Koert, As Mark said, I have already refactored the API so that nothing is catalyst is exposed (and users won't need them anyway). Data types, Row interfaces are both outside catalyst package and in org.apache.spark.sql.
On Tue, Jan 27, 2015 at 9:08 AM, Koert Kuipers <ko...@tresata.com> wrote: > hey matei, > i think that stuff such as SchemaRDD, columar storage and perhaps also > query planning can be re-used by many systems that do analysis on > structured data. i can imagine panda-like systems, but also datalog or > scalding-like (which we use at tresata and i might rebase on SchemaRDD at > some point). SchemaRDD should become the interface for all these. and > columnar storage abstractions should be re-used between all these. > > currently the sql tie in is way beyond just the (perhaps unfortunate) > naming convention. for example a core part of the SchemaRD abstraction is > Row, which is org.apache.spark.sql.catalyst.expressions.Row, forcing anyone > that want to build on top of SchemaRDD to dig into catalyst, a SQL Parser > (if i understand it correctly, i have not used catalyst, but it looks > neat). i should not need to include a SQL parser just to use structured > data in say a panda-like framework. > > best, koert > > > On Mon, Jan 26, 2015 at 8:31 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 >> >> >> >> >> >> >
