream(stream, "f1 as myInt, f0 as myLong");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val stream: DataStream[(Long, Int)] = ...

// convert DataStream into Table with default field names "_1" and "_2"
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with field "_2" only
val table: Table = tableEnv.fromDataStream(stream, '_2)

// convert DataStream into Table with swapped fields
val table: Table = tableEnv.fromDataStream(stream, '_2, '_1)

// convert DataStream into Table with swapped fields and field names "myInt" and "myLong"
val table: Table = tableEnv.fromDataStream(stream, '_2 as 'myInt, '_1 as 'myLong)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

DataStream<Long> stream = ...

// convert DataStream into Table with default field name "f0"
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with field name "myLong"
Table table = tableEnv.fromDataStream(stream, "myLong");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val stream: DataStream[Long] = ...

// convert DataStream into Table with default field name "f0"
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with field name "myLong"
val table: Table = tableEnv.fromDataStream(stream, 'myLong)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

DataStream<Tuple2<Long, String>> stream = ...

// convert DataStream into Table with default field names "f0", "f1"
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with renamed field names "myLong", "myString" (position-based)
Table table = tableEnv.fromDataStream(stream, "myLong, myString");

// convert DataStream into Table with reordered fields "f1", "f0" (name-based)
Table table = tableEnv.fromDataStream(stream, "f1, f0");

// convert DataStream into Table with projected field "f1" (name-based)
Table table = tableEnv.fromDataStream(stream, "f1");

// convert DataStream into Table with reordered and aliased fields "myString", "myLong" (name-based)
Table table = tableEnv.fromDataStream(stream, "f1 as 'myString', f0 as 'myLong'");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val stream: DataStream[(Long, String)] = ...

// convert DataStream into Table with renamed default field names '_1, '_2
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with field names "myLong", "myString" (position-based)
val table: Table = tableEnv.fromDataStream(stream, 'myLong, 'myString)

// convert DataStream into Table with reordered fields "_2", "_1" (name-based)
val table: Table = tableEnv.fromDataStream(stream, '_2, '_1)

// convert DataStream into Table with projected field "_2" (name-based)
val table: Table = tableEnv.fromDataStream(stream, '_2)

// convert DataStream into Table with reordered and aliased fields "myString", "myLong" (name-based)
val table: Table = tableEnv.fromDataStream(stream, '_2 as 'myString, '_1 as 'myLong)

// 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 table = tableEnv.fromDataStream(streamCC)

// convert DataStream into Table with field names 'myName, 'myAge (position-based)
val table = tableEnv.fromDataStream(streamCC, 'myName, 'myAge)

// convert DataStream into Table with reordered and aliased fields "myAge", "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'age as 'myAge, 'name as 'myName)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// Person is a POJO with fields "name" and "age"
DataStream<Person> stream = ...

// convert DataStream into Table with default field names "age", "name" (fields are ordered by name!)
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with renamed fields "myAge", "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, "age as myAge, name as myName");

// convert DataStream into Table with projected field "name" (name-based)
Table table = tableEnv.fromDataStream(stream, "name");

// convert DataStream into Table with projected and renamed field "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, "name as myName");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// Person is a POJO with field names "name" and "age"
val stream: DataStream[Person] = ...

// convert DataStream into Table with default field names "age", "name" (fields are ordered by name!)
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with renamed fields "myAge", "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'age as 'myAge, 'name as 'myName)

// convert DataStream into Table with projected field "name" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'name)

// convert DataStream into Table with projected and renamed field "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'name as 'myName)

// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo`
DataStream<Row> stream = ...

