You've got to be a little bit careful here. "NA" in systems like R or pandas may have special meaning that is distinct from "null".
See, e.g. http://www.r-bloggers.com/r-na-vs-null/ On Wed, Jan 28, 2015 at 4:42 PM, Reynold Xin <r...@databricks.com> wrote: > 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 > > >> >> > > >> >> > > > > > > > > >
