Table API

The Table API is a unified, relational API for stream and batch processing. Table API queries can be run on batch or streaming input without modifications. The Table API is a super set of the SQL language and is specially designed for working with Apache Flink. The Table API is a language-integrated API for Scala and Java. Instead of specifying queries as String values as common with SQL, Table API queries are defined in a language-embedded style in Java or Scala with IDE support like autocompletion and syntax validation.

The Table API shares many concepts and parts of its API with Flink’s SQL integration. Have a look at the Common Concepts & API to learn how to register tables or to create a Table object. The Streaming Concepts pages discuss streaming specific concepts such as dynamic tables and time attributes.

The following examples assume a registered table called Orders with attributes (a, b, c, rowtime). The rowtime field is either a logical time attribute in streaming or a regular timestamp field in batch.

Overview & Examples

The Table API is available for Scala and Java. The Scala Table API leverages on Scala expressions, the Java Table API is based on strings which are parsed and converted into equivalent expressions.

The following example shows the differences between the Scala and Java Table API. The table program is executed in a batch environment. It scans the Orders table, groups by field a, and counts the resulting rows per group. The result of the table program is converted into a DataSet of type Row and printed.

The Java Table API is enabled by importing org.apache.flink.table.api.java.*. The following example shows how a Java Table API program is constructed and how expressions are specified as strings.

import org.apache.flink.table.api._
import org.apache.flink.table.api.java._

// environment configuration
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tEnv = BatchTableEnvironment.create(env);

// register Orders table in table environment
// ...

// specify table program
Table orders = tEnv.scan("Orders"); // schema (a, b, c, rowtime)

Table counts = orders
        .groupBy("a")
        .select("a, b.count as cnt");

// conversion to DataSet
DataSet<Row> result = tEnv.toDataSet(counts, Row.class);
result.print();

The Scala Table API is enabled by importing org.apache.flink.api.scala._ and org.apache.flink.table.api.scala._.

The following example shows how a Scala Table API program is constructed. Table attributes are referenced using Scala Symbols, which start with an apostrophe character (').

import org.apache.flink.api.scala._
import org.apache.flink.table.api._
import org.apache.flink.table.api.scala._

// environment configuration
val env = ExecutionEnvironment.getExecutionEnvironment
val tEnv = BatchTableEnvironment.create(env)

// register Orders table in table environment
// ...

// specify table program
val orders = tEnv.scan("Orders") // schema (a, b, c, rowtime)

val result = orders
               .groupBy('a)
               .select('a, 'b.count as 'cnt)
               .toDataSet[Row] // conversion to DataSet
               .print()

使用from pyflink.table import *来导入Python Table API。

下面这个例子演示了如何组织一个Python Table API程序,以及字符串形式的表达式用法。

from pyflink.table import *
from pyflink.dataset import *

# environment configuration
env = ExecutionEnvironment.get_execution_environment()
t_env = TableEnvironment.create(env, TableConfig())

# register Orders table and Result table sink in table environment
# ...

# specify table program
orders = t_env.scan("Orders")  # schema (a, b, c, rowtime)

orders.group_by("a").select("a, b.count as cnt").insert_into("result")

t_env.execute("python_job")

The next example shows a more complex Table API program. The program scans again the Orders table. It filters null values, normalizes the field a of type String, and calculates for each hour and product a the average billing amount b.

// environment configuration
// ...

// specify table program
Table orders = tEnv.scan("Orders"); // schema (a, b, c, rowtime)

Table result = orders
        .filter("a.isNotNull && b.isNotNull && c.isNotNull")
        .select("a.lowerCase() as a, b, rowtime")
        .window(Tumble.over("1.hour").on("rowtime").as("hourlyWindow"))
        .groupBy("hourlyWindow, a")
        .select("a, hourlyWindow.end as hour, b.avg as avgBillingAmount");
// environment configuration
// ...

// specify table program
val orders: Table = tEnv.scan("Orders") // schema (a, b, c, rowtime)

val result: Table = orders
        .filter('a.isNotNull && 'b.isNotNull && 'c.isNotNull)
        .select('a.lowerCase() as 'a, 'b, 'rowtime)
        .window(Tumble over 1.hour on 'rowtime as 'hourlyWindow)
        .groupBy('hourlyWindow, 'a)
        .select('a, 'hourlyWindow.end as 'hour, 'b.avg as 'avgBillingAmount)
# environment configuration
# ...

# specify table program
orders = t_env.scan("Orders")  # schema (a, b, c, rowtime)

result = orders.filter("a.isNotNull && b.isNotNull && c.isNotNull") \
               .select("a.lowerCase() as a, b, rowtime") \
               .window(Tumble.over("1.hour").on("rowtime").alias("hourlyWindow")) \
               .group_by("hourlyWindow, a") \
               .select("a, hourlyWindow.end as hour, b.avg as avgBillingAmount")

Since the Table API is a unified API for batch and streaming data, both example programs can be executed on batch and streaming inputs without any modification of the table program itself. In both cases, the program produces the same results given that streaming records are not late (see Streaming Concepts for details).

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Operations

The Table API supports the following operations. Please note that not all operations are available in both batch and streaming yet; they are tagged accordingly.

Scan, Projection, and Filter

Operators Description
Scan
Batch Streaming

Similar to the FROM clause in a SQL query. Performs a scan of a registered table.

Table orders = tableEnv.scan("Orders");
Select
Batch Streaming

Similar to a SQL SELECT statement. Performs a select operation.

Table orders = tableEnv.scan("Orders");
Table result = orders.select("a, c as d");

You can use star (*) to act as a wild card, selecting all of the columns in the table.

Table result = orders.select("*");
As
Batch Streaming

Renames fields.

Table orders = tableEnv.scan("Orders");
Table result = orders.as("x, y, z, t");
Where / Filter
Batch Streaming

Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate.

Table orders = tableEnv.scan("Orders");
Table result = orders.where("b === 'red'");
or
Table orders = tableEnv.scan("Orders");
Table result = orders.filter("a % 2 === 0");
Operators Description
Scan
Batch Streaming

Similar to the FROM clause in a SQL query. Performs a scan of a registered table.

val orders: Table = tableEnv.scan("Orders")
Select
Batch Streaming

Similar to a SQL SELECT statement. Performs a select operation.

val orders: Table = tableEnv.scan("Orders")
val result = orders.select('a, 'c as 'd)

You can use star (*) to act as a wild card, selecting all of the columns in the table.

val orders: Table = tableEnv.scan("Orders")
val result = orders.select('*)
As
Batch Streaming

Renames fields.

val orders: Table = tableEnv.scan("Orders").as('x, 'y, 'z, 't)
Where / Filter
Batch Streaming

Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate.

val orders: Table = tableEnv.scan("Orders")
val result = orders.filter('a % 2 === 0)
or
val orders: Table = tableEnv.scan("Orders")
val result = orders.where('b === "red")
操作 描述
Scan
批处理 流处理

类似于SQL请求中的FROM子句,将一个环境中已注册的表转换成Table对象。

orders = table_env.scan("Orders")
Select
批处理 流处理

类似于SQL请求中的SELECT子句,执行一个select操作。

orders = table_env.scan("Orders")
result = orders.select("a, c as d")

您可以使用星号 (*) 表示选择表中的所有列。

result = orders.select("*")
Alias
批处理 流处理

重命名字段。

orders = table_env.scan("Orders")
result = orders.alias("x, y, z, t")
Where / Filter
批处理 流处理

类似于SQL请求中的WHERE子句,过滤掉表中不满足条件的行。

orders = table_env.scan("Orders")
result = orders.where("b === 'red'")
or
orders = table_env.scan("Orders")
result = orders.filter("a % 2 === 0")

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Column Operations

Operators Description
AddColumns
Batch Streaming

Performs a field add operation. It will throw an exception if the added fields already exist.

Table orders = tableEnv.scan("Orders");
Table result = orders.addColumns("concat(c, 'sunny')");
AddOrReplaceColumns
Batch Streaming

Performs a field add operation. 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.

Table orders = tableEnv.scan("Orders");
Table result = orders.addOrReplaceColumns("concat(c, 'sunny') as desc");
DropColumns
Batch Streaming

Performs a field drop operation. The field expressions should be field reference expressions, and only existing fields can be dropped.

Table orders = tableEnv.scan("Orders");
Table result = orders.dropColumns("b, c");
RenameColumns
Batch Streaming

Performs a field rename operation. The field expressions should be alias expressions, and only the existing fields can be renamed.

