I have been looking around, how can we map Variant type in Flink. I have not found any existing type which we could use, but Flink already have some JSON parsing capabilities [1] for string fields.
So until we have native support in Flink for something similar to Vartiant type, I expect that we need to map it to JSON strings in RowData. Based on that, here are my preferences: 1. I'm ok with adapting Spark Variant type, if we build our own Iceberg serializer/deserializer module for it 2. I prefer to move the spec to Iceberg, so we own it, and extend it, if needed. This could be important in the first phase. Later when it is more stable we might donate it to some other project, like Parquet 3. I would prefer to support only a single type, and Variant is more expressive, but having a standard way to convert between JSON and Variant would be useful for Flink users. 4. On subcolumnarization: I think Flink will only use this feature as much as the Iceberg readers implement this, so I would like to see as much as possible of it in the common Iceberg code Thanks, Peter [1] - https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/functions/systemfunctions/#json-functions On Tue, Jul 23, 2024, 06:36 Micah Kornfield <emkornfi...@gmail.com> wrote: > Sorry for the late reply. I agree with the sentiments on 1 and 3 that > have already been posted (adopt the Spark encoding, and only have the > Variant type). As mentioned on the doc for 3, I think it would be good to > specify how to map scalar types to a JSON representation so there can be > consistency between engines that don't support variant. > > >> Regarding point 2, I also feel Iceberg is more natural to host such a >> subproject for variant spec and implementation. But let me reach out to the >> Spark community to discuss. > > > The only other place I can think of that might be a good home for Variant > spec could be in Apache Arrow as a canonical extension type. There is an > issue for this [1]. I think the main thing on where this is housed is > which types are intended to be supported. I believe Arrow is currently a > superset of the Iceberg type system (UUID is supported as a canonical > extension type [2]). > > For point 4 subcolumnarization, I think ideally this belongs in Iceberg > (and if Iceberg and Delta Lake can agree on how to do it that would be > great) with potential consultation with Parquet/ORC communities to > potentially add better native support. > > Thanks, > Micah > > > > [1] https://github.com/apache/arrow/issues/42069 > [2] https://arrow.apache.org/docs/format/CanonicalExtensions.html > > On Sat, Jul 20, 2024 at 5:54 PM Aihua Xu <aihu...@gmail.com> wrote: > >> Thanks for the discussion and feedback. >> >> Do we have the consensus on point 1 and point 3 to move forward with >> Spark variant encoding and support Variant type only? Or let me know how to >> proceed from here. >> >> Regarding point 2, I also feel Iceberg is more natural to host such a >> subproject for variant spec and implementation. But let me reach out to the >> Spark community to discuss. >> >> Thanks, >> Aihua >> >> >> On Fri, Jul 19, 2024 at 9:35 AM Yufei Gu <flyrain...@gmail.com> wrote: >> >>> Agreed with point 1. >>> >>> For point 2, I also prefer to hold the spec and reference implementation >>> under Iceberg. Here are the reasons: >>> 1. It is unconventional and impractical for one engine to depend on >>> another for data types. For instance, it is not ideal for Trino to rely on >>> data types defined by the Spark engine. >>> 2. Iceberg serves as a bridge between engines and file formats. By >>> centralizing the specification in Iceberg, any future optimizations or >>> updates to file formats can be referred to within Iceberg, ensuring >>> consistency and reducing dependencies. >>> >>> For point 3, I'd prefer to support the variant type only at this moment. >>> >>> Yufei >>> >>> >>> On Thu, Jul 18, 2024 at 12:55 PM Ryan Blue <b...@databricks.com.invalid> >>> wrote: >>> >>>> Similarly, I'm aligned with point 1 and I'd choose to support only >>>> variant for point 3. >>>> >>>> We'll need to work with the Spark community to find a good place for >>>> the library and spec, since it touches many different projects. I'd also >>>> prefer Iceberg as the home. >>>> >>>> I also think it's a good idea to get subcolumnarization into our spec >>>> when we update. Without that I think the feature will be fairly limited. >>>> >>>> On Thu, Jul 18, 2024 at 10:56 AM Russell Spitzer < >>>> russell.spit...