I also feel the same as Reynold. I agree we should minimize API breaks and focus on fixing things around the edge that were mistakes (e.g. exposing Guava and Akka) rather than any overhaul that could fragment the community. Ideally a major release is a lightweight process we can do every couple of years, with minimal impact for users.
- Patrick On Tue, Nov 10, 2015 at 3:35 PM, Nicholas Chammas < nicholas.cham...@gmail.com> wrote: > > For this reason, I would *not* propose doing major releases to break > substantial API's or perform large re-architecting that prevent users from > upgrading. Spark has always had a culture of evolving architecture > incrementally and making changes - and I don't think we want to change this > model. > > +1 for this. The Python community went through a lot of turmoil over the > Python 2 -> Python 3 transition because the upgrade process was too painful > for too long. The Spark community will benefit greatly from our explicitly > looking to avoid a similar situation. > > > 3. Assembly-free distribution of Spark: don’t require building an > enormous assembly jar in order to run Spark. > > Could you elaborate a bit on this? I'm not sure what an assembly-free > distribution means. > > Nick > > On Tue, Nov 10, 2015 at 6:11 PM Reynold Xin <r...@databricks.com> wrote: > >> I’m starting a new thread since the other one got intermixed with feature >> requests. Please refrain from making feature request in this thread. Not >> that we shouldn’t be adding features, but we can always add features in >> 1.7, 2.1, 2.2, ... >> >> First - I want to propose a premise for how to think about Spark 2.0 and >> major releases in Spark, based on discussion with several members of the >> community: a major release should be low overhead and minimally disruptive >> to the Spark community. A major release should not be very different from a >> minor release and should not be gated based on new features. The main >> purpose of a major release is an opportunity to fix things that are broken >> in the current API and remove certain deprecated APIs (examples follow). >> >> For this reason, I would *not* propose doing major releases to break >> substantial API's or perform large re-architecting that prevent users from >> upgrading. Spark has always had a culture of evolving architecture >> incrementally and making changes - and I don't think we want to change this >> model. In fact, we’ve released many architectural changes on the 1.X line. >> >> If the community likes the above model, then to me it seems reasonable to >> do Spark 2.0 either after Spark 1.6 (in lieu of Spark 1.7) or immediately >> after Spark 1.7. It will be 18 or 21 months since Spark 1.0. A cadence of >> major releases every 2 years seems doable within the above model. >> >> Under this model, here is a list of example things I would propose doing >> in Spark 2.0, separated into APIs and Operation/Deployment: >> >> >> APIs >> >> 1. Remove interfaces, configs, and modules (e.g. Bagel) deprecated in >> Spark 1.x. >> >> 2. Remove Akka from Spark’s API dependency (in streaming), so user >> applications can use Akka (SPARK-5293). We have gotten a lot of complaints >> about user applications being unable to use Akka due to Spark’s dependency >> on Akka. >> >> 3. Remove Guava from Spark’s public API (JavaRDD Optional). >> >> 4. Better class package structure for low level developer API’s. In >> particular, we have some DeveloperApi (mostly various listener-related >> classes) added over the years. Some packages include only one or two public >> classes but a lot of private classes. A better structure is to have public >> classes isolated to a few public packages, and these public packages should >> have minimal private classes for low level developer APIs. >> >> 5. Consolidate task metric and accumulator API. Although having some >> subtle differences, these two are very similar but have completely >> different code path. >> >> 6. Possibly making Catalyst, Dataset, and DataFrame more general by >> moving them to other package(s). They are already used beyond SQL, e.g. in >> ML pipelines, and will be used by streaming also. >> >> >> Operation/Deployment >> >> 1. Scala 2.11 as the default build. We should still support Scala 2.10, >> but it has been end-of-life. >> >> 2. Remove Hadoop 1 support. >> >> 3. Assembly-free distribution of Spark: don’t require building an >> enormous assembly jar in order to run Spark. >> >>
