[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21143 @cloud-fan, here's a commit that demonstrates the idea and implementation: https://github.com/rdblue/spark/commit/b41eb1ef2af38c510f5426a096c586a93e4a5556 That adds `residualFilters` to the `InputPartition` and updates `DataSourceRDD` to compile and run the filter expressions if there are any. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21143 I think we would only need `DataSourceReader` to implement `SupportsPushDownFilter` because it is primarily used to push filters to the data source and the query's filters are determined while planning. The interface that we need is for tasks to pass filters back to Spark. For that, I would just add a method to `InputPartition`: `residualFilter`. If that returns `Some(filter)` then Spark would generate a filter for the records coming out of the source. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/21143 If we don't care about whole stage codegen, I think it's possible to dynamically change the filter condition at executors and codegen it. Now the problem becomes, how would the data source API look like? I'd image some systems have to do filter pushdown at the driver side, so that they can connect to the metadata service only once. One possible way is to allow users to mixin `SupportsPushDownFilter` to both `DataSourceReader` and `DataReaderFactory`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21143 @cloud-fan, that's kind of the point I was trying to make. It is too difficult to do whole-stage codegen, but we could add a codegen filter before whole-stage codegen. Why make the implementation handle it using Spark internals when we could do it more cleanly in Spark? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/21143 Hi @rdblue , you can also create a codegen-ed filter in your data source, but the real problem is whole-stage-codegen. The code is generated at the driver side, sent to and compiled at the executor side. It is non-trivial to change it to generating code at executor side. If union doesn't work, let's think more about this and write a design doc first. I'm willing to join the discussion and help to make a design, thanks! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21143 @cloud-fan, union doesn't really help. I already have support for mixed-formats working just fine. The format isn't the problem, it is filtering (and a similar problem with projection). Parquet allows you to push down filters, while Avro doesn't. Right now, I'm running filters inside my data source to ensure that the result always matches pushed filters, which is okay but doesn't use codegen. Since we already have a need for per-split filters for residuals, we could do something similar in Spark instead of in the data sources and allow each split to return a residual. Then Spark would add a codegen'ed filter before proceeding with the rest of the physical plan. You might think of it as a `ResidualFilter` node, where the filter expression changes, instead of a separate physical plan. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/21143 LGTM. Thanks! Merged to master. --- - 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 #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/21143 @rdblue After some more thoughts, I don't think Spark can support per-split physical plan changing in the near future. My best suggestion for mixed format data source is to have different implementations and union them at the end. We can add some kind of union API to data source v2 to make this thing easier. What do you think? --- - 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 #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/21143 LGTM except above comments. This PR does not change anything else but just clean the implementation of existing Data Source V2 API --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/21143 This is a very good point! Unfortunately Spark SQL doesn't support change the physical plan at a per-split fashion, and I'd say this feature is non-trivial to implement, and needs a design doc. After we have this feature, it might make sense to shift some of the mixin traits from `DataSourceReader` to `DataReaderFactory`, so that they can be applied per split. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters
Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21143 Thanks for working on this, @cloud-fan! I was thinking about needing it just recently so that data sources can delegate to Spark when needed. I'll have a thorough look at it tomorrow, but one quick high-level question: should we make these residuals based on the input split instead? Input splits might have different residual filters that need to be applied. For example, if you have a time range query, `ts > X`, and are storing data by day, then you know that when `day(ts) > day(X)` that `ts > X` *must* be true, but when `day(ts) = day(X)`, `ts > X` *might* be true. So for only some splits, when scanning the boundary day, you need to run the original filter, but not for any other splits. Another use case for a per-split residual is when splits might be different file formats. Parquet allows pushing down filters, but Avro doesn't. In a mixed table format it would be great for Avro splits to return the entire expression as a residual, while Parquet splits do the filtering. What do you think? --- - 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 #21143: [SPARK-24072][SQL] clearly define pushed filters
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