使用flink Table &Sql api来构建批量和流式应用(2)Table API概述

从flink的官方文档，我们知道flink的编程模型分为四层，sql层是最高层的api，Table api是中间层，DataStream/DataSet Api 是核心，stateful Streaming process层是底层实现。

 

 

 

其中，

flink dataset api使用及原理 介绍了DataSet Api 

flink DataStream API使用及原理介绍了DataStream Api 

flink中的时间戳如何使用？---Watermark使用及原理 介绍了底层实现的基础Watermark

flink window实例分析 介绍了window的概念及使用原理

Flink中的状态与容错 介绍了State的概念及checkpoint，savepoint的容错机制

 上篇<使用flink Table &Sql api来构建批量和流式应用(1)Table的基本概念>介绍了Table的基本概念及使用方法

本篇主要看看Table Api有哪些功能？

org.apache.flink.table.api.Table抽象了Table Api的功能

/**
 * A Table is the core component of the Table API.
 * Similar to how the batch and streaming APIs have DataSet and DataStream,
 * the Table API is built around {@link Table}.
 *
 * <p>Use the methods of {@link Table} to transform data. Use {@code TableEnvironment} to convert a
 * {@link Table} back to a {@code DataSet} or {@code DataStream}.
 *
 * <p>When using Scala a {@link Table} can also be converted using implicit conversions.
 *
 * <p>Java Example:
 *
 * <pre>
 * {@code
 *   ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
 *   BatchTableEnvironment tEnv = BatchTableEnvironment.create(env);
 *
 *   DataSet<Tuple2<String, Integer>> set = ...
 *   tEnv.registerTable("MyTable", set, "a, b");
 *
 *   Table table = tEnv.scan("MyTable").select(...);
 *   ...
 *   Table table2 = ...
 *   DataSet<MyType> set2 = tEnv.toDataSet(table2, MyType.class);
 * }
 * </pre>
 *
 * <p>Scala Example:
 *
 * <pre>
 * {@code
 *   val env = ExecutionEnvironment.getExecutionEnvironment
 *   val tEnv = BatchTableEnvironment.create(env)
 *
 *   val set: DataSet[(String, Int)] = ...
 *   val table = set.toTable(tEnv, 'a, 'b)
 *   ...
 *   val table2 = ...
 *   val set2: DataSet[MyType] = table2.toDataSet[MyType]
 * }
 * </pre>
 *
 * <p>Operations such as {@code join}, {@code select}, {@code where} and {@code groupBy} either
 * take arguments in a Scala DSL or as an expression String. Please refer to the documentation for
 * the expression syntax.
 */

(1) 查询select 

/**
     * Performs a selection operation. Similar to a SQL SELECT statement. The field expressions
     * can contain complex expressions and aggregations.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.select("key, value.avg + ' The average' as average")
     * }
     * </pre>
     */
    Table select(String fields);

    /**
     * Performs a selection operation. Similar to a SQL SELECT statement. The field expressions
     * can contain complex expressions and aggregations.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.select('key, 'value.avg + " The average" as 'average)
     * }
     * </pre>
     */
    Table select(Expression... fields);

(2) 条件where 

/**
     * Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE
     * clause.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.where("name = 'Fred'")
     * }
     * </pre>
     */
    Table where(String predicate);

    /**
     * Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE
     * clause.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.where('name === "Fred")
     * }
     * </pre>
     */
    Table where(Expression predicate);

(3)过滤Filter

    /**
     * Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE
     * clause.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.filter("name = 'Fred'")
     * }
     * </pre>
     */
    Table filter(String predicate);

    /**
     * Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE
     * clause.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.filter('name === "Fred")
     * }
     * </pre>
     */
    Table filter(Expression predicate);

(4) distinct

    /**
     * Removes duplicate values and returns only distinct (different) values.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.select("key, value").distinct()
     * }
     * </pre>
     */
    Table distinct();

(5) group by 

    /**
     * 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.groupBy("key").select("key, value.avg")
     * }
     * </pre>
     */
    GroupedTable groupBy(String fields);

    /**
     * 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.groupBy('key).select('key, 'value.avg)
     * }
     * </pre>
     */
    GroupedTable groupBy(Expression... fields);

(6) order by

    /**
     * Sorts the given {@link Table}. Similar to SQL ORDER BY.
     * The resulting Table is sorted globally sorted across all parallel partitions.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.orderBy("name.desc")
     * }
     * </pre>
     */
    Table orderBy(String fields);