Table orders = tableEnv.scan("Orders");
Table result = orders.renameColumns("b as b2, c as c2");
Operators Description
AddColumns
Batch Streaming

Performs a field add operation. It will throw an exception if the added fields already exist.

val orders = tableEnv.scan("Orders");
val result = orders.addColumns(concat('c, "Sunny"))
AddOrReplaceColumns
Batch Streaming

Performs a field add operation. 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.

val orders = tableEnv.scan("Orders");
val result = orders.addOrReplaceColumns(concat('c, "Sunny") as 'desc)
DropColumns
Batch Streaming

Performs a field drop operation. The field expressions should be field reference expressions, and only existing fields can be dropped.

val orders = tableEnv.scan("Orders");
val result = orders.dropColumns('b, 'c)
RenameColumns
Batch Streaming

Performs a field rename operation. The field expressions should be alias expressions, and only the existing fields can be renamed.

val orders = tableEnv.scan("Orders");
val result = orders.renameColumns('b as 'b2, 'c as 'c2)
操作 描述
AddColumns
批处理 流处理

执行新增字段操作。如果欲添加字段已经存在,将会抛出异常。

orders = table_env.scan("Orders")
result = orders.add_columns("concat(c, 'sunny')")
AddOrReplaceColumns
批处理 流处理

执行新增字段操作。如果欲添加字段已经存在,将会替换该字段。如果新增字段列表中有同名字段,取最靠后的为有效字段。

orders = table_env.scan("Orders")
result = orders.add_or_replace_columns("concat(c, 'sunny') as desc")
DropColumns
批处理 流处理

执行删除字段操作。参数必须是字段列表,并且必须是已经存在的字段才能被删除。

orders = table_env.scan("Orders")
result = orders.drop_columns("b, c")
RenameColumns
批处理 流处理

执行重命名字段操作。参数必须是字段别名(例:b as b2)列表,并且必须是已经存在的字段才能被重命名。

orders = table_env.scan("Orders")
result = orders.rename_columns("b as b2, c as c2")

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Aggregations

Operators Description
GroupBy Aggregation
Batch Streaming
Result Updating

Similar to a SQL GROUP BY clause. Groups the rows on the grouping keys with a following running aggregation operator to aggregate rows group-wise.

Table orders = tableEnv.scan("Orders");
Table result = orders.groupBy("a").select("a, b.sum as d");

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the type of aggregation and the number of distinct grouping keys. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

GroupBy Window Aggregation
Batch Streaming

Groups and aggregates a table on a group window and possibly one or more grouping keys.

Table orders = tableEnv.scan("Orders");
Table result = orders
    .window(Tumble.over("5.minutes").on("rowtime").as("w")) // define window
    .groupBy("a, w") // group by key and window
    .select("a, w.start, w.end, w.rowtime, b.sum as d"); // access window properties and aggregate
Over Window Aggregation
Streaming

Similar to a SQL OVER clause. Over window aggregates are computed for each row, based on a window (range) of preceding and succeeding rows. See the over windows section for more details.

Table orders = tableEnv.scan("Orders");
Table result = orders
    // define window
    .window(Over
      .partitionBy("a")
      .orderBy("rowtime")
      .preceding("UNBOUNDED_RANGE")
      .following("CURRENT_RANGE")
      .as("w"))
    .select("a, b.avg over w, b.max over w, b.min over w"); // sliding aggregate

Note: All aggregates must be defined over the same window, i.e., same partitioning, sorting, and range. Currently, only windows with PRECEDING (UNBOUNDED and bounded) to CURRENT ROW range are supported. Ranges with FOLLOWING are not supported yet. ORDER BY must be specified on a single time attribute.

Distinct Aggregation
Batch Streaming
Result Updating

Similar to a SQL DISTINCT aggregation clause such as COUNT(DISTINCT a). Distinct aggregation declares that an aggregation function (built-in or user-defined) is only applied on distinct input values. Distinct can be applied to GroupBy Aggregation, GroupBy Window Aggregation and Over Window Aggregation.

Table orders = tableEnv.scan("Orders");
// Distinct aggregation on group by
Table groupByDistinctResult = orders
    .groupBy("a")
    .select("a, b.sum.distinct as d");
// Distinct aggregation on time window group by
Table groupByWindowDistinctResult = orders
    .window(Tumble.over("5.minutes").on("rowtime").as("w")).groupBy("a, w")
    .select("a, b.sum.distinct as d");
// Distinct aggregation on over window
Table result = orders
    .window(Over
        .partitionBy("a")
        .orderBy("rowtime")
        .preceding("UNBOUNDED_RANGE")
        .as("w"))
    .select("a, b.avg.distinct over w, b.max over w, b.min over w");

User-defined aggregation function can also be used with DISTINCT modifiers. To calculate the aggregate results only for distinct values, simply add the distinct modifier towards the aggregation function.

Table orders = tEnv.scan("Orders");

// Use distinct aggregation for user-defined aggregate functions
tEnv.registerFunction("myUdagg", new MyUdagg());
orders.groupBy("users").select("users, myUdagg.distinct(points) as myDistinctResult");

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct fields. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Distinct
Batch Streaming
Result Updating

Similar to a SQL DISTINCT clause. Returns records with distinct value combinations.

Table orders = tableEnv.scan("Orders");
Table result = orders.distinct();

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct fields. Please provide a query configuration with valid retention interval to prevent excessive state size. If state cleaning is enabled, distinct have to emit messages to prevent too early state eviction of downstream operators which makes distinct contains result updating. See Query Configuration for details.

Operators Description
GroupBy Aggregation
Batch Streaming
Result Updating

Similar to a SQL GROUP BY clause. Groups the rows on the grouping keys with a following running aggregation operator to aggregate rows group-wise.

val orders: Table = tableEnv.scan("Orders")
val result = orders.groupBy('a).select('a, 'b.sum as 'd)

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the type of aggregation and the number of distinct grouping keys. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

GroupBy Window Aggregation
Batch Streaming

Groups and aggregates a table on a group window and possibly one or more grouping keys.

val orders: Table = tableEnv.scan("Orders")
val result: Table = orders
    .window(Tumble over 5.minutes on 'rowtime as 'w) // define window
    .groupBy('a, 'w) // group by key and window
    .select('a, w.start, 'w.end, 'w.rowtime, 'b.sum as 'd) // access window properties and aggregate
Over Window Aggregation
Streaming

Similar to a SQL OVER clause. Over window aggregates are computed for each row, based on a window (range) of preceding and succeeding rows. See the over windows section for more details.

val orders: Table = tableEnv.scan("Orders")
val result: Table = orders
    // define window
    .window(Over
      partitionBy 'a
      orderBy 'rowtime
      preceding UNBOUNDED_RANGE
      following CURRENT_RANGE
      as 'w)
    .select('a, 'b.avg over 'w, 'b.max over 'w, 'b.min over 'w) // sliding aggregate

Note: All aggregates must be defined over the same window, i.e., same partitioning, sorting, and range. Currently, only windows with PRECEDING (UNBOUNDED and bounded) to CURRENT ROW range are supported. Ranges with FOLLOWING are not supported yet. ORDER BY must be specified on a single time attribute.

Distinct Aggregation
Batch Streaming
Result Updating

Similar to a SQL DISTINCT AGGREGATION clause such as COUNT(DISTINCT a). Distinct aggregation declares that an aggregation function (built-in or user-defined) is only applied on distinct input values. Distinct can be applied to GroupBy Aggregation, GroupBy Window Aggregation and Over Window Aggregation.

val orders: Table = tableEnv.scan("Orders");
// Distinct aggregation on group by
val groupByDistinctResult = orders
    .groupBy('a)
    .select('a, 'b.sum.distinct as 'd)
// Distinct aggregation on time window group by
val groupByWindowDistinctResult = orders
    .window(Tumble over 5.minutes on 'rowtime as 'w).groupBy('a, 'w)
    .select('a, 'b.sum.distinct as 'd)
// Distinct aggregation on over window
val result = orders
    .window(Over
        partitionBy 'a
        orderBy 'rowtime
        preceding UNBOUNDED_RANGE
        as 'w)
    .select('a, 'b.avg.distinct over 'w, 'b.max over 'w, 'b.min over 'w)

User-defined aggregation function can also be used with DISTINCT modifiers. To calculate the aggregate results only for distinct values, simply add the distinct modifier towards the aggregation function.

val orders: Table = tEnv.scan("Orders");

// Use distinct aggregation for user-defined aggregate functions
val myUdagg = new MyUdagg();
orders.groupBy('users).select('users, myUdagg.distinct('points) as 'myDistinctResult);

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct fields. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Distinct
Batch

Similar to a SQL DISTINCT clause. Returns records with distinct value combinations.

val orders: Table = tableEnv.scan("Orders")
val result = orders.distinct()

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct fields. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

操作 描述
GroupBy Aggregation
批处理 流处理
结果持续更新

类似于SQL的GROUP BY子句。将数据按照指定字段进行分组,之后对各组内数据执行聚合操作。

orders = table_env.scan("Orders")
result = orders.group_by("a").select("a, b.sum as d")

注意: 对于流式查询,计算查询结果所需的状态(state)可能会无限增长,具体情况取决于聚合操作的类型和分组的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

