[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16575044#comment-16575044
]
Kyle Prifogle commented on SPARK-12449:
---
[~oae] as far as I can tell the issue lives on here:
https://issues.apache.org/jira/browse/SPARK-22386
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
>Priority: Major
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
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]
Johannes Zillmann commented on SPARK-12449:
---
I'm a bit confused. Reading
https://www.snowflake.com/snowflake-spark-part-2-pushing-query-processing/ and
https://github.com/snowflakedb/spark-snowflake/pull/8/files it looks like what
the ticket is describing has already been realized ?
Can somebody shed light on this !?
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
>Priority: Major
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16548140#comment-16548140
]
Kyle Prifogle commented on SPARK-12449:
---
What happened to this initiative? I came here trying to figure out why
".limit(10)" seemed to scan the entire table.
Is slow down in some of this (seemingly critical) work an indication that the
breaks have been put on open source spark and that databricks run time is the
only future?
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
>Priority: Major
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16156622#comment-16156622
]
Andrew Ash commented on SPARK-12449:
[~velvia] I'm not involved with the CatalystSource or SAP HANAVora, so can't
comment on the direction that project is going right now.
However there is an effort to add a new Datasources V2 API happening at
https://issues.apache.org/jira/browse/SPARK-15689 and on the email list right
now that could grow to encompass the goals of this issue.
[~stephank85] if you are able to comment on SPARK-15689 your input would be
very valuable to that API design.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16138856#comment-16138856
]
Evan Chan commented on SPARK-12449:
---
Andrew and others:
Is there a plan to make this CatalystSource available or contribute it back to
Spark somehow?
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16138501#comment-16138501
]
Andrew Ash commented on SPARK-12449:
Relevant slides:
https://www.slideshare.net/SparkSummit/the-pushdown-of-everything-by-stephan-kessler-and-santiago-mola
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15153931#comment-15153931
]
Stephan Kessler commented on SPARK-12449:
-
To keep the interfaces stable, the possibility of extending might be a good
option.
Following the logic of the catalyst source model, we could still "ask" the
datasource if it supports a certain expression/aggregate/... if yes, we push it
down and do not care anymore on Spark level. The default answer for that
"asking" method should be "false", keeping the interface stable when we extend
it.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15153920#comment-15153920
]
Stephan Kessler commented on SPARK-12449:
-
In our github repo (https://github.com/SAP/HANAVora-Extensions) we have the
current implementation we use for HANA Vora.
More specifically, i guess you are interested in the CatalystSource Strategy,
which basically shows the interaction between the datasource and
PhysicalPlanning stage
https://github.com/SAP/HANAVora-Extensions/blob/0988a166739270f7332a3e7ca3347878e963d0d0/core/src/main/scala/org/apache/spark/sql/execution/datasources/CatalystSourceStrategy.scala
Obviously for a gradual approach this will change!
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15151904#comment-15151904
]
Max Seiden commented on SPARK-12449:
[~stephank85] Dropping the partitioned bit makes sense for a first shot. As-per
the discussion above, it seems pretty useful to keep the ability to ask the
source about pushdown support for plans and exprs.
Also, one thing I liked in your CatalystSource model was the extra work you did
to inject subqueries into the plan; getting a good design for a select over a
single table / subquery seems like a reasonable starting point IMO. It would
also be nice to strive for the property of rendering a sources.* plan into SQL,
but that may be a bit of a stretch. :-)
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Max Seiden commented on SPARK-12449:
Yea, that seems to be the case. There's code in the DataSourceStrategy that
specifically resolves aliases, but the filtered scan case is pretty narrow
relative to an expression tree.
+1 for a generic way to avoid double execution of operations. On the flip, a
boolean check would drop a neat property of "unhandledFilters" which is that it
can accept a subset of what the planner tries to push down.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Evan Chan commented on SPARK-12449:
---
I think in the case of sources.Expressions, by the time they are pushed down,
all aliases etc should have been resolved already, so that should not be an
issue, right?
Agree that capabilities would be important. If that didn’t exist, then the
default would be to not compute the expressions and let Spark’s default
aggregators do it, which means it would be like the filtering today where there
is double filtering.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Max Seiden commented on SPARK-12449:
Very interested in checking out that PR! It would be prudent to have a holistic
high-level design for any work here too, mostly to answer a few major
questions. A random sample of such Qs:
+ Should there be a new trait for each new `sources.*` type, or a single trait
that communicates capabilities to the planner (i.e. the CatalystSource design)?
a) a new trait for each source could get unwieldy given the potential #
of permutations
b) a single, generic trait is powerful, but it puts a lot of burden on
the implementer to cover more cases than they may want
+ Depending on the above, should source plans be a tree of operators or a list
of operators to be applied in-order?
