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From Jonathan Share <jon.sh...@gmail.com>
Subject Re: S3 checkpointing in AWS in Frankfurt
Date Wed, 23 Nov 2016 17:48:05 GMT
Hi Scott,

Thanks for the suggestion, it sounds like you and I think alike, going over
to hdfs sounds to me like the simplest solution.

There are no requirements to use S3, just another team member who is
generally sceptical fearing that adding HDFS will add a new class of
maintenance problems to our stack, and the project has a general goal of
using managed services as much as possible so we wanted to try and make it
work.

Regards,
Jonathan


On 23 November 2016 at 16:38, Scott Kidder <kidder.scott@gmail.com> wrote:

> Hi Jonathan,
>
> You might be better off creating a small Hadoop HDFS cluster just for the
> purpose of storing Flink checkpoint & savepoint data. Like you, I tried
> using S3 to persist Flink state, but encountered AWS SDK issues and felt
> like I was going down an ill-advised path. I then created a small 3-node
> HDFS cluster in the same region as my Flink hosts but distributed across 3
> AZs. The checkpointing is very fast and, most importantly, just works.
>
> Is there a firm requirement to use S3, or could you use HDFS instead?
>
> Best,
>
> --Scott Kidder
>
> On Tue, Nov 22, 2016 at 11:52 PM, Jonathan Share <jon.share@gmail.com>
> wrote:
>
>> Hi,
>>
>> I'm interested in hearing if anyone else has experience with using Amazon
>> S3 as a state backend in the Frankfurt region. For political reasons we've
>> been asked to keep all European data in Amazon's Frankfurt region. This
>> causes a problem as the S3 endpoint in Frankfurt requires the use of AWS
>> Signature Version 4 "This new Region supports only Signature Version 4"
>> [1] and this doesn't appear to work with the Hadoop version that Flink is
>> built against [2].
>>
>> After some hacking we have managed to create a docker image with a build
>> of Flink 1.2 master, copying over jar files from the hadoop
>> 3.0.0-alpha1 package and this appears to work, for the most part but we
>> still suffer from some classpath problems (conflicts between AWS API used
>> in hadoop and those we want to use in out streams for interacting with
>> Kinesis) and the whole thing feels a little fragile. Has anyone else tried
>> this? Is there a simpler solution?
>>
>> As a follow-up question, we saw that with checkpointing on three
>> relatively simple streams set to 1 second, our S3 costs were higher than
>> the EC2 costs for our entire infrastructure. This seems slightly
>> disproportionate. For now we have reduced checkpointing interval to 10
>> seconds and that has greatly improved the cost projections graphed via
>> Amazon Cloud Watch, but I'm interested in hearing other peoples experience
>> with this. Is that the kind of billing level we can expect or is this a
>> symptom of a mis-configuration? Is this a setup others are using? As we are
>> using Kinesis as the source for all streams I don't see a huge risk with
>> larger checkpoint intervals and our Sinks are designed to mostly tolerate
>> duplicates (some improvements can be made).
>>
>> Thanks in advance
>> Jonathan
>>
>>
>> [1] https://aws.amazon.com/blogs/aws/aws-region-germany/
>> [2] https://issues.apache.org/jira/browse/HADOOP-13324
>>
>
>

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