[jira] [Updated] (FLINK-32040) The WatermarkStrategy defined with the Function(with_idleness) report an error
[
https://issues.apache.org/jira/browse/FLINK-32040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-32040:
Description:
*version:* upgrade pyflink1.15.2 to pyflink1.16.1
*Report an error:*
Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
characteristic set to 'ProcessingTime', or did you forget to call
'data_stream.assign_timestamps_and_watermarks(...)'?
The application before with version 1.15.2 has never reported the error.
*Example:*
{code:java}
```python```
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp: int) -> int:
return value['version']
sql="""
select columns,version(milliseconds) from kafka_source
"""
table = st_env.sql_query(sql)
stream = st_env.to_changelog_stream(table)
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
stream = stream.key_by(CommonKeySelector()) \
.window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
.process(WindowFunction(), typeInfo){code}
Try to debug to trace
??pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks??
and find ??watermark_strategy._timestamp_assigner?? is none.
*Solution:*
Remove the function ??with_idleness(Duration.of_seconds(10))??
{code:java}
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner())) {code}
Is this a bug?
was:
*version:* upgrade pyflink1.15.2 to pyflink1.16.1
*Report an error:*
Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
characteristic set to 'ProcessingTime', or did you forget to call
'data_stream.assign_timestamps_and_watermarks(...)'?
The application before with version 1.15.2 has never reported the error.
*Example:*
{code:java}
```python```
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp: int) -> int:
return value['version']
sql="""
select columns,version(milliseconds) from kafka_source
"""
table = st_env.sql_query(sql)
stream = st_env.to_changelog_stream(table)
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
stream = stream.key_by(CommonKeySelector()) \
.window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
.process(WindowFunction(), typeInfo){code}
Try to debug to trace
pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks and
find watermark_strategy._timestamp_assigner is none.
*Solution:*
Remove function–with_idleness(Duration.of_seconds(10))
{code:java}
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner())) {code}
Is this a bug?
> The WatermarkStrategy defined with the Function(with_idleness) report an error
> --
>
> Key: FLINK-32040
> URL: https://issues.apache.org/jira/browse/FLINK-32040
> Project: Flink
> Issue Type: Bug
> Components: API / Python
>Reporter: Joekwal
>Priority: Blocker
>
> *version:* upgrade pyflink1.15.2 to pyflink1.16.1
>
> *Report an error:*
> Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
> characteristic set to 'ProcessingTime', or did you forget to call
> 'data_stream.assign_timestamps_and_watermarks(...)'?
> The application before with version 1.15.2 has never reported the error.
>
> *Example:*
> {code:java}
> ```python```
> class MyTimestampAssigner(TimestampAssigner):
>def extract_timestamp(self, value, record_timestamp: int) -> int:
>return value['version']
> sql="""
> select columns,version(milliseconds) from kafka_source
> """
> table = st_env.sql_query(sql)
> stream = st_env.to_changelog_stream(table)
> stream = stream.assign_timestamps_and_watermarks(
> WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
>
> .with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
> stream = stream.key_by(CommonKeySelector()) \
> .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
> .process(WindowFunction(), typeInfo){code}
>
> Try to debug to trace
> ??pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks??
> and find
[jira] [Updated] (FLINK-32040) The WatermarkStrategy defined with the Function(with_idleness) report an error
[
https://issues.apache.org/jira/browse/FLINK-32040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-32040:
Description:
*version:* upgrade pyflink1.15.2 to pyflink1.16.1
*Report an error:*
Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
characteristic set to 'ProcessingTime', or did you forget to call
'data_stream.assign_timestamps_and_watermarks(...)'?
The application before with version 1.15.2 has never reported the error.