    /**
     * Sorts the given {@link Table}. Similar to SQL ORDER BY.
     * The resulting Table is globally sorted across all parallel partitions.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.orderBy('name.desc)
     * }
     * </pre>
     */
    Table orderBy(Expression... fields);

(7) map

    /**
     * Performs a map operation with an user-defined scalar function or a built-in scalar function.
     * The output will be flattened if the output type is a composite type.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   ScalarFunction func = new MyMapFunction();
     *   tableEnv.registerFunction("func", func);
     *   tab.map("func(c)");
     * }
     * </pre>
     */
    Table map(String mapFunction);

    /**
     * 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   val func = new MyMapFunction()
     *   tab.map(func('c))
     * }
     * </pre>
     */
    Table map(Expression mapFunction);

    /**
     * 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   TableFunction func = new MyFlatMapFunction();
     *   tableEnv.registerFunction("func", func);
     *   table.flatMap("func(c)");
     * }
     * </pre>
     */
    Table flatMap(String tableFunction);

    /**
     * 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   val func = new MyFlatMapFunction
     *   table.flatMap(func('c))
     * }
     * </pre>
     */
    Table flatMap(Expression tableFunction);

(8) aggregate

/**
     * Performs a global aggregate operation with an aggregate function. You have to close the
     * {@link #aggregate(String)} with a select statement. The output will be flattened if the
     * output type is a composite type.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   AggregateFunction aggFunc = new MyAggregateFunction()
     *   tableEnv.registerFunction("aggFunc", aggFunc);
     *   table.aggregate("aggFunc(a, b) as (f0, f1, f2)")
     *     .select("f0, f1")
     * }
     * </pre>
     */
    AggregatedTable aggregate(String aggregateFunction);

    /**
     * Performs a global aggregate operation with an aggregate function. You have to close the
     * {@link #aggregate(Expression)} with a select statement. The output will be flattened if the
     * output type is a composite type.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   val aggFunc = new MyAggregateFunction
     *   table.aggregate(aggFunc('a, 'b) as ('f0, 'f1, 'f2))
     *     .select('f0, 'f1)
     * }
     * </pre>
     */
    AggregatedTable aggregate(Expression aggregateFunction);

    /**
     * Perform a global flatAggregate without groupBy. FlatAggregate takes a TableAggregateFunction
     * which returns multiple rows. Use a selection after the flatAggregate.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   TableAggregateFunction tableAggFunc = new MyTableAggregateFunction();
     *   tableEnv.registerFunction("tableAggFunc", tableAggFunc);
     *   tab.flatAggregate("tableAggFunc(a, b) as (x, y, z)")
     *     .select("x, y, z")
     * }
     * </pre>
     */
    FlatAggregateTable flatAggregate(String tableAggregateFunction);

    /**
     * Perform a global flatAggregate without groupBy. FlatAggregate takes a TableAggregateFunction
     * which returns multiple rows. Use a selection after the flatAggregate.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   val tableAggFunc = new MyTableAggregateFunction
     *   tab.flatAggregate(tableAggFunc('a, 'b) as ('x, 'y, 'z))
     *     .select('x, 'y, 'z)
     * }
     * </pre>
     */
    FlatAggregateTable flatAggregate(Expression tableAggregateFunction);

(9)列的管理

    /**
     * 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.
     *
     * <p>Example:
     * <pre>
     * {@code
     *   tab.addColumns("a + 1 as a1, concat(b, 'sunny') as b1")
     * }
     * </pre>
     */
    Table addColumns(String fields);

    /**
     * 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.addColumns('a + 1 as 'a1, concat('b, "sunny") as 'b1)
     * }
     * </pre>
     */
    Table addColumns(Expression... fields);

    /**
     * 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 add columns name is the same as the existing column name. Moreover, if the added
     * fields have duplicate field name, then the last one is used.
     *
     * <p>Example:
     * <pre>
     * {@code
     *   tab.addOrReplaceColumns("a + 1 as a1, concat(b, 'sunny') as b1")
     * }
     * </pre>
     */
    Table addOrReplaceColumns(String fields);

    /**
     * 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.
     *
     * <p>Scala Example:
     * <pre>
     * {@code
     *   tab.addOrReplaceColumns('a + 1 as 'a1, concat('b, "sunny") as 'b1)
     * }
     * </pre>
     */
    Table addOrReplaceColumns(Expression... fields);

    /**
     * Renames existing columns. Similar to a field alias statement. The field expressions
     * should be alias expressions, and only the existing fields can be renamed.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.renameColumns("a as a1, b as b1")
     * }
     * </pre>
     */
    Table renameColumns(String fields);

