Hi Imran, I don't think it's a good idea to start creating multiple types of Iceberg tables. Iceberg's main selling point is compatibility between engines. If we don't have readers and writers for all types of tables, then we remove compatibility from the equation and engine specific formats always win. OTOH, if we write readers and writers for all types of tables then we are back on square one.
Identifier fields are a table schema concept and used in many cases during query planning and execution. This is why they are defined as part of the SQL spec, and this is why Iceberg defines them as well. One use case is where they can be used to merge deletes (independently of how they are manifested) and subsequent inserts, into updates. Flink SQL doesn't allow creating tables with partition transforms, so no new table could be created by Flink SQL using transforms, but tables created by other engines could still be used (both read an write). Also you can create such tables in Flink using the Java API. Requiring partition columns be part of the identifier fields is coming from the practical consideration, that you want to limit the scope of the equality deletes as much as possible. Otherwise all of the equality deletes should be table global, and they should be read by every reader. We could write those, we just decided that we don't want to allow the user to do this, as it is most cases a bad idea. I hope this helps, Peter On Fri, Nov 8, 2024, 22:01 Imran Rashid <iras...@cloudera.com.invalid> wrote: > I'm not down in the weeds at all myself on implementation details, so > forgive me if I'm wrong about the details here. > > I can see all the viewpoints -- both that equality deletes enable some use > cases, but also make others far more difficult. What surprised me the most > is that Iceberg does not provide a way to distinguish these two table > "types". > > At first, I thought the presence of an identifier-field ( > https://iceberg.apache.org/spec/#identifier-field-ids) indicated that the > table was a target for equality deletes. But, then it turns out > identifier-fields are also useful for changelog views even without equality > deletes -- IIUC, they show that a delete + insert should actually be > interpreted as an update in changelog view. > > To be perfectly honest, I'm confused about all of these details -- from my > read, the spec does not indicate this relationship between > identifier-fields and equality_ids in equality delete files ( > https://iceberg.apache.org/spec/#equality-delete-files), but I think that > is the way Flink works. Flink itself seems to have even more limitations > -- no partition transforms are allowed, and all partition columns must be a > subset of the identifier fields. Is that just a Flink limitation, or is > that the intended behavior in the spec? (Or maybe user-error on my part?) > Those seem like very reasonable limitations, from an implementation > point-of-view. But OTOH, as a user, this seems to be directly contrary to > some of the promises of Iceberg. > > Its easy to see if a table already has equality deletes in it, by looking > at the metadata. But is there any way to indicate that a table (or branch > of a table) _must not_ have equality deletes added to it? > > If that were possible, it seems like we could support both use cases. We > could continue to optimize for the streaming ingestion use cases using > equality deletes. But we could also build more optimizations into the > "non-streaming-ingestion" branches. And we could document the tradeoff so > it is much clearer to end users. > > To maintain compatibility, I suppose that the change would be that > equality deletes continue to be allowed by default, but we'd add a new > field to indicate that for some tables (or branches of a table), equality > deletes would not be allowed. And it would be an error for an engine to > make an update which added an equality delete to such a table. > > Maybe that change would even be possible in V3. > > And if all the performance improvements to equality deletes make this a > moot point, we could drop the field in v4. But it seems like a mistake to > both limit the non-streaming use-case AND have confusing limitations for > the end-user in the meantime. > > I would happily be corrected about my understanding of all of the above. > > thanks! > Imran > > On Tue, Nov 5, 2024 at 9:16 AM Bryan Keller <brya...@gmail.com> wrote: > >> I also feel we should keep equality deletes until we have an alternative >> solution for streaming updates/deletes. >> >> -Bryan >> >> On Nov 4, 2024, at 8:33 AM, Péter Váry <peter.vary.apa...@gmail.com> >> wrote: >> >> Well, it seems like I'm a little late, so most of the arguments are >> voiced. >> >> I agree that we should not deprecate the equality deletes until we have a >> replacement feature. >> I think one of the big advantages of Iceberg is that it supports batch >> processing and streaming ingestion too. >> For streaming ingestion we need a way to update existing data in a >> performant way, but restricting deletes for the primary keys seems like >> enough from the streaming perspective. >> >> Equality deletes allow a very wide range of applications, which we might >> be able to narrow down a bit, but still keep useful. So if we want to go >> down this road, we need to start collecting the requirements. >> >> Thanks, >> Peter >> >> Shani Elharrar <sh...@upsolver.com.invalid> ezt írta (időpont: 2024. >> nov. 1., P, 19:22): >> >>> I understand how it makes sense for batch jobs, but it damages stream >>> jobs, using equality deletes works much better for streaming (which have a >>> strict SLA for delays), and in order to decrease the performance penalty - >>> systems can rewrite the equality deletes to positional deletes. >>> >>> Shani. >>> >>> On 1 Nov 2024, at 20:06, Steven Wu <stevenz...@gmail.com> wrote: >>> >>> >>> Fundamentally, it is very difficult to write position deletes with >>> concurrent writers and conflicts for batch jobs too, as the inverted index >>> may become invalid/stale. >>> >>> The position deletes are created during the write phase. But conflicts >>> are only detected at the commit stage. I assume the batch job should fail >>> in this case. >>> >>> On Fri, Nov 1, 2024 at 10:57 AM Steven Wu <stevenz...@gmail.com> wrote: >>> >>>> Shani, >>>> >>>> That is a good point. It is certainly a limitation for the Flink job to >>>> track the inverted index internally (which is what I had in mind). It can't >>>> be shared/synchronized with other Flink jobs or other engines writing to >>>> the same table. >>>> >>>> Thanks, >>>> Steven >>>> >>>> On Fri, Nov 1, 2024 at 10:50 AM Shani Elharrar >>>> <sh...@upsolver.com.invalid> wrote: >>>> >>>>> Even if Flink can create this state, it would have to be maintained >>>>> against the Iceberg table, we wouldn't like duplicates (keys) if other >>>>> systems / users update the table (e.g manual insert / updates using DML). >>>>> >>>>> Shani. >>>>> >>>>> On 1 Nov 2024, at 18:32, Steven Wu <stevenz...@gmail.com> wrote: >>>>> >>>>> >>>>> > Add support for inverted indexes to reduce the cost of position >>>>> lookup. This is fairly tricky to implement for streaming use cases without >>>>> an external system. >>>>> >>>>> Anton, that is also what I was saying earlier. In Flink, the inverted >>>>> index of (key, committed data files) can be tracked in Flink state. >>>>> >>>>> On Fri, Nov 1, 2024 at 2:16 AM Anton Okolnychyi <aokolnyc...@gmail.com> >>>>> wrote: >>>>> >>>>>> I was a bit skeptical when we were adding equality deletes, but >>>>>> nothing beats their performance during writes. We have to find an >>>>>> alternative before deprecating. >>>>>> >>>>>> We are doing a lot of work to improve streaming, like reducing the >>>>>> cost of commits, enabling a large (potentially infinite) number of >>>>>> snapshots, changelog reads, and so on. It is a project goal to excel in >>>>>> streaming. >>>>>> >>>>>> I was going to focus on equality deletes after completing the DV >>>>>> work. I believe we have these options: >>>>>> >>>>>> - Revisit the existing design of equality deletes (e.g. add more >>>>>> restrictions, improve compaction, offer new writers). >>>>>> - Standardize on the view-based approach [1] to handle streaming >>>>>> upserts and CDC use cases, potentially making this part of the spec. >>>>>> - Add support for inverted indexes to reduce the cost of position >>>>>> lookup. This is fairly tricky to implement for streaming use cases >>>>>> without >>>>>> an external system. Our runtime filtering in Spark today is equivalent to >>>>>> looking up positions in an inverted index represented by another Iceberg >>>>>> table. That may still not be enough for some streaming use cases. >>>>>> >>>>>> [1] - https://www.tabular.io/blog/hello-world-of-cdc/ >>>>>> >>>>>> - Anton >>>>>> >>>>>> чт, 31 жовт. 2024 р. о 21:31 Micah Kornfield <emkornfi...