*Example:*
{code:java}
```python```
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp: int) -> int:
return value['version']
sql="""
select columns,version(milliseconds) from kafka_source
"""
table = st_env.sql_query(sql)
stream = st_env.to_changelog_stream(table)
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
stream = stream.key_by(CommonKeySelector()) \
.window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
.process(WindowFunction(), typeInfo){code}
Try to debug to trace
pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks and
find watermark_strategy._timestamp_assigner is none.
*Solution:*
Remove function–with_idleness(Duration.of_seconds(10))
{code:java}
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner())) {code}
Is this a bug?
was:
version: upgrade pyflink1.15.2 to pyflink1.16.1
Report an error:
Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
characteristic set to 'ProcessingTime', or did you forget to call
'data_stream.assign_timestamps_and_watermarks(...)'?
The application before with version 1.15.2 has never reported the error.
Example1 report an error:
{code:java}
```python```
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp: int) -> int:
return value['version']
sql="""
select columns,version(milliseconds) from kafka_source
"""
table = st_env.sql_query(sql)
stream = st_env.to_changelog_stream(table)
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
stream = stream.key_by(CommonKeySelector()) \
.window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
.process(WindowFunction(), typeInfo){code}
Try to debug to trace
pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks and
find
watermark_strategy._timestamp_assigner is none.
Solution:
Remove function–with_idleness(Duration.of_seconds(10))
{code:java}
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner())) {code}
Is this a bug?
> The WatermarkStrategy defined with the Function(with_idleness) report an error
> --
>
> Key: FLINK-32040
> URL: https://issues.apache.org/jira/browse/FLINK-32040
> Project: Flink
> Issue Type: Bug
> Components: API / Python
>Reporter: Joekwal
>Priority: Blocker
>
> *version:* upgrade pyflink1.15.2 to pyflink1.16.1
>
> *Report an error:*
> Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
> characteristic set to 'ProcessingTime', or did you forget to call
> 'data_stream.assign_timestamps_and_watermarks(...)'?
> The application before with version 1.15.2 has never reported the error.
>
> *Example:*
> {code:java}
> ```python```
> class MyTimestampAssigner(TimestampAssigner):
>def extract_timestamp(self, value, record_timestamp: int) -> int:
>return value['version']
> sql="""
> select columns,version(milliseconds) from kafka_source
> """
> table = st_env.sql_query(sql)
> stream = st_env.to_changelog_stream(table)
> stream = stream.assign_timestamps_and_watermarks(
> WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
>
> .with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
> stream = stream.key_by(CommonKeySelector()) \
> .window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
> .process(WindowFunction(), typeInfo){code}
>
> Try to debug to trace
> pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks
> and find watermark_strategy._timestamp_assigner
[jira] [Created] (FLINK-32040) The WatermarkStrategy defined with the Function(with_idleness) report an error
Joekwal created FLINK-32040:
---
Summary: The WatermarkStrategy defined with the
Function(with_idleness) report an error
Key: FLINK-32040
URL: https://issues.apache.org/jira/browse/FLINK-32040
Project: Flink
Issue Type: Bug
Components: API / Python
Reporter: Joekwal
version: upgrade pyflink1.15.2 to pyflink1.16.1
Report an error:
Record has Java Long.MIN_VALUE timestamp (= no timestamp marker). Is the time
characteristic set to 'ProcessingTime', or did you forget to call
'data_stream.assign_timestamps_and_watermarks(...)'?
The application before with version 1.15.2 has never reported the error.
Example1 report an error:
{code:java}
```python```
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp: int) -> int:
return value['version']
sql="""
select columns,version(milliseconds) from kafka_source
"""
table = st_env.sql_query(sql)
stream = st_env.to_changelog_stream(table)
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner()).with_idleness(Duration.of_seconds(10)))
stream = stream.key_by(CommonKeySelector()) \
.window(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8))) \
.process(WindowFunction(), typeInfo){code}
Try to debug to trace
pyflink.datastream.data_stream.DataStream.assign_timestamps_and_watermarks and
find
watermark_strategy._timestamp_assigner is none.
