Package org.apache.flink.table.api.internal

Class TableImpl

    • Method Detail

      • getResolvedSchema

        public org.apache.flink.table.catalog.ResolvedSchema getResolvedSchema()
        Description copied from interface: Table
        Returns the resolved schema of this table.
        Specified by:
        getResolvedSchema in interface Table

      • printSchema

        public void printSchema()
        Description copied from interface: Table
        Prints the schema of this table to the console in a summary format.
        Specified by:
        printSchema in interface Table

      • getQueryOperation

        public QueryOperation getQueryOperation()
        Description copied from interface: Table
        Returns underlying logical representation of this table.
        Specified by:
        getQueryOperation in interface Table

      • select

        public Table select​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Performs a selection operation. Similar to a SQL SELECT statement. The field expressions can contain complex expressions and aggregations.

        Java Example: 

        
 tab.select($("key"), $("value").avg().plus(" The average").as("average"));

        Scala Example: 

        
 tab.select($"key", $"value".avg + " The average" as "average")
        Specified by:
        select in interface Table

      • createTemporalTableFunction

        public org.apache.flink.table.functions.TemporalTableFunction createTemporalTableFunction​(org.apache.flink.table.expressions.Expression timeAttribute,
                                                                                          org.apache.flink.table.expressions.Expression primaryKey)
        Description copied from interface: Table
        Creates TemporalTableFunction backed up by this table as a history table. Temporal Tables represent a concept of a table that changes over time and for which Flink keeps track of those changes. TemporalTableFunction provides a way how to access those data.

        For more information please check Flink's documentation on Temporal Tables.

        Currently TemporalTableFunctions are only supported in streaming.

        Specified by:
        createTemporalTableFunction in interface Table
        Parameters:
        timeAttribute - Must points to a time indicator. Provides a way to compare which records are a newer or older version.
        primaryKey - Defines the primary key. With primary key it is possible to update a row or to delete it.
        Returns:
        TemporalTableFunction which is an instance of TableFunction. It takes one single argument, the timeAttribute, for which it returns matching version of the Table, from which TemporalTableFunction was created.

      • as

        public Table as​(String field,
                String... fields)
        Description copied from interface: Table
        Renames the fields of the expression result. Use this to disambiguate fields before joining to operations.

        Example: 

        
 tab.as("a", "b")
        Specified by:
        as in interface Table

      • as

        public Table as​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Renames the fields of the expression result. Use this to disambiguate fields before joining to operations.

        Java Example: 

        
 tab.as($("a"), $("b"))

        Scala Example: 

        
 tab.as($"a", $"b")
        Specified by:
        as in interface Table

      • filter

        public Table filter​(org.apache.flink.table.expressions.Expression predicate)
        Description copied from interface: Table
        Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE clause.

        Java Example: 

        
 tab.filter($("name").isEqual("Fred"));

        Scala Example: 

        
 tab.filter($"name" === "Fred")
        Specified by:
        filter in interface Table

      • where

        public Table where​(org.apache.flink.table.expressions.Expression predicate)
        Description copied from interface: Table
        Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE clause.

        Java Example: 

        
 tab.where($("name").isEqual("Fred"));

        Scala Example: 

        
 tab.where($"name" === "Fred")
        Specified by:
        where in interface Table

      • groupBy

        public GroupedTable groupBy​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Groups the elements on some grouping keys. Use this before a selection with aggregations to perform the aggregation on a per-group basis. Similar to a SQL GROUP BY statement.

        Java Example: 

        
 tab.groupBy($("key")).select($("key"), $("value").avg());

        Scala Example: 

        
 tab.groupBy($"key").select($"key", $"value".avg)
        Specified by:
        groupBy in interface Table

      • distinct

        public Table distinct()
        Description copied from interface: Table
        Removes duplicate values and returns only distinct (different) values.

        Example: 

        
 tab.select($("key"), $("value")).distinct();
        Specified by:
        distinct in interface Table

      • join

        public Table join​(Table right)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL join. The fields of the two joined operations must not overlap, use as to rename fields if necessary. You can use where and select clauses after a join to further specify the behaviour of the join.

        Note: Both tables must be bound to the same TableEnvironment .

        Example: 

        
 left.join(right)
     .where($("a").isEqual($("b")).and($("c").isGreater(3))
     .select($("a"), $("b"), $("d"));
        Specified by:
        join in interface Table

      • join

        public Table join​(Table right,
                  org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL join. The fields of the two joined operations must not overlap, use as to rename fields if necessary.

        Note: Both tables must be bound to the same TableEnvironment .

        Java Example: 

        
 left.join(right, $("a").isEqual($("b")))
     .select($("a"), $("b"), $("d"));

        Scala Example: 

        
 left.join(right, $"a" === $"b")
     .select($"a", $"b", $"d")
        Specified by:
        join in interface Table

      • leftOuterJoin

        public Table leftOuterJoin​(Table right)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL left outer join. The fields of the two joined operations must not overlap, use as to rename fields if necessary.

        Note: Both tables must be bound to the same TableEnvironment and its TableConfig must have null check enabled (default).

        Example: 

        
 left.leftOuterJoin(right)
     .select($("a"), $("b"), $("d"));
        Specified by:
        leftOuterJoin in interface Table

      • leftOuterJoin

        public Table leftOuterJoin​(Table right,
                           org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL left outer join. The fields of the two joined operations must not overlap, use as to rename fields if necessary.

        Note: Both tables must be bound to the same TableEnvironment and its TableConfig must have null check enabled (default).

        Java Example: 

        
 left.leftOuterJoin(right, $("a").isEqual($("b")))
     .select($("a"), $("b"), $("d"));

        Scala Example: 

        
 left.leftOuterJoin(right, $"a" === $"b")
     .select($"a", $"b", $"d")
        Specified by:
        leftOuterJoin in interface Table

      • rightOuterJoin

        public Table rightOuterJoin​(Table right,
                            org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL right outer join. The fields of the two joined operations must not overlap, use as to rename fields if necessary.

        Note: Both tables must be bound to the same TableEnvironment and its TableConfig must have null check enabled (default).

        Java Example: 

        
 left.rightOuterJoin(right, $("a").isEqual($("b")))
     .select($("a"), $("b"), $("d"));

        Scala Example: 

        
 left.rightOuterJoin(right, $"a" === $"b")
     .select($"a", $"b", $"d")
        Specified by:
        rightOuterJoin in interface Table

      • fullOuterJoin

        public Table fullOuterJoin​(Table right,
                           org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins two Tables. Similar to a SQL full outer join. The fields of the two joined operations must not overlap, use as to rename fields if necessary.

        Note: Both tables must be bound to the same TableEnvironment and its TableConfig must have null check enabled (default).

        Java Example: 

        
 left.fullOuterJoin(right, $("a").isEqual($("b")))
     .select($("a"), $("b"), $("d"));

        Scala Example: 

        
 left.fullOuterJoin(right, $"a" === $"b")
     .select($"a", $"b", $"d")
        Specified by:
        fullOuterJoin in interface Table

      • joinLateral

        public Table joinLateral​(org.apache.flink.table.expressions.Expression tableFunctionCall)
        Description copied from interface: Table
        Joins this Table with an user-defined TableFunction. This join is similar to a SQL inner join with ON TRUE predicate but works with a table function. Each row of the table is joined with all rows produced by the table function.

        Java Example: 

        
 class MySplitUDTF extends TableFunction<String> {
   public void eval(String str) {
     str.split("#").forEach(this::collect);
   }
 }

 table.joinLateral(call(MySplitUDTF.class, $("c")).as("s"))
      .select($("a"), $("b"), $("c"), $("s"));

        Scala Example: 

        
 class MySplitUDTF extends TableFunction[String] {
   def eval(str: String): Unit = {
     str.split("#").foreach(collect)
   }
 }

 val split = new MySplitUDTF()
 table.joinLateral(split($"c") as "s")
      .select($"a", $"b", $"c", $"s")
        Specified by:
        joinLateral in interface Table

      • joinLateral

        public Table joinLateral​(org.apache.flink.table.expressions.Expression tableFunctionCall,
                         org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins this Table with an user-defined TableFunction. This join is similar to a SQL inner join but works with a table function. Each row of the table is joined with all rows produced by the table function.

        Java Example: 

        
 class MySplitUDTF extends TableFunction<String> {
   public void eval(String str) {
     str.split("#").forEach(this::collect);
   }
 }

 table.joinLateral(call(MySplitUDTF.class, $("c")).as("s"), $("a").isEqual($("s")))
      .select($("a"), $("b"), $("c"), $("s"));

        Scala Example: 

        
 class MySplitUDTF extends TableFunction[String] {
   def eval(str: String): Unit = {
     str.split("#").foreach(collect)
   }
 }

 val split = new MySplitUDTF()
 table.joinLateral(split($"c") as "s", $"a" === $"s")
      .select($"a", $"b", $"c", $"s")
        Specified by:
        joinLateral in interface Table

      • leftOuterJoinLateral

        public Table leftOuterJoinLateral​(org.apache.flink.table.expressions.Expression tableFunctionCall)
        Description copied from interface: Table
        Joins this Table with an user-defined TableFunction. This join is similar to a SQL left outer join with ON TRUE predicate but works with a table function. Each row of the table is joined with all rows produced by the table function. If the table function does not produce any row, the outer row is padded with nulls.

