What about exposing transforms that make it easy to coerce data to what the
method needs? Instead of passing a dataframe, you’d pass df.toSet to isin
Assuming toSet returns a local list, wouldn’t that have the problem of not
being able to handle extremely large lists? In contrast, I believe SQL’s IN
is implemented in such a way that the inner query being referenced by IN
does not need to be collected locally. Did I understand your suggestion
correctly?
I think having .isin() accept a Column potentially makes more sense, since
that matches what happens in SQL in terms of semantics, and would hopefully
also preserve the distributed nature of the operation.
For example, I believe in most cases we’d want this
(table1
.where(
table1['name'].isin(
table2.select('name')
# table2['name'] # per Reynold's suggestion
)))
and this
(table1
.join(table2, on='name')
.select(table1['*']))
to compile down to the same physical plan. No?
Nick
On Thu, Apr 19, 2018 at 7:13 PM Reynold Xin <[email protected]> wrote:
> Perhaps we can just have a function that turns a DataFrame into a Column?
> That'd work for both correlated and uncorrelated case, although in the
> correlated case we'd need to turn off eager analysis (otherwise there is no
> way to construct a valid DataFrame).
>
>
> On Thu, Apr 19, 2018 at 4:08 PM, Ryan Blue <[email protected]>
> wrote:
>
>> Nick, thanks for raising this.
>>
>> It looks useful to have something in the DF API that behaves like
>> sub-queries, but I’m not sure that passing a DF works. Making every method
>> accept a DF that may contain matching data seems like it puts a lot of work
>> on the API — which now has to accept a DF all over the place.
>>
>> What about exposing transforms that make it easy to coerce data to what
>> the method needs? Instead of passing a dataframe, you’d pass df.toSet to
>> isin:
>>
>> val subQ = spark.sql("select distinct filter_col from source")
>> val df = table.filter($"col".isin(subQ.toSet))
>>
>> That also distinguishes between a sub-query and a correlated sub-query
>> that uses values from the outer query. We would still need to come up with
>> syntax for the correlated case, unless there’s a proposal already.
>>
>> rb
>>
>>
>> On Mon, Apr 9, 2018 at 3:56 PM, Nicholas Chammas <
>> [email protected]> wrote:
>>
>>> I just submitted SPARK-23945
>>> <https://issues.apache.org/jira/browse/SPARK-23945> but wanted to
>>> double check here to make sure I didn't miss something fundamental.
>>>
>>> Correlated subqueries are tracked at a high level in SPARK-18455
>>> <https://issues.apache.org/jira/browse/SPARK-18455>, but it's not clear
>>> to me whether they are "design-appropriate" for the DataFrame API.
>>>
>>> Are correlated subqueries a thing we can expect to have in the DataFrame
>>> API?
>>>
>>> Nick
>>>
>>>
>>
>>
>> --
>> Ryan Blue
>> Software Engineer
>> Netflix
>>
>
>