GroupBy Window Aggregation
批处理 流处理

在一个窗口上分组和聚合数据,可包含其它分组字段。

orders = table_env.scan("Orders")
result = orders.window(Tumble.over("5.minutes").on("rowtime").alias("w")) \ 
               .group_by("a, w") \
               .select("a, w.start, w.end, w.rowtime, b.sum as d")
Over Window Aggregation
流处理

类似于SQL中的OVER开窗函数。Over窗口聚合对每一行都进行一次聚合计算,聚合的对象是以当前行的位置为基准,向前向后取一个区间范围内的所有数据。详情请见Over窗口一节。

orders = table_env.scan("Orders")
result = orders.over_window(Over.partition_by("a").order_by("rowtime")
                            .preceding("UNBOUNDED_RANGE").following("CURRENT_RANGE")
                            .alias("w")) \
    .select("a, b.avg over w, b.max over w, b.min over w")

注意: 所有的聚合操作必须在同一个窗口上定义,即分组,排序,范围等属性必须一致。目前,窗口区间范围的向前(PRECEDING)取值没有限制,可以为无界(UNBOUNDED),但是向后(FOLLOWING)只支持当前行(CURRENT ROW),其它向后范围取值暂不支持。排序(ORDER BY)属性必须指定单个时间属性

Distinct Aggregation
批处理 流处理
结果持续更新

类似于SQL聚合函数中的的DISTINCT关键字比如COUNT(DISTINCT a)。带有distinct标记的聚合函数只会接受不重复的输入,重复输入将被丢弃。这个去重特性可以在分组聚合(GroupBy Aggregation)分组窗口聚合(GroupBy Window Aggregation)以及Over窗口聚合(Over Window Aggregation)上使用。

orders = table_env.scan("Orders")
# Distinct aggregation on group by
group_by_distinct_result = orders.group_by("a") \
                                 .select("a, b.sum.distinct as d")
# Distinct aggregation on time window group by
group_by_window_distinct_result = orders.window(
    Tumble.over("5.minutes").on("rowtime").alias("w")).groupBy("a, w") \
    .select("a, b.sum.distinct as d")
# Distinct aggregation on over window
result = orders.over_window(Over
                       .partition_by("a")
                       .order_by("rowtime")
                       .preceding("UNBOUNDED_RANGE")
                       .alias("w")) \
                       .select(
                       "a, b.avg.distinct over w, b.max over w, b.min over w")

注意: 对于流式查询,计算查询结果所需的状态(state)可能会无限增长,具体情况取决于执行去重判断时参与判断的字段的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

Distinct
批处理 流处理
结果持续更新

类似于SQL中的DISTINCT子句。返回去重后的数据。

orders = table_env.scan("Orders")
result = orders.distinct()

注意: 对于流式查询,计算查询结果所需的状态(state)可能会无限增长,具体情况取决于执行去重判断时参与判断的字段的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

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Joins

Operators Description
Inner Join
Batch Streaming

Similar to a SQL JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined through join operator or using a where or filter operator.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.join(right).where("a = d").select("a, b, e");

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Outer Join
Batch Streaming Result Updating

Similar to SQL LEFT/RIGHT/FULL OUTER JOIN clauses. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");

Table leftOuterResult = left.leftOuterJoin(right, "a = d").select("a, b, e");
Table rightOuterResult = left.rightOuterJoin(right, "a = d").select("a, b, e");
Table fullOuterResult = left.fullOuterJoin(right, "a = d").select("a, b, e");

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Time-windowed Join
Batch Streaming

Note: Time-windowed joins are a subset of regular joins that can be processed in a streaming fashion.

A time-windowed join requires at least one equi-join predicate and a join condition that bounds the time on both sides. Such a condition can be defined by two appropriate range predicates (<, <=, >=, >) or a single equality predicate that compares time attributes of the same type (i.e., processing time or event time) of both input tables.

For example, the following predicates are valid window join conditions:

  • ltime === rtime
  • ltime >= rtime && ltime < rtime + 10.minutes
Table left = tableEnv.fromDataSet(ds1, "a, b, c, ltime.rowtime");
Table right = tableEnv.fromDataSet(ds2, "d, e, f, rtime.rowtime");

Table result = left.join(right)
  .where("a = d && ltime >= rtime - 5.minutes && ltime < rtime + 10.minutes")
  .select("a, b, e, ltime");
Inner Join with Table Function (UDTF)
Batch Streaming

Joins a table with the results of a table function. Each row of the left (outer) table is joined with all rows produced by the corresponding call of the table function. A row of the left (outer) table is dropped, if its table function call returns an empty result.

// register User-Defined Table Function
TableFunction<String> split = new MySplitUDTF();
tableEnv.registerFunction("split", split);

// join
Table orders = tableEnv.scan("Orders");
Table result = orders
    .joinLateral("split(c).as(s, t, v)")
    .select("a, b, s, t, v");
Left Outer Join with Table Function (UDTF)
Batch Streaming

Joins a table with the results of a table function. Each row of the left (outer) table is joined with all rows produced by the corresponding call of the table function. If a table function call returns an empty result, the corresponding outer row is preserved and the result padded with null values.

Note: Currently, the predicate of a table function left outer join can only be empty or literal true.

// register User-Defined Table Function
TableFunction<String> split = new MySplitUDTF();
tableEnv.registerFunction("split", split);

// join
Table orders = tableEnv.scan("Orders");
Table result = orders
    .leftOuterJoinLateral("split(c).as(s, t, v)")
    .select("a, b, s, t, v");
Join with Temporal Table
Streaming

Temporal tables are tables that track changes over time.

A temporal table function provides access to the state of a temporal table at a specific point in time. The syntax to join a table with a temporal table function is the same as in Inner Join with Table Function.

Currently only inner joins with temporal tables are supported.

Table ratesHistory = tableEnv.scan("RatesHistory");

// register temporal table function with a time attribute and primary key
TemporalTableFunction rates = ratesHistory.createTemporalTableFunction(
    "r_proctime",
    "r_currency");
tableEnv.registerFunction("rates", rates);

// join with "Orders" based on the time attribute and key
Table orders = tableEnv.scan("Orders");
Table result = orders
    .joinLateral("rates(o_proctime)", "o_currency = r_currency")

For more information please check the more detailed temporal tables concept description.

Operators Description
Inner Join
Batch Streaming

Similar to a SQL JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined through join operator or using a where or filter operator.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'd, 'e, 'f)
val result = left.join(right).where('a === 'd).select('a, 'b, 'e)

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Outer Join
Batch Streaming Result Updating

Similar to SQL LEFT/RIGHT/FULL OUTER JOIN clauses. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.

val left = tableEnv.fromDataSet(ds1, 'a, 'b, 'c)
val right = tableEnv.fromDataSet(ds2, 'd, 'e, 'f)

val leftOuterResult = left.leftOuterJoin(right, 'a === 'd).select('a, 'b, 'e)
val rightOuterResult = left.rightOuterJoin(right, 'a === 'd).select('a, 'b, 'e)
val fullOuterResult = left.fullOuterJoin(right, 'a === 'd).select('a, 'b, 'e)

Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Time-windowed Join
Batch Streaming

Note: Time-windowed joins are a subset of regular joins that can be processed in a streaming fashion.

A time-windowed join requires at least one equi-join predicate and a join condition that bounds the time on both sides. Such a condition can be defined by two appropriate range predicates (<, <=, >=, >) or a single equality predicate that compares time attributes of the same type (i.e., processing time or event time) of both input tables.

For example, the following predicates are valid window join conditions:

  • 'ltime === 'rtime
  • 'ltime >= 'rtime && 'ltime < 'rtime + 10.minutes
val left = ds1.toTable(tableEnv, 'a, 'b, 'c, 'ltime.rowtime)
val right = ds2.toTable(tableEnv, 'd, 'e, 'f, 'rtime.rowtime)

val result = left.join(right)
  .where('a === 'd && 'ltime >= 'rtime - 5.minutes && 'ltime < 'rtime + 10.minutes)
  .select('a, 'b, 'e, 'ltime)
Inner Join with Table Function (UDTF)
Batch Streaming

Joins a table with the results of a table function. Each row of the left (outer) table is joined with all rows produced by the corresponding call of the table function. A row of the left (outer) table is dropped, if its table function call returns an empty result.

// instantiate User-Defined Table Function
val split: TableFunction[_] = new MySplitUDTF()

// join
val result: Table = table
    .joinLateral(split('c) as ('s, 't, 'v))
    .select('a, 'b, 's, 't, 'v)
Left Outer Join with Table Function (UDTF)
Batch Streaming

Joins a table with the results of a table function. Each row of the left (outer) table is joined with all rows produced by the corresponding call of the table function. If a table function call returns an empty result, the corresponding outer row is preserved and the result padded with null values.

Note: Currently, the predicate of a table function left outer join can only be empty or literal true.

