I want to address the issue that Matei raised about the heavy lifting required for a full SQL support. It is amazing that even after 30 years of research there is not a single good open source columnar database like Vertica. There is a column store option in MySQL, but it is not nearly as sophisticated as Vertica or MonetDB. But there's a true need for such a system. I wonder why so and it's high time to change that. On Jan 26, 2015 5:47 PM, "Sandy Ryza" <sandy.r...@cloudera.com> wrote:
> 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 > > > > >
