Hi Ricardo,

one option is to use s3p for checkpointing (Presto) and s3a for custom
applications and attach different configurations.

In general, I'd recommend to use a cluster per application to exactly avoid
such issues. I'd use K8s and put the respective IAM roles on each
application pod (e.g. with kiam).

On Thu, Jun 18, 2020 at 1:46 AM Ricardo Cardante <
ricardocarda...@tutanota.com> wrote:

> Hi!
>
>
> We are working in a use case where we have a shared Flink cluster to
> deploy multiple jobs from different teams. With this strategy, we are
> facing a challenge regarding the interaction with S3. Given that we already
> configured S3 for the state backend (through flink-conf.yaml) every time we
> use API functions that communicate with the file system (e.g., DataStream
> readFile) the applicational configurations appear to be overridden by those
> of the cluster while attempting to communicate with external S3 buckets.
> What we've thought so far:
>
>
> 1. Provide a core-site.xml resource file targeting the external S3 buckets
> we want to interact with. We've tested, and the credentials ultimately seem
> to be ignored in behalf of the IAM roles that are pre-loaded with the
> instances;
>
> 2. Load the cluster instances with multiple IAM roles. The problem with
> this is that we would allow each job to interact with out-of-scope buckets;
>
> 3. Spin multiple clusters with different configurations - we would like to
> avoid this since we started from the premise of sharing a single cluster
> per context;
>
>
> What would be a clean/recommended solution to interact with multiple S3
> buckets with different security policies from a shared Flink cluster?
>
> Thanks in advance.
>


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