Hi,
据我所知，Flink 1.10 官方没有支持Elasticsearch 5.x 版本的 sql connector。
Best，
Jark
On Tue, 16 Jun 2020 at 16:08, Dian Fu <dian0511...@gmail.com> wrote:
> 可以发一下完整的异常吗？
>
> 在 2020年6月16日，下午3:45，jack <wslyk...@163.com> 写道：
>
> 连接的版本部分我本地已经修改为 5了，发生了下面的报错；
>
> >> st_env.connect(
> >> Elasticsearch()
> >> .version("5")
> >> .host("localhost", 9200, "http")
> >> .index("taxiid-cnts")
> >> .document_type('taxiidcnt')
> >> .key_delimiter("$")) \
>
>
>
>
>
>
> 在 2020-06-16 15:38:28，"Dian Fu" <dian0511...@gmail.com> 写道：
> >I guess it's because the ES version specified in the job is `6`, however,
> >the jar used is `5`.
> >
> >> 在 2020年6月16日，下午1:47，jack <wslyk...@163.com> 写道：
> >>
> >> 我这边使用的是pyflink连接es的一个例子，我这边使用的es为5.4.1的版本，pyflink为1.10.1，连接jar包我使用的是
> >> flink-sql-connector-elasticsearch5_2.11-1.10.1.jar，kafka，json的连接包也下载了，连接kafka测试成功了。
> >> 连接es的时候报错，findAndCreateTableSink failed。
> >> 是不是es的连接jar包原因造成的？哪位遇到过类似问题还请指导一下，感谢。
> >>
> >> Caused by Could not find a suitable factory for
> >> ‘org.apache.flink.table.factories.TableSinkFactory’ in the classpath.
> >> Reason: Required context properties mismatch
> >>
> >>
> >>
> >> from pyflink.datastream import StreamExecutionEnvironment,
> >> TimeCharacteristic
> >> from pyflink.table import StreamTableEnvironment, DataTypes,
> >> EnvironmentSettings
> >> from pyflink.table.descriptors import Schema, Kafka, Json, Rowtime,
> >> Elasticsearch
> >>
> >>
> >> def area_cnts():
> >> s_env = StreamExecutionEnvironment.get_execution_environment()
> >> s_env.set_stream_time_characteristic(TimeCharacteristic.EventTime)
> >> s_env.set_parallelism(1)
> >>
> >> # use blink table planner
> >> st_env = StreamTableEnvironment \
> >> .create(s_env, environment_settings=EnvironmentSettings
> >> .new_instance()
> >> .in_streaming_mode()
> >> .use_blink_planner().build())
> >>
> >> # register source and sink
> >> register_rides_source(st_env)
> >> register_cnt_sink(st_env)
> >>
> >> # query
> >> st_env.from_path("source")\
> >> .group_by("taxiId")\
> >> .select("taxiId, count(1) as cnt")\
> >> .insert_into("sink")
> >>
> >> # execute
> >> st_env.execute("6-write_with_elasticsearch")
> >>
> >>
> >> def register_rides_source(st_env):
> >> st_env \
> >> .connect( # declare the external system to connect to
> >> Kafka()
> >> .version("universal")
> >> .topic("Rides")
> >> .start_from_earliest()
> >> .property("zookeeper.connect", "zookeeper:2181")
> >> .property("bootstrap.servers", "kafka:9092")) \
> >> .with_format( # declare a format for this system
> >> Json()
> >> .fail_on_missing_field(True)
> >> .schema(DataTypes.ROW([
> >> DataTypes.FIELD("rideId", DataTypes.BIGINT()),
> >> DataTypes.FIELD("isStart", DataTypes.BOOLEAN()),
> >> DataTypes.FIELD("eventTime", DataTypes.TIMESTAMP()),
> >> DataTypes.FIELD("lon", DataTypes.FLOAT()),
> >> DataTypes.FIELD("lat", DataTypes.FLOAT()),
> >> DataTypes.FIELD("psgCnt", DataTypes.INT()),
> >> DataTypes.FIELD("taxiId", DataTypes.BIGINT())]))) \
> >> .with_schema( # declare the schema of the table
> >> Schema()
> >> .field("rideId", DataTypes.BIGINT())
> >> .field("taxiId", DataTypes.BIGINT())
> >> .field("isStart", DataTypes.BOOLEAN())
> >> .field("lon", DataTypes.FLOAT())
> >> .field("lat", DataTypes.FLOAT())
> >> .field("psgCnt", DataTypes.INT())
> >> .field("rideTime", DataTypes.TIMESTAMP())
> >> .rowtime(
> >> Rowtime()
> >> .timestamps_from_field("eventTime")
> >> .watermarks_periodic_bounded(60000))) \
> >> .in_append_mode() \
> >> .register_table_source("source")
> >>
> >>
> >> def register_cnt_sink(st_env):
> >> st_env.connect(
> >> Elasticsearch()
> >> .version("6")
> >> .host("elasticsearch", 9200, "http")
> >> .index("taxiid-cnts")
> >> .document_type('taxiidcnt')
> >> .key_delimiter("$")) \
> >> .with_schema(
> >> Schema()
> >> .field("taxiId", DataTypes.BIGINT())
> >> .field("cnt", DataTypes.BIGINT())) \
> >> .with_format(
> >> Json()
> >> .derive_schema()) \
> >> .in_upsert_mode() \
> >> .register_table_sink("sink")
> >>
> >>
> >> if __name__ == '__main__':
> >> area_cnts()
> >>
> >
>
>
>
