![]() alias ( 'd' )) # Distinct aggregation on over window result = orders. alias ( 'd' )) # Distinct aggregation on time window group by group_by_window_distinct_result = orders. from_path ( "Orders" ) # Distinct aggregation on group by group_by_distinct_result = orders. Similar to a SQL DISTINCT aggregation clause such as COUNT(DISTINCT a).ĭistinct aggregation declares that an aggregation function (built-in or user-defined) is only applied on distinct input values.ĭistinct can be applied to GroupBy Aggregation, GroupBy Window Aggregation and Over Window Aggregation.įrom import col, lit, UNBOUNDED_RANGE from import Over, Tumble orders = t_env. ORDER BY must be specified on a single time attribute. Ranges with FOLLOWING are not supported yet. Currently, only windows with PRECEDING (UNBOUNDED and bounded) to CURRENT ROW range are supported. over ( col ( 'w' )))Īll aggregates must be defined over the same window, i.e., same partitioning, sorting, and range. Similar to the FROM clause in a SQL query.įrom import Over from import col, UNBOUNDED_RANGE, CURRENT_RANGE orders = t_env. Please note that not all operations are available in both batch and streaming yet they are tagged accordingly. The Table API supports the following operations. ![]() In both cases, the program produces the same results given that streaming records are not late (see Streaming Concepts for details). 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. select ( col ( 'a' ), col ( 'hourly_window' ). group_by ( col ( 'hourly_window' ), col ( 'a' )) \ from_path ( "Orders" ) # schema (a, b, c, rowtime) result = orders. # specify table program from import col, lit from import Tumble orders = t_env. It filters null values, normalizes the field a of type String, and calculates for each hour and product a the average billing amount b. The program scans again the Orders table. The next example shows a more complex Table API program. ![]() ![]() from_path ( "Orders" ) # schema (a, b, c, rowtime) orders. execute_sql ( sink_ddl ) # specify table program orders = t_env. execute_sql ( source_ddl ) sink_ddl = f """ WATERMARK FOR rowtime AS rowtime - INTERVAL '1' SECOND in_batch_mode ()) # register Orders table and Result table sink in table environment source_data_path = "/path/to/source/directory/" result_data_path = "/path/to/result/directory/" source_ddl = f """ create ( environment_settings = EnvironmentSettings. It scans the Orders table, groups by field a, and counts the resulting rows per group.įrom pyflink.table import * from import col # environment configuration t_env = TableEnvironment. The table program is executed in a batch environment. The following example shows the differences between the Scala, Java and Python Table API. 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 Table API is available for Scala, Java and Python. The rowtime field is either a logical time attribute in streaming or a regular timestamp field in batch. The following examples assume a registered table called Orders with attributes (a, b, c, rowtime). The Streaming Concepts pages discuss streaming specific concepts such as dynamic tables and time attributes. Have a look at the Common Concepts & API to learn how to register tables or to create a Table object. The Table API shares many concepts and parts of its API with Flink’s SQL integration. 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 is a language-integrated API for Scala, Java and Python. The Table API is a super set of the SQL language and is specially designed for working with Apache Flink. Table API queries can be run on batch or streaming input without modifications. The Table API is a unified, relational API for stream and batch processing. Hadoop MapReduce compatibility with Flink.Conversions between Table and DataStream.Conversions between PyFlink Table and Pandas DataFrame.
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