// instantiate User-Defined Table Function
val split: TableFunction[_] = new MySplitUDTF()

// join
val result: Table = table
    .leftOuterJoinLateral(split('c) as ('s, 't, 'v))
    .select('a, 'b, 's, 't, 'v)
Join with Temporal Table
Streaming

Temporal tables are tables that track their changes over time.

A temporal table function provides access to the state of a temporal table at a specific point in time. The syntax to join a table with a temporal table function is the same as in Inner Join with Table Function.

Currently only inner joins with temporal tables are supported.

val ratesHistory = tableEnv.scan("RatesHistory")

// register temporal table function with a time attribute and primary key
val rates = ratesHistory.createTemporalTableFunction('r_proctime, 'r_currency)

// join with "Orders" based on the time attribute and key
val orders = tableEnv.scan("Orders")
val result = orders
    .joinLateral(rates('o_rowtime), 'r_currency === 'o_currency)

For more information please check the more detailed temporal tables concept description.

操作 描述
Inner Join
批处理 流处理

类似于SQL的JOIN子句。对两张表执行内连接操作。两张表必须具有不同的字段名称,并且必须在join方法或者随后的where或filter方法中定义至少一个等值连接条件。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("d, e, f")
result = left.join(right).where("a = d").select("a, b, e")

注意: 对于流式查询,计算查询结果所需的状态(state)可能会无限增长,具体取决于不重复的输入行的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

Outer Join
批处理 流处理 结果持续更新

类似于SQL的LEFT/RIGHT/FULL OUTER JOIN子句。对两张表执行外连接操作。两张表必须具有不同的字段名称,并且必须定义至少一个等值连接条件。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("d, e, f")

left_outer_result = left.left_outer_join(right, "a = d").select("a, b, e")
right_outer_result = left.right_outer_join(right, "a = d").select("a, b, e")
full_outer_result = left.full_outer_join(right, "a = d").select("a, b, e")

注意: 对于流式查询,计算查询结果所需的状态(state)可能会无限增长,具体取决于不重复的输入行的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

Time-windowed Join
批处理 流处理

Python API暂不支持。

Inner Join with Table Function (UDTF)
批处理 流处理

将一张表与一个表函数的执行结果执行内连接操作。左表的每一行都会进行一次表函数调用,调用将会返回0个,1个或多个结果,再与这些结果执行连接操作。如果一行数据对应的表函数调用返回了一个空的结果集,则这行数据会被丢弃。

# register Java User-Defined Table Function
table_env.register_java_function("split", "com.my.udf.MySplitUDTF")

# join
orders = table_env.scan("Orders")
result = orders.join_lateral("split(c).as(s, t, v)").select("a, b, s, t, v")
Left Outer Join with Table Function (UDTF)
批处理 流处理

将一张表与一个表函数的执行结果执行左连接操作。左表的每一行都会进行一次表函数调用,调用将会返回0个,1个或多个结果,再与这些结果执行连接操作。如果一行数据对应的表函数调用返回了一个空的结果集,这行数据依然会被保留,对应的右表数值用null(python为None)填充。

注意:目前,表函数的左连接操作的连接条件(join predicate)只能为空或者为"true"常量。

# register Java User-Defined Table Function
table_env.register_java_function("split", "com.my.udf.MySplitUDTF")

# join
orders = table_env.scan("Orders")
result = orders.left_outer_join_lateral("split(c).as(s, t, v)").select("a, b, s, t, v")
Join with Temporal Table
流处理

Python API暂不支持。

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Set Operations

Operators Description
Union
Batch

Similar to a SQL UNION clause. Unions two tables with duplicate records removed. Both tables must have identical field types.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.union(right);
UnionAll
Batch Streaming

Similar to a SQL UNION ALL clause. Unions two tables. Both tables must have identical field types.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.unionAll(right);
Intersect
Batch

Similar to a SQL INTERSECT clause. Intersect returns records that exist in both tables. If a record is present one or both tables more than once, it is returned just once, i.e., the resulting table has no duplicate records. Both tables must have identical field types.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.intersect(right);
IntersectAll
Batch

Similar to a SQL INTERSECT ALL clause. 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. Both tables must have identical field types.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.intersectAll(right);
Minus
Batch

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.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.minus(right);
MinusAll
Batch

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.

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.minusAll(right);
In
Batch Streaming

Similar to a SQL IN clause. In returns true if an expression exists in a given table sub-query. The sub-query table must consist of one column. This column must have the same data type as the expression.

Table left = ds1.toTable(tableEnv, "a, b, c");
Table right = ds2.toTable(tableEnv, "a");

// using implicit registration
Table result = left.select("a, b, c").where("a.in(" + right + ")");

// using explicit registration
tableEnv.registerTable("RightTable", right);
Table result = left.select("a, b, c").where("a.in(RightTable)");

Note: For streaming queries the operation is rewritten in a join and group operation. The required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Operators Description
Union
Batch

Similar to a SQL UNION clause. Unions two tables with duplicate records removed, both tables must have identical field types.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'a, 'b, 'c)
val result = left.union(right)
UnionAll
Batch Streaming

Similar to a SQL UNION ALL clause. Unions two tables, both tables must have identical field types.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'a, 'b, 'c)
val result = left.unionAll(right)
Intersect
Batch

Similar to a SQL INTERSECT clause. 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. Both tables must have identical field types.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'e, 'f, 'g)
val result = left.intersect(right)
IntersectAll
Batch

Similar to a SQL INTERSECT ALL clause. 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. Both tables must have identical field types.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'e, 'f, 'g)
val result = left.intersectAll(right)
Minus
Batch

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.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'a, 'b, 'c)
val result = left.minus(right)
MinusAll
Batch

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.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'a, 'b, 'c)
val result = left.minusAll(right)
In
Batch Streaming

Similar to a SQL IN clause. In returns true if an expression exists in a given table sub-query. The sub-query table must consist of one column. This column must have the same data type as the expression.

val left = ds1.toTable(tableEnv, 'a, 'b, 'c)
val right = ds2.toTable(tableEnv, 'a)
val result = left.select('a, 'b, 'c).where('a.in(right))

Note: For streaming queries the operation is rewritten in a join and group operation. The required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.

Operators Description
Union
批处理

类似于SQL的UNION子句。将两张表组合成一张表,这张表拥有二者去除重复后的全部数据。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.union(right)
UnionAll
批处理 流处理

类似于SQL的UNION ALL子句。将两张表组合成一张表,这张表拥有二者的全部数据。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.union_all(right)
Intersect
批处理

类似于SQL的INTERSECT子句。Intersect返回在两张表中都存在的数据。如果一个记录在两张表中不止出现一次,则只返回一次,即结果表没有重复记录。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.intersect(right)
IntersectAll
批处理

类似于SQL的INTERSECT ALL子句。IntersectAll返回在两张表中都存在的数据。如果一个记录在两张表中不止出现一次,则按照它在两张表中都出现的次数返回,即结果表可能包含重复数据。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.intersect_all(right)
Minus
批处理

类似于SQL的EXCEPT子句。Minus返回仅存在于左表,不存在于右表中的数据。左表中的相同数据只会返回一次,即数据会被去重。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.minus(right);
MinusAll
批处理

类似于SQL的EXCEPT ALL子句。MinusAll返回仅存在于左表,不存在于右表中的数据。如果一条数据在左表中出现了n次,在右表中出现了m次,最终这条数据将会被返回(n - m)次,即按右表中出现的次数来移除数据。两张表的字段和类型必须完全一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a, b, c")
result = left.minus_all(right)
In
批处理 流处理

类似于SQL的IN子句。如果In左边表达式的值在给定的子查询结果中则返回true。子查询的结果必须为单列。此列数据类型必须和表达式一致。

left = table_env.scan("Source1").select("a, b, c")
right = table_env.scan("Source2").select("a")

# using implicit registration
result = left.select("a, b, c").where("a.in(%s)" % right)

# using explicit registration
table_env.register_table("RightTable", right)
result = left.select("a, b, c").where("a.in(RightTable)")

注意: 对于流式查询,这个操作会被替换成一个连接操作和一个分组操作。计算查询结果所需的状态(state)可能会无限增长,具体取决于不重复的输入行的数量。您可能需要在查询配置中设置状态保留时间,以防止状态过大。详情请看查询配置

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OrderBy, Offset & Fetch

Operators Description
Order By
Batch

Similar to a SQL ORDER BY clause. Returns records globally sorted across all parallel partitions.

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.orderBy("a.asc");
Offset & Fetch
Batch

Similar to the SQL OFFSET and FETCH clauses. Offset and Fetch limit the number of records returned from a sorted result. Offset and Fetch are technically part of the Order By operator and thus must be preceded by it.

