I love a good flame war :P > On 21 May 2019, at 22:57, Jacques Nadeau <jacq...@dremio.com> wrote: > > > That's my point, truly independent writers (two Spark jobs, or a Spark job > and Dremio job) means a distributed transaction. It would need yet another > external transaction coordinator on top of both Spark and Dremio, Iceberg by > itself > cannot solve this. > > I'm not ready to accept this. Iceberg already supports a set of semantics > around multiple writers committing simultaneously and how conflict resolution > is done. The same can be done here.
MVCC (which is what Iceberg tries to implement) requires a total ordering of snapshots. Also the snapshots need to be non-conflicting. I really don't see how any metadata data structures can solve this without an outside coordinator. Consider this: Snapshot 0: (K,A) = 1 Job X: UPDATE K SET A=A+1 Job Y: UPDATE K SET A=10 What should the final value of A be and who decides ? > > By single writer, I don't mean single process, I mean multiple coordinated > processes like Spark executors coordinated by Spark driver. The coordinator > ensures that the data is pre-partitioned on > each executor, and the coordinator commits the snapshot. > > Note however that single writer job/multiple concurrent reader jobs is > perfectly feasible, i.e. it shouldn't be a problem to write from a Spark job > and read from multiple Dremio queries concurrently (for example) > > :D This is still "single process" from my perspective. That process may be > coordinating other processes to do distributed work but ultimately it is a > single process. Fair enough > > I'm not sure what you mean exactly. If we can't enforce uniqueness we > shouldn't assume it. > > I disagree. We can specify that as a requirement and state that you'll get > unintended consequences if you provide your own keys and don't maintain this. There's no need for unintended consequences, we can specify consistent behaviour (and I believe the document says what that is) > > We do expect that most of the time the natural key is unique, but the eager > and lazy with natural key designs can handle duplicates > consistently. Basically it's not a problem to have duplicate natural keys, > everything works fine. > > That heavily depends on how things are implemented. For example, we may write > a bunch of code that generates internal data structures based on this > expectation. If we have to support duplicate matches, all of sudden we can no > longer size various data structures to improve performance and may be unable > to preallocate memory associated with a guaranteed completion. Again we need to operate on the assumption that this is a large scale distributed compute/remote storage scenario. Key matching is done with shuffles with data movement across the network, such optimizations would really have little impact on overall performance. Not to mention that most query engines would already optimize the shuffle already as much as it can be optimized. It is true that if actual duplicate keys would make the key matching join (anti-join) somewhat more expensive, however it can be done in such a way that if the keys are in practice unique the join is as efficient as it can be. > > Let me try and clarify each point: > > - lookup for query or update on a non-(partition/bucket/sort) key predicate > implies scanning large amounts of data - because these are the only data > structures that can narrow down the lookup, right ? One could argue that the > min/max index (file skipping) can be applied to any column, but in reality if > that column is not sorted the min/max intervals can have huge overlaps so it > may be next to useless. > - remote storage - this is a critical architecture decision - implementations > on local storage imply a vastly different design for the entire system, > storage and compute. > - deleting single records per snapshot is unfeasible in eager but also > particularly in the lazy design: each deletion creates a very small snapshot. > Deleting 1 million records one at a time would create 1 million small files, > and 1 million RPC calls. > > Why is this unfeasible? If I have a dataset of 100mm files including 1mm > small files, is that a major problem? It seems like your usecase isn't one > where you want to support single record deletes but it is definitely > something important to many people. 100 mm total files or 1 mm files per dataset is definitely a problem on HDFS, and I believe on S3 too. Single key delete would work just fine, but it's simply not optimal to do that on remote storage. This is a very well known problem with HDFS, and one of the very reasons to have something like Iceberg in the first place. Basically the users would be able to do single key mutation, but it's not the use case we should be optimizing for, but it's really not advisable. > > Eager is conceptually just lazy + compaction done, well, eagerly. The logic > for both is exactly the same, the trade-off is just that with eager you > implicitly compact every time so that you don't do any work on read, while > with lazy > you want to amortize the cost of compaction over multiple snapshots. > > Basically there should be no difference between the two conceptually, or with > regard to keys, etc. The only difference is some mechanics in implementation. > > I think you have deconstruct the problem too much to say these are the same > (or at least that is what I'm starting to think given this thread). It seems > like real world implementation decisions (per our discussion here) are in > conflict. For example, you just argued against having a 1mm arbitrary > mutations but I think that is because you aren't thinking about things over > time with a delta implementation. Having 10,000 mutations a day where we do > delta compaction once a week > and local file mappings (key to offset sparse bitmaps) seems like it could > result in very good performance in a case where we're mutating small amounts > of data. In this scenario, you may not do major compaction ever unless you > get to a high enough percentage of records that have been deleted in the > original dataset. That drives a very different set of implementation > decisions from a situation where you're trying to restate an entire partition > at once. We operate on 1 billion mutations per day at least. This is the problem Iceberg wants to solve, I believe it's stated upfront. 10000/day is not a big data problem. It can be done fairly trivially and it would be supported, but there's not much point in extra optimizing for this use case I believe.
