For which version to target, I think we should survey the Druid community and get input. In your case, which version are you currently deploying? Historical experience tells me we should target current and current-1 (3.4.x and 3.3.x)
In terms of the writer (Spark writes to Druid), what's the user workflow you envision? Would you think the user would trigger a spark job from Druid? Or is this user who is submitting a Spark job to target a Druid cluster? The former allows other systems, like compaction, for example, to use Spark as a runner. In terms of the reader (Spark reads Druid). I'm most curious to find out what experience you are imagining. Should the reader be reading Druid segment files or would the reader issue queries to Druid (maybe even to historicals?) so that query can be parallelized? Of the two, there is a lot more interest in the writer from the people I've been talking to. Regards, Will On Tue, Aug 8, 2023 at 8:50 AM Julian Jaffe <julianfja...@gmail.com> wrote: > Hey all, > > There was talk earlier this year about resurrecting the effort to add > direct Spark readers and writers to Druid. Rather than repeat the previous > attempt and parachute in with updated connectors, I’d like to start by > building a little more consensus around what the Druid dev community wants > as potential maintainers. > > To begin with, I want to solicit opinions on two topics: > > Should these connectors be written in Scala or Java? The benefits of Scala > would be that the existing connectors are written in Scala, as are most > open source references for Spark Datasource V2 implementations. The > benefits of Java are that Druid is written in Java, and so engineers > interested in contributing to Druid wouldn’t need to switch between > languages. Additionally, existing tooling, static checkers, etc. could be > used with minimal effort, conforming code style and developer ergonomics > across Druid instead of needing to keep an alternate Scala tool chain in > sync. > Which Spark version should this effort target? The most recently released > version of Spark is 3.4.1. Should we aim to integrate with the latest Spark > minor version under the assumption that this will give us the longest > window of support, or should we build against an older minor line (3.3? > 3.2?) since most Spark users tend to lag? For reference, there are > currently 3 stable Spark release versions, 3.2.4, 3.3.2, and 3.4.1. From a > user’s point of view, the API is mostly compatible across a major version > (i.e. 3.x), while developer APIs such as the ones we would use to build > these connectors can change between minor versions. > There are quite a few nuances and trade offs inherent to the decisions > above, and my hope is that by hashing these choices out before presenting > an implementation we can build buy-in from the Druid maintainer community > that will result in this effort succeeding where the first effort failed. > > Thanks, > Julian
