[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-08 Thread rdblue
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.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-08 Thread rdblue
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.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-02 Thread cloud-fan
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`.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-02 Thread rdblue
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?


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-02 Thread cloud-fan
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!


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-05-01 Thread rdblue
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.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-30 Thread gatorsmile
Github user gatorsmile commented on the issue:

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LGTM. Thanks! Merged to master.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-27 Thread cloud-fan
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?


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-27 Thread gatorsmile
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


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-25 Thread cloud-fan
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.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-25 Thread rdblue
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?


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-25 Thread gengliangwang
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retest this please.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-25 Thread cloud-fan
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retest this please


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-24 Thread SparkQA
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https://github.com/apache/spark/pull/21143
  
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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-24 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/21143
  
**[Test build #89795 has 
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 for PR 21143 at commit 
[`abc6426`](https://github.com/apache/spark/commit/abc64263288a39660ca9e1c59513fea2388bc080).
 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-24 Thread SparkQA
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https://github.com/apache/spark/pull/21143
  
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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

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[GitHub] spark issue #21143: [SPARK-24072][SQL] clearly define pushed filters

2018-04-24 Thread SparkQA
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https://github.com/apache/spark/pull/21143
  
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