Isn't that just "null" in SQL? On Wed, Jan 28, 2015 at 4:41 PM, Evan Chan <velvia.git...@gmail.com> wrote:
> I believe that most DataFrame implementations out there, like Pandas, > supports the idea of missing values / NA, and some support the idea of > Not Meaningful as well. > > Does Row support anything like that? That is important for certain > applications. I thought that Row worked by being a mutable object, > but haven't looked into the details in a while. > > -Evan > > On Wed, Jan 28, 2015 at 4:23 PM, Reynold Xin <r...@databricks.com> wrote: > > It shouldn't change the data source api at all because data sources > create > > RDD[Row], and that gets converted into a DataFrame automatically > (previously > > to SchemaRDD). > > > > > https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala > > > > One thing that will break the data source API in 1.3 is the location of > > types. Types were previously defined in sql.catalyst.types, and now > moved to > > sql.types. After 1.3, sql.catalyst is hidden from users, and all public > APIs > > have first class classes/objects defined in sql directly. > > > > > > > > On Wed, Jan 28, 2015 at 4:20 PM, Evan Chan <velvia.git...@gmail.com> > wrote: > >> > >> Hey guys, > >> > >> How does this impact the data sources API? I was planning on using > >> this for a project. > >> > >> +1 that many things from spark-sql / DataFrame is universally > >> desirable and useful. > >> > >> By the way, one thing that prevents the columnar compression stuff in > >> Spark SQL from being more useful is, at least from previous talks with > >> Reynold and Michael et al., that the format was not designed for > >> persistence. > >> > >> I have a new project that aims to change that. It is a > >> zero-serialisation, high performance binary vector library, designed > >> from the outset to be a persistent storage friendly. May be one day > >> it can replace the Spark SQL columnar compression. > >> > >> Michael told me this would be a lot of work, and recreates parts of > >> Parquet, but I think it's worth it. LMK if you'd like more details. > >> > >> -Evan > >> > >> On Tue, Jan 27, 2015 at 4:35 PM, Reynold Xin <r...@databricks.com> > wrote: > >> > Alright I have merged the patch ( > >> > https://github.com/apache/spark/pull/4173 > >> > ) since I don't see any strong opinions against it (as a matter of > fact > >> > most were for it). We can still change it if somebody lays out a > strong > >> > argument. > >> > > >> > On Tue, Jan 27, 2015 at 12:25 PM, Matei Zaharia > >> > <matei.zaha...@gmail.com> > >> > wrote: > >> > > >> >> The type alias means your methods can specify either type and they > will > >> >> work. It's just another name for the same type. But Scaladocs and > such > >> >> will > >> >> show DataFrame as the type. > >> >> > >> >> Matei > >> >> > >> >> > On Jan 27, 2015, at 12:10 PM, Dirceu Semighini Filho < > >> >> dirceu.semigh...@gmail.com> wrote: > >> >> > > >> >> > Reynold, > >> >> > But with type alias we will have the same problem, right? > >> >> > If the methods doesn't receive schemardd anymore, we will have to > >> >> > change > >> >> > our code to migrade from schema to dataframe. Unless we have an > >> >> > implicit > >> >> > conversion between DataFrame and SchemaRDD > >> >> > > >> >> > > >> >> > > >> >> > 2015-01-27 17:18 GMT-02:00 Reynold Xin <r...@databricks.com>: > >> >> > > >> >> >> Dirceu, > >> >> >> > >> >> >> That is not possible because one cannot overload return types. > >> >> >> > >> >> >> SQLContext.parquetFile (and many other methods) needs to return > some > >> >> type, > >> >> >> and that type cannot be both SchemaRDD and DataFrame. > >> >> >> > >> >> >> In 1.3, we will create a type alias for DataFrame called SchemaRDD > >> >> >> to > >> >> not > >> >> >> break source compatibility for Scala. > >> >> >> > >> >> >> > >> >> >> On Tue, Jan 27, 2015 at 6:28 AM, Dirceu Semighini Filho < > >> >> >> dirceu.semigh...@gmail.com> wrote: > >> >> >> > >> >> >>> Can't the SchemaRDD remain the same, but deprecated, and be > removed > >> >> >>> in > >> >> the > >> >> >>> release 1.5(+/- 1) for example, and the new code been added to > >> >> DataFrame? > >> >> >>> With this, we don't impact in existing code for the next few > >> >> >>> releases. > >> >> >>> > >> >> >>> > >> >> >>> > >> >> >>> 2015-01-27 0:02 GMT-02:00 Kushal Datta <kushal.da...@gmail.com>: > >> >> >>> > >> >> >>>> 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 > >> >> >>>>>> > >> >> >>>>>> > >> >> >>>>> > >> >> >>>> > >> >> >>> > >> >> >> > >> >> >> > >> >> > >> >> > >> >> --------------------------------------------------------------------- > >> >> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > >> >> For additional commands, e-mail: dev-h...@spark.apache.org > >> >> > >> >> > > > > >
