先编译正确后，再debug
tangzhi8...@gmail.com 于2021年6月28日周一 下午3:02写道：
> 目的：想在本地环境IDEA远程调试Flink
> 步骤：
> 1.这是Debug的配置项
> 2.报错堆栈信息：
> org.apache.flink.client.program.ProgramInvocationException: The main
> method caused an error: Failed to execute job 'Streaming WordCount'.
> at
>
FWIW I had to do something similar in the past. My solution was to…
1. Create a custom reader that added the source directory to the input data (so
I had a Tuple2
2. Create a job that reads from all source directories, using HadoopInputFormat
for text
3. Constrain the parallelism of this
Hi Adrian,
Could you share your state backend configuration ?
Regards,
Maciek
pt., 9 lip 2021 o 19:09 Adrian Bednarz napisał(a):
>
> Hello,
>
> We are experimenting with lookup joins in Flink 1.13.0. Unfortunately, we
> unexpectedly hit significant performance degradation when changing the
Hi
只要enable了checkpoint，一定会生成checkpoint的，这与你的运行模式无关。可以检查一下日志，看看JM端是否正常触发了checkpoint
祝好
唐云
From: casel.chen
Sent: Tuesday, June 29, 2021 9:55
To: user-zh@flink.apache.org
Subject: local运行模式下不会生成checkpoint吗？
我在本地使用local运行模式运行flink sql，将数据从kafka写到mongodb，mongodb
Hello,
We are experimenting with lookup joins in Flink 1.13.0. Unfortunately, we
unexpectedly hit significant performance degradation when changing the
state backend to RocksDB.
We performed tests with two tables: fact table TXN and dimension table
CUSTOMER with the following schemas:
TXN:
|--
flink运行在原生k8s上，现在想要修改Root Logger Level和动态添加 Logger Name -> Logger
Level，以及用户可以传入自定义的日志模板，目前有办法做到么？
Hi!
You can define your sink with the following schema:
CREATE TABLE kafka_sink (
employee ROW
) WITH (
'connector' = 'kafka',
'format' = 'json'
// other properties...
);
You can also insert into this sink with the following SQL:
INSERT INTO kafka_sink SELECT ROW(id, name) FROM
Hi community,
I'll receive json message from Kafka, convert flat json to nested json and send
it back to Kafka.
receive message from Kafka: {“id”:"001","name":"wang"}
send message back to Kafka: {"employee":{“id”:"001","name":"wang"}}
How to do it in Flink sql?
1095193...@qq.com
Hi,
We have a Flink 1.11.1 Version streaming pipeline in production which reads
from Kafka.
Kafka Server version is 2.5.0 - confluent 5.5.0
Kafka Client Version is 2.4.1 -
{"component":"org.apache.kafka.common.utils.AppInfoParser$AppInfo","message":"Kafka
version: 2.4.1","method":""}
我定义了一个kafka来源的table，sql查询时调了自定义函数， 但是发现参数不能被正确传递给自定义函数eval.
我用的flink版本是1.10.0.
l json 的ddl如下：
private static final String personKafkaTable = "CREATE TABLE
hw_person_normal_t(\n"
+ " data ARRAY>,\n"
+ " key STRING,\n"
+ " operation STRING\n"
+ ") with (\n"
+
Gen is right with his explanation why the dead TM discovery can be faster
with Flink < 1.12.
Concerning flaky TaskManager connections:
2.1 I think the problem is that the receiving TM does not know the
container ID of the sending TM. It only knows its address. But this is
something one could
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