        Java Example: 

        
 class MySplitUDTF extends TableFunction<String> {
   public void eval(String str) {
     str.split("#").forEach(this::collect);
   }
 }

 table.leftOuterJoinLateral(call(MySplitUDTF.class, $("c")).as("s"))
      .select($("a"), $("b"), $("c"), $("s"));

        Scala Example: 

        
 class MySplitUDTF extends TableFunction[String] {
   def eval(str: String): Unit = {
     str.split("#").foreach(collect)
   }
 }

 val split = new MySplitUDTF()
 table.leftOuterJoinLateral(split($"c") as "s")
      .select($"a", $"b", $"c", $"s")
        Specified by:
        leftOuterJoinLateral in interface Table

      • leftOuterJoinLateral

        public Table leftOuterJoinLateral​(org.apache.flink.table.expressions.Expression tableFunctionCall,
                                  org.apache.flink.table.expressions.Expression joinPredicate)
        Description copied from interface: Table
        Joins this Table with an user-defined TableFunction. This join is similar to a SQL left outer join with ON TRUE predicate but works with a table function. Each row of the table is joined with all rows produced by the table function. If the table function does not produce any row, the outer row is padded with nulls.

        Java Example: 

        
 class MySplitUDTF extends TableFunction<String> {
   public void eval(String str) {
     str.split("#").forEach(this::collect);
   }
 }

 table.leftOuterJoinLateral(call(MySplitUDTF.class, $("c")).as("s"), $("a").isEqual($("s")))
      .select($("a"), $("b"), $("c"), $("s"));

        Scala Example: 

        
 class MySplitUDTF extends TableFunction[String] {
   def eval(str: String): Unit = {
     str.split("#").foreach(collect)
   }
 }

 val split = new MySplitUDTF()
 table.leftOuterJoinLateral(split($"c") as "s", $"a" === $"s")
      .select($"a", $"b", $"c", $"s")
        Specified by:
        leftOuterJoinLateral in interface Table

      • minus

        public Table minus​(Table right)
        Description copied from interface: Table
        Minus of two Tables with duplicate records removed. Similar to a SQL EXCEPT clause. Minus returns records from the left table that do not exist in the right table. Duplicate records in the left table are returned exactly once, i.e., duplicates are removed. Both tables must have identical field types.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.minus(right);
        Specified by:
        minus in interface Table

      • minusAll

        public Table minusAll​(Table right)
        Description copied from interface: Table
        Minus of two Tables. Similar to a SQL EXCEPT ALL. Similar to a SQL EXCEPT ALL clause. MinusAll returns the records that do not exist in the right table. A record that is present n times in the left table and m times in the right table is returned (n - m) times, i.e., as many duplicates as are present in the right table are removed. Both tables must have identical field types.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.minusAll(right);
        Specified by:
        minusAll in interface Table

      • union

        public Table union​(Table right)
        Description copied from interface: Table
        Unions two Tables with duplicate records removed. Similar to a SQL UNION. The fields of the two union operations must fully overlap.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.union(right);
        Specified by:
        union in interface Table

      • unionAll

        public Table unionAll​(Table right)
        Description copied from interface: Table
        Unions two Tables. Similar to a SQL UNION ALL. The fields of the two union operations must fully overlap.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.unionAll(right);
        Specified by:
        unionAll in interface Table

      • intersect

        public Table intersect​(Table right)
        Description copied from interface: Table
        Intersects two Tables with duplicate records removed. Intersect returns records that exist in both tables. If a record is present in one or both tables more than once, it is returned just once, i.e., the resulting table has no duplicate records. Similar to a SQL INTERSECT. The fields of the two intersect operations must fully overlap.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.intersect(right);
        Specified by:
        intersect in interface Table

      • intersectAll

        public Table intersectAll​(Table right)
        Description copied from interface: Table
        Intersects two Tables. IntersectAll returns records that exist in both tables. If a record is present in both tables more than once, it is returned as many times as it is present in both tables, i.e., the resulting table might have duplicate records. Similar to an SQL INTERSECT ALL. The fields of the two intersect operations must fully overlap.

