+1 (binding) > On Sep 11, 2017, at 5:54 PM, Hyukjin Kwon <gurwls...@gmail.com> wrote: > > +1 (non-binding) > > > 2017-09-12 9:52 GMT+09:00 Yin Huai <yh...@databricks.com>: > +1 > > On Mon, Sep 11, 2017 at 5:47 PM, Sameer Agarwal <sam...@databricks.com> wrote: > +1 (non-binding) > > On Thu, Sep 7, 2017 at 9:10 PM, Bryan Cutler <cutl...@gmail.com> wrote: > +1 (non-binding) for the goals and non-goals of this SPIP. I think it's fine > to work out the minor details of the API during review. > > Bryan > > On Wed, Sep 6, 2017 at 5:17 AM, Takuya UESHIN <ues...@happy-camper.st> wrote: > Hi all, > > Thank you for voting and suggestions. > > As Wenchen mentioned and also we're discussing at JIRA, we need to discuss > the size hint for the 0-parameter UDF. > But I believe we got a consensus about the basic APIs except for the size > hint, I'd like to submit a pr based on the current proposal and continue > discussing in its review. > > https://github.com/apache/spark/pull/19147 > > I'd keep this vote open to wait for more opinions. > > Thanks. > > > On Wed, Sep 6, 2017 at 9:48 AM, Wenchen Fan <cloud0...@gmail.com> wrote: > +1 on the design and proposed API. > > One detail I'd like to discuss is the 0-parameter UDF, how we can specify the > size hint. This can be done in the PR review though. > > On Sat, Sep 2, 2017 at 2:07 AM, Felix Cheung <felixcheun...@hotmail.com> > wrote: > +1 on this and like the suggestion of type in string form. > > Would it be correct to assume there will be data type check, for example the > returned pandas data frame column data types match what are specified. We > have seen quite a bit of issues/confusions with that in R. > > Would it make sense to have a more generic decorator name so that it could > also be useable for other efficient vectorized format in the future? Or do we > anticipate the decorator to be format specific and will have more in the > future? > > From: Reynold Xin <r...@databricks.com> > Sent: Friday, September 1, 2017 5:16:11 AM > To: Takuya UESHIN > Cc: spark-dev > Subject: Re: [VOTE][SPIP] SPARK-21190: Vectorized UDFs in Python > > Ok, thanks. > > +1 on the SPIP for scope etc > > > On API details (will deal with in code reviews as well but leaving a note > here in case I forget) > > 1. I would suggest having the API also accept data type specification in > string form. It is usually simpler to say "long" then "LongType()". > > 2. Think about what error message to show when the rows numbers don't match > at runtime. > > > On Fri, Sep 1, 2017 at 12:29 PM Takuya UESHIN <ues...@happy-camper.st> wrote: > Yes, the aggregation is out of scope for now. > I think we should continue discussing the aggregation at JIRA and we will be > adding those later separately. > > Thanks. > > > On Fri, Sep 1, 2017 at 6:52 PM, Reynold Xin <r...@databricks.com> wrote: > Is the idea aggregate is out of scope for the current effort and we will be > adding those later? > > On Fri, Sep 1, 2017 at 8:01 AM Takuya UESHIN <ues...@happy-camper.st> wrote: > Hi all, > > We've been discussing to support vectorized UDFs in Python and we almost got > a consensus about the APIs, so I'd like to summarize and call for a vote. > > Note that this vote should focus on APIs for vectorized UDFs, not APIs for > vectorized UDAFs or Window operations. > > https://issues.apache.org/jira/browse/SPARK-21190 > > > Proposed API > > We introduce a @pandas_udf decorator (or annotation) to define vectorized > UDFs which takes one or more pandas.Series or one integer value meaning the > length of the input value for 0-parameter UDFs. The return value should be > pandas.Series of the specified type and the length of the returned value > should be the same as input value. > > We can define vectorized UDFs as: > > @pandas_udf(DoubleType()) > def plus(v1, v2): > return v1 + v2 > > or we can define as: > > plus = pandas_udf(lambda v1, v2: v1 + v2, DoubleType()) > > We can use it similar to row-by-row UDFs: > > df.withColumn('sum', plus(df.v1, df.v2)) > > As for 0-parameter UDFs, we can define and use as: > > @pandas_udf(LongType()) > def f0(size): > return pd.Series(1).repeat(size) > > df.select(f0()) > > > > The vote will be up for the next 72 hours. Please reply with your vote: > > +1: Yeah, let's go forward and implement the SPIP. > +0: Don't really care. > -1: I don't think this is a good idea because of the following technical > reasons. > > Thanks! > > -- > Takuya UESHIN > Tokyo, Japan > > http://twitter.com/ueshin > > > > -- > Takuya UESHIN > Tokyo, Japan > > http://twitter.com/ueshin > > > > > -- > Takuya UESHIN > Tokyo, Japan > > http://twitter.com/ueshin > > > > > -- > Sameer Agarwal > Software Engineer | Databricks Inc. > http://cs.berkeley.edu/~sameerag > >
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