Both SchemaRDD and DataFrame sound fine to me, though I like the former slightly better because it's more descriptive.
Even if SchemaRDD's needs to rely on Spark SQL under the covers, it would be more clear from a user-facing perspective to at least choose a package name for it that omits "sql". I would also be in favor of adding a separate Spark Schema module for Spark SQL to rely on, but I imagine that might be too large a change at this point? -Sandy On Mon, Jan 26, 2015 at 5:32 PM, Matei Zaharia <matei.zaha...@gmail.com> wrote: > (Actually when we designed Spark SQL we thought of giving it another name, > like Spark Schema, but we decided to stick with SQL since that was the most > obvious use case to many users.) > > Matei > > > On Jan 26, 2015, at 5:31 PM, Matei Zaharia <matei.zaha...@gmail.com> > wrote: > > > > While it might be possible to move this concept to Spark Core long-term, > supporting structured data efficiently does require quite a bit of the > infrastructure in Spark SQL, such as query planning and columnar storage. > The intent of Spark SQL though is to be more than a SQL server -- it's > meant to be a library for manipulating structured data. Since this is > possible to build over the core API, it's pretty natural to organize it > that way, same as Spark Streaming is a library. > > > > Matei > > > >> On Jan 26, 2015, at 4:26 PM, Koert Kuipers <ko...@tresata.com> wrote: > >> > >> "The context is that SchemaRDD is becoming a common data format used for > >> bringing data into Spark from external systems, and used for various > >> components of Spark, e.g. MLlib's new pipeline API." > >> > >> i agree. this to me also implies it belongs in spark core, not sql > >> > >> On Mon, Jan 26, 2015 at 6:11 PM, Michael Malak < > >> michaelma...@yahoo.com.invalid> wrote: > >> > >>> And in the off chance that anyone hasn't seen it yet, the Jan. 13 Bay > Area > >>> Spark Meetup YouTube contained a wealth of background information on > this > >>> idea (mostly from Patrick and Reynold :-). > >>> > >>> https://www.youtube.com/watch?v=YWppYPWznSQ > >>> > >>> ________________________________ > >>> From: Patrick Wendell <pwend...@gmail.com> > >>> To: Reynold Xin <r...@databricks.com> > >>> Cc: "dev@spark.apache.org" <dev@spark.apache.org> > >>> Sent: Monday, January 26, 2015 4:01 PM > >>> Subject: Re: renaming SchemaRDD -> DataFrame > >>> > >>> > >>> One thing potentially not clear from this e-mail, there will be a 1:1 > >>> correspondence where you can get an RDD to/from a DataFrame. > >>> > >>> > >>> On Mon, Jan 26, 2015 at 2:18 PM, Reynold Xin <r...@databricks.com> > wrote: > >>>> Hi, > >>>> > >>>> We are considering renaming SchemaRDD -> DataFrame in 1.3, and wanted > to > >>>> get the community's opinion. > >>>> > >>>> The context is that SchemaRDD is becoming a common data format used > for > >>>> bringing data into Spark from external systems, and used for various > >>>> components of Spark, e.g. MLlib's new pipeline API. We also expect > more > >>> and > >>>> more users to be programming directly against SchemaRDD API rather > than > >>> the > >>>> core RDD API. SchemaRDD, through its less commonly used DSL originally > >>>> designed for writing test cases, always has the data-frame like API. > In > >>>> 1.3, we are redesigning the API to make the API usable for end users. > >>>> > >>>> > >>>> There are two motivations for the renaming: > >>>> > >>>> 1. DataFrame seems to be a more self-evident name than SchemaRDD. > >>>> > >>>> 2. SchemaRDD/DataFrame is actually not going to be an RDD anymore > (even > >>>> though it would contain some RDD functions like map, flatMap, etc), > and > >>>> calling it Schema*RDD* while it is not an RDD is highly confusing. > >>> Instead. > >>>> DataFrame.rdd will return the underlying RDD for all RDD methods. > >>>> > >>>> > >>>> My understanding is that very few users program directly against the > >>>> SchemaRDD API at the moment, because they are not well documented. > >>> However, > >>>> oo maintain backward compatibility, we can create a type alias > DataFrame > >>>> that is still named SchemaRDD. This will maintain source compatibility > >>> for > >>>> Scala. That said, we will have to update all existing materials to use > >>>> DataFrame rather than SchemaRDD. > >>> > >>> --------------------------------------------------------------------- > >>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > >>> For additional commands, e-mail: dev-h...@spark.apache.org > >>> > >>> --------------------------------------------------------------------- > >>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > >>> For additional commands, e-mail: dev-h...@spark.apache.org > >>> > >>> > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > >
