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From Evan Chan <velvia.git...@gmail.com>
Subject Re: renaming SchemaRDD -> DataFrame
Date Sun, 01 Feb 2015 08:31:17 GMT
It is true that you can persist SchemaRdds / DataFrames to disk via
Parquet, but a lot of time and inefficiencies is lost.   The in-memory
columnar cached representation is completely different from the
Parquet file format, and I believe there has to be a translation into
a Row (because ultimately Spark SQL traverses Row's -- even the
InMemoryColumnarTableScan has to then convert the columns into Rows
for row-based processing).   On the other hand, traditional data
frames process in a columnar fashion.   Columnar storage is good, but
nowhere near as good as columnar processing.

Another issue, which I don't know if it is solved yet, but it is
difficult for Tachyon to efficiently cache Parquet files without
understanding the file format itself.

I gave a talk at last year's Spark Summit on this topic.

I'm working on efforts to change this, however.  Shoot me an email at
velvia at gmail if you're interested in joining forces.

On Thu, Jan 29, 2015 at 1:59 PM, Cheng Lian <lian.cs.zju@gmail.com> wrote:
> Yes, when a DataFrame is cached in memory, it's stored in an efficient
> columnar format. And you can also easily persist it on disk using Parquet,
> which is also columnar.
>
> Cheng
>
>
> On 1/29/15 1:24 PM, Koert Kuipers wrote:
>>
>> to me the word DataFrame does come with certain expectations. one of them
>> is that the data is stored columnar. in R data.frame internally uses a
>> list
>> of sequences i think, but since lists can have labels its more like a
>> SortedMap[String, Array[_]]. this makes certain operations very cheap
>> (such
>> as adding a column).
>>
>> in Spark the closest thing would be a data structure where per Partition
>> the data is also stored columnar. does spark SQL already use something
>> like
>> that? Evan mentioned "Spark SQL columnar compression", which sounds like
>> it. where can i find that?
>>
>> thanks
>>
>> On Thu, Jan 29, 2015 at 2:32 PM, Evan Chan <velvia.github@gmail.com>
>> wrote:
>>
>>> +1.... having proper NA support is much cleaner than using null, at
>>> least the Java null.
>>>
>>> On Wed, Jan 28, 2015 at 6:10 PM, Evan R. Sparks <evan.sparks@gmail.com>
>>> wrote:
>>>>
>>>> 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 <rxin@databricks.com>
>>>
>>> wrote:
>>>>>
>>>>> Isn't that just "null" in SQL?
>>>>>
>>>>> On Wed, Jan 28, 2015 at 4:41 PM, Evan Chan <velvia.github@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 <rxin@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.github@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 <rxin@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.zaharia@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.semighini@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 <rxin@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.semighini@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.datta@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.ryza@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.zaharia@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.zaharia@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 <
>>>>>>
>>>>>> koert@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 <
>>>>>>>>>>>>>>>>>> michaelmalak@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 <pwendell@gmail.com>
>>>>>>>>>>>>>>>>>>> To: Reynold Xin
<rxin@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 <
>>>>>>>>>>>>>
>>>>>>>>>>>>> rxin@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.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>> ---------------------------------------------------------------------
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>>>
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>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> For additional
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>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> To unsubscribe,
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>>>
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>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> For additional
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>>>>>>>>>>>>>>>>>>> dev-help@spark.apache.org
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>> ---------------------------------------------------------------------
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>>>>>>>>>>>>>>>> For additional commands,
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>>>
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>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
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>>>>>>>>>>
>>>>>>>>>>
>>>>>>>
>>>>
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