Hi,
I'd add another one regarding Java hashCode() and its practical
usability for distributed systems [1], although practically all (Java
based) data processing systems rely on it.
One bug directly related to this I once saw was, that using an Enum
inside other object used as partitioning key results in really hard to
debug bugs. Mostly because during local testing everything works just
fine, problem arises only when multiple JVMs are involved. This is
caused by the fact, that hashCode() of Enum is derived from associated
memory position.
Jan
[1]
https://martin.kleppmann.com/2012/06/18/java-hashcode-unsafe-for-distributed-systems.html
On 10/1/19 11:45 AM, Piotr Nowojski wrote:
Hi,
Are you asking about bugs in Flink, in libraries that Flink is using or bugs in
applications that were using Flink? From my perspective/what I have seen:
The most problematic bugs while developing features for Flink:
Dead locks & data losses caused by concurrency issues in network stack
after changing some trivial things in new data notifications.
Data visibility issues for concurrent writes/reads when implementing S3
connector.
The most problematic bug/type of bugs in the Dependencies:
Dead locks in the external connector (for example
https://issues.apache.org/jira/browse/KAFKA-6132
<https://issues.apache.org/jira/browse/KAFKA-6132> ). Integration with external
systems is always difficult. If you add concurrency issues to the mix…
The most problematic bug in the Flink application:
Being unaware that for some reasons, some unknown to me code was
interrupting (SIGINT) threads spawned by a custom SourceFunction, that were
emitting the data, when the job was back pressured. This was causing records
serialisation very rarerly to be interrupted in the middle showing up on the
down stream receiver as deserialisation errors.
Piotrek
On 1 Oct 2019, at 04:18, Konstantinos Kallas <konstantinos.kal...@hotmail.com>
wrote:
Hi everyone.
I wanted to ask Flink users what are the most subtle Flink bugs that
people have witnessed. The cause of the bugs could be anything (e.g.
wrong assumptions on data, parallelism of non-parallel operator, simple
mistakes).
We are developing a testing framework for Flink and it would be
interesting to have examples of difficult to spot bugs to evaluate our
testing framework on.
Thanks,
Konstantinos Kallas