    /**
     * Renames existing columns. Similar to a field alias statement. The field expressions
     * should be alias expressions, and only the existing fields can be renamed.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.renameColumns('a as 'a1, 'b as 'b1)
     * }
     * </pre>
     */
    Table renameColumns(Expression... fields);

    /**
     * Drops existing columns. The field expressions should be field reference expressions.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.dropColumns("a, b")
     * }
     * </pre>
     */
    Table dropColumns(String fields);

    /**
     * Drops existing columns. The field expressions should be field reference expressions.
     *
     * <p>Scala Example:
     * <pre>
     * {@code
     *   tab.dropColumns('a, 'b)
     * }
     * </pre>
     */
    Table dropColumns(Expression... fields);

(10) window操作

    /**
     * Groups the records of a table by assigning them to windows defined by a time or row interval.
     *
     * <p>For streaming tables of infinite size, grouping into windows is required to define finite
     * groups on which group-based aggregates can be computed.
     *
     * <p>For batch tables of finite size, windowing essentially provides shortcuts for time-based
     * groupBy.
     *
     * <p><b>Note</b>: Computing windowed aggregates on a streaming table is only a parallel operation
     * if additional grouping attributes are added to the {@code groupBy(...)} clause.
     * If the {@code groupBy(...)} only references a GroupWindow alias, the streamed table will be
     * processed by a single task, i.e., with parallelism 1.
     *
     * @param groupWindow groupWindow that specifies how elements are grouped.
     * @return A windowed table.
     */
    GroupWindowedTable window(GroupWindow groupWindow);

    /**
     * Defines over-windows on the records of a table.
     *
     * <p>An over-window defines for each record an interval of records over which aggregation
     * functions can be computed.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   table
     *     .window(Over partitionBy 'c orderBy 'rowTime preceding 10.seconds as 'ow)
     *     .select('c, 'b.count over 'ow, 'e.sum over 'ow)
     * }
     * </pre>
     *
     * <p><b>Note</b>: 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.
     *
     * <p><b>Note</b>: Over-windows for batch tables are currently not supported.
     *
     * @param overWindows windows that specify the record interval over which aggregations are
     *                    computed.
     * @return An OverWindowedTable to specify the aggregations.
     */
    OverWindowedTable window(OverWindow... overWindows);

(11) 表关联

包括Inner join和OuterJoin

    /**
     * Joins two {@link Table}s. Similar to a SQL join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary. You can use
     * where and select clauses after a join to further specify the behaviour of the join.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} .
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.join(right).where("a = b && c > 3").select("a, b, d")
     * }
     * </pre>
     */
    Table join(Table right);

    /**
     * Joins two {@link Table}s. Similar to a SQL join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} .
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.join(right, "a = b")
     * }
     * </pre>
     */
    Table join(Table right, String joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} .
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   left.join(right, 'a === 'b).select('a, 'b, 'd)
     * }
     * </pre>
     */
    Table join(Table right, Expression joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL left outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.leftOuterJoin(right).select("a, b, d")
     * }
     * </pre>
     */
    Table leftOuterJoin(Table right);

    /**
     * Joins two {@link Table}s. Similar to a SQL left outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.leftOuterJoin(right, "a = b").select("a, b, d")
     * }
     * </pre>
     */
    Table leftOuterJoin(Table right, String joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL left outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   left.leftOuterJoin(right, 'a === 'b).select('a, 'b, 'd)
     * }
     * </pre>
     */
    Table leftOuterJoin(Table right, Expression joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL right outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.rightOuterJoin(right, "a = b").select("a, b, d")
     * }
     * </pre>
     */
    Table rightOuterJoin(Table right, String joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL right outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   left.rightOuterJoin(right, 'a === 'b).select('a, 'b, 'd)
     * }
     * </pre>
     */
    Table rightOuterJoin(Table right, Expression joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL full outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.fullOuterJoin(right, "a = b").select("a, b, d")
     * }
     * </pre>
     */
    Table fullOuterJoin(Table right, String joinPredicate);

