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, Java and Python. Instead of specifying queries as String values as common with SQL, Table API queries are defined in a language-embedded style in Java, Scala or Python 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.
The Table API is available for Scala, Java and Python. The Scala Table API leverages on Scala expressions, the Java Table API supports both Expression DSL and strings which are parsed and converted into equivalent expressions, the Python Table API currently only supports strings which are parsed and converted into equivalent expressions.
The following example shows the differences between the Scala, Java and Python 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 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.
For the Expression DSL it is also necessary to import static org.apache.flink.table.api.Expressions.*
The Scala Table API is enabled by importing org.apache.flink.table.api._
, org.apache.flink.api.scala._
, and org.apache.flink.table.api.bridge.scala._
(for bridging to/from DataStream).
The following example shows how a Scala Table API program is constructed. Table fields are referenced using Scala’s String interpolation using a dollar character ($
).
The following example shows how a Python Table API program is constructed and how expressions are specified as strings.
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
.
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).
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.
Operators | Description |
---|---|
From Batch Streaming |
Similar to the FROM clause in a SQL query. Performs a scan of a registered table. |
Values Batch Streaming |
Similar to the VALUES clause in a SQL query. Produces an inline table out of the provided rows. You can use a `row(...)` expression to create composite rows: will produce a Table with a schema as follows: The method will derive the types automatically from the input expressions. If types at a certain position differ, the method will try to find a common super type for all types. If a common super type does not exist, an exception will be thrown. You can also specify the requested type explicitly. It might be helpful for assigning more generic types like e.g. DECIMAL or naming the columns. will produce a Table with a schema as follows: |
Select Batch Streaming |
Similar to a SQL SELECT statement. Performs a select operation. You can use star ( |
As Batch Streaming |
Renames fields. |
Where / Filter Batch Streaming |
Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate. or |
Operators | Description |
---|---|
From Batch Streaming |
Similar to the FROM clause in a SQL query. Performs a scan of a registered table. |
Values Batch Streaming |
Similar to the VALUES clause in a SQL query. Produces an inline table out of the provided rows. You can use a `row(...)` expression to create composite rows: will produce a Table with a schema as follows: The method will derive the types automatically from the input expressions. If types at a certain position differ, the method will try to find a common super type for all types. If a common super type does not exist, an exception will be thrown. You can also specify the requested type explicitly. It might be helpful for assigning more generic types like e.g. DECIMAL or naming the columns. will produce a Table with a schema as follows: |
Select Batch Streaming |
Similar to a SQL SELECT statement. Performs a select operation. You can use star ( |
As Batch Streaming |
Renames fields. |
Where / Filter Batch Streaming |
Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate. or |
Operators | Description |
---|---|
From Batch Streaming |
Similar to the FROM clause in a SQL query. Performs a scan of a registered table. |
FromElements Batch Streaming |
Similar to the VALUES clause in a SQL query. Produces an inline table out of the provided rows. will produce a Table with a schema as follows: The method will derive the types automatically from the input data. The elements types must be acceptable atomic types or acceptable composite types. All elements must be of the same type. If the elements types are composite types, the composite types must be strictly equal, and its subtypes must also be acceptable types, e.g. if the elements are tuples, the length of the tuples must be equal, the element types of the tuples must be equal in order. The built-in acceptable atomic element types contains: int, long, str, unicode, bool, float, bytearray, datetime.date, datetime.time, datetime.datetime, datetime.timedelta, decimal.Decimal The built-in acceptable composite element types contains: list, tuple, dict, array, pyflink.table.Row You can also specify the requested type explicitly. It might be helpful for assigning more generic types like e.g. DECIMAL or naming the columns. will produce a Table with a schema as follows: |
Select Batch Streaming |
Similar to a SQL SELECT statement. Performs a select operation. You can use star ( |
Alias Batch Streaming |
Renames fields. |
Where / Filter Batch Streaming |
Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate. or |
Operators | Description |
---|---|
AddColumns Batch Streaming |
Performs a field add operation. It will throw an exception if the added fields already exist. |
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. |
DropColumns Batch Streaming |
Performs a field drop operation. The field expressions should be field reference expressions, and only existing fields can be dropped. |
RenameColumns Batch Streaming |
Performs a field rename operation. The field expressions should be alias expressions, and only the existing fields can be renamed. |
Operators | Description |
---|---|
AddColumns Batch Streaming |
Performs a field add operation. It will throw an exception if the added fields already exist. |
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. |
DropColumns Batch Streaming |
Performs a field drop operation. The field expressions should be field reference expressions, and only existing fields can be dropped. |
RenameColumns Batch Streaming |
Performs a field rename operation. The field expressions should be alias expressions, and only the existing fields can be renamed. |
Operators | Description |
---|---|
AddColumns Batch Streaming |
Performs a field add operation. It will throw an exception if the added fields already exist. |
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. |
DropColumns Batch Streaming |
Performs a field drop operation. The field expressions should be field reference expressions, and only existing fields can be dropped. |
RenameColumns Batch Streaming |
Performs a field rename operation. The field expressions should be alias expressions, and only the existing fields can be renamed. |
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. 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. |
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. 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. 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. 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. 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. 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. |
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. 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. 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. 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. 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. 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. |
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. 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. 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. 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. |
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. 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. 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. |
Interval Join Batch Streaming |
Note: Interval joins are a subset of regular joins that can be processed in a streaming fashion. An interval 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 ( For example, the following predicates are valid interval join conditions:
|
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. |
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 |
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. 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. 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. 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. |
Interval Join Batch Streaming |
Note: Interval joins are a subset of regular joins that can be processed in a streaming fashion. An interval 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 ( For example, the following predicates are valid interval join conditions:
|
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. |
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 |
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. 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. 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. 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. |