// convert DataStream into Table with default field names "name", "age"
Table table = tableEnv.fromDataStream(stream);

// convert DataStream into Table with renamed field names "myName", "myAge" (position-based)
Table table = tableEnv.fromDataStream(stream, "myName, myAge");

// convert DataStream into Table with renamed fields "myName", "myAge" (name-based)
Table table = tableEnv.fromDataStream(stream, "name as myName, age as myAge");

// convert DataStream into Table with projected field "name" (name-based)
Table table = tableEnv.fromDataStream(stream, "name");

// convert DataStream into Table with projected and renamed field "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, "name as myName");

// get a TableEnvironment
val tableEnv = StreamTableEnvironment.create(env)

// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo`
val stream: DataStream[Row] = ...

// convert DataStream into Table with default field names "name", "age"
val table: Table = tableEnv.fromDataStream(stream)

// convert DataStream into Table with renamed field names "myName", "myAge" (position-based)
val table: Table = tableEnv.fromDataStream(stream, 'myName, 'myAge)

// convert DataStream into Table with renamed fields "myName", "myAge" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'name as 'myName, 'age as 'myAge)

// convert DataStream into Table with projected field "name" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'name)

// convert DataStream into Table with projected and renamed field "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, 'name as 'myName)

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

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);

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = StreamTableEnvironment.create(env)

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)

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

table1 = t_env.from_elements([(1, "hello")], ["count", "word"])
table2 = t_env.from_elements([(1, "hello")], ["count", "word"])
table = table1 \
    .where("LIKE(word, 'F%')") \
    .union_all(table2)

explanation = t_env.explain(table)
print(explanation)

== Abstract Syntax Tree ==
LogicalUnion(all=[true])
  LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')])
    FlinkLogicalDataStreamScan(id=[1], fields=[count, word])
  FlinkLogicalDataStreamScan(id=[2], fields=[count, word])

== Optimized Logical Plan ==
DataStreamUnion(all=[true], union all=[count, word])
  DataStreamCalc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')])
    DataStreamScan(id=[1], fields=[count, word])
  DataStreamScan(id=[2], fields=[count, word])

== 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, _UTF-16LE'F%')), select: (count, word)
			ship_strategy : FORWARD

			Stage 5 : Operator
				content : from: (count, word)
				ship_strategy : REBALANCE

== Abstract Syntax Tree ==
LogicalUnion(all=[true])
  LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')])
    FlinkLogicalDataStreamScan(id=[1], fields=[count, word])
  FlinkLogicalDataStreamScan(id=[2], fields=[count, word])

== Optimized Logical Plan ==
DataStreamUnion(all=[true], union all=[count, word])
  DataStreamCalc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')])
    DataStreamScan(id=[1], fields=[count, word])
  DataStreamScan(id=[2], fields=[count, word])

== 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, _UTF-16LE'F%')), select: (count, word)
			ship_strategy : FORWARD

			Stage 5 : Operator
				content : from: (count, word)
				ship_strategy : REBALANCE

== Abstract Syntax Tree ==
LogicalUnion(all=[true])
  LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')])
    FlinkLogicalDataStreamScan(id=[3], fields=[count, word])
  FlinkLogicalDataStreamScan(id=[6], fields=[count, word])

== Optimized Logical Plan ==
DataStreamUnion(all=[true], union all=[count, word])
  DataStreamCalc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')])
    DataStreamScan(id=[3], fields=[count, word])
  DataStreamScan(id=[6], fields=[count, word])

== Physical Execution Plan ==
Stage 1 : Data Source
	content : collect elements with CollectionInputFormat

	Stage 2 : Operator
		content : Flat Map
		ship_strategy : FORWARD

		Stage 3 : Operator
			content : Map
			ship_strategy : FORWARD

Stage 4 : Data Source
	content : collect elements with CollectionInputFormat

	Stage 5 : Operator
		content : Flat Map
		ship_strategy : FORWARD

		Stage 6 : Operator
			content : Map
			ship_strategy : FORWARD

			Stage 7 : Operator
				content : Map
				ship_strategy : FORWARD

				Stage 8 : Operator
					content : where: (LIKE(word, _UTF-16LE'F%')), select: (count, word)
					ship_strategy : FORWARD

					Stage 9 : Operator
						content : Map
						ship_strategy : FORWARD