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From Andriy Redko <>
Subject Re: Perpetual support problems using Spark for dependency link aggregation
Date Tue, 19 Mar 2019 11:20:19 GMT
Hi Adrian,

First of all, I want to confirm from the personal experiences, the dependencies
are often built after the fact, so there is a real need for this kind of job/component. 
There are many choices, either to use the data processing engines you mentioned,
or onboard the data store with aggregation capabalities (may ClickHouse fe). What
do you think would be the best route for Zipkin? Keep the Spark but look for
maintenance help? Or (re)write it altogether, ideally with no data engines
needed? Just trying to understand how you envision it.

Best Regards,
    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> 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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