        Note: Both tables must be bound to the same TableEnvironment.

        Example: 

        
 left.intersectAll(right);
        Specified by:
        intersectAll in interface Table

      • orderBy

        public Table orderBy​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Sorts the given Table. Similar to SQL ORDER BY.

        The resulting Table is globally sorted across all parallel partitions.

        Java Example: 

        
 tab.orderBy($("name").desc());

        Scala Example: 

        
 tab.orderBy($"name".desc)

        For unbounded tables, this operation requires a sorting on a time attribute or a subsequent fetch operation.

        Specified by:
        orderBy in interface Table

      • offset

        public Table offset​(int offset)
        Description copied from interface: Table
        Limits a (possibly sorted) result from an offset position.

        This method can be combined with a preceding Table.orderBy(Expression...) call for a deterministic order and a subsequent Table.fetch(int) call to return n rows after skipping the first o rows. 

        
 // skips the first 3 rows and returns all following rows.
 tab.orderBy($("name").desc()).offset(3);
 // skips the first 10 rows and returns the next 5 rows.
 tab.orderBy($("name").desc()).offset(10).fetch(5);

        For unbounded tables, this operation requires a subsequent fetch operation.

        Specified by:
        offset in interface Table
        Parameters:
        offset - number of records to skip

      • fetch

        public Table fetch​(int fetch)
        Description copied from interface: Table
        Limits a (possibly sorted) result to the first n rows.

        This method can be combined with a preceding Table.orderBy(Expression...) call for a deterministic order and Table.offset(int) call to return n rows after skipping the first o rows. 

        
 // returns the first 3 records.
 tab.orderBy($("name").desc()).fetch(3);
 // skips the first 10 rows and returns the next 5 rows.
 tab.orderBy($("name").desc()).offset(10).fetch(5);
        Specified by:
        fetch in interface Table
        Parameters:
        fetch - the number of records to return. Fetch must be >= 0.

      • window

        public GroupWindowedTable window​(GroupWindow groupWindow)
        Description copied from interface: Table
        Groups the records of a table by assigning them to windows defined by a time or row interval.

        For streaming tables of infinite size, grouping into windows is required to define finite groups on which group-based aggregates can be computed.

        For batch tables of finite size, windowing essentially provides shortcuts for time-based groupBy.

        Note: Computing windowed aggregates on a streaming table is only a parallel operation if additional grouping attributes are added to the groupBy(...) clause. If the groupBy(...) only references a GroupWindow alias, the streamed table will be processed by a single task, i.e., with parallelism 1.

        Specified by:
        window in interface Table
        Parameters:
        groupWindow - groupWindow that specifies how elements are grouped.
        Returns:
        A windowed table.

      • window

        public OverWindowedTable window​(OverWindow... overWindows)
        Description copied from interface: Table
        Defines over-windows on the records of a table.

        An over-window defines for each record an interval of records over which aggregation functions can be computed.

        Java Example: 

        
 table
   .window(Over.partitionBy($("c")).orderBy($("rowTime")).preceding(lit(10).seconds()).as("ow")
   .select($("c"), $("b").count().over($("ow")), $("e").sum().over($("ow")));

        Scala Example: 

        
 table
   .window(Over partitionBy $"c" orderBy $"rowTime" preceding 10.seconds as "ow")
   .select($"c", $"b".count over $"ow", $"e".sum over $"ow")

        Note: Computing over window aggregates on a streaming table is only a parallel operation if the window is partitioned. Otherwise, the whole stream will be processed by a single task, i.e., with parallelism 1.

        Note: Over-windows for batch tables are currently not supported.

        Specified by:
        window in interface Table
        Parameters:
        overWindows - windows that specify the record interval over which aggregations are computed.
        Returns:
        An OverWindowedTable to specify the aggregations.

      • addColumns

        public Table addColumns​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Adds additional columns. Similar to a SQL SELECT statement. The field expressions can contain complex expressions, but can not contain aggregations. It will throw an exception if the added fields already exist.

        Java Example: 

        
 tab.addColumns(
    $("a").plus(1).as("a1"),
    concat($("b"), "sunny").as("b1")
 );

        Scala Example: 

        
 tab.addColumns(
    $"a" + 1 as "a1",
    concat($"b", "sunny") as "b1"
 )
        Specified by:
        addColumns in interface Table

      • addOrReplaceColumns

        public Table addOrReplaceColumns​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Adds additional columns. Similar to a SQL SELECT statement. The field expressions can contain complex expressions, but can not contain aggregations. Existing fields will be replaced. If the added fields have duplicate field name, then the last one is used.

