One more thing I missed, commit metadata for the batch N must be written
"after" all other parts of the checkpoint are successfully written for the
batch N.
So you seem to find a way to do asynchronous commit on "custom state store
provider" - as I commented before, it's being tied to the task
Thank you for the reply. For our use case, it's okay to not have
exactly-once semantics. Given this use case of not needing exactly-once
a) Is there any negative implications if one were to use a custom state
store provider which asynchronously committed under the hood
b) Is there any other option
I see some points making async checkpoint be tricky to add in micro-batch;
one example is "end to end exactly-once", as the commit phase in sink for
the batch N can be run "after" the batch N + 1 has been started and write
for batch N + 1 can happen before committing batch N. state store
Hi,
I have been experimenting with the Continuous mode and the Micro batch mode
in Spark Structured Streaming. When enabling checkpoint to S3 instead of
the local File System we see that Continuous mode has no change in latency
(expected due to async checkpointing) however the Micro-batch mode
hey, Yi Wu
Looks like it's just an apt installation problem, we should do apt update
to refresh the local package cache list before we install the "gnupg".
I opened a issue on jira [1] , and try to fix it in [2], hope this helps.
[1] https://issues.apache.org/jira/browse/SPARK-34820
[2]
Hi devs,
It seems like the K8s Integration test is unable to run recently because of
the unavailable libs:
Err:20 http://security.debian.org/debian-security buster/updates/main
amd64 libldap-common all 2.4.47+dfsg-3+deb10u4
404 Not Found [IP: 151.101.194.132 80]
Err:21
