[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 Thanks for the update! Enjoy your vacation, and thanks for letting me know. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 I'm on vacation and will be back next week, will have a more thorough review at that time, sorry for the inconvenience! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 @cloud-fan, is there anything else that needs to be updated, or is this ready to be merged? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 @cloud-fan, can you have a look at this? I've made the requested changes and tests are passing. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 Okay, I rebased again after SPARK-23303 was reverted. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 @cloud-fan, I've rebased and made the requested changes. #20603 reverts the last commit that adds back support for user-supplied schemas that are identical to the source schema. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 > Sorry, what do you want to change? Nothing, just a potential use case to support creating `DataSourceOptions` in `DataSourceV2Relation`. If there are a lot of places like this, it's painful to duplicate the logic of merging extra entries to `DataSourceOptions`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 > See FindDataSourceTable.readDataSourceTable about how we handle the path option. Sorry, what do you want to change? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 > We've added a resolution rule from UnresolvedRelation to DataSourceV2Relation that uses our implementation. UnresolvedRelation needs to pass its TableIdentifier to the v2 relation, which is why I added this. I've been thinking about this a little more. This is actually an existing problem for file-based data sources. The solution is, when converting an unresolved relation to data source relation, we add some new options to the existing data source options before passing the options to data source relation. See `FindDataSourceTable.readDataSourceTable` about how we handle the path option. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 @cloud-fan: Rebased and removed path. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 Will do. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 I think there will be a lot of discussion about data source v2 table support. For now how about we remove the table/path stuff and get this PR in ASAP? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 > Can you give a use case about this? We've added a resolution rule from `UnresolvedRelation` to `DataSourceV2Relation` that uses our implementation. `UnresolvedRelation` needs to pass its `TableIdentifier` to the v2 relation, which is why I added this. We could separate this out, but I think it makes sense to get the options for an immutable plan node right from the start. And I think we agree that `TableIdentifier` will be passed this way. I just don't see any benefit to separating this out. I agree that there isn't a requirement in this PR and I can remove it if necessary. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 > The reader is to be created and configured by the relation, then the relation needs to be able to set the table, path, and other properties. This adds necessary data to the relation that is no longer be passed directly to the reader from DataFrameReader. This is an interesting thing that I missed before. Can you give a use case about this? If we have a requirement for it, I totally I agree we should put them in `DataSourceV2Relation`. > I don't see why we wouldn't want to have these options in the immutable relation node from the start. My main concern is, this PR itself doesn't show a strong use case for putting these information into `DataSourceV2Relation`. If we do need it in the future, we can add it when we really need it. `DataSourceV2Relation` is not a public API so I think we don't need to rush. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 The reader is to be created and configured by the relation, then the relation needs to be able to set the table, path, and other properties. This adds necessary data to the relation that is no longer be passed directly to the reader from `DataFrameReader`. From the other thread on this, I think we agree that minimizing the number of places that work with `DataSourceOptions` and the specific option strings is a good idea. So it makes sense to define the relation using `TableIdentifier`. Other paths that create `DataSourceV2Relation` need the table name to be passed like this. I guess we *could* revert the change and add it in a separate commit, but I don't see a reason for the extra work. It would be impractical to backport a later `TableIdentifier` change without this immutability change. Similarly, why would someone want to move to an immutable plan, but leave some left-over logic for configuration in `DataFrameReader`? I don't see why we wouldn't want to have these options in the immutable relation node from the start. Do you have a case in mind that I'm missing? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 This PR does 3 things: 1. make `DataSourceV2Relation` immutable. This extends the constructor of `DataSourceV2Relation` to include pushed filters and pruned columns. 2. carry some standard information(table, path, etc.) into `DataSourceV2Relation`. This extends the constructor of `DataSourceV2Relation` to include table identifier, path string etc. 3. replace the new operator pushdown rule with `PhysicalOperation`. It will be great if we only focus on 1, but I'm also OK if we do 1 and 3 together. I don't think we should include 2 here, as it's unclear what's the benefit. These stardard information are only used to create `DataSourceOptions` inside `DataSourceV2Relation`, which can also be done in `DataFrameReader`. I suggest we don't change this part and just keep the `DataSourceOption` in the constructor of `DataSourceV2Relation`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 @cloud-fan, this is a single commit on purpose because predicate push-down makes plan changes. I think it's best to do these at once to avoid unnecessary work. That's why I started looking more closely at push-down in the first place: updating the other push-down code for immutable plans was a mess. I also think it is unlikely that we will need to revert the push-down changes here. If we end up redesigning push-down, then it is unlikely that the easiest starting point is to roll back this fix. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 I'm OK to replace the new push down implementation with `PhysicalOperation`, but please do that in an individual PR. If we do find the new implementation is necessary, it's easier for us to bring it back, if it was removed in an individual commit. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 > For safety, I wanna keep it unchanged, and start something new for data source v2 only. I disagree. * **#20476 addresses a bug caused by the new implementation that is not a problem if we reuse the current push-down code.** Using an entirely new implementation to push filters and projection is going to introduce bugs, and that problem demonstrates that it is a real risk. * **Using unreliable push-down code is going to make it more difficult for anyone to use the v2 API.** * **This approach throws away work that has accumulated over the past few years that give us confidence in the current push-down code.** The other code paths have push-down tests that will help us catch bugs in the new push-down logic. If we limit the scope of this change to v2, we will not be able to reuse those tests and will have to write entirely new ones that cover all cases. Lastly, I think it is clear that we need a design for a new push-down mechanism. **Adding this to DataSourceV2 as feature creep is not a good way to redesign it.** I'd like to see a design document that addresses some of the open questions. I'd also prefer that this new implementation be removed from the v2 code path for 2.3.0. @marmbrus, what do you think? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20387 For doing pushdown at logical or physical phase, I don't have a strong preference. I think at logical phase we should try our best to push down data-size-reduction operators(like filter, aggregate, limit, etc.) close to the bottom of the plan, as it should always be good. We should apply pushdown to data sources at physical phase, as it's not always good and we need to consider the cost. Currently it's done in logical phase because of the `computeStats` problem. Eventually we should compute the statistics and apply pushdown to data sources in physical phase. About how to apply pushdown to data sources, I think `PhysicalOperation` is in the right direction and the new pushdown rule also follows it. Generally the logical phase is responsible for pushing down data-size-reduction operators close to the data source relation, and in the physical phase we collect supported operators(currently it's only project and filter) above the data source relation and apply the pushdown once, so this doesn't need to be incremental. We definitely need to document the contract for ordering and interactions between different types of pushdowns. For now we don't need to worry about it as we only support column pruning and filter push down, and these 2 are orthogonal, it doesn't matter if data source run project first or filter first. Here are some initial thoughts on how to define the contract. Let's say Data Source V2 framework supports pushing down required columns(column pruning), filter, limit, aggregate. Semantically filter, limit and aggregate are not exchangeable, we should respect their order in the query. If we have all these operators in a query, how to tell the data source about the order of these operators? My proposal is, since `DataSourceReader` is mutable(not the plan!), we can ask the data source to remember which operators have been pushed down, via the order of when these `pushXXX` methods are called. And data source implementations should respect the order of pushdown when applying them internally. About `PhysicalOperation`, it's pretty simple and we probably need to change it a lot when adding more operator pushdown. Another concern is, `PhysicalOperation` is used in a lot of places, not only data source pushdown. For safety, I wanna keep it unchanged, and start something new for data source v2 only. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user marmbrus commented on the issue: https://github.com/apache/spark/pull/20387 Regarding, `computeStats`, the logical plan seems like it might not be the right place. As we move towards more CBO it seems like we are going to need to pick physical operators before we can really reason about the cost of a subplan. With the caveat that I haven't though hard about this, I'd be supportive of moving these kinds of metrics to physical plan. +1 that we need to be able to consider pushdown when producing stats either way. On the second point, I don't think I understand DataSourceV2 enough yet to know the answer, but you ask a lot of questions that I think need to be defined as part of the API (if we haven't already). What is the contract for ordering and interactions between different types of pushdown? Is it valid to pushdown in pieces or will we only call the method once? (sorry if this is written down and I've just missed it). My gut feeling is that we don't really want to fuse incrementally. Its seems hard to reason about correctness and interactions between different things that have been pushed. As I hinted at before, I think its most natural to split the concerns of pushdown within a query plan and fusing of operators. But maybe this is limited in someway I don't realize. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/20387 > Why pushdown is happening in logical optimization and not during query planning. My first instinct would be to have the optimizer get operators as close to the leaves as possible and then fuse (or push down) as we convert to physical plan. I'm probably missing something. I think there are two reasons, but I'm not fully convinced by either one: * [`computeStats`](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala#L232) is defined on logical plans, so the result of filter push-down needs to be a logical plan if we want to be able to use accurate stats for a scan. I'm interested here to ensure that we correctly produce broadcast relations based on the actual scan stats, not the table-level stats. Maybe there's another way to do this? * One of the tests for DSv2 ends up invoking the push-down rule twice, which made me think about whether or not that should be valid. I think it probably should be. For example, what if a plan has nodes that can all be pushed, but they aren't in the right order? Or what if a projection wasn't pushed through a filter because of a rule problem, but it can still be pushed down? Incremental fusing during optimization might be an extensible way to handle odd cases, or it may be useless. I'm not quite sure yet. It would be great to hear your perspective on these. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
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[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
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[GitHub] spark issue #20387: [SPARK-23203][SQL]: DataSourceV2: Use immutable logical ...
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