    /**
     * Joins two {@link Table}s. Similar to a SQL full outer join. The fields of the two joined
     * operations must not overlap, use {@code as} to rename fields if necessary.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment} and its
     * {@code TableConfig} must have null check enabled (default).
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   left.fullOuterJoin(right, 'a === 'b).select('a, 'b, 'd)
     * }
     * </pre>
     */
    Table fullOuterJoin(Table right, Expression joinPredicate);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   class MySplitUDTF extends TableFunction<String> {
     *     public void eval(String str) {
     *       str.split("#").forEach(this::collect);
     *     }
     *   }
     *
     *   TableFunction<String> split = new MySplitUDTF();
     *   tableEnv.registerFunction("split", split);
     *   table.joinLateral("split(c) as (s)").select("a, b, c, s");
     * }
     * </pre>
     */
    Table joinLateral(String tableFunctionCall);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   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)
     * }
     * </pre>
     */
    Table joinLateral(Expression tableFunctionCall);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   class MySplitUDTF extends TableFunction<String> {
     *     public void eval(String str) {
     *       str.split("#").forEach(this::collect);
     *     }
     *   }
     *
     *   TableFunction<String> split = new MySplitUDTF();
     *   tableEnv.registerFunction("split", split);
     *   table.joinLateral("split(c) as (s)", "a = s").select("a, b, c, s");
     * }
     * </pre>
     */
    Table joinLateral(String tableFunctionCall, String joinPredicate);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   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)
     * }
     * </pre>
     */
    Table joinLateral(Expression tableFunctionCall, Expression joinPredicate);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   class MySplitUDTF extends TableFunction<String> {
     *     public void eval(String str) {
     *       str.split("#").forEach(this::collect);
     *     }
     *   }
     *
     *   TableFunction<String> split = new MySplitUDTF();
     *   tableEnv.registerFunction("split", split);
     *   table.leftOuterJoinLateral("split(c) as (s)").select("a, b, c, s");
     * }
     * </pre>
     */
    Table leftOuterJoinLateral(String tableFunctionCall);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   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)
     * }
     * </pre>
     */
    Table leftOuterJoinLateral(Expression tableFunctionCall);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   class MySplitUDTF extends TableFunction<String> {
     *     public void eval(String str) {
     *       str.split("#").forEach(this::collect);
     *     }
     *   }
     *
     *   TableFunction<String> split = new MySplitUDTF();
     *   tableEnv.registerFunction("split", split);
     *   table.leftOuterJoinLateral("split(c) as (s)", "a = s").select("a, b, c, s");
     * }
     * </pre>
     */
    Table leftOuterJoinLateral(String tableFunctionCall, String joinPredicate);

    /**
     * Joins this {@link Table} with an user-defined {@link 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.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   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)
     * }
     * </pre>
     */
    Table leftOuterJoinLateral(Expression tableFunctionCall, Expression joinPredicate);

(12) 集合操作

    /**
     * Minus of two {@link Table}s 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.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.minus(right)
     * }
     * </pre>
     */
    Table minus(Table right);

    /**
     * Minus of two {@link Table}s. 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.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.minusAll(right)
     * }
     * </pre>
     */
    Table minusAll(Table right);

    /**
     * Unions two {@link Table}s with duplicate records removed.
     * Similar to a SQL UNION. The fields of the two union operations must fully overlap.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.union(right)
     * }
     * </pre>
     */
    Table union(Table right);

    /**
     * Unions two {@link Table}s. Similar to a SQL UNION ALL. The fields of the two union
     * operations must fully overlap.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.unionAll(right)
     * }
     * </pre>
     */
    Table unionAll(Table right);

    /**
     * Intersects two {@link Table}s 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.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.intersect(right)
     * }
     * </pre>
     */
    Table intersect(Table right);

    /**
     * Intersects two {@link Table}s. 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.
     *
     * <p>Note: Both tables must be bound to the same {@code TableEnvironment}.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   left.intersectAll(right)
     * }
     * </pre>
     */
    Table intersectAll(Table right);

(13) 创建临时表

/**
     * Creates {@link 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. {@link TemporalTableFunction} provides a way how to
     * access those data.
     *
     * <p>For more information please check Flink's documentation on Temporal Tables.
     *
     * <p>Currently {@link TemporalTableFunction}s are only supported in streaming.
     *
     * @param timeAttribute Must points to a time attribute. Provides a way to compare which
     *                      records are a newer or older version.
     * @param primaryKey    Defines the primary key. With primary key it is possible to update
     *                      a row or to delete it.
     * @return {@link TemporalTableFunction} which is an instance of {@link TableFunction}.
     *        It takes one single argument, the {@code timeAttribute}, for which it returns
     *        matching version of the {@link Table}, from which {@link TemporalTableFunction}
     *        was created.
     */
    TemporalTableFunction createTemporalTableFunction(String timeAttribute, String primaryKey);