        Java Example: 

        
 tab.addOrReplaceColumns(
    $("a").plus(1).as("a1"),
    concat($("b"), "sunny").as("b1")
 );

        Scala Example: 

        
 tab.addOrReplaceColumns(
    $"a" + 1 as "a1",
    concat($"b", "sunny") as "b1"
 )
        Specified by:
        addOrReplaceColumns in interface Table

      • renameColumns

        public Table renameColumns​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Renames existing columns. Similar to a field alias statement. The field expressions should be alias expressions, and only the existing fields can be renamed.

        Java Example: 

        
 tab.renameColumns(
    $("a").as("a1"),
    $("b").as("b1")
 );

        Scala Example: 

        
 tab.renameColumns(
    $"a" as "a1",
    $"b" as "b1"
 )
        Specified by:
        renameColumns in interface Table

      • dropColumns

        public Table dropColumns​(org.apache.flink.table.expressions.Expression... fields)
        Description copied from interface: Table
        Drops existing columns. The field expressions should be field reference expressions.

        Java Example: 

        
 tab.dropColumns($("a"), $("b"));

        Scala Example: 

        
 tab.dropColumns($"a", $"b")
        Specified by:
        dropColumns in interface Table

      • map

        public Table map​(org.apache.flink.table.expressions.Expression mapFunction)
        Description copied from interface: Table
        Performs a map operation with an user-defined scalar function or built-in scalar function. The output will be flattened if the output type is a composite type.

        Java Example: 

        
 tab.map(call(MyMapFunction.class, $("c")))

        Scala Example: 

        
 val func = new MyMapFunction()
 tab.map(func($"c"))
        Specified by:
        map in interface Table

      • flatMap

        public Table flatMap​(org.apache.flink.table.expressions.Expression tableFunction)
        Description copied from interface: Table
        Performs a flatMap operation with an user-defined table function or built-in table function. The output will be flattened if the output type is a composite type.

        Java Example: 

        
 tab.flatMap(call(MyFlatMapFunction.class, $("c")))

        Scala Example: 

        
 val func = new MyFlatMapFunction()
 tab.flatMap(func($"c"))
        Specified by:
        flatMap in interface Table

      • aggregate

        public AggregatedTable aggregate​(org.apache.flink.table.expressions.Expression aggregateFunction)
        Description copied from interface: Table
        Performs a global aggregate operation with an aggregate function. You have to close the Table.aggregate(Expression) with a select statement. The output will be flattened if the output type is a composite type.

        Java Example: 

        
 tab.aggregate(call(MyAggregateFunction.class, $("a"), $("b")).as("f0", "f1", "f2"))
   .select($("f0"), $("f1"));

        Scala Example: 

        
 val aggFunc = new MyAggregateFunction
 table.aggregate(aggFunc($"a", $"b") as ("f0", "f1", "f2"))
   .select($"f0", $"f1")
        Specified by:
        aggregate in interface Table

      • flatAggregate

        public FlatAggregateTable flatAggregate​(org.apache.flink.table.expressions.Expression tableAggregateFunction)
        Description copied from interface: Table
        Perform a global flatAggregate without groupBy. FlatAggregate takes a TableAggregateFunction which returns multiple rows. Use a selection after the flatAggregate.

        Java Example: 

        
 tab.flatAggregate(call(MyTableAggregateFunction.class, $("a"), $("b")).as("x", "y", "z"))
   .select($("x"), $("y"), $("z"));

        Scala Example: 

        
 val tableAggFunc: TableAggregateFunction = new MyTableAggregateFunction
 tab.flatAggregate(tableAggFunc($"a", $"b") as ("x", "y", "z"))
   .select($"x", $"y", $"z")
        Specified by:
        flatAggregate in interface Table

      • insertInto

        public TablePipeline insertInto​(String tablePath,
                                boolean overwrite)
        Description copied from interface: Table
        Declares that the pipeline defined by the given Table object should be written to a table (backed by a DynamicTableSink) that was registered under the specified path.

        See the documentation of TableEnvironment.useDatabase(String) or TableEnvironment.useCatalog(String) for the rules on the path resolution.