Table in = tableEnv.fromDataSet(ds, "a, b, c");

// returns the first 5 records from the sorted result
Table result1 = in.orderBy("a.asc").fetch(5);

// skips the first 3 records and returns all following records from the sorted result
Table result2 = in.orderBy("a.asc").offset(3);

// skips the first 10 records and returns the next 5 records from the sorted result
Table result3 = in.orderBy("a.asc").offset(10).fetch(5);
Operators Description
Order By
Batch

Similar to a SQL ORDER BY clause. Returns records globally sorted across all parallel partitions.

val in = ds.toTable(tableEnv, 'a, 'b, 'c)
val result = in.orderBy('a.asc)
Offset & Fetch
Batch

Similar to the SQL OFFSET and FETCH clauses. Offset and Fetch limit the number of records returned from a sorted result. Offset and Fetch are technically part of the Order By operator and thus must be preceded by it.

val in = ds.toTable(tableEnv, 'a, 'b, 'c)

// returns the first 5 records from the sorted result
val result1: Table = in.orderBy('a.asc).fetch(5)

// skips the first 3 records and returns all following records from the sorted result
val result2: Table = in.orderBy('a.asc).offset(3)

// skips the first 10 records and returns the next 5 records from the sorted result
val result3: Table = in.orderBy('a.asc).offset(10).fetch(5)
操作 描述
Order By
批处理

类似于SQL的ORDER BY子句。返回包括所有子并发分区内所有数据的全局排序结果。

in = table_env.scan("Source1").select("a, b, c")
result = in.order_by("a.asc")
Offset & Fetch
批处理

类似于SQL的OFFSET和FETCH子句。Offset和Fetch从已排序的结果中返回指定数量的数据。Offset和Fetch在技术上是Order By操作的一部分,因此必须紧跟其后出现。

in = table_env.scan("Source1").select("a, b, c")

# returns the first 5 records from the sorted result
result1 = in.order_by("a.asc").fetch(5)

# skips the first 3 records and returns all following records from the sorted result
result2 = in.order_by("a.asc").offset(3)

# skips the first 10 records and returns the next 5 records from the sorted result
result3 = in.order_by("a.asc").offset(10).fetch(5)

Insert

Operators Description
Insert Into
Batch Streaming

Similar to the INSERT INTO clause in a SQL query. Performs a insertion into a registered output table.

Output tables must be registered in the TableEnvironment (see Register a TableSink). Moreover, the schema of the registered table must match the schema of the query.

Table orders = tableEnv.scan("Orders");
orders.insertInto("OutOrders");
Operators Description
Insert Into
Batch Streaming

Similar to the INSERT INTO clause in a SQL query. Performs a insertion into a registered output table.

Output tables must be registered in the TableEnvironment (see Register a TableSink). Moreover, the schema of the registered table must match the schema of the query.

val orders: Table = tableEnv.scan("Orders")
orders.insertInto("OutOrders")
操作 描述
Insert Into
批处理 流处理

类似于SQL请求中的INSERT INTO子句。将数据输出到一个已注册的输出表中。

输出表必须先在TableEnvironment中注册(详见注册一个TableSink)。此外,注册的表的模式(schema)必须和请求的结果的模式(schema)相匹配。

orders = table_env.scan("Orders");
orders.insert_into("OutOrders");

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Group Windows

Group window aggregates group rows into finite groups based on time or row-count intervals and evaluate aggregation functions once per group. For batch tables, windows are a convenient shortcut to group records by time intervals.

Windows are defined using the window(w: GroupWindow) clause and require an alias, which is specified using the as clause. In order to group a table by a window, the window alias must be referenced in the groupBy(...) clause like a regular grouping attribute. The following example shows how to define a window aggregation on a table.

Table table = input
  .window([GroupWindow w].as("w"))  // define window with alias w
  .groupBy("w")  // group the table by window w
  .select("b.sum");  // aggregate
val table = input
  .window([w: GroupWindow] as 'w)  // define window with alias w
  .groupBy('w)   // group the table by window w
  .select('b.sum)  // aggregate
# define window with alias w, group the table by window w, then aggregate
table = input.window([GroupWindow w].alias("w")) \
             .group_by("w").select("b.sum")

In streaming environments, window aggregates can only be computed in parallel if they group on one or more attributes in addition to the window, i.e., the groupBy(...) clause references a window alias and at least one additional attribute. A groupBy(...) clause that only references a window alias (such as in the example above) can only be evaluated by a single, non-parallel task. The following example shows how to define a window aggregation with additional grouping attributes.

Table table = input
  .window([GroupWindow w].as("w"))  // define window with alias w
  .groupBy("w, a")  // group the table by attribute a and window w
  .select("a, b.sum");  // aggregate
val table = input
  .window([w: GroupWindow] as 'w) // define window with alias w
  .groupBy('w, 'a)  // group the table by attribute a and window w
  .select('a, 'b.sum)  // aggregate
# define window with alias w, group the table by attribute a and window w,
# then aggregate
table = input.window([GroupWindow w].alias("w")) \
             .group_by("w, a").select("b.sum")

Window properties such as the start, end, or rowtime timestamp of a time window can be added in the select statement as a property of the window alias as w.start, w.end, and w.rowtime, respectively. The window start and rowtime timestamps are the inclusive lower and upper window boundaries. In contrast, the window end timestamp is the exclusive upper window boundary. For example a tumbling window of 30 minutes that starts at 2pm would have 14:00:00.000 as start timestamp, 14:29:59.999 as rowtime timestamp, and 14:30:00.000 as end timestamp.

Table table = input
  .window([GroupWindow w].as("w"))  // define window with alias w
  .groupBy("w, a")  // group the table by attribute a and window w
  .select("a, w.start, w.end, w.rowtime, b.count"); // aggregate and add window start, end, and rowtime timestamps
val table = input
  .window([w: GroupWindow] as 'w)  // define window with alias w
  .groupBy('w, 'a)  // group the table by attribute a and window w
  .select('a, 'w.start, 'w.end, 'w.rowtime, 'b.count) // aggregate and add window start, end, and rowtime timestamps
# define window with alias w, group the table by attribute a and window w,
# then aggregate and add window start, end, and rowtime timestamps
table = input.window([GroupWindow w].alias("w")) \
             .group_by("w, a") \
             .select("a, w.start, w.end, w.rowtime, b.count")

The Window parameter defines how rows are mapped to windows. Window is not an interface that users can implement. Instead, the Table API provides a set of predefined Window classes with specific semantics, which are translated into underlying DataStream or DataSet operations. The supported window definitions are listed below.

Tumble (Tumbling Windows)

A tumbling window assigns rows to non-overlapping, continuous windows of fixed length. For example, a tumbling window of 5 minutes groups rows in 5 minutes intervals. Tumbling windows can be defined on event-time, processing-time, or on a row-count.

Tumbling windows are defined by using the Tumble class as follows:

Method Description
over Defines the length the window, either as time or row-count interval.
on The time attribute to group (time interval) or sort (row count) on. For batch queries this might be any Long or Timestamp attribute. For streaming queries this must be a declared event-time or processing-time time attribute.
as Assigns an alias to the window. The alias is used to reference the window in the following groupBy() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause.
// Tumbling Event-time Window
.window(Tumble.over("10.minutes").on("rowtime").as("w"));

// Tumbling Processing-time Window (assuming a processing-time attribute "proctime")
.window(Tumble.over("10.minutes").on("proctime").as("w"));

// Tumbling Row-count Window (assuming a processing-time attribute "proctime")
.window(Tumble.over("10.rows").on("proctime").as("w"));
// Tumbling Event-time Window
.window(Tumble over 10.minutes on 'rowtime as 'w)

// Tumbling Processing-time Window (assuming a processing-time attribute "proctime")
.window(Tumble over 10.minutes on 'proctime as 'w)

// Tumbling Row-count Window (assuming a processing-time attribute "proctime")
.window(Tumble over 10.rows on 'proctime as 'w)
# Tumbling Event-time Window
.window(Tumble.over("10.minutes").on("rowtime").alias("w"))

# Tumbling Processing-time Window (assuming a processing-time attribute "proctime")
.window(Tumble.over("10.minutes").on("proctime").alias("w"))

# Tumbling Row-count Window (assuming a processing-time attribute "proctime")
.window(Tumble.over("10.rows").on("proctime").alias("w"));

Slide (Sliding Windows)

A sliding window has a fixed size and slides by a specified slide interval. If the slide interval is smaller than the window size, sliding windows are overlapping. Thus, rows can be assigned to multiple windows. For example, a sliding window of 15 minutes size and 5 minute slide interval assigns each row to 3 different windows of 15 minute size, which are evaluated in an interval of 5 minutes. Sliding windows can be defined on event-time, processing-time, or on a row-count.

