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From Adrian Cole <>
Subject Re: Perpetual support problems using Spark for dependency link aggregation
Date Wed, 20 Mar 2019 14:39:20 GMT
Definitely appreciated.

The most primary concern is knowing if there is something about our
job that precludes it from running across multiple hosts and how in
simplest sense to validate such a thing (ideally in a testcontainers
I don't think generically speaking spark team can influence the
elasticsearch drift issue, but if at least we can get a review it can
stop the bleeding. We've invested a lot in this job.. years.. it would
be best to be able to continue.

Thanks again,

On Wed, Mar 20, 2019 at 8:25 PM Andriy Redko <> wrote:
> Hi Adrian,
> Got it, I am drafting an email to send to Spark users list, asking for help, there are
> that someone either steps in or pass the message around, so we could at least try to
> some contributors. Do you think it makes sense? Thanks!
> Best Regards,
>     Andriy Redko
> AC> Hi, Andriy
> AC> Thanks for responding. I dont think we can assume there will always be a choice
for streaming or online aggregation.
> AC> The two easiest ways out would be a spark guru (ideally gurus) steeping forward
or an easier to support alternative
> AC> for after the fact aggregation over large datasets that minimally works with mysql
ES and Cassandra.
> AC> -A
> AC> On Tue, Mar 19, 2019, 7:20 PM Andriy Redko <> wrote:
> AC> Hi Adrian,
> AC>  First of all, I want to confirm from the personal experiences, the dependencies
> AC>  are often built after the fact, so there is a real need for this kind of job/component.
> AC>  There are many choices, either to use the data processing engines you mentioned,
> AC>  or onboard the data store with aggregation capabalities (may ClickHouse fe).
> AC>  do you think would be the best route for Zipkin? Keep the Spark but look for
> AC>  maintenance help? Or (re)write it altogether, ideally with no data engines
> AC>  needed? Just trying to understand how you envision it.
> AC>  Best Regards,
> AC>      Andriy Redko
>  AC>> Hi, team.
>  AC>> A long time ago, we arbitrarily used spark for dependency link
>  AC>> aggregation (porting the work from Eirik's hadoop job). The initial
>  AC>> spark job was created incomplete then abandoned by the author. I've
>  AC>> tried a lot to support it, but it has been perpetual maintenance and
>  AC>> most of us have no idea how to support it. Yet, we get a lot of user
>  AC>> questions about it and the support load is higher than most of our
>  AC>> projects.
>  AC>> The Elasticsearch part is landmines from the "wan only" stuff, to them
>  AC>> having a narrow supported range of versions. It is rev-locked to a JRE
>  AC>> (even if will change later). We've had users complain about CVE
>  AC>> maintenance and actively ask for a non-spark option. General support
>  AC>> comes in questions about cluster distribution which no-one knows the
>  AC>> answer to. I've recently in desperation added a change to help show
>  AC>> where Spark support is.
>  AC>>
>  AC>> All this said, despite the problems running distributed or with
>  AC>> elasticsearch, most can start the zipkin-dependencies job as a
>  AC>> one-shot cron job without much help.
>  AC>> I think we have to be honest about the fact that since this project
>  AC>> started, we've rarely had anyone able to support it. I hope we can get
>  AC>> out of the mutually disappointing support swamp. Does anyone have any
>  AC>> ideas?
>  AC>> I would like to think someone could come in and save us, but seems we
>  AC>> should also consider other tools as that usually doesn't happen, and
>  AC>> one person saving us isn't sustainable (usually we need a few people
>  AC>> to know a tool in order to realistically support it). It is possible
>  AC>> to recruit for this, but we need significant close buy-in from people
>  AC>> who know spark imho, like actually helping with support, if we want to
>  AC>> continue this path.
>  AC>> I know there's a Kafka streaming option [1]. I also know some have
>  AC>> used Flink, and some have had interest in Pulsar. I think we should
>  AC>> have streaming options, but fact is many don't use any buffer like
>  AC>> Kafka (direct http), which leads me to think we still need an
>  AC>> after-the-fact option (pull from storage). Moreover spark's embedded
>  AC>> mode is nice as it can be treated as a dumb cron job.
>  AC>> Looking for ideas,
>  AC>> -A
>  AC>> [1]
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