a) the first option is more natural, but is smells a lot like catalyst --
not a bad thing if it's a separate, stable API though
+ the more that's pushed down via sources.Expressions, the more complex things
may get for implementers
a) for example, if Aliases are pushed down, there's a lot more
opportunity for resolution bugs in the source impl
b) a definitive stance would be needed for exprs like UDFs or those
dealing with complex types
c) without a way to signal capabilities (implicitly or explicitly) to the
planner, there'd likely need to be a way to "bail out"
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15151345#comment-15151345
]
Evan Chan commented on SPARK-12449:
---
[~stephank85] would you have any code to share? :D
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Stephan Kessler commented on SPARK-12449:
-
[~maxseiden] good idea! In order to simplify things even more - we could get
rid (at least in the first shot) of the partitioned and holistic approach,
since we aim for databases as datasources. What do you think on keeping the
ability to kind of ask the datasource if it supports the pushdown of a
well-defined operation? This would simplify the implementation of the
datasource as well as the Strategy for the planner.
[~velvia] i am currently working heavily on the pushdown of partial aggregates
in combination with Tungsten, so i am happy to contribute in that direction.
Should i try to formulate a new/simplified design doc that covers the gradual
approach? I am very happy to help with the PR and the definitions of tasks as
well.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15151268#comment-15151268
]
Evan Chan commented on SPARK-12449:
---
I agree with [~maxseiden] on a gradual approach to push more down into the data
sources API.Since I was going to explore a path like this anyways, I'd be
willing to submit a PR to explore a `sources.Expression` kind of pushdown.
There is also some stuff in 2.0 that might interact with this, such as
vectorization and the whole query code gen, that we need to be aware of.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Max Seiden commented on SPARK-12449:
[~rxin] Given that predicate pushdown via `sources.Filter` is (afaik) a stable
API, conceivably that model could be extended to support ever richer operations
(i.e. sources.Expression, sources.Limit, sources.Join, sources.Aggregation). In
this case, the stable APIs remain a derivative of the Catalyst plans and all
that needs to change between releases is the compilation from Catalyst =>
Sources.
cc [~marmbrus] since we talked briefly about this idea in person at Spark Summit
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Takeshi Yamamuro commented on SPARK-12449:
--
I agree though, there are many jira tickets(12506, 12126, 12686, 9182, 10195,
...) related in this topic and it is hard to redesign datasource codes to
satisfy all the requirements...
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15141243#comment-15141243
]
Evan Chan commented on SPARK-12449:
---
[~rxin] I agree with [~stephank85] and others that this would be a huge help.
At the very least, if the expressions could be pushed down that would help a
lot. Many databases are doing custom work to get the pushdowns needed, and I
was thinking of doing something very similar and was going to propose something
just like this.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Santiago M. Mola commented on SPARK-12449:
--
Implementing this interface or an equivalent one would help standarize a lot of
advanced features that data sources have been doing for some time. And while
doing so, it would prevent them from creating their own SQLContext variants or
patching the running SQLContext at runtime (using extraStrategies).
Here's a list of data source that are currently this approach. It would also be
good to take them into account for this JIRA. The proposed interface and
strategy should probably support all of these use cases. Some of them also use
their own catalog implementation, but that should be something for a separate
JIRA.
*spark-sql-on-hbase*
Already mentioned by [~yzhou2001]. They are using HBaseContext with
extraStrategies that inject HBaseStrategies doing aggregation push down:
https://github.com/Huawei-Spark/Spark-SQL-on-HBase/blob/master/src/main/scala/org/apache/spark/sql/hbase/execution/HBaseStrategies.scala
*memsql-spark-connector*
They offer both their own SQLContext or inject their MemSQL-specific push down
strategy on runtime.
They do match Catalyst's LogicalPlan in the same way we're proposing to push
down filters, projects, aggregates, limits, sorts and joins:
https://github.com/memsql/memsql-spark-connector/blob/master/connectorLib/src/main/scala/com/memsql/spark/pushdown/MemSQLPushdownStrategy.scala
*spark-iqmulus*
Strategy injected to push down counts and some aggregates:
https://github.com/IGNF/spark-iqmulus/blob/master/src/main/scala/fr/ign/spark/iqmulus/ExtraStrategies.scala
*druid-olap*
They use SparkPlanner, Strategy and LogicalPlan APIs to do extensive push down.