@gmail.com> wrote: >>>> >>>>> I'm aligned with point 1. >>>>> >>>>> For point 2 I think we should choose quickly, I honestly do think this >>>>> would be fine as part of the Iceberg Spec directly but understand it may >>>>> be >>>>> better for the more broad community if it was a sub project. As a >>>>> sub-project I would still prefer it being an Iceberg Subproject since we >>>>> are engine/file-format agnostic. >>>>> >>>>> 3. I support adding just Variant. >>>>> >>>>> On Thu, Jul 18, 2024 at 12:54 AM Aihua Xu <aihu...@apache.org> wrote: >>>>> >>>>>> Hello community, >>>>>> >>>>>> It’s great to sync up with some of you on Variant and >>>>>> SubColumarization support in Iceberg again. Apologize that I didn’t >>>>>> record >>>>>> the meeting but here are some key items that we want to follow up with >>>>>> the >>>>>> community. >>>>>> >>>>>> 1. Adopt Spark Variant encoding >>>>>> Those present were in favor of adopting the Spark variant encoding >>>>>> for Iceberg Variant with extensions to support other Iceberg types. We >>>>>> would like to know if anyone has an objection to this to reuse an open >>>>>> source encoding. >>>>>> >>>>>> 2. Movement of the Spark Variant Spec to another project >>>>>> To avoid introducing Apache Spark as a dependency for the engines and >>>>>> file formats, we discussed separating Spark Variant encoding spec and >>>>>> implementation from the Spark Project to a neutral location. We thought >>>>>> up >>>>>> several solutions but didn’t have consensus on any of them. We are >>>>>> looking >>>>>> for more feedback on this topic from the community either in terms of >>>>>> support for one of these options or another idea on how to support the >>>>>> spec. >>>>>> >>>>>> Options Proposed: >>>>>> * Leave the Spec in Spark (Difficult for versioning and other engines) >>>>>> * Copying the Spec into Iceberg Project Directly (Difficult for other >>>>>> Table Formats) >>>>>> * Creating a Sub-Project of Apache Iceberg and moving the spec and >>>>>> reference implementation there (Logistically complicated) >>>>>> * Creating a Sub-Project of Apache Spark and moving the spec and >>>>>> reference implementation there (Logistically complicated) >>>>>> >>>>>> 3. Add Variant type vs. Variant and JSON types >>>>>> Those who were present were in favor of adding only the Variant type >>>>>> to Iceberg. We are looking for anyone who has an objection to going >>>>>> forward >>>>>> with just the Variant Type and no Iceberg JSON Type. We were favoring >>>>>> adding Variant type only because: >>>>>> * Introducing a JSON type would require engines that only support >>>>>> VARIANT to do write time validation of their input to a JSON column. If >>>>>> they don’t have a JSON type an engine wouldn’t support this. >>>>>> * Engines which don’t support Variant will work most of the time but >>>>>> can have fallback strings defined in the spec for reading unsupported >>>>>> types. Writing a JSON into a Variant will always work. >>>>>> >>>>>> 4. Support for Subcolumnization spec (shredding in Spark) >>>>>> We have no action items on this but would like to follow up on >>>>>> discussions on Subcolumnization in the future. >>>>>> * We had general agreement that this should be included in Iceberg V3 >>>>>> or else adding variant may not be useful. >>>>>> * We are interested in also adopting the shredding spec from Spark >>>>>> and would like to move it to whatever place we decided the Variant spec >>>>>> is >>>>>> going to live. >>>>>> >>>>>> Let us know if missed anything and if you have any additional >>>>>> thoughts or suggestions. >>>>>> >>>>>> Thanks >>>>>> Aihua >>>>>> >>>>>> >>>>>> On 2024/07/15 18:32:22 Aihua Xu wrote: >>>>>> > Thanks for the discussion. >>>>>> > >>>>>> > I will move forward to work on spec PR. >>>>>> > >>>>>> > Regarding the implementation, we will have module for Variant >>>>>> support in Iceberg so we will not have to bring in Spark libraries. >>>>>> > >>>>>> > I'm reposting the meeting invite in case it's not clear in my >>>>>> original email since I included in the end. Looks like we don't have >>>>>> major >>>>>> objections/diverges but let's sync up and have consensus. >>>>>> > >>>>>> > Meeting invite: >>>>>> > >>>>>> > Wednesday, July 17 · 9:00 – 10:00am >>>>>> > Time zone: America/Los_Angeles >>>>>> > Google Meet joining info >>>>>> > Video call link: https://meet.google.com/pbm-ovzn-aoq >>>>>> > Or dial: (US) +1 650-449-9343 PIN: 170 576 525# >>>>>> > More phone numbers: https://tel.meet/pbm-ovzn-aoq?pin=4079632691790 >>>>>> > >>>>>> > Thanks, >>>>>> > Aihua >>>>>> > >>>>>> > On 2024/07/12 20:55:01 Micah Kornfield wrote: >>>>>> > > I don't think this needs to hold up the PR but I think coming to a >>>>>> > > consensus on the exact set of types supported is worthwhile (and >>>>>> if the >>>>>> > > goal is to maintain the same set as specified by the Spark >>>>>> Variant type or >>>>>> > > if divergence is expected/allowed). From a fragmentation >>>>>> perspective it >>>>>> > > would be a shame if they diverge, so maybe a next step is also >>>>>> suggesting >>>>>> > > support to the Spark community on the missing existing Iceberg >>>>>> types? >>>>>> > > >>>>>> > > Thanks, >>>>>> > > Micah >>>>>> > > >>>>>> > > On Fri, Jul 12, 2024 at 1:44 PM Russell Spitzer < >>>>>> russell.spit...@gmail.com> >>>>>> > > wrote: >>>>>> > > >>>>>> > > > Just talked with Aihua and he's working on the Spec PR now. We >>>>>> can get >>>>>> > > > feedback there from everyone. >>>>>> > > > >>>>>> > > > On Fri, Jul 12, 2024 at 3:41 PM Ryan Blue >>>>>> <b...@databricks.com.invalid> >>>>>> > > > wrote: >>>>>> > > > >>>>>> > > >> Good idea, but I'm hoping that we can continue to get their >>>>>> feedback in >>>>>> > > >> parallel to getting the spec changes started. Piotr didn't >>>>>> seem to object >>>>>> > > >> to the encoding from what I read of his comments. Hopefully he >>>>>> (and others) >>>>>> > > >> chime in here. >>>>>> > > >> >>>>>> > > >> On Fri, Jul 12, 2024 at 1:32 PM Russell Spitzer < >>>>>> > > >> russell.spit...@gmail.com> wrote: >>>>>> > > >> >>>>>> > > >>> I just want to make sure we get Piotr and Peter on board as >>>>>> > > >>> representatives of Flink and Trino engines. Also make sure we >>>>>> have anyone >>>>>> > > >>> else chime in who has experience with Ray if possible. >>>>>> > > >>> >>>>>> > > >>> Spec changes feel like the right next step. >>>>>> > > >>> >>>>>> > > >>> On Fri, Jul 12, 2024 at 3:14 PM Ryan Blue >>>>>> <b...@databricks.com.invalid> >>>>>> > > >>> wrote: >>>>>> > > >>> >>>>>> > > >>>> Okay, what are the next steps here? This proposal has been >>>>>> out for >>>>>> > > >>>> quite a while and I don't see any major objections to using >>>>>> the Spark >>>>>> > > >>>> encoding. It's quite well designed and fits the need well. >>>>>> It can also be >>>>>> > > >>>> extended to support additional types that are missing if >>>>>> that's a priority. >>>>>> > > >>>> >>>>>> > > >>>> Should we move forward by starting a draft of the changes to >>>>>> the table >>>>>> > > >>>> spec? Then we can vote on committing those changes and get >>>>>> moving on an >>>>>> > > >>>> implementation (or possibly do the implementation in >>>>>> parallel). >>>>>> > > >>>> >>>>>> > > >>>> On Fri, Jul 12, 2024 at 1:08 PM Russell Spitzer < >>>>>> > > >>>> russell.spit...@gmail.com> wrote: >>>>>> > > >>>> >>>>>> > > >>>>> That's fair, I'm sold on an Iceberg Module. >>>>>> > > >>>>> >>>>>> > > >>>>> On Fri, Jul 12, 2024 at 2:53 PM Ryan Blue >>>>>> <b...@databricks.com.invalid> >>>>>> > > >>>>> wrote: >>>>>> > > >>>>> >>>>>> > > >>>>>> > Feels like eventually the encoding should land in >>>>>> parquet proper >>>>>> > > >>>>>> right? >>>>>> > > >>>>>> >>>>>> > > >>>>>> What about using it in ORC? I don't know where it should >>>>>> end up. >>>>>> > > >>>>>> Maybe Iceberg should make a standalone module from it? >>>>>> > > >>>>>> >>>>>> > > >>>>>> On Fri, Jul 12, 2024 at 12:38 PM Russell Spitzer < >>>>>> > > >>>>>> russell.spit...@gmail.com> wrote: >>>>>> > > >>>>>> >>>>>> > > >>>>>>> Feels like eventually the encoding should land in parquet >>>>>> proper >>>>>> > > >>>>>>> right? I'm fine with us just copying into Iceberg though >>>>>> for the time >>>>>> > > >>>>>>> being. >>>>>> > > >>>>>>> >>>>>> > > >>>>>>> On Fri, Jul 12, 2024 at 2:31 PM Ryan Blue >>>>>> > > >>>>>>> <b...@databricks.com.invalid> wrote: >>>>>> > > >>>>>>> >>>>>> > > >>>>>>>> Oops, it looks like I missed where Aihua brought this up >>>>>> in his >>>>>> > > >>>>>>>> last email: >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> > do we have an issue to directly use Spark >>>>>> implementation in >>>>>> > > >>>>>>>> Iceberg? >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> Yes, I think that we do have an issue using the Spark >>>>>> library. What >>>>>> > > >>>>>>>> do you think about a Java implementation in Iceberg? >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> Ryan >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> On Fri, Jul 12, 2024 at 12:28 PM Ryan Blue < >>>>>> b...