Sliding windows are defined by using the Slide class as follows:

Method Description
over Defines the length of the window, either as time or row-count interval.
every Defines the slide interval, either as time or row-count interval. The slide interval must be of the same type as the size interval.
on The time attribute to group (time interval) or sort (row count) on. For batch queries this might be any Long or Timestamp attribute. For streaming queries this must be a declared event-time or processing-time time attribute.
as Assigns an alias to the window. The alias is used to reference the window in the following groupBy() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause.
// Sliding Event-time Window
.window(Slide.over("10.minutes").every("5.minutes").on("rowtime").as("w"));

// Sliding Processing-time window (assuming a processing-time attribute "proctime")
.window(Slide.over("10.minutes").every("5.minutes").on("proctime").as("w"));

// Sliding Row-count window (assuming a processing-time attribute "proctime")
.window(Slide.over("10.rows").every("5.rows").on("proctime").as("w"));
// Sliding Event-time Window
.window(Slide over 10.minutes every 5.minutes on 'rowtime as 'w)

// Sliding Processing-time window (assuming a processing-time attribute "proctime")
.window(Slide over 10.minutes every 5.minutes on 'proctime as 'w)

// Sliding Row-count window (assuming a processing-time attribute "proctime")
.window(Slide over 10.rows every 5.rows on 'proctime as 'w)
# Sliding Event-time Window
.window(Slide.over("10.minutes").every("5.minutes").on("rowtime").alias("w"))

# Sliding Processing-time window (assuming a processing-time attribute "proctime")
.window(Slide.over("10.minutes").every("5.minutes").on("proctime").alias("w"))

# Sliding Row-count window (assuming a processing-time attribute "proctime")
.window(Slide.over("10.rows").every("5.rows").on("proctime").alias("w"))

Session (Session Windows)

Session windows do not have a fixed size but their bounds are defined by an interval of inactivity, i.e., a session window is closes if no event appears for a defined gap period. For example a session window with a 30 minute gap starts when a row is observed after 30 minutes inactivity (otherwise the row would be added to an existing window) and is closed if no row is added within 30 minutes. Session windows can work on event-time or processing-time.

A session window is defined by using the Session class as follows:

Method Description
withGap Defines the gap between two windows as time interval.
on The time attribute to group (time interval) or sort (row count) on. For batch queries this might be any Long or Timestamp attribute. For streaming queries this must be a declared event-time or processing-time time attribute.
as Assigns an alias to the window. The alias is used to reference the window in the following groupBy() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause.
// Session Event-time Window
.window(Session.withGap("10.minutes").on("rowtime").as("w"));

// Session Processing-time Window (assuming a processing-time attribute "proctime")
.window(Session.withGap("10.minutes").on("proctime").as("w"));
// Session Event-time Window
.window(Session withGap 10.minutes on 'rowtime as 'w)

// Session Processing-time Window (assuming a processing-time attribute "proctime")
.window(Session withGap 10.minutes on 'proctime as 'w)
# Session Event-time Window
.window(Session.with_gap("10.minutes").on("rowtime").alias("w"))

# Session Processing-time Window (assuming a processing-time attribute "proctime")
.window(Session.with_gap("10.minutes").on("proctime").alias("w"))

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Over Windows

Over window aggregates are known from standard SQL (OVER clause) and defined in the SELECT clause of a query. Unlike group windows, which are specified in the GROUP BY clause, over windows do not collapse rows. Instead over window aggregates compute an aggregate for each input row over a range of its neighboring rows.

Over windows are defined using the window(w: OverWindow*) clause (在Python API中为over_window(*OverWindow)) and referenced via an alias in the select() method. The following example shows how to define an over window aggregation on a table.

Table table = input
  .window([OverWindow w].as("w"))           // define over window with alias w
  .select("a, b.sum over w, c.min over w"); // aggregate over the over window w
val table = input
  .window([w: OverWindow] as 'w)              // define over window with alias w
  .select('a, 'b.sum over 'w, 'c.min over 'w) // aggregate over the over window w
# define over window with alias w and aggregate over the over window w
table = input.over_window([OverWindow w].alias("w")) \
    .select("a, b.sum over w, c.min over w")

The OverWindow defines a range of rows over which aggregates are computed. OverWindow is not an interface that users can implement. Instead, the Table API provides the Over class to configure the properties of the over window. Over windows can be defined on event-time or processing-time and on ranges specified as time interval or row-count. The supported over window definitions are exposed as methods on Over (and other classes) and are listed below:

Method Required Description
partitionBy Optional

Defines a partitioning of the input on one or more attributes. Each partition is individually sorted and aggregate functions are applied to each partition separately.

Note: In streaming environments, over window aggregates can only be computed in parallel if the window includes a partition by clause. Without partitionBy(...) the stream is processed by a single, non-parallel task.

orderBy Required

Defines the order of rows within each partition and thereby the order in which the aggregate functions are applied to rows.

Note: For streaming queries this must be a declared event-time or processing-time time attribute. Currently, only a single sort attribute is supported.

preceding Optional

Defines the interval of rows that are included in the window and precede the current row. The interval can either be specified as time or row-count interval.

Bounded over windows are specified with the size of the interval, e.g., 10.minutes for a time interval or 10.rows for a row-count interval.

Unbounded over windows are specified using a constant, i.e., UNBOUNDED_RANGE for a time interval or UNBOUNDED_ROW for a row-count interval. Unbounded over windows start with the first row of a partition.

If the preceding clause is omitted, UNBOUNDED_RANGE and CURRENT_RANGE are used as the default preceding and following for the window.

following Optional

Defines the window interval of rows that are included in the window and follow the current row. The interval must be specified in the same unit as the preceding interval (time or row-count).

At the moment, over windows with rows following the current row are not supported. Instead you can specify one of two constants:

  • CURRENT_ROW sets the upper bound of the window to the current row.
  • CURRENT_RANGE sets the upper bound of the window to sort key of the current row, i.e., all rows with the same sort key as the current row are included in the window.

If the following clause is omitted, the upper bound of a time interval window is defined as CURRENT_RANGE and the upper bound of a row-count interval window is defined as CURRENT_ROW.

as Required

Assigns an alias to the over window. The alias is used to reference the over window in the following select() clause.

Note: Currently, all aggregation functions in the same select() call must be computed of the same over window.

Unbounded Over Windows

// Unbounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("unbounded_range").as("w"));

// Unbounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("unbounded_range").as("w"));

// Unbounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("unbounded_row").as("w"));

// Unbounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("unbounded_row").as("w"));
// Unbounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over partitionBy 'a orderBy 'rowtime preceding UNBOUNDED_RANGE as 'w)

// Unbounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over partitionBy 'a orderBy 'proctime preceding UNBOUNDED_RANGE as 'w)

// Unbounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over partitionBy 'a orderBy 'rowtime preceding UNBOUNDED_ROW as 'w)

// Unbounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over partitionBy 'a orderBy 'proctime preceding UNBOUNDED_ROW as 'w)
# Unbounded Event-time over window (assuming an event-time attribute "rowtime")
.over_window(Over.partition_by("a").order_by("rowtime").preceding("unbounded_range").alias("w"))

# Unbounded Processing-time over window (assuming a processing-time attribute "proctime")
.over_window(Over.partition_by("a").order_by("proctime").preceding("unbounded_range").alias("w"))

# Unbounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.over_window(Over.partition_by("a").order_by("rowtime").preceding("unbounded_row").alias("w"))
 
# Unbounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.over_window(Over.partition_by("a").order_by("proctime").preceding("unbounded_row").alias("w"))

Bounded Over Windows

// Bounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("1.minutes").as("w"))

// Bounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("1.minutes").as("w"))

// Bounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("10.rows").as("w"))

// Bounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("10.rows").as("w"))
// Bounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over partitionBy 'a orderBy 'rowtime preceding 1.minutes as 'w)

// Bounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over partitionBy 'a orderBy 'proctime preceding 1.minutes as 'w)

// Bounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over partitionBy 'a orderBy 'rowtime preceding 10.rows as 'w)

// Bounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over partitionBy 'a orderBy 'proctime preceding 10.rows as 'w)
# Bounded Event-time over window (assuming an event-time attribute "rowtime")
.over_window(Over.partition_by("a").order_by("rowtime").preceding("1.minutes").alias("w"))

# Bounded Processing-time over window (assuming a processing-time attribute "proctime")
.over_window(Over.partition_by("a").order_by("proctime").preceding("1.minutes").alias("w"))

# Bounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.over_window(Over.partition_by("a").order_by("rowtime").preceding("10.rows").alias("w"))
 
# Bounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.over_window(Over.partition_by("a").order_by("proctime").preceding("10.rows").alias("w"))

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Row-based Operations

The row-based operations generate outputs with multiple columns.

Operators Description
Map
Batch Streaming

Performs a map operation with a user-defined scalar function or built-in scalar function. The output will be flattened if the output type is a composite type.

public class MyMapFunction extends ScalarFunction {
    public Row eval(String a) {
        return Row.of(a, "pre-" + a);
    }

    @Override
    public TypeInformation<?> getResultType(Class<?>[] signature) {
        return Types.ROW(Types.STRING(), Types.STRING());
    }
}

ScalarFunction func = new MyMapFunction();
tableEnv.registerFunction("func", func);

Table table = input
  .map("func(c)").as("a, b")
FlatMap
Batch Streaming

Performs a flatMap operation with a table function.

public class MyFlatMapFunction extends TableFunction<Row> {

    public void eval(String str) {
        if (str.contains("#")) {
            String[] array = str.split("#");
            for (int i = 0; i < array.length; ++i) {
                collect(Row.of(array[i], array[i].length()));
            }
        }
    }

    @Override
    public TypeInformation<Row> getResultType() {
        return Types.ROW(Types.STRING(), Types.INT());
    }
}

TableFunction func = new MyFlatMapFunction();
tableEnv.registerFunction("func", func);

Table table = input
  .flatMap("func(c)").as("a, b")
Aggregate
Batch Streaming Result Updating

Performs an aggregate operation with an aggregate function. You have to close the "aggregate" with a select statement and the select statement does not support aggregate functions. The output of aggregate will be flattened if the output type is a composite type.

public class MyMinMaxAcc {
    public int min = 0;
    public int max = 0;
}

public class MyMinMax extends AggregateFunction<Row, MyMinMaxAcc> {

    public void accumulate(MyMinMaxAcc acc, int value) {
        if (value < acc.min) {
            acc.min = value;
        }
        if (value > acc.max) {
            acc.max = value;
        }
    }

    @Override
    public MyMinMaxAcc createAccumulator() {
        return new MyMinMaxAcc();
    }

    public void resetAccumulator(MyMinMaxAcc acc) {
        acc.min = 0;
        acc.max = 0;
    }

    @Override
    public Row getValue(MyMinMaxAcc acc) {
        return Row.of(acc.min, acc.max);
    }

    @Override
    public TypeInformation<Row> getResultType() {
        return new RowTypeInfo(Types.INT, Types.INT);
    }
}

AggregateFunction myAggFunc = new MyMinMax();
tableEnv.registerFunction("myAggFunc", myAggFunc);
Table table = input
  .groupBy("key")
  .aggregate("myAggFunc(a) as (x, y)")
  .select("key, x, y")
FlatAggregate
Streaming
Result Updating

Similar to a GroupBy Aggregation. Groups the rows on the grouping keys with the following running table aggregation operator to aggregate rows group-wise. The difference from an AggregateFunction is that TableAggregateFunction may return 0 or more records for a group. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.

Instead of using emitValue to output results, you can also use the emitUpdateWithRetract method. Different from emitValue, emitUpdateWithRetract is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The emitUpdateWithRetract method will be used in preference to the emitValue method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than emitValue as it can output values incrementally. See Table Aggregation Functions for details.

/**
 * Accumulator for Top2.
 */
public class Top2Accum {
    public Integer first;
    public Integer second;
}

/**
 * The top2 user-defined table aggregate function.
 */
public class Top2 extends TableAggregateFunction<Tuple2<Integer, Integer>, Top2Accum> {

    @Override
    public Top2Accum createAccumulator() {
        Top2Accum acc = new Top2Accum();
        acc.first = Integer.MIN_VALUE;
        acc.second = Integer.MIN_VALUE;
        return acc;
    }


    public void accumulate(Top2Accum acc, Integer v) {
        if (v > acc.first) {
            acc.second = acc.first;
            acc.first = v;
        } else if (v > acc.second) {
            acc.second = v;
        }
    }

    public void merge(Top2Accum acc, java.lang.Iterable<Top2Accum> iterable) {
        for (Top2Accum otherAcc : iterable) {
            accumulate(acc, otherAcc.first);
            accumulate(acc, otherAcc.second);
        }
    }

    public void emitValue(Top2Accum acc, Collector<Tuple2<Integer, Integer>> out) {
        // emit the value and rank
        if (acc.first != Integer.MIN_VALUE) {
            out.collect(Tuple2.of(acc.first, 1));
        }
        if (acc.second != Integer.MIN_VALUE) {
            out.collect(Tuple2.of(acc.second, 2));
        }
    }
}

tEnv.registerFunction("top2", new Top2());
Table orders = tableEnv.scan("Orders");
Table result = orders
    .groupBy("key")
    .flatAggregate("top2(a) as (v, rank)")
    .select("key, v, rank");

Note: For streaming queries, the required state to compute the query result might grow infinitely depending on the type of aggregation and the number of distinct grouping keys. Please provide a query configuration with a valid retention interval to prevent excessive state size. See Query Configuration for details.

Group Window FlatAggregate
Streaming

Groups and aggregates a table on a group window and possibly one or more grouping keys. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.

tableEnv.registerFunction("top2", new Top2());
Table orders = tableEnv.scan("Orders");
Table result = orders
    .window(Tumble.over("5.minutes").on("rowtime").as("w")) // define window
    .groupBy("a, w") // group by key and window
    .flatAggregate("top2(b) as (v, rank)")
    .select("a, w.start, w.end, w.rowtime, v, rank"); // access window properties and aggregate results
Operators Description
Map
Batch Streaming

Performs a map operation with a user-defined scalar function or built-in scalar function. The output will be flattened if the output type is a composite type.

class MyMapFunction extends ScalarFunction {
  def eval(a: String): Row = {
    Row.of(a, "pre-" + a)
  }

  override def getResultType(signature: Array[Class[_]]): TypeInformation[_] =
    Types.ROW(Types.STRING, Types.STRING)
}

val func = new MyMapFunction()
val table = input
  .map(func('c)).as('a, 'b)
FlatMap
Batch Streaming

Performs a flatMap operation with a table function.

class MyFlatMapFunction extends TableFunction[Row] {
  def eval(str: String): Unit = {
    if (str.contains("#")) {
      str.split("#").foreach({ s =>
        val row = new Row(2)
        row.setField(0, s)
        row.setField(1, s.length)
        collect(row)
      })
    }
  }

  override def getResultType: TypeInformation[Row] = {
    Types.ROW(Types.STRING, Types.INT)
  }
}

val func = new MyFlatMapFunction
val table = input
  .flatMap(func('c)).as('a, 'b)
Aggregate
Batch Streaming Result Updating

Performs an aggregate operation with an aggregate function. You have to close the "aggregate" with a select statement and the select statement does not support aggregate functions. The output of aggregate will be flattened if the output type is a composite type.

case class MyMinMaxAcc(var min: Int, var max: Int)

class MyMinMax extends AggregateFunction[Row, MyMinMaxAcc] {

  def accumulate(acc: MyMinMaxAcc, value: Int): Unit = {
    if (value < acc.min) {
      acc.min = value
    }
    if (value > acc.max) {
      acc.max = value
    }
  }

  override def createAccumulator(): MyMinMaxAcc = MyMinMaxAcc(0, 0)

  def resetAccumulator(acc: MyMinMaxAcc): Unit = {
    acc.min = 0
    acc.max = 0
  }

  override def getValue(acc: MyMinMaxAcc): Row = {
    Row.of(Integer.valueOf(acc.min), Integer.valueOf(acc.max))
  }

  override def getResultType: TypeInformation[Row] = {
    new RowTypeInfo(Types.INT, Types.INT)
  }
}

val myAggFunc: AggregateFunction = new MyMinMax
val table = input
  .groupBy('key)
  .aggregate(myAggFunc('a) as ('x, 'y))
  .select('key, 'x, 'y)
FlatAggregate
Streaming
Result Updating

Similar to a GroupBy Aggregation. Groups the rows on the grouping keys with the following running table aggregation operator to aggregate rows group-wise. The difference from an AggregateFunction is that TableAggregateFunction may return 0 or more records for a group. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.

Instead of using emitValue to output results, you can also use the emitUpdateWithRetract method. Different from emitValue, emitUpdateWithRetract is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The emitUpdateWithRetract method will be used in preference to the emitValue method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than emitValue as it can output values incrementally. See Table Aggregation Functions for details.

import java.lang.{Integer => JInteger}
import org.apache.flink.table.api.Types
import org.apache.flink.table.functions.TableAggregateFunction

/**
 * Accumulator for top2.
 */
class Top2Accum {
  var first: JInteger = _
  var second: JInteger = _
}

/**
 * The top2 user-defined table aggregate function.
 */
class Top2 extends TableAggregateFunction[JTuple2[JInteger, JInteger], Top2Accum] {

  override def createAccumulator(): Top2Accum = {
    val acc = new Top2Accum
    acc.first = Int.MinValue
    acc.second = Int.MinValue
    acc
  }

  def accumulate(acc: Top2Accum, v: Int) {
    if (v > acc.first) {
      acc.second = acc.first
      acc.first = v
    } else if (v > acc.second) {
      acc.second = v
    }
  }

  def merge(acc: Top2Accum, its: JIterable[Top2Accum]): Unit = {
    val iter = its.iterator()
    while (iter.hasNext) {
      val top2 = iter.next()
      accumulate(acc, top2.first)
      accumulate(acc, top2.second)
    }
  }

  def emitValue(acc: Top2Accum, out: Collector[JTuple2[JInteger, JInteger]]): Unit = {
    // emit the value and rank
    if (acc.first != Int.MinValue) {
      out.collect(JTuple2.of(acc.first, 1))
    }
    if (acc.second != Int.MinValue) {
      out.collect(JTuple2.of(acc.second, 2))
    }
  }
}

val top2 = new Top2
val orders: Table = tableEnv.scan("Orders")
val result = orders
    .groupBy('key)
    .flatAggregate(top2('a) as ('v, 'rank))
    .select('key, 'v, 'rank)

Note: For streaming queries, the required state to compute the query result might grow infinitely depending on the type of aggregation and the number of distinct grouping keys. Please provide a query configuration with a valid retention interval to prevent excessive state size. See Query Configuration for details.

Group Window FlatAggregate
Streaming

Groups and aggregates a table on a group window and possibly one or more grouping keys. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.

val top2 = new Top2
val orders: Table = tableEnv.scan("Orders")
val result = orders
    .window(Tumble over 5.minutes on 'rowtime as 'w) // define window
    .groupBy('a, 'w) // group by key and window
    .flatAggregate(top2('b) as ('v, 'rank))
    .select('a, w.start, 'w.end, 'w.rowtime, 'v, 'rank) // access window properties and aggregate results

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Data Types

The Table API is built on top of Flink’s DataSet and DataStream APIs. Internally, it also uses Flink’s TypeInformation to define data types. Fully supported types are listed in org.apache.flink.table.api.Types. The following table summarizes the relation between Table API types, SQL types, and the resulting Java class.

Table API SQL Java type
Types.STRING VARCHAR java.lang.String
Types.BOOLEAN BOOLEAN java.lang.Boolean
Types.BYTE TINYINT java.lang.Byte
Types.SHORT SMALLINT java.lang.Short
Types.INT INTEGER, INT java.lang.Integer
Types.LONG BIGINT java.lang.Long
Types.FLOAT REAL, FLOAT java.lang.Float
Types.DOUBLE DOUBLE java.lang.Double
Types.DECIMAL DECIMAL java.math.BigDecimal
Types.SQL_DATE DATE java.sql.Date
Types.SQL_TIME TIME java.sql.Time
Types.SQL_TIMESTAMP TIMESTAMP(3) java.sql.Timestamp
Types.INTERVAL_MONTHS INTERVAL YEAR TO MONTH java.lang.Integer
Types.INTERVAL_MILLIS INTERVAL DAY TO SECOND(3) java.lang.Long
Types.PRIMITIVE_ARRAY ARRAY e.g. int[]
Types.OBJECT_ARRAY ARRAY e.g. java.lang.Byte[]
Types.MAP MAP java.util.HashMap
Types.MULTISET MULTISET e.g. java.util.HashMap<String, Integer> for a multiset of String
Types.ROW ROW org.apache.flink.types.Row

Generic types and (nested) composite types (e.g., POJOs, tuples, rows, Scala case classes) can be fields of a row as well.

Fields of composite types with arbitrary nesting can be accessed with value access functions.

Generic types are treated as a black box and can be passed on or processed by user-defined functions.

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Expression Syntax

Some of the operators in previous sections expect one or more expressions. Expressions can be specified using an embedded Scala DSL or as Strings. Please refer to the examples above to learn how expressions can be specified.

This is the EBNF grammar for expressions:

expressionList = expression , { "," , expression } ;

expression = overConstant | alias ;

alias = logic | ( logic , "as" , fieldReference ) | ( logic , "as" , "(" , fieldReference , { "," , fieldReference } , ")" ) ;

logic = comparison , [ ( "&&" | "||" ) , comparison ] ;

comparison = term , [ ( "=" | "==" | "===" | "!=" | "!==" | ">" | ">=" | "<" | "<=" ) , term ] ;

term = product , [ ( "+" | "-" ) , product ] ;

product = unary , [ ( "*" | "/" | "%") , unary ] ;

unary = [ "!" | "-" | "+" ] , composite ;

composite = over | suffixed | nullLiteral | prefixed | atom ;

suffixed = interval | suffixAs | suffixCast | suffixIf | suffixDistinct | suffixFunctionCall | timeIndicator ;

prefixed = prefixAs | prefixCast | prefixIf | prefixDistinct | prefixFunctionCall ;

interval = timeInterval | rowInterval ;

timeInterval = composite , "." , ("year" | "years" | "quarter" | "quarters" | "month" | "months" | "week" | "weeks" | "day" | "days" | "hour" | "hours" | "minute" | "minutes" | "second" | "seconds" | "milli" | "millis") ;

rowInterval = composite , "." , "rows" ;

suffixCast = composite , ".cast(" , dataType , ")" ;

prefixCast = "cast(" , expression , dataType , ")" ;

dataType = "BYTE" | "SHORT" | "INT" | "LONG" | "FLOAT" | "DOUBLE" | "BOOLEAN" | "STRING" | "DECIMAL" | "SQL_DATE" | "SQL_TIME" | "SQL_TIMESTAMP" | "INTERVAL_MONTHS" | "INTERVAL_MILLIS" | ( "MAP" , "(" , dataType , "," , dataType , ")" ) | ( "PRIMITIVE_ARRAY" , "(" , dataType , ")" ) | ( "OBJECT_ARRAY" , "(" , dataType , ")" ) ;

suffixAs = composite , ".as(" , fieldReference , ")" ;

prefixAs = "as(" , expression, fieldReference , ")" ;

suffixIf = composite , ".?(" , expression , "," , expression , ")" ;

prefixIf = "?(" , expression , "," , expression , "," , expression , ")" ;

suffixDistinct = composite , "distinct.()" ;

prefixDistinct = functionIdentifier , ".distinct" , [ "(" , [ expression , { "," , expression } ] , ")" ] ;

suffixFunctionCall = composite , "." , functionIdentifier , [ "(" , [ expression , { "," , expression } ] , ")" ] ;

prefixFunctionCall = functionIdentifier , [ "(" , [ expression , { "," , expression } ] , ")" ] ;

atom = ( "(" , expression , ")" ) | literal | fieldReference ;

fieldReference = "*" | identifier ;

nullLiteral = "nullOf(" , dataType , ")" ;

timeIntervalUnit = "YEAR" | "YEAR_TO_MONTH" | "MONTH" | "QUARTER" | "WEEK" | "DAY" | "DAY_TO_HOUR" | "DAY_TO_MINUTE" | "DAY_TO_SECOND" | "HOUR" | "HOUR_TO_MINUTE" | "HOUR_TO_SECOND" | "MINUTE" | "MINUTE_TO_SECOND" | "SECOND" ;

timePointUnit = "YEAR" | "MONTH" | "DAY" | "HOUR" | "MINUTE" | "SECOND" | "QUARTER" | "WEEK" | "MILLISECOND" | "MICROSECOND" ;

over = composite , "over" , fieldReference ;

overConstant = "current_row" | "current_range" | "unbounded_row" | "unbounded_row" ;

timeIndicator = fieldReference , "." , ( "proctime" | "rowtime" ) ;

Literals: Here, literal is a valid Java literal. String literals can be specified using single or double quotes. Duplicate the quote for escaping (e.g. 'It''s me.' or "I ""like"" dogs.").

Null literals: Null literals must have a type attached. Use nullOf(type) (e.g. nullOf(INT)) for creating a null value.

Field references: The fieldReference specifies a column in the data (or all columns if * is used), and functionIdentifier specifies a supported scalar function. The column names and function names follow Java identifier syntax.

Function calls: Expressions specified as strings can also use prefix notation instead of suffix notation to call operators and functions.

Decimals: If working with exact numeric values or large decimals is required, the Table API also supports Java’s BigDecimal type. In the Scala Table API decimals can be defined by BigDecimal("123456") and in Java by appending a “p” for precise e.g. 123456p.

Time representation: In order to work with temporal values the Table API supports Java SQL’s Date, Time, and Timestamp types. In the Scala Table API literals can be defined by using java.sql.Date.valueOf("2016-06-27"), java.sql.Time.valueOf("10:10:42"), or java.sql.Timestamp.valueOf("2016-06-27 10:10:42.123"). The Java and Scala Table API also support calling "2016-06-27".toDate(), "10:10:42".toTime(), and "2016-06-27 10:10:42.123".toTimestamp() for converting Strings into temporal types. Note: Since Java’s temporal SQL types are time zone dependent, please make sure that the Flink Client and all TaskManagers use the same time zone.

Temporal intervals: Temporal intervals can be represented as number of months (Types.INTERVAL_MONTHS) or number of milliseconds (Types.INTERVAL_MILLIS). Intervals of same type can be added or subtracted (e.g. 1.hour + 10.minutes). Intervals of milliseconds can be added to time points (e.g. "2016-08-10".toDate + 5.days).

Scala expressions: Scala expressions use implicit conversions. Therefore, make sure to add the wildcard import org.apache.flink.table.api.scala._ to your programs. In case a literal is not treated as an expression, use .toExpr such as 3.toExpr to force a literal to be converted.

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