Solution:
Remove function–with_idleness(Duration.of_seconds(10))
{code:java}
stream = stream.assign_timestamps_and_watermarks(
WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_minutes(1))
.with_timestamp_assigner(MyTimestampAssigner())) {code}
Is this a bug?
--
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(v8.20.10#820010)
[jira] [Closed] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
[
https://issues.apache.org/jira/browse/FLINK-30977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal closed FLINK-30977.
---
Resolution: Fixed
> flink tumbling window stream converting to pandas dataframe not work
>
>
> Key: FLINK-30977
> URL: https://issues.apache.org/jira/browse/FLINK-30977
> Project: Flink
> Issue Type: Bug
> Components: API / Python
> Environment: pyflink1.15.2
>Reporter: Joekwal
>Priority: Major
>
> I want to know if tumbling window supported to convert to pandas?
> {code:java}
> code... #create env
> kafka_src = """
> CREATE TABLE if not exists `kafka_src` (
> ...
> `event_time` as CAST(`end_time` as TIMESTAMP(3)),
> WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
> )
> with (
> 'connector' = 'kafka',
> 'topic' = 'topic',
> 'properties.bootstrap.servers' = '***',
> 'properties.group.id' = '***',
> 'scan.startup.mode' = 'earliest-offset',
> 'value.format' = 'debezium-json'
> );
> """
>
> t_env.execute_sql(kafka_src)
> table = st_env.sql_query("SELECT columns,`event_time` \
> FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
> MINUTES))")
> table.execute().print() #could print the result
> df = table.to_pandas()
> #schema is correct!
> schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
> ...
> ])
> table = st_env.from_pandas(df,schema=schema)
> st_env.create_temporary_view("view_table",table)
> st_env.sql_query("select * from view_table").execute().print() # Not
> work!Can't print the result {code}
> Tumbling window stream from kafka source convert to pandas dataframe and it
> can't print the result.The schema is right.I have tested in another job with
> using batch stream from jdbc source.It can print the result.The only
> different thing is the input stream.Is tumbling windows supported to convert
> to Pandas?
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
[jira] [Updated] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
[
https://issues.apache.org/jira/browse/FLINK-30977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-30977:
Description:
I want to know if tumbling window supported to convert to pandas?
{code:java}
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result {code}
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.Is tumbling windows supported to convert to Pandas?
was:
I want to know if tumbling window supported to convert to pandas?
{code:java}
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result {code}
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
> flink tumbling window stream converting to pandas dataframe not work
>
>
> Key: FLINK-30977
> URL: https://issues.apache.org/jira/browse/FLINK-30977
> Project: Flink
> Issue Type: Bug
> Components: API / Python
> Environment: pyflink1.15.2
>Reporter: Joekwal
>Priority: Major
>
> I want to know if tumbling window supported to convert to pandas?
> {code:java}
> code... #create env
> kafka_src = """
> CREATE TABLE if not exists `kafka_src` (
> ...
> `event_time` as CAST(`end_time` as TIMESTAMP(3)),
> WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
> )
> with (
> 'connector' = 'kafka',
> 'topic' = 'topic',
> 'properties.bootstrap.servers' = '***',
> 'properties.group.id' = '***',
> 'scan.startup.mode' = 'earliest-offset',
> 'value.format' = 'debezium-json'
> );
> """
>
> t_env.execute_sql(kafka_src)
> table = st_env.sql_query("SELECT columns,`event_time` \
> FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
> MINUTES))")
> table.execute().print() #could print the result
> df = table.to_pandas()
> #schema is correct!
> schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
> ...
> ])
> table = st_env.from_pandas(df,schema=schema)
> st_env.create_temporary_view("view_table",table)
> st_env.sql_query("select * from view_table").execute().print() # Not
> work!Can't print the result {code}
> Tumbling window stream from kafka source convert to pandas dataframe and it
> can't print the result.The schema is right.I have tested in another job with
> using batch stream from jdbc source.It can print the result.The only
> different thing is the input stream.Is tumbling windows supported to convert
> to Pandas?