@gmail.com> >>>>>> пише: >>>>>> >>>>>>> I agree that equality deletes have their place in streaming. I >>>>>>> think the ultimate decision here is how opinionated Iceberg wants to be >>>>>>> on >>>>>>> its use-cases. If it really wants to stick to its origins of "slow >>>>>>> moving >>>>>>> data", then removing equality deletes would be inline with this. I >>>>>>> think >>>>>>> the other high level question is how much we allow for partially >>>>>>> compatible >>>>>>> features (the row lineage use-case feature was explicitly approved >>>>>>> excluding equality deletes, and people seemed OK with it at the time. >>>>>>> If >>>>>>> all features need to work together, then maybe we need to rethink the >>>>>>> design here so it can be forward compatible with equality deletes). >>>>>>> >>>>>>> I think one issue with equality deletes as stated in the spec is >>>>>>> that they are overly broad. I'd be interested if people have any use >>>>>>> cases >>>>>>> that differ, but I think one way of narrowing (and probably a necessary >>>>>>> building block for building something better) the specification scope >>>>>>> on >>>>>>> equality deletes is to focus on upsert/Streaming deletes. Two >>>>>>> proposals in >>>>>>> this regard are: >>>>>>> >>>>>>> 1. Require that equality deletes can only correspond to unique >>>>>>> identifiers for the table. >>>>>>> 2. Consider requiring that for equality deletes on partitioned >>>>>>> tables, that the primary key must contain a partition column (I believe >>>>>>> Flink at least already does this). It is less clear to me that this >>>>>>> would >>>>>>> meet all existing use-cases. But having this would allow for better >>>>>>> incremental data-structures, which could then be partition based. >>>>>>> >>>>>>> Narrow scope to unique identifiers would allow for further building >>>>>>> blocks already mentioned, like a secondary index (possible via LSM >>>>>>> tree), >>>>>>> that would allow for better performance overall. >>>>>>> >>>>>>> I generally agree with the sentiment that we shouldn't deprecate >>>>>>> them until there is a viable replacement. With all due respect to my >>>>>>> employer, let's not fall into the Google trap [1] :) >>>>>>> >>>>>>> Cheers, >>>>>>> Micah >>>>>>> >>>>>>> [1] https://goomics.net/50/ >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Thu, Oct 31, 2024 at 12:35 PM Alexander Jo < >>>>>>> alex...@starburstdata.com> wrote: >>>>>>> >>>>>>>> Hey all, >>>>>>>> >>>>>>>> Just to throw my 2 cents in, I agree with Steven and others that we >>>>>>>> do need some kind of replacement before deprecating equality deletes. >>>>>>>> They certainly have their problems, and do significantly increase >>>>>>>> complexity as they are now, but the writing of position deletes is too >>>>>>>> expensive for certain pipelines. >>>>>>>> >>>>>>>> We've been investigating using equality deletes for some of our >>>>>>>> workloads at Starburst, the key advantage we were hoping to leverage is >>>>>>>> cheap, effectively random access lookup deletes. >>>>>>>> Say you have a UUID column that's unique in a table and want to >>>>>>>> delete a row by UUID. With position deletes each delete is expensive >>>>>>>> without an index on that UUID. >>>>>>>> With equality deletes each delete is cheap and while >>>>>>>> reads/compaction is expensive but when updates are frequent and reads >>>>>>>> are >>>>>>>> sporadic that's a reasonable tradeoff. >>>>>>>> >>>>>>>> Pretty much what Jason and Steven have already said. >>>>>>>> >>>>>>>> Maybe there are some incremental improvements on equality deletes >>>>>>>> or tips from similar systems that might alleviate some of their >>>>>>>> problems? >>>>>>>> >>>>>>>> - Alex Jo >>>>>>>> >>>>>>>> On Thu, Oct 31, 2024 at 10:58 AM Steven Wu <stevenz...