Hi Shilpa,
Shuiqiang is right. Currently, we recommend to use SQL DDL until the
connect API is updated. See here:
https://ci.apache.org/projects/flink/flink-docs-master/docs/dev/table/sql/create/#create-table
Especially the WATERMARK section shows how to declare a rowtime attribute.
Regards,
Timo
On 05.03.21 08:47, Shuiqiang Chen wrote:
Hi Shilpa,
There might be something wrong when defining the rowtime field with the
Connector descriptor, it’s recommended to use SQL DDL to create tables,
and do queries with table API.
Best,
Shuiqiang
Shilpa Shankar <sshan...@bandwidth.com <mailto:sshan...@bandwidth.com>>
于2021年3月4日周四 下午9:29写道：
Hello,
We are using pyflink's datastream api v1.12.1 to consume from kafka
and want to use one of the fields to act as the "rowtime" for windowing.
We realize we need to convert BIGINT to TIMESTAMP before we use it
as "rowtime".
py4j.protocol.Py4JJavaError: An error occurred while calling o91.select.
: org.apache.flink.table.api.ValidationException: A group window
expects a time attribute for grouping in a stream environment.
But we are not sure where and how that needs to be implemented.
Some help here would be really appreciated.
Thanks,
Shilpa
import os
from pyflink.table.expressions import lit, Expression
from pyflink.datastream import StreamExecutionEnvironment,
TimeCharacteristic
from pyflink.datastream import CheckpointingMode,
ExternalizedCheckpointCleanup
from pyflink.table import StreamTableEnvironment, DataTypes,
EnvironmentSettings, CsvTableSink, TableConfig
from pyflink.table.descriptors import Schema, Rowtime, Json, Kafka
from pyflink.table.window import Slide
def main():
env = StreamExecutionEnvironment.get_execution_environment()
env.set_parallelism(1)
env.set_stream_time_characteristic(TimeCharacteristic.EventTime)
env.enable_checkpointing(60000, CheckpointingMode.EXACTLY_ONCE)
config = env.get_checkpoint_config()
config.enable_externalized_checkpoints(ExternalizedCheckpointCleanup.DELETE_ON_CANCELLATION)
st_env = StreamTableEnvironment.create(
env,
environment_settings=EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
)
register_kafka_source(st_env)
register_transactions_sink_into_csv(st_env)
#Filter
st_env.from_path("source") \
.window(Slide.over(lit(2).minutes).every(lit(1).minutes).on("rowtime").alias("w"))
\
.group_by("customer_id, w") \
.select("""customer_id as customer_id,
count(*) as total_counts,
w.start as start_time,
w.end as end_time
""") \
.insert_into("sink_into_csv")
def register_kafka_source(st_env):
# Add Source
st_env.connect(
Kafka() \
.version("universal") \
.topic("topic1") \
.property("group.id <http://group.id>", "topic_consumer") \
.property("security.protocol", "SASL_PLAINTEXT") \
.property("sasl.mechanism", "PLAIN") \
.property("bootstrap.servers", "<bootsptrap_servers>") \
.property("sasl.jaas.config", "<user,password>") \
.start_from_earliest()
).with_format(
Json()
.fail_on_missing_field(False)
.schema(
DataTypes.ROW([
DataTypes.FIELD("customer_id", DataTypes.STRING()),
DataTypes.FIELD("time_in_epoch_milliseconds",
DataTypes.BIGINT())
])
)
).with_schema(
Schema()
.field("customer_id", DataTypes.STRING())
.field("rowtime", DataTypes.BIGINT())
.rowtime(
Rowtime()
.timestamps_from_field("time_in_epoch_milliseconds")
.watermarks_periodic_bounded(10)
)
).in_append_mode(
).create_temporary_table(
"source"
)
def register_transactions_sink_into_csv(env):
result_file = "/opt/examples/data/output/output_file.csv"
if os.path.exists(result_file):
os.remove(result_file)
env.register_table_sink("sink_into_csv",
CsvTableSink(["customer_id",
"total_count",
"start_time",
"end_time"],
[DataTypes.STRING(),
DataTypes.DOUBLE(),
DataTypes.TIMESTAMP(3),
DataTypes.TIMESTAMP(3)],
result_file))
if __name__ == "__main__":
main()
