Hi, I would like to add my opinions regarding the discussion of shredding into multiple types. For the sake of argument, imagine we shred a column "A" into int64 and string and we keep track of min/max for int64s and strings. 1. In our experience, a significant portion of the benefit of shredding comes from compile time pruning, i.e., pruning based on metadata layer stats. If a query has a filter on "A"::primite_type, where primitive type can be any primitive type including int64 and string, metadata layer cannot prune based on any min/max. This is actually true even for runtime pruning. This is because, for any file, we would need to know the min/max across all values in that file when the values are cast to the primitive_type. Any min/max derived from a subset of values will not be useful. For example, if the rows are 2, "4", "A"::int64 > 3 should not be pruned.
2. We have not seen an important use case where values for a shredded path is of different types across a majority of files. For cases like an int64 changes into string, usually only a few of the files will have a mix of different types and after a while, majority of the files will have strings. In other cases, strings can indicate errors or exceptions, and again they are not common. The case we have seen to be more common is encoding integers, timestamps or dates in strings. In such a case, what will be useful is casting all string values to INT like primitive types, compute min/max based on those primitive types and prune accordingly. For example, for an simple example, a column which is "2", "4", "5" can have two min/max types, one based on string and one based on int. Then iceberg can store these min/max values with different types and can prune both when the column is accessed as a string and as in INT. Column itself can still be shredded as string. Keeping track of min/max in multiple target types can actually be done for a case where a path has both INT and string values as well. Selcuk On Fri, Jul 12, 2024 at 6:02 PM Ryan Blue <b...@databricks.com> 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 >