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> We probably all agree with the downside of equality deletes: it >>>>>>>>> postpones all the work on the read path. >>>>>>>>> >>>>>>>>> In theory, we can implement position deletes only in the Flink >>>>>>>>> streaming writer. It would require the tracking of last committed data >>>>>>>>> files per key, which can be stored in Flink state (checkpointed). >>>>>>>>> This is >>>>>>>>> obviously quite expensive/challenging, but possible. >>>>>>>>> >>>>>>>>> I like to echo one benefit of equality deletes that Russel called >>>>>>>>> out in the original email. Equality deletes would never have >>>>>>>>> conflicts. >>>>>>>>> that is important for streaming writers (Flink, Kafka connect, ...) >>>>>>>>> that >>>>>>>>> commit frequently (minutes or less). Assume Flink can write position >>>>>>>>> deletes only and commit every 2 minutes. The long-running nature of >>>>>>>>> streaming jobs can cause frequent commit conflicts with background >>>>>>>>> delete >>>>>>>>> compaction jobs. >>>>>>>>> >>>>>>>>> Overall, the streaming upsert write is not a well solved problem >>>>>>>>> in Iceberg. This probably affects all streaming engines (Flink, Kafka >>>>>>>>> connect, Spark streaming, ...). We need to come up with some better >>>>>>>>> alternatives before we can deprecate equality deletes. >>>>>>>>> >>>>>>>>> >>>>>>>>> On Thu, Oct 31, 2024 at 8:38 AM Russell Spitzer < >>>>>>>>> russell.spit...@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> For users of Equality Deletes, what are the key benefits to >>>>>>>>>> Equality Deletes that you would like to preserve and could you >>>>>>>>>> please share >>>>>>>>>> some concrete examples of the queries you want to run (and the >>>>>>>>>> schemas and >>>>>>>>>> data sizes you would like to run them against) and the latencies >>>>>>>>>> that would >>>>>>>>>> be acceptable? >>>>>>>>>> >>>>>>>>>> On Thu, Oct 31, 2024 at 10:05 AM Jason Fine >>>>>>>>>> <ja...@upsolver.com.invalid> wrote: >>>>>>>>>> >>>>>>>>>>> Hi, >>>>>>>>>>> >>>>>>>>>>> Representing Upsolver here, we also make use of Equality Deletes >>>>>>>>>>> to deliver high frequency low latency updates to our clients at >>>>>>>>>>> scale. We >>>>>>>>>>> have customers using them at scale and demonstrating the need and >>>>>>>>>>> viability. We automate the process of converting them into >>>>>>>>>>> positional >>>>>>>>>>> deletes (or fully applying them) for more efficient engine queries >>>>>>>>>>> in the >>>>>>>>>>> background giving our users both low latency and good query >>>>>>>>>>> performance. >>>>>>>>>>> >>>>>>>>>>> Equality Deletes were added since there isn't a good way to >>>>>>>>>>> solve frequent updates otherwise. It would require some sort of >>>>>>>>>>> index >>>>>>>>>>> keeping track of every record in the table (by a predetermined PK) >>>>>>>>>>> and >>>>>>>>>>> maintaining such an index is a huge task that every tool interested >>>>>>>>>>> in this >>>>>>>>>>> would need to re-implement. It also becomes a bottleneck limiting >>>>>>>>>>> table >>>>>>>>>>> sizes. >>>>>>>>>>> >>>>>>>>>>> I don't think they should be removed without providing an >>>>>>>>>>> alternative. Positional Deletes have a different performance profile >>>>>>>>>>> inherently, requiring more upfront work proportional to the table >>>>>>>>>>> size. >>>>>>>>>>> >>>>>>>>>>> On Thu, Oct 31, 2024 at 2:45 PM Jean-Baptiste Onofré < >>>>>>>>>>> j...@nanthrax.net> wrote: >>>>>>>>>>> >>>>>>>>>>>> Hi Russell >>>>>>>>>>>> >>>>>>>>>>>> Thanks for the nice writeup and the proposal. >>>>>>>>>>>> >>>>>>>>>>>> I agree with your analysis, and I have the same feeling. >>>>>>>>>>>> However, I >>>>>>>>>>>> think there are more than Flink that write equality delete >>>>>>>>>>>> files. So, >>>>>>>>>>>> I agree to deprecate in V3, but maybe be more "flexible" about >>>>>>>>>>>> removal >>>>>>>>>>>> in V4 in order to give time to engines to update. >>>>>>>>>>>> I think that by deprecating equality deletes, we are clearly >>>>>>>>>>>> focusing >>>>>>>>>>>> on read performance and "consistency" (more than write). It's >>>>>>>>>>>> not >>>>>>>>>>>> necessarily a bad thing but the streaming platform and data >>>>>>>>>>>> ingestion >>>>>>>>>>>> platforms will be probably concerned about that (by using >>>>>>>>>>>> positional >>>>>>>>>>>> deletes, they will have to scan/read all datafiles to find the >>>>>>>>>>>> position, so painful). >>>>>>>>>>>> >>>>>>>>>>>> So, to summarize: >>>>>>>>>>>> 1. Agree to deprecate equality deletes, but -1 to commit any >>>>>>>>>>>> target >>>>>>>>>>>> for deletion before having a clear path for streaming platforms >>>>>>>>>>>> (Flink, Beam, ...) >>>>>>>>>>>> 2. In the meantime (during the deprecation period), I propose to >>>>>>>>>>>> explore possible improvements for streaming platforms (maybe >>>>>>>>>>>> finding a >>>>>>>>>>>> way to avoid full data files scan, ...) >>>>>>>>>>>> >>>>>>>>>>>> Thanks ! >>>>>>>>>>>> Regards >>>>>>>>>>>> JB >>>>>>>>>>>> >>>>>>>>>>>> On Wed, Oct 30, 2024 at 10:06 PM Russell Spitzer >>>>>>>>>>>> <russell.spit...