Their API usage could be limited to LogicalPlan only if this JIRA is
implemented:
https://github.com/SparklineData/spark-druid-olap/blob/master/src/main/scala/org/apache/spark/sql/sources/druid/
*magellan* _(probably out of scope)_
Does its own BroadcastJoin. Although, it seems to me that this usage would be
out of scope for us.
https://github.com/harsha2010/magellan/blob/master/src/main/scala/magellan/execution/MagellanStrategies.scala
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15088334#comment-15088334
]
Yan commented on SPARK-12449:
-
Stephan, thanks for your explanations and questions. My answers are as follows:
1) This is actually one point of having a "physical plan pruning interface" as
part of the DataSource interface. From just a logical plan, it'd be probably
hard to take advantage of data distribution info that Spark SQL is actually
capable of. Another advantage of a pluggable physical plan pruner is the
flexibility of making use of datasources' various capabilities, including
partial aggregation, some types of predicate/expression evaluations, ..., etc.
We feel the pain of the lack of such a "physical plan pruner" in developing the
Astro project
(http://spark-packages.org/package/Huawei-Spark/Spark-SQL-on-HBase) which
forces us to use a separate SQLContext to incorporate many advanced planning
optimizations for HBase.
In fact, the current datasource API already supports predicate pruning in
*physical* plan, in a limited way though, in the method of "unhandledFilters".
2) I don't think current Spark SQL planning has the capability, and the "plan
later" is for a different purpose.
3) Right, this just a bit more details to 2). The idea is the same: physical
plan pruning.
The point seems to be: the question of logical plan pruning vs. physical plan
pruning is actually a question of what types of capabilities of a data source
are to be valued here, physical or logical. My take is physical, for Spark's
powerful capabilities in dataset/dataframe/SQL In fact, the
"isMultiplePartitionExecution" field of the proposed "CatalystSource"
interface, if true, signifies the willingness of leaving some *physical*
operations such as shuffling to the Spark engine. It might make more sense for
a SQL federation engine to do logical plan pruning. But Spark are much more
capable than a federated engine, I guess.
Admittedly, the stability and complexity of such an interface will be a big
issue as pointed out by Reynold. I'd just keep my eyes open on any
progresses/ideas/topics made in this field.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Yan commented on SPARK-12449:
-
Stephan,
By "partial op" I mean, for instance, partial map-side aggregation. There is
also a Jira (SPARK-12686) that seems to echo this scenario as well.
Spark-10978 deals with predicate pushdown that used to get double evaluated,
which is not related to the discussion of logical plan vs physical plan push
down.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Stephan Kessler commented on SPARK-12449:
-
Hi Yan,
this is only true for the {{DataSourceStrategy}} built in Spark currently
(there are some changes for 1.6 dealing with this issue, [SPARK-10978]). For
the proposed CatalystSourceStrategy this is not the case. We would just return
the RDD {{logicalPlanToRDD}} and no additional duplicated operations.
Best,
Stephan
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Stephan Kessler commented on SPARK-12449:
-
Thank you for your thoughts - unfortunately i did not get all of them, could
you help me out?
1) The partitioning in the case we are currently handling comes from the
datasource itself. Rule-of-a-thumb: If multiple instances of the data source
exists, it is "partitioned". We do not make any assumption about data
distributions. Obviously this might be beneficial for further operations (e.g.,
joins) if we do so, but currently we don't. The assumption behind it is that
data source and Spark execution are loosly coupled, and Spark does not control
loading or distribution of data.
2) This exactly is done with the physical planner strategy. It kind of "probes"
if a datasource is capable of processing a fraction of the logical plan, if
not, it does not return any physical operation (i.e.
{{org.apache.spark.sql.execution}}) other planning strategies come first and
might result in fractions of the plan, that are "planned later". On those left
out parts, the data source strategy is called again.
3) If i get it right, this is the result of 2)? i guess i should add a more
complex example to the design document, to make things more clear at that stage.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Stephan Kessler commented on SPARK-12449:
-
Hi Reynold,
i fully agree that we need the flexibility for the logical plan. What about
marking this API as DeveloperAPI or even Experimental? In this case, each
datasource implementing those interfaces will safely work with a specific Spark
Version, and might need modifications for a new one (which might be the case
anyways).
What do you think?
Best,
Stephan
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
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]
Yan commented on SPARK-12449:
-
Conceivably, if only logical plan is used, the Spark SQL execution would
probably redo the already-pushed-down partial op, which would amount to a
no-op. It might work but just not be super efficient.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15070100#comment-15070100
]
Santiago M. Mola commented on SPARK-12449:
--
Well, at least with the implementation presented at the Spark Summit, only the
logical plan is required. The physical plan is handled only by the planner
strategy, which would be internal to Spark.
The strategy has all the logic required to split partial ops and push down only
one part.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15070088#comment-15070088
]
Yan commented on SPARK-12449:
-
To push down map-