        Example: 

        
 Table table = tableEnv.sqlQuery("SELECT * FROM MyTable");
 TablePipeline tablePipeline = table.insertInto("MySinkTable", true);
 TableResult tableResult = tablePipeline.execute();
 tableResult.await();

        One can execute the returned TablePipeline using Executable.execute(), or compile it to a CompiledPlan using Compilable.compilePlan().

        If multiple pipelines should insert data into one or more sink tables as part of a single execution, use a StatementSet (see TableEnvironment.createStatementSet()).

        Specified by:
        insertInto in interface Table
        Parameters:
        tablePath - The path of the registered table (backed by a DynamicTableSink).
        overwrite - Indicates whether existing data should be overwritten.
        Returns:
        The complete pipeline from one or more source tables to a sink table.

      • insertInto

        public TablePipeline insertInto​(TableDescriptor descriptor)
        Description copied from interface: Table
        Declares that the pipeline defined by the given Table object should be written to a table (backed by a DynamicTableSink) expressed via the given TableDescriptor.

        The descriptor won't be registered in the catalog, but it will be propagated directly in the operation tree. Note that calling this method multiple times, even with the same descriptor, results in multiple sink tables instances.

        This method allows to declare a Schema for the sink descriptor. The declaration is similar to a CREATE TABLE DDL in SQL and allows to:

        • overwrite automatically derived columns with a custom DataType
        • add metadata columns next to the physical columns
        • declare a primary key

        It is possible to declare a schema without physical/regular columns. In this case, those columns will be automatically derived and implicitly put at the beginning of the schema declaration.

        Examples: 

        
 Schema schema = Schema.newBuilder()
   .column("f0", DataTypes.STRING())
   .build();

 Table table = tableEnv.from(TableDescriptor.forConnector("datagen")
   .schema(schema)
   .build());

 table.insertInto(TableDescriptor.forConnector("blackhole")
   .schema(schema)
   .build());

        One can execute the returned TablePipeline using Executable.execute(), or compile it to a CompiledPlan using Compilable.compilePlan().

        If multiple pipelines should insert data into one or more sink tables as part of a single execution, use a StatementSet (see TableEnvironment.createStatementSet()).

        Specified by:
        insertInto in interface Table
        Parameters:
        descriptor - Descriptor describing the sink table into which data should be inserted.
        Returns:
        The complete pipeline from one or more source tables to a sink table.

      • insertInto

        public TablePipeline insertInto​(TableDescriptor descriptor,
                                boolean overwrite)
        Description copied from interface: Table
        Declares that the pipeline defined by the given Table object should be written to a table (backed by a DynamicTableSink) expressed via the given TableDescriptor.

        The descriptor won't be registered in the catalog, but it will be propagated directly in the operation tree. Note that calling this method multiple times, even with the same descriptor, results in multiple sink tables being registered.

        This method allows to declare a Schema for the sink descriptor. The declaration is similar to a CREATE TABLE DDL in SQL and allows to:

        • overwrite automatically derived columns with a custom DataType
        • add metadata columns next to the physical columns
        • declare a primary key

        It is possible to declare a schema without physical/regular columns. In this case, those columns will be automatically derived and implicitly put at the beginning of the schema declaration.

        Examples: 

        
 Schema schema = Schema.newBuilder()
   .column("f0", DataTypes.STRING())
   .build();

 Table table = tableEnv.from(TableDescriptor.forConnector("datagen")
   .schema(schema)
   .build());

 table.insertInto(TableDescriptor.forConnector("blackhole")
   .schema(schema)
   .build(), true);

        One can execute the returned TablePipeline using Executable.execute(), or compile it to a CompiledPlan using Compilable.compilePlan().

        If multiple pipelines should insert data into one or more sink tables as part of a single execution, use a StatementSet (see TableEnvironment.createStatementSet()).

        Specified by:
        insertInto in interface Table
        Parameters:
        descriptor - Descriptor describing the sink table into which data should be inserted.
        overwrite - Indicates whether existing data should be overwritten.
        Returns:
        The complete pipeline from one or more source tables to a sink table.

      • explain

        public String explain​(ExplainFormat format,
                      ExplainDetail... extraDetails)
        Description copied from interface: Explainable
        Returns the AST of this object and the execution plan to compute the result of the given statement.
        Specified by:
        explain in interface Explainable<Table>
        Parameters:
        format - The output format of explained plan
        extraDetails - The extra explain details which the result of this method should include, e.g. estimated cost, changelog mode for streaming
        Returns:
        AST and the execution plan.