@gmail.com> wrote: >>>>>>>>>>>> > >>>>>>>>>>>> > Background: >>>>>>>>>>>> > >>>>>>>>>>>> > 1) Position Deletes >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > Writers determine what rows are deleted and mark them in a 1 >>>>>>>>>>>> for 1 representation. With delete vectors this means every data >>>>>>>>>>>> file has at >>>>>>>>>>>> most 1 delete vector that it is read in conjunction with to excise >>>>>>>>>>>> deleted >>>>>>>>>>>> rows. Reader overhead is more or less constant and is very >>>>>>>>>>>> predictable. >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > The main cost of this mode is that deletes must be determined >>>>>>>>>>>> at write time which is expensive and can be more difficult for >>>>>>>>>>>> conflict >>>>>>>>>>>> resolution >>>>>>>>>>>> > >>>>>>>>>>>> > 2) Equality Deletes >>>>>>>>>>>> > >>>>>>>>>>>> > Writers write out reference to what values are deleted (in a >>>>>>>>>>>> partition or globally). There can be an unlimited number of >>>>>>>>>>>> equality >>>>>>>>>>>> deletes and they all must be checked for every data file that is >>>>>>>>>>>> read. The >>>>>>>>>>>> cost of determining deleted rows is essentially given to the >>>>>>>>>>>> reader. >>>>>>>>>>>> > >>>>>>>>>>>> > Conflicts almost never happen since data files are not >>>>>>>>>>>> actually changed and there is almost no cost to the writer to >>>>>>>>>>>> generate >>>>>>>>>>>> these. Almost all costs related to equality deletes are passed on >>>>>>>>>>>> to the >>>>>>>>>>>> reader. >>>>>>>>>>>> > >>>>>>>>>>>> > Proposal: >>>>>>>>>>>> > >>>>>>>>>>>> > Equality deletes are, in my opinion, unsustainable and we >>>>>>>>>>>> should work on deprecating and removing them from the >>>>>>>>>>>> specification. At >>>>>>>>>>>> this time, I know of only one engine (Apache Flink) which produces >>>>>>>>>>>> these >>>>>>>>>>>> deletes but almost all engines have implementations to read them. >>>>>>>>>>>> The cost >>>>>>>>>>>> of implementing equality deletes on the read path is difficult and >>>>>>>>>>>> unpredictable in terms of memory usage and compute complexity. >>>>>>>>>>>> We’ve had >>>>>>>>>>>> suggestions of implementing rocksdb inorder to handle ever growing >>>>>>>>>>>> sets of >>>>>>>>>>>> equality deletes which in my opinion shows that we are going down >>>>>>>>>>>> the wrong >>>>>>>>>>>> path. >>>>>>>>>>>> > >>>>>>>>>>>> > Outside of performance, Equality deletes are also difficult >>>>>>>>>>>> to use in conjunction with many other features. For example, any >>>>>>>>>>>> features >>>>>>>>>>>> requiring CDC or Row lineage are basically impossible when >>>>>>>>>>>> equality deletes >>>>>>>>>>>> are in use. When Equality deletes are present, the state of the >>>>>>>>>>>> table can >>>>>>>>>>>> only be determined with a full scan making it difficult to update >>>>>>>>>>>> differential structures. This means materialized views or indexes >>>>>>>>>>>> need to >>>>>>>>>>>> essentially be fully rebuilt whenever an equality delete is added >>>>>>>>>>>> to the >>>>>>>>>>>> table. >>>>>>>>>>>> > >>>>>>>>>>>> > Equality deletes essentially remove complexity from the write >>>>>>>>>>>> side but then add what I believe is an unacceptable level of >>>>>>>>>>>> complexity to >>>>>>>>>>>> the read side. >>>>>>>>>>>> > >>>>>>>>>>>> > Because of this I suggest we deprecate Equality Deletes in V3 >>>>>>>>>>>> and slate them for full removal from the Iceberg Spec in V4. >>>>>>>>>>>> > >>>>>>>>>>>> > I know this is a big change and compatibility breakage so I >>>>>>>>>>>> would like to introduce this idea to the community and solicit >>>>>>>>>>>> feedback >>>>>>>>>>>> from all stakeholders. I am very flexible on this issue and would >>>>>>>>>>>> like to >>>>>>>>>>>> hear the best issues both for and against removal of Equality >>>>>>>>>>>> Deletes. >>>>>>>>>>>> > >>>>>>>>>>>> > Thanks everyone for your time, >>>>>>>>>>>> > >>>>>>>>>>>> > Russ Spitzer >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> >>>>>>>>>>> *Jason Fine* >>>>>>>>>>> Chief Software Architect >>>>>>>>>>> ja...@upsolver.com | www.upsolver.com >>>>>>>>>>> >>>>>>>>>> >>
