Just the fields specified, IMO. When in doubt, copy SQL. (and I mean SQL generally, not just Beam SQL)
Kenn On Wed, Jan 13, 2021 at 11:17 AM Reuven Lax <re...@google.com> wrote: > Definitely could be a top-level transform. Should it automatically unnest > all arrays, or just the fields specified? > > We do have to define the semantics for nested arrays as well. > > On Wed, Jan 13, 2021 at 10:57 AM Robert Bradshaw <rober...@google.com> > wrote: > >> Ah, thanks for the clarification. UNNEST does sound like what you want >> here, and would likely make sense as a top-level relational transform as >> well as being supported by SQL. >> >> On Wed, Jan 13, 2021 at 10:53 AM Tao Li <t...@zillow.com> wrote: >> >>> @Kyle Weaver <kcwea...@google.com> sure thing! So the input/output >>> definition for the Flatten.Iterables >>> <https://beam.apache.org/releases/javadoc/2.25.0/org/apache/beam/sdk/transforms/Flatten.Iterables.html> >>> is: >>> >>> >>> >>> Input: PCollection<Iterable<T> >>> >>> Output: PCollection<T> >>> >>> >>> >>> The input/output for a explode transform would look like this: >>> >>> Input: PCollection<Row> The row schema has a field which is an array of >>> T >>> >>> Output: PCollection<Row> The array type field from input schema is >>> replaced with a new field of type T. The elements from the array type field >>> are flattened into multiple rows in the new table (other fields of input >>> table are just duplicated. >>> >>> >>> >>> Hope this clarification helps! >>> >>> >>> >>> *From: *Kyle Weaver <kcwea...@google.com> >>> *Reply-To: *"user@beam.apache.org" <user@beam.apache.org> >>> *Date: *Tuesday, January 12, 2021 at 4:58 PM >>> *To: *"user@beam.apache.org" <user@beam.apache.org> >>> *Cc: *Reuven Lax <re...@google.com> >>> *Subject: *Re: Is there an array explode function/transform? >>> >>> >>> >>> @Reuven Lax <re...@google.com> yes I am aware of that transform, but >>> that’s different from the explode operation I was referring to: >>> https://spark.apache.org/docs/latest/api/sql/index.html#explode >>> <https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fapi%2Fsql%2Findex.html%23explode&data=04%7C01%7Ctaol%40zillow.com%7C1226a5d9efee43fc7d5508d8b75e5bfd%7C033464830d1840e7a5883784ac50e16f%7C0%7C0%7C637460963191408293%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=IjXWhmHTGsbpgbxa1gJ5LcOFI%2BoiGIDYBwXPnukQfxk%3D&reserved=0> >>> >>> >>> >>> How is it different? It'd help if you could provide the signature (input >>> and output PCollection types) of the transform you have in mind. >>> >>> >>> >>> On Tue, Jan 12, 2021 at 4:49 PM Tao Li <t...@zillow.com> wrote: >>> >>> @Reuven Lax <re...@google.com> yes I am aware of that transform, but >>> that’s different from the explode operation I was referring to: >>> https://spark.apache.org/docs/latest/api/sql/index.html#explode >>> <https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fapi%2Fsql%2Findex.html%23explode&data=04%7C01%7Ctaol%40zillow.com%7C1226a5d9efee43fc7d5508d8b75e5bfd%7C033464830d1840e7a5883784ac50e16f%7C0%7C0%7C637460963191418249%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=XuUUmNB3fgBasjDj0Dq1Z2g6%2Bc5fbvluf%2BnAp2m8cuE%3D&reserved=0> >>> >>> >>> >>> *From: *Reuven Lax <re...@google.com> >>> *Reply-To: *"user@beam.apache.org" <user@beam.apache.org> >>> *Date: *Tuesday, January 12, 2021 at 2:04 PM >>> *To: *user <user@beam.apache.org> >>> *Subject: *Re: Is there an array explode function/transform? >>> >>> >>> >>> Have you tried Flatten.iterables >>> >>> >>> >>> On Tue, Jan 12, 2021, 2:02 PM Tao Li <t...@zillow.com> wrote: >>> >>> Hi community, >>> >>> >>> >>> Is there a beam function to explode an array (similarly to spark sql’s >>> explode())? I did some research but did not find anything. >>> >>> >>> >>> BTW I think we can potentially use FlatMap to implement the explode >>> functionality, but a Beam provided function would be very handy. >>> >>> >>> >>> Thanks a lot! >>> >>>