@databricks.com> >>>>>> > > >>>>>>>> wrote: >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>>> I raised the same point from Peter's email in a comment >>>>>> on the doc >>>>>> > > >>>>>>>>> as well. There is a spark-variant_2.13 artifact that >>>>>> would be a much >>>>>> > > >>>>>>>>> smaller scope than relying on large portions of Spark, >>>>>> but I even then I >>>>>> > > >>>>>>>>> doubt that it is a good idea for Iceberg to depend on >>>>>> that because it is a >>>>>> > > >>>>>>>>> Scala artifact and we would need to bring in a ton of >>>>>> Scala libs. I think >>>>>> > > >>>>>>>>> what makes the most sense is to have an independent >>>>>> implementation of the >>>>>> > > >>>>>>>>> spec in Iceberg. >>>>>> > > >>>>>>>>> >>>>>> > > >>>>>>>>> On Fri, Jul 12, 2024 at 11:51 AM Péter Váry < >>>>>> > > >>>>>>>>> peter.vary.apa...@gmail.com> wrote: >>>>>> > > >>>>>>>>> >>>>>> > > >>>>>>>>>> Hi Aihua, >>>>>> > > >>>>>>>>>> Long time no see :) >>>>>> > > >>>>>>>>>> Would this mean, that every engine which plans to >>>>>> support Variant >>>>>> > > >>>>>>>>>> data type needs to add Spark as a dependency? Like >>>>>> Flink/Trino/Hive etc? >>>>>> > > >>>>>>>>>> Thanks, Peter >>>>>> > > >>>>>>>>>> >>>>>> > > >>>>>>>>>> >>>>>> > > >>>>>>>>>> On Fri, Jul 12, 2024, 19:10 Aihua Xu < >>>>>> aihu...@apache.org> wrote: >>>>>> > > >>>>>>>>>> >>>>>> > > >>>>>>>>>>> Thanks Ryan. >>>>>> > > >>>>>>>>>>> >>>>>> > > >>>>>>>>>>> Yeah. That's another reason we want to pursue Spark >>>>>> encoding to >>>>>> > > >>>>>>>>>>> keep compatibility for the open source engines. >>>>>> > > >>>>>>>>>>> >>>>>> > > >>>>>>>>>>> One more question regarding the encoding >>>>>> implementation: do we >>>>>> > > >>>>>>>>>>> have an issue to directly use Spark implementation in >>>>>> Iceberg? Russell >>>>>> > > >>>>>>>>>>> pointed out that Trino doesn't have Spark dependency >>>>>> and that could be a >>>>>> > > >>>>>>>>>>> problem? >>>>>> > > >>>>>>>>>>> >>>>>> > > >>>>>>>>>>> Thanks, >>>>>> > > >>>>>>>>>>> Aihua >>>>>> > > >>>>>>>>>>> >>>>>> > > >>>>>>>>>>> On 2024/07/12 15:02:06 Ryan Blue wrote: >>>>>> > > >>>>>>>>>>> > Thanks, Aihua! >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > I think that the encoding choice in the current doc >>>>>> is a good >>>>>> > > >>>>>>>>>>> one. I went >>>>>> > > >>>>>>>>>>> > through the Spark encoding in detail and it looks >>>>>> like a >>>>>> > > >>>>>>>>>>> better choice than >>>>>> > > >>>>>>>>>>> > the other candidate encodings for quickly accessing >>>>>> nested >>>>>> > > >>>>>>>>>>> fields. >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > Another reason to use the Spark type is that this >>>>>> is what >>>>>> > > >>>>>>>>>>> Delta's variant >>>>>> > > >>>>>>>>>>> > type is based on, so Parquet files in tables >>>>>> written by Delta >>>>>> > > >>>>>>>>>>> could be >>>>>> > > >>>>>>>>>>> > converted or used in Iceberg tables without needing >>>>>> to rewrite >>>>>> > > >>>>>>>>>>> variant >>>>>> > > >>>>>>>>>>> > data. (Also, note that I work at Databricks and >>>>>> have an >>>>>> > > >>>>>>>>>>> interest in >>>>>> > > >>>>>>>>>>> > increasing format compatibility.) >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > Ryan >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > On Thu, Jul 11, 2024 at 11:21 AM Aihua Xu < >>>>>> > > >>>>>>>>>>> aihua...@snowflake.com.invalid> >>>>>> > > >>>>>>>>>>> > wrote: >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > > [Discuss] Consensus for Variant Encoding >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > It’s great to be able to present the Variant type >>>>>> proposal >>>>>> > > >>>>>>>>>>> in the >>>>>> > > >>>>>>>>>>> > > community sync yesterday and I’m looking to host >>>>>> a meeting >>>>>> > > >>>>>>>>>>> next week >>>>>> > > >>>>>>>>>>> > > (targeting for 9am, July 17th) to go over any >>>>>> further >>>>>> > > >>>>>>>>>>> concerns about the >>>>>> > > >>>>>>>>>>> > > encoding of the Variant type and any other >>>>>> questions on the >>>>>> > > >>>>>>>>>>> first phase of >>>>>> > > >>>>>>>>>>> > > the proposal >>>>>> > > >>>>>>>>>>> > > < >>>>>> > > >>>>>>>>>>> >>>>>> https://docs.google.com/document/d/1QjhpG_SVNPZh3anFcpicMQx90ebwjL7rmzFYfUP89Iw/edit >>>>>> > > >>>>>>>>>>> >. >>>>>> > > >>>>>>>>>>> > > We are hoping that anyone who is interested in >>>>>> the proposal >>>>>> > > >>>>>>>>>>> can either join >>>>>> > > >>>>>>>>>>> > > or reply with their comments so we can discuss >>>>>> them. Summary >>>>>> > > >>>>>>>>>>> of the >>>>>> > > >>>>>>>>>>> > > discussion and notes will be sent to the mailing >>>>>> list for >>>>>> > > >>>>>>>>>>> further