--
This message was
[jira] [Updated] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
[
https://issues.apache.org/jira/browse/FLINK-30977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-30977:
Description:
I want to know if tumbling window supported to convert to pandas?
{code:java}
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result {code}
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
was:
I want to know if tumbling window supported to convert to pandas?
{code:java}
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result {code}
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
> flink tumbling window stream converting to pandas dataframe not work
>
>
> Key: FLINK-30977
> URL: https://issues.apache.org/jira/browse/FLINK-30977
> Project: Flink
> Issue Type: Bug
> Environment: pyflink1.15.2
>Reporter: Joekwal
>Priority: Major
>
> I want to know if tumbling window supported to convert to pandas?
> {code:java}
> code... #create env
> kafka_src = """
> CREATE TABLE if not exists `kafka_src` (
> ...
> `event_time` as CAST(`end_time` as TIMESTAMP(3)),
> WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
> )
> with (
> 'connector' = 'kafka',
> 'topic' = 'topic',
> 'properties.bootstrap.servers' = '***',
> 'properties.group.id' = '***',
> 'scan.startup.mode' = 'earliest-offset',
> 'value.format' = 'debezium-json'
> );
> """
>
> t_env.execute_sql(kafka_src)
> table = st_env.sql_query("SELECT columns,`event_time` \
> FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
> MINUTES))")
> table.execute().print() #could print the result
> df = table.to_pandas()
> #schema is correct!
> schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
> ...
> ])
> table = st_env.from_pandas(df,schema=schema)
> st_env.create_temporary_view("view_table",table)
> st_env.sql_query("select * from view_table").execute().print() # Not
> work!Can't print the result {code}
> Tumbling window stream from kafka source convert to pandas dataframe and it
> can't print the result.The schema is right.I have tested in another job with
> using batch stream from jdbc source.It can print the result.The only
> different thing is the input stream.As doc mentioned, the bounded stream is
> supported to convert to
[jira] [Updated] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
[
https://issues.apache.org/jira/browse/FLINK-30977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-30977:
Description:
I want to know if tumbling window supported to convert to pandas?
{code:java}
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result {code}
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
was:
I want to know if tumbling window supported to convert to pandas?
```
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result
```
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
> flink tumbling window stream converting to pandas dataframe not work
>
>
> Key: FLINK-30977
> URL: https://issues.apache.org/jira/browse/FLINK-30977
> Project: Flink
> Issue Type: Bug
> Environment: pyflink1.15.2
>Reporter: Joekwal
>Priority: Major
>
> I want to know if tumbling window supported to convert to pandas?
> {code:java}
> code... #create env
> kafka_src = """
> CREATE TABLE if not exists `kafka_src` (
> ...
> `event_time` as CAST(`end_time` as TIMESTAMP(3)),
> WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
> )
> with (
> 'connector' = 'kafka',
> 'topic' = 'topic',
> 'properties.bootstrap.servers' = '***',
> 'properties.group.id' = '***',
> 'scan.startup.mode' = 'earliest-offset',
> 'value.format' = 'debezium-json'
> );
> """
> t_env.execute_sql(kafka_src)
> table = st_env.sql_query("SELECT columns,`event_time` \
> FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
> MINUTES))")
> table.execute().print() #could print the result
> df = table.to_pandas()
> #schema is correct!
> schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
> ...
> ])
> table = st_env.from_pandas(df,schema=schema)
> st_env.create_temporary_view("view_table",table)
> st_env.sql_query("select * from view_table").execute().print() # Not
> work!Can't print the result {code}
> Tumbling window stream from kafka source convert to pandas dataframe and it
> can't print the result.The schema is right.I have tested in another job with
> using batch stream from jdbc source.It can print the result.The only
> different thing is the input stream.As doc mentioned, the bounded stream is
> supported to convert to pandas.So
[jira] [Updated] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
[
https://issues.apache.org/jira/browse/FLINK-30977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joekwal updated FLINK-30977:
Description:
I want to know if tumbling window supported to convert to pandas?
