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import logging
import sys

from pyflink.common.time import Instant

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import (DataTypes, TableDescriptor, Schema, StreamTableEnvironment)
from pyflink.table.expressions import lit, col
from pyflink.table.udf import udaf
from pyflink.table.window import Tumble


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(col('name'), col('w')) \
                 .select(col('name'), mean_udaf(col('price')), col("w").start, col("w").end)

    # submit for execution
    table.execute_insert('sink') \
         .wait()
    # remove .wait if submitting to a remote cluster, refer to
    # https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/faq/#wait-for-jobs-to-finish-when-executing-jobs-in-mini-cluster
    # for more details


if __name__ == '__main__':
    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s")

    pandas_udaf()