    /**
     * Creates {@link 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. {@link TemporalTableFunction} provides a way how to
     * access those data.
     *
     * <p>For more information please check Flink's documentation on Temporal Tables.
     *
     * <p>Currently {@link TemporalTableFunction}s are only supported in streaming.
     *
     * @param timeAttribute Must points to a time indicator. Provides a way to compare which
     *                      records are a newer or older version.
     * @param primaryKey    Defines the primary key. With primary key it is possible to update
     *                      a row or to delete it.
     * @return {@link TemporalTableFunction} which is an instance of {@link TableFunction}.
     *        It takes one single argument, the {@code timeAttribute}, for which it returns
     *        matching version of the {@link Table}, from which {@link TemporalTableFunction}
     *        was created.
     */
    TemporalTableFunction createTemporalTableFunction(Expression timeAttribute, Expression primaryKey);

(14) 重命名

/**
     * Renames the fields of the expression result. Use this to disambiguate fields before
     * joining to operations.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.as("a, b")
     * }
     * </pre>
     */
    Table as(String fields);

    /**
     * Renames the fields of the expression result. Use this to disambiguate fields before
     * joining to operations.
     *
     * <p>Scala Example:
     *
     * <pre>
     * {@code
     *   tab.as('a, 'b)
     * }
     * </pre>
     */
    Table as(Expression... fields);

    /**
     * Filters out elements that don't pass the filter predicate. Similar to a SQL WHERE
     * clause.
     *
     * <p>Example:
     *
     * <pre>
     * {@code
     *   tab.filter("name = 'Fred'")
     * }
     * </pre>
     */

(15)插入数据表

    /**
     * Writes the {@link Table} to a {@link TableSink} that was registered under the specified path.
     * For the path resolution algorithm see {@link TableEnvironment#useDatabase(String)}.
     *
     * <p>A batch {@link Table} can only be written to a
     * {@code org.apache.flink.table.sinks.BatchTableSink}, a streaming {@link Table} requires a
     * {@code org.apache.flink.table.sinks.AppendStreamTableSink}, a
     * {@code org.apache.flink.table.sinks.RetractStreamTableSink}, or an
     * {@code org.apache.flink.table.sinks.UpsertStreamTableSink}.
     *
     * @param tablePath The first part of the path of the registered {@link TableSink} to which the {@link Table} is
     *        written. This is to ensure at least the name of the {@link TableSink} is provided.
     * @param tablePathContinued The remaining part of the path of the registered {@link TableSink} to which the
     *        {@link Table} is written.
     */
    void insertInto(String tablePath, String... tablePathContinued);

    /**
     * Writes the {@link Table} to a {@link TableSink} that was registered under the specified name
     * in the initial default catalog.
     *
     * <p>A batch {@link Table} can only be written to a
     * {@code org.apache.flink.table.sinks.BatchTableSink}, a streaming {@link Table} requires a
     * {@code org.apache.flink.table.sinks.AppendStreamTableSink}, a
     * {@code org.apache.flink.table.sinks.RetractStreamTableSink}, or an
     * {@code org.apache.flink.table.sinks.UpsertStreamTableSink}.
     *
     * @param tableName The name of the {@link TableSink} to which the {@link Table} is written.
     * @param conf The {@link QueryConfig} to use.
     * @deprecated use {@link #insertInto(QueryConfig, String, String...)}
     */
    @Deprecated
    void insertInto(String tableName, QueryConfig conf);

    /**
     * Writes the {@link Table} to a {@link TableSink} that was registered under the specified path.
     * For the path resolution algorithm see {@link TableEnvironment#useDatabase(String)}.
     *
     * <p>A batch {@link Table} can only be written to a
     * {@code org.apache.flink.table.sinks.BatchTableSink}, a streaming {@link Table} requires a
     * {@code org.apache.flink.table.sinks.AppendStreamTableSink}, a
     * {@code org.apache.flink.table.sinks.RetractStreamTableSink}, or an
     * {@code org.apache.flink.table.sinks.UpsertStreamTableSink}.
     *
     * @param conf The {@link QueryConfig} to use.
     * @param tablePath The first part of the path of the registered {@link TableSink} to which the {@link Table} is
     *        written. This is to ensure at least the name of the {@link TableSink} is provided.
     * @param tablePathContinued The remaining part of the path of the registered {@link TableSink} to which the
     *        {@link Table} is written.
     */
    void insertInto(QueryConfig conf, String tablePath, String... tablePathContinued);

总结：

本篇抓住Table api的核心类Table来发现其拥有的功能，并提供了使用用例。Flink Table Api 主要包括了查询select，条件where，过滤filter，排序order by，分组group by，去重distinct，表关联join，重命名as等常规sql操作，也提供了flink自身特性的操作：

窗口操作window，表聚合操作，map操作，aggregate操作。