In particular the performance tricks are in SpecificMutableRow. On Wed, Jan 28, 2015 at 5:49 PM, Evan Chan <velvia.git...@gmail.com> wrote:
> Yeah, it's "null". I was worried you couldn't represent it in Row > because of primitive types like Int (unless you box the Int, which > would be a performance hit). Anyways, I'll take another look at the > Row API again :-p > > 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 > >> >> >> > >> >> >> > >> > > >> > > > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > >
