def pandas_udaf(): env = StreamExecutionEnvironment.get_execution_environment() env.set_parallelism(1) t_env = StreamTableEnvironment.create(stream_execution_environment=env) # define the source with watermark definition ds = env.from_collection( collection=[ (Instant.of_epoch_milli(1000), 'Alice', 110.1), (Instant.of_epoch_milli(4000), 'Bob', 30.2), (Instant.of_epoch_milli(3000), 'Alice', 20.0), (Instant.of_epoch_milli(2000), 'Bob', 53.1), (Instant.of_epoch_milli(5000), 'Alice', 13.1), (Instant.of_epoch_milli(3000), 'Bob', 3.1), (Instant.of_epoch_milli(7000), 'Bob', 16.1), (Instant.of_epoch_milli(10000), 'Alice', 20.1) ], type_info=Types.ROW([Types.INSTANT(), Types.STRING(), Types.FLOAT()])) table = t_env.from_data_stream( ds, Schema.new_builder() .column_by_expression("ts", "CAST(f0 AS TIMESTAMP_LTZ(3))") .column("f1", DataTypes.STRING()) .column("f2", DataTypes.FLOAT()) .watermark("ts", "ts - INTERVAL '3' SECOND") .build() ).alias("ts, name, price") # define the sink t_env.create_temporary_table( 'sink', TableDescriptor.for_connector('print') .schema(Schema.new_builder() .column('name', DataTypes.STRING()) .column('total_price', DataTypes.FLOAT()) .column('w_start', DataTypes.TIMESTAMP_LTZ()) .column('w_end', DataTypes.TIMESTAMP_LTZ()) .build()) .build()) @udaf(result_type=DataTypes.FLOAT(), func_type="pandas") def mean_udaf(v): return v.mean() # define the tumble window operation table = table.window(Tumble.over(lit(5).seconds).on(col("ts")).alias("w")) \ .group_by(table.name, col('w')) \ .select(table.name, mean_udaf(table.price), col("w").start, col("w").end) # submit for execution table.execute_insert('sink') \ .wait()
def tumble_window_demo(): env = StreamExecutionEnvironment.get_execution_environment() env.set_parallelism(1) t_env = StreamTableEnvironment.create(stream_execution_environment=env) # define the source with watermark definition ds = env.from_collection( collection=[ (Instant.of_epoch_milli(1000), 'Alice', 110.1), (Instant.of_epoch_milli(4000), 'Bob', 30.2), (Instant.of_epoch_milli(3000), 'Alice', 20.0), (Instant.of_epoch_milli(2000), 'Bob', 53.1), (Instant.of_epoch_milli(5000), 'Alice', 13.1), (Instant.of_epoch_milli(3000), 'Bob', 3.1), (Instant.of_epoch_milli(7000), 'Bob', 16.1), (Instant.of_epoch_milli(10000), 'Alice', 20.1) ], type_info=Types.ROW([Types.INSTANT(), Types.STRING(), Types.FLOAT()])) table = t_env.from_data_stream( ds, Schema.new_builder() .column_by_expression("ts", "CAST(f0 AS TIMESTAMP(3))") .column("f1", DataTypes.STRING()) .column("f2", DataTypes.FLOAT()) .watermark("ts", "ts - INTERVAL '3' SECOND") .build() ).alias("ts", "name", "price") # define the sink t_env.create_temporary_table( 'sink', TableDescriptor.for_connector('print') .schema(Schema.new_builder() .column('name', DataTypes.STRING()) .column('total_price', DataTypes.FLOAT()) .build()) .build()) # define the over window operation table = table.over_window( Over.partition_by(col("name")) .order_by(col("ts")) .preceding(row_interval(2)) .following(CURRENT_ROW) .alias('w')) \ .select(table.name, table.price.max.over(col('w'))) # submit for execution table.execute_insert('sink') \ .wait()