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