```
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result
```
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
was:
I want to know if tumbling window supported to convert to pandas?
```
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
# table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result
```
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
> flink tumbling window stream converting to pandas dataframe not work
>
>
> Key: FLINK-30977
> URL: https://issues.apache.org/jira/browse/FLINK-30977
> Project: Flink
> Issue Type: Bug
> Environment: pyflink1.15.2
>Reporter: Joekwal
>Priority: Major
>
> I want to know if tumbling window supported to convert to pandas?
> ```
> code... #create env
> kafka_src = """
> CREATE TABLE if not exists `kafka_src` (
> ...
> `event_time` as CAST(`end_time` as TIMESTAMP(3)),
> WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
> )
> with (
> 'connector' = 'kafka',
> 'topic' = 'topic',
> 'properties.bootstrap.servers' = '***',
> 'properties.group.id' = '***',
> 'scan.startup.mode' = 'earliest-offset',
> 'value.format' = 'debezium-json'
> );
> """
> t_env.execute_sql(kafka_src)
> table = st_env.sql_query("SELECT columns,`event_time` \
> FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
> MINUTES))")
> table.execute().print() #could print the result
> df = table.to_pandas()
> #schema is correct!
> schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
> ...
> ])
> table = st_env.from_pandas(df,schema=schema)
> st_env.create_temporary_view("view_table",table)
> st_env.sql_query("select * from view_table").execute().print() # Not
> work!Can't print the result
> ```
> Tumbling window stream from kafka source convert to pandas dataframe and it
> can't print the result.The schema is right.I have tested in another job with
> using batch stream from jdbc source.It can print the result.The only
> different thing is the input stream.As doc mentioned, the bounded stream is
> supported to convert to pandas.So what could
[jira] [Created] (FLINK-30977) flink tumbling window stream converting to pandas dataframe not work
Joekwal created FLINK-30977:
---
Summary: flink tumbling window stream converting to pandas
dataframe not work
Key: FLINK-30977
URL: https://issues.apache.org/jira/browse/FLINK-30977
Project: Flink
Issue Type: Bug
Environment: pyflink1.15.2
Reporter: Joekwal
I want to know if tumbling window supported to convert to pandas?
```
code... #create env
kafka_src = """
CREATE TABLE if not exists `kafka_src` (
...
`event_time` as CAST(`end_time` as TIMESTAMP(3)),
WATERMARK FOR event_time as event_time - INTERVAL '5' SECOND
)
with (
'connector' = 'kafka',
'topic' = 'topic',
'properties.bootstrap.servers' = '***',
'properties.group.id' = '***',
'scan.startup.mode' = 'earliest-offset',
'value.format' = 'debezium-json'
);
"""
t_env.execute_sql(kafka_src)
table = st_env.sql_query("SELECT columns,`event_time` \
FROM TABLE(TUMBLE(TABLE table_name, DESCRIPTOR(event_time), INTERVAL '1'
MINUTES))")
# table.execute().print() #could print the result
df = table.to_pandas()
#schema is correct!
schema = DataTypes.ROW([DataTypes.FIELD("column1", DataTypes.STRING()),
...
])
table = st_env.from_pandas(df,schema=schema)
st_env.create_temporary_view("view_table",table)
st_env.sql_query("select * from view_table").execute().print() # Not work!Can't
print the result
```
Tumbling window stream from kafka source convert to pandas dataframe and it
can't print the result.The schema is right.I have tested in another job with
using batch stream from jdbc source.It can print the result.The only different
thing is the input stream.As doc mentioned, the bounded stream is supported to
convert to pandas.So what could have gone wrong?
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