comment >>>>>> > > >>>>>>>>>>> > > there. >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > - >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > What should be the underlying binary >>>>>> representation >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > We have evaluated a few encodings in the doc >>>>>> including ION, >>>>>> > > >>>>>>>>>>> JSONB, and >>>>>> > > >>>>>>>>>>> > > Spark encoding.Choosing the underlying encoding >>>>>> is an >>>>>> > > >>>>>>>>>>> important first step >>>>>> > > >>>>>>>>>>> > > here and we believe we have general support for >>>>>> Spark’s >>>>>> > > >>>>>>>>>>> Variant encoding. >>>>>> > > >>>>>>>>>>> > > We would like to hear if anyone else has strong >>>>>> opinions in >>>>>> > > >>>>>>>>>>> this space. >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > - >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Should we support multiple logical types or >>>>>> just Variant? >>>>>> > > >>>>>>>>>>> Variant vs. >>>>>> > > >>>>>>>>>>> > > Variant + JSON. >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > This is to discuss what logical data type(s) to >>>>>> be supported >>>>>> > > >>>>>>>>>>> in Iceberg - >>>>>> > > >>>>>>>>>>> > > Variant only vs. Variant + JSON. Both types would >>>>>> share the >>>>>> > > >>>>>>>>>>> same underlying >>>>>> > > >>>>>>>>>>> > > encoding but would imply different limitations on >>>>>> engines >>>>>> > > >>>>>>>>>>> working with >>>>>> > > >>>>>>>>>>> > > those types. >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > From the sync up meeting, we are more favoring >>>>>> toward >>>>>> > > >>>>>>>>>>> supporting Variant >>>>>> > > >>>>>>>>>>> > > only and we want to have a consensus on the >>>>>> supported >>>>>> > > >>>>>>>>>>> type(s). >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > - >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > How should we move forward with >>>>>> Subcolumnization? >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Subcolumnization is an optimization for Variant >>>>>> type by >>>>>> > > >>>>>>>>>>> separating out >>>>>> > > >>>>>>>>>>> > > subcolumns with their own metadata. This is not >>>>>> critical for >>>>>> > > >>>>>>>>>>> choosing the >>>>>> > > >>>>>>>>>>> > > initial encoding of the Variant type so we were >>>>>> hoping to >>>>>> > > >>>>>>>>>>> gain consensus on >>>>>> > > >>>>>>>>>>> > > leaving that for a follow up spec. >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Thanks >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Aihua >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Meeting invite: >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > Wednesday, July 17 · 9:00 – 10:00am >>>>>> > > >>>>>>>>>>> > > Time zone: America/Los_Angeles >>>>>> > > >>>>>>>>>>> > > Google Meet joining info >>>>>> > > >>>>>>>>>>> > > Video call link: >>>>>> https://meet.google.com/pbm-ovzn-aoq >>>>>> > > >>>>>>>>>>> > > Or dial: (US) +1 650-449-9343 PIN: 170 576 >>>>>> 525# >>>>>> > > >>>>>>>>>>> > > More phone numbers: >>>>>> > > >>>>>>>>>>> https://tel.meet/pbm-ovzn-aoq?pin=4079632691790 >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > > On Tue, May 28, 2024 at 9:21 PM Aihua Xu < >>>>>> > > >>>>>>>>>>> aihua...@snowflake.com> wrote: >>>>>> > > >>>>>>>>>>> > > >>>>>> > > >>>>>>>>>>> > >> Hello, >>>>>> > > >>>>>>>>>>> > >> >>>>>> > > >>>>>>>>>>> > >> We have drafted the proposal >>>>>> > > >>>>>>>>>>> > >> < >>>>>> > > >>>>>>>>>>> >>>>>> https://docs.google.com/document/d/1QjhpG_SVNPZh3anFcpicMQx90ebwjL7rmzFYfUP89Iw/edit >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > >> for Variant data type. Please help review and >>>>>> comment. >>>>>> > > >>>>>>>>>>> > >> >>>>>> > > >>>>>>>>>>> > >> Thanks, >>>>>> > > >>>>>>>>>>> > >> Aihua >>>>>> > > >>>>>>>>>>> > >> >>>>>> > > >>>>>>>>>>> > >> On Thu, May 16, 2024 at 12:45 PM Jack Ye < >>>>>> > > >>>>>>>>>>> yezhao...@gmail.com> wrote: >>>>>> > > >>>>>>>>>>> > >> >>>>>> > > >>>>>>>>>>> > >>> +10000 for a JSON/BSON type. We also had the >>>>>> same >>>>>> > > >>>>>>>>>>> discussion internally >>>>>> > > >>>>>>>>>>> > >>> and a JSON type would really play well with for >>>>>> example >>>>>> > > >>>>>>>>>>> the SUPER type in >>>>>> > > >>>>>>>>>>> > >>> Redshift: >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> >>>>>> https://docs.aws.amazon.com/redshift/latest/dg/r_SUPER_type.html, >>>>>> > > >>>>>>>>>>> and >>>>>> > > >>>>>>>>>>> > >>> can also provide better integration with the >>>>>> Trino JSON >>>>>> > > >>>>>>>>>>> type. >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> > >>> Looking forward to the proposal! >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> > >>> Best, >>>>>> > > >>>>>>>>>>> > >>> Jack Ye >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> > >>> On Wed, May 15, 2024 at 9:37 AM Tyler Akidau >>>>>> > > >>>>>>>>>>> > >>> <tyler.aki...