Interval Join Batch Streaming |
Note: Interval joins are a subset of regular joins that can be processed in a streaming fashion. An interval 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 ( For example, the following predicates are valid interval join conditions:
|
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. |
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 |
Join with Temporal Table Streaming |
Currently not supported in Python Table API. |
Operators | Description |
---|---|
Union Batch |
Similar to a SQL UNION clause. Unions two tables with duplicate records removed. Both tables must have identical field types. |
UnionAll Batch Streaming |
Similar to a SQL UNION ALL clause. Unions two tables. Both tables must have identical field types. |
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. |
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. |
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. |
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. |
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. 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. |
UnionAll Batch Streaming |
Similar to a SQL UNION ALL clause. Unions two tables, both tables must have identical field types. |
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. |
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. |
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. |
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. |
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. 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. |
UnionAll Batch Streaming |
Similar to a SQL UNION ALL clause. Unions two tables. Both tables must have identical field types. |
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. |
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. |
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. |
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. |
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. 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 |
---|---|
Order By Batch |
Similar to a SQL ORDER BY clause. Returns records globally sorted across all parallel partitions. |
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. |
Operators | Description |
---|---|
Order By Batch |
Similar to a SQL ORDER BY clause. Returns records globally sorted across all parallel partitions. |
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. |
Operators | Description |
---|---|
Order By Batch |
Similar to a SQL ORDER BY clause. Returns records globally sorted across all parallel partitions. |
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. |
Operators | Description |
---|---|
Insert Into Batch Streaming |
Similar to the `INSERT INTO` clause in a SQL query, the method performs an insertion into a registered output table. The `executeInsert()` method will immediately submit a Flink job which execute the insert operation. Output tables must be registered in the TableEnvironment (see Connector tables). Moreover, the schema of the registered table must match the schema of the query. |
Operators | Description |
---|---|
Insert Into Batch Streaming |
Similar to the `INSERT INTO` clause in a SQL query, the method performs an insertion into a registered output table. The `executeInsert()` method will immediately submit a Flink job which execute the insert operation. Output tables must be registered in the TableEnvironment (see Connector tables). Moreover, the schema of the registered table must match the schema of the query. |
Operators | Description |
---|---|
Insert Into Batch Streaming |
Similar to the INSERT INTO clause in a SQL query. Performs a insertion into a registered output table. The executeInsert method will immediately submit a flink job which execute the insert operation. 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. |
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(GroupWindow w)
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.
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.
Windows are defined using the window(w: GroupWindow)
clause and require an alias, which is specified using the alias
clause. In order to group a table by a window, the window alias must be referenced in the group_by(...)
clause like a regular grouping attribute.
The following example shows how to define a window aggregation on a table.
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.
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.
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 group_by(...)
clause references a window alias and at least one additional attribute. A group_by(...)
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.
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.
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.
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 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 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. |
alias |
Assigns an alias to the window. The alias is used to reference the window in the following group_by() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause. |
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 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 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. |
alias |
Assigns an alias to the window. The alias is used to reference the window in the following group_by() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause. |
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. |
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. |
A session window is defined by using the Session
class as follows:
Method | Description |
---|---|
with_gap |
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. |
alias |
Assigns an alias to the window. The alias is used to reference the window in the following group_by() clause and optionally to select window properties such as window start, end, or rowtime timestamps in the select() clause. |
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 (using over_window(*OverWindow)
in Python API) and referenced via an alias in the select()
method. The following example shows how to define an over window aggregation on a table.
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 |
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., Unbounded over windows are specified using a constant, i.e., If the |
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:
If the |
as |
Required |
Assigns an alias to the over window. The alias is used to reference the over window in the following |
Method | Required | Description |
---|---|---|
partition_by |
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 |
order_by |
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., Unbounded over windows are specified using a constant, i.e., If the |
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:
If the |
alias |
Required |
Assigns an alias to the over window. The alias is used to reference the over window in the following |
Note: Currently, all aggregation functions in the same select()
call must be computed of the same over window.
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. |
FlatMap Batch Streaming |
Performs a flatMap operation with a table function. |
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. |
Group Window Aggregate Batch Streaming |
Groups and aggregates a table on a group window and possibly one or more grouping keys. You have to close the "aggregate" with a select statement. And the select statement does not support "*" or aggregate functions. |
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 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. |
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. |
FlatMap Batch Streaming |
Performs a flatMap operation with a table function. |
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. |
Group Window Aggregate Batch Streaming |
Groups and aggregates a table on a group window and possibly one or more grouping keys. You have to close the "aggregate" with a select statement. And the select statement does not support "*" or aggregate functions. |
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 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. |
Operators | Description |
---|---|
Map Batch Streaming |
Currently not supported in Python Table API. |
FlatMap Batch Streaming |
Currently not supported in Python Table API. |
Aggregate Batch Streaming Result Updating |
Currently not supported in Python Table API. |
Group Window Aggregate Batch Streaming |
Currently not supported in Python Table API. |
FlatAggregate Streaming Result Updating |
Currently not supported in Python Table API. |
Group Window FlatAggregate Streaming |
Currently not supported in Python Table API. |
Please see the dedicated page about data types.
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.
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:
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._
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.