@snowflake.com.invalid> wrote: >>>>>> > > >>>>>>>>>>> > >>> >>>>>> > > >>>>>>>>>>> > >>>> On Tue, May 14, 2024 at 7:58 PM Gang Wu < >>>>>> ust...@gmail.com> >>>>>> > > >>>>>>>>>>> wrote: >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>>> > We may need some guidance on just how many >>>>>> we need to >>>>>> > > >>>>>>>>>>> look at; >>>>>> > > >>>>>>>>>>> > >>>>> > we were planning on Spark and Trino, but >>>>>> weren't sure >>>>>> > > >>>>>>>>>>> how much >>>>>> > > >>>>>>>>>>> > >>>>> > further down the rabbit hole we needed to >>>>>> go。 >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>>> There are some engines living outside the >>>>>> Java world. It >>>>>> > > >>>>>>>>>>> would be >>>>>> > > >>>>>>>>>>> > >>>>> good if the proposal could cover the effort >>>>>> it takes to >>>>>> > > >>>>>>>>>>> integrate >>>>>> > > >>>>>>>>>>> > >>>>> variant type to them (e.g. velox, datafusion, >>>>>> etc.). >>>>>> > > >>>>>>>>>>> This is something >>>>>> > > >>>>>>>>>>> > >>>>> that >>>>>> > > >>>>>>>>>>> > >>>>> some proprietary iceberg vendors also care >>>>>> about. >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>> Ack, makes sense. We can make sure to share >>>>>> some >>>>>> > > >>>>>>>>>>> perspective on this. >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>> > Not necessarily, no. As long as there's a >>>>>> binary type >>>>>> > > >>>>>>>>>>> and Iceberg and >>>>>> > > >>>>>>>>>>> > >>>>> > the query engines are aware that the binary >>>>>> column >>>>>> > > >>>>>>>>>>> needs to be >>>>>> > > >>>>>>>>>>> > >>>>> > interpreted as a variant, that should be >>>>>> sufficient. >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>>> From the perspective of interoperability, it >>>>>> would be >>>>>> > > >>>>>>>>>>> good to support >>>>>> > > >>>>>>>>>>> > >>>>> native >>>>>> > > >>>>>>>>>>> > >>>>> type from file specs. Life will be easier for >>>>>> projects >>>>>> > > >>>>>>>>>>> like Apache >>>>>> > > >>>>>>>>>>> > >>>>> XTable. >>>>>> > > >>>>>>>>>>> > >>>>> File format could also provide finer-grained >>>>>> statistics >>>>>> > > >>>>>>>>>>> for variant >>>>>> > > >>>>>>>>>>> > >>>>> type which >>>>>> > > >>>>>>>>>>> > >>>>> facilitates data skipping. >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>> Agreed, there can definitely be additional >>>>>> value in >>>>>> > > >>>>>>>>>>> native file format >>>>>> > > >>>>>>>>>>> > >>>> integration. Just wanted to highlight that >>>>>> it's not a >>>>>> > > >>>>>>>>>>> strict requirement. >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>> -Tyler >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>> >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>>> Gang >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>>> On Wed, May 15, 2024 at 6:49 AM Tyler Akidau >>>>>> > > >>>>>>>>>>> > >>>>> <tyler.aki...@snowflake.com.invalid> wrote: >>>>>> > > >>>>>>>>>>> > >>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> Good to see you again as well, JB! Thanks! >>>>>> > > >>>>>>>>>>> > >>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> -Tyler >>>>>> > > >>>>>>>>>>> > >>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> On Tue, May 14, 2024 at 1:04 PM >>>>>> Jean-Baptiste Onofré < >>>>>> > > >>>>>>>>>>> j...@nanthrax.net> >>>>>> > > >>>>>>>>>>> > >>>>>> wrote: >>>>>> > > >>>>>>>>>>> > >>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> Hi Tyler, >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> Super happy to see you there :) It reminds >>>>>> me our >>>>>> > > >>>>>>>>>>> discussions back in >>>>>> > > >>>>>>>>>>> > >>>>>>> the start of Apache Beam :) >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> Anyway, the thread is pretty interesting. I >>>>>> remember >>>>>> > > >>>>>>>>>>> some discussions >>>>>> > > >>>>>>>>>>> > >>>>>>> about JSON datatype for spec v3. The binary >>>>>> data type >>>>>> > > >>>>>>>>>>> is already >>>>>> > > >>>>>>>>>>> > >>>>>>> supported in the spec v2. >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> I'm looking forward to the proposal and >>>>>> happy to help >>>>>> > > >>>>>>>>>>> on this ! >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> Regards >>>>>> > > >>>>>>>>>>> > >>>>>>> JB >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>>> On Sat, May 11, 2024 at 7:06 AM Tyler Akidau >>>>>> > > >>>>>>>>>>> > >>>>>>> <tyler.aki...@snowflake.com.invalid> wrote: >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > Hello, >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > We (Tyler, Nileema, Selcuk, Aihua) are >>>>>> working on a >>>>>> > > >>>>>>>>>>> proposal for >>>>>> > > >>>>>>>>>>> > >>>>>>> which we’d like to get early feedback from >>>>>> the >>>>>> > > >>>>>>>>>>> community. As you may know, >>>>>> > > >>>>>>>>>>> > >>>>>>> Snowflake has embraced Iceberg as its open >>>>>> Data Lake >>>>>> > > >>>>>>>>>>> format. Having made >>>>>> > > >>>>>>>>>>> > >>>>>>> good progress on our own adoption of the >>>>>> Iceberg >>>>>> > > >>>>>>>>>>> standard, we’re now in a >>>>>> > > >>>>>>>>>>> > >>>>>>> position where there are features not yet >>>>>> supported in >>>>>> > > >>>>>>>>>>> Iceberg which we >>>>>> > > >>>>>>>>>>> > >>>>>>> think would be valuable for our users, and >>>>>> that we >>>>>> > > >>>>>>>>>>> would like to discuss >>>>>> > > >>>>>>>>>>> > >>>>>>> with and help contribute to the Iceberg >>>>>> community. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > The first two such features we’d like to >>>>>> discuss are >>>>>> > > >>>>>>>>>>> in support of >>>>>> > > >>>>>>>>>>> > >>>>>>> efficient querying of dynamically typed, >>>>>> > > >>>>>>>>>>> semi-structured data: variant data >>>>>> > > >>>>>>>>>>> > >>>>>>> types, and subcolumnarization of variant >>>>>> columns. In >>>>>> > > >>>>>>>>>>> more detail, for >>>>>> > > >>>>>>>>>>> > >>>>>>> anyone who may not already be familiar: >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > 1. Variant data types >>>>>> > > >>>>>>>>>>> > >>>>>>> > Variant types allow for the efficient >>>>>> binary >>>>>> > > >>>>>>>>>>> encoding of dynamic >>>>>> > > >>>>>>>>>>> > >>>>>>> semi-structured data such as JSON, Avro, >>>>>> etc. By >>>>>> > > >>>>>>>>>>> encoding semi-structured >>>>>> > > >>>>>>>>>>> > >>>>>>> data as a variant column, we retain the >>>>>> flexibility of >>>>>> > > >>>>>>>>>>> the source data, >>>>>> > > >>>>>>>>>>> > >>>>>>> while allowing query engines to more >>>>>> efficiently >>>>>> > > >>>>>>>>>>> operate on the data. >>>>>> > > >>>>>>>>>>> > >>>>>>> Snowflake has supported the variant data >>>>>> type on >>>>>> > > >>>>>>>>>>> Snowflake tables for many >>>>>> > > >>>>>>>>>>> > >>>>>>> years [1]. As more and more users utilize >>>>>> Iceberg >>>>>> > > >>>>>>>>>>> tables in Snowflake, >>>>>> > > >>>>>>>>>>> > >>>>>>> we’re hearing an increasing chorus of >>>>>> requests for >>>>>> > > >>>>>>>>>>> variant support. >>>>>> > > >>>>>>>>>>> > >>>>>>> Additionally, other query engines such as >>>>>> Apache Spark >>>>>> > > >>>>>>>>>>> have begun adding >>>>>> > > >>>>>>>>>>> > >>>>>>> variant support [2]. As such, we believe it >>>>>> would be >>>>>> > > >>>>>>>>>>> beneficial to the >>>>>> > > >>>>>>>>>>> > >>>>>>> Iceberg community as a whole to standardize >>>>>> on the >>>>>> > > >>>>>>>>>>> variant data type >>>>>> > > >>>>>>>>>>> > >>>>>>> encoding used across Iceberg tables. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > One specific point to make here is that, >>>>>> since an >>>>>> > > >>>>>>>>>>> Apache OSS >>>>>> > > >>>>>>>>>>> > >>>>>>> version of variant encoding already exists >>>>>> in Spark, >>>>>> > > >>>>>>>>>>> it likely makes sense >>>>>> > > >>>>>>>>>>> > >>>>>>> to simply adopt the Spark encoding as the >>>>>> Iceberg >>>>>> > > >>>>>>>>>>> standard as well. The >>>>>> > > >>>>>>>>>>> > >>>>>>> encoding we use internally today in >>>>>> Snowflake is >>>>>> > > >>>>>>>>>>> slightly different, but >>>>>> > > >>>>>>>>>>> > >>>>>>> essentially equivalent, and we see no >>>>>> particular value >>>>>> > > >>>>>>>>>>> in trying to clutter >>>>>> > > >>>>>>>>>>> > >>>>>>> the space with another >>>>>> equivalent-but-incompatible >>>>>> > > >>>>>>>>>>> encoding. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > 2. Subcolumnarization >>>>>> > > >>>>>>>>>>> > >>>>>>> > Subcolumnarization of variant columns >>>>>> allows query >>>>>> > > >>>>>>>>>>> engines to >>>>>> > > >>>>>>>>>>> > >>>>>>> efficiently prune datasets when subcolumns >>>>>> (i.e., >>>>>> > > >>>>>>>>>>> nested fields) within a >>>>>> > > >>>>>>>>>>> > >>>>>>> variant column are queried, and also allows >>>>>> optionally >>>>>> > > >>>>>>>>>>> materializing some >>>>>> > > >>>>>>>>>>> > >>>>>>> of the nested fields as a column on their >>>>>> own, >>>>>> > > >>>>>>>>>>> affording queries on these >>>>>> > > >>>>>>>>>>> > >>>>>>> subcolumns the ability to read less data >>>>>> and spend >>>>>> > > >>>>>>>>>>> less CPU on extraction. >>>>>> > > >>>>>>>>>>> > >>>>>>> When subcolumnarizing, the system managing >>>>>> table >>>>>> > > >>>>>>>>>>> metadata and data tracks >>>>>> > > >>>>>>>>>>> > >>>>>>> individual pruning statistics (min, max, >>>>>> null, etc.) >>>>>> > > >>>>>>>>>>> for some subset of the >>>>>> > > >>>>>>>>>>> > >>>>>>> nested fields within a variant, and also >>>>>> manages any >>>>>> > > >>>>>>>>>>> optional >>>>>> > > >>>>>>>>>>> > >>>>>>> materialization. Without >>>>>> subcolumnarization, any query >>>>>> > > >>>>>>>>>>> which touches a >>>>>> > > >>>>>>>>>>> > >>>>>>> variant column must read, parse, extract, >>>>>> and filter >>>>>> > > >>>>>>>>>>> every row for which >>>>>> > > >>>>>>>>>>> > >>>>>>> that column is non-null. Thus, by providing >>>>>> a >>>>>> > > >>>>>>>>>>> standardized way of tracking >>>>>> > > >>>>>>>>>>> > >>>>>>> subcolum metadata and data for variant >>>>>> columns, >>>>>> > > >>>>>>>>>>> Iceberg can make >>>>>> > > >>>>>>>>>>> > >>>>>>> subcolumnar optimizations accessible across >>>>>> various >>>>>> > > >>>>>>>>>>> catalogs and query >>>>>> > > >>>>>>>>>>> > >>>>>>> engines. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > Subcolumnarization is a non-trivial >>>>>> topic, so we >>>>>> > > >>>>>>>>>>> expect any >>>>>> > > >>>>>>>>>>> > >>>>>>> concrete proposal to include not only the >>>>>> set of >>>>>> > > >>>>>>>>>>> changes to Iceberg >>>>>> > > >>>>>>>>>>> > >>>>>>> metadata that allow compatible query >>>>>> engines to >>>>>> > > >>>>>>>>>>> interopate on >>>>>> > > >>>>>>>>>>> > >>>>>>> subcolumnarization data for variant >>>>>> columns, but also >>>>>> > > >>>>>>>>>>> reference >>>>>> > > >>>>>>>>>>> > >>>>>>> documentation explaining subcolumnarization >>>>>> principles >>>>>> > > >>>>>>>>>>> and recommended best >>>>>> > > >>>>>>>>>>> > >>>>>>> practices. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > It sounds like the recent Geo proposal >>>>>> [3] may be a >>>>>> > > >>>>>>>>>>> good starting >>>>>> > > >>>>>>>>>>> > >>>>>>> point for how to approach this, so our plan >>>>>> is to >>>>>> > > >>>>>>>>>>> write something up in >>>>>> > > >>>>>>>>>>> > >>>>>>> that vein that covers the proposed spec >>>>>> changes, >>>>>> > > >>>>>>>>>>> backwards compatibility, >>>>>> > > >>>>>>>>>>> > >>>>>>> implementor burdens, etc. But we wanted to >>>>>> first reach >>>>>> > > >>>>>>>>>>> out to the community >>>>>> > > >>>>>>>>>>> > >>>>>>> to introduce ourselves and the idea, and >>>>>> see if >>>>>> > > >>>>>>>>>>> there’s any early feedback >>>>>> > > >>>>>>>>>>> > >>>>>>> we should incorporate before we spend too >>>>>> much time on >>>>>> > > >>>>>>>>>>> a concrete proposal. >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > Thank you! >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > [1] >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> >>>>>> https://docs.snowflake.com/en/sql-reference/data-types-semistructured >>>>>> > > >>>>>>>>>>> > >>>>>>> > [2] >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> >>>>>> https://github.com/apache/spark/blob/master/common/variant/README.md >>>>>> > > >>>>>>>>>>> > >>>>>>> > [3] >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> >>>>>> https://docs.google.com/document/d/1iVFbrRNEzZl8tDcZC81GFt01QJkLJsI9E2NBOt21IRI/edit >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> > -Tyler, Nileema, Selcuk, Aihua >>>>>> > > >>>>>>>>>>> > >>>>>>> > >>>>>> > > >>>>>>>>>>> > >>>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> > -- >>>>>> > > >>>>>>>>>>> > Ryan Blue >>>>>> > > >>>>>>>>>>> > Databricks >>>>>> > > >>>>>>>>>>> > >>>>>> > > >>>>>>>>>>> >>>>>> > > >>>>>>>>>> >>>>>> > > >>>>>>>>> >>>>>> > > >>>>>>>>> -- >>>>>> > > >>>>>>>>> Ryan Blue >>>>>> > > >>>>>>>>> Databricks >>>>>> > > >>>>>>>>> >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>>> -- >>>>>> > > >>>>>>>> Ryan Blue >>>>>> > > >>>>>>>> Databricks >>>>>> > > >>>>>>>> >>>>>> > > >>>>>>> >>>>>> > > >>>>>> >>>>>> > > >>>>>> -- >>>>>> > > >>>>>> Ryan Blue >>>>>> > > >>>>>> Databricks >>>>>> > > >>>>>> >>>>>> > > >>>>> >>>>>> > > >>>> >>>>>> > > >>>> -- >>>>>> > > >>>> Ryan Blue >>>>>> > > >>>> Databricks >>>>>> > > >>>> >>>>>> > > >>> >>>>>> > > >> >>>>>> > > >> -- >>>>>> > > >> Ryan Blue >>>>>> > > >> Databricks >>>>>> > > >> >>>>>> > > > >>>>>> > > >>>>>> > >>>>>> >>>>> >>>> >>>> -- >>>> Ryan Blue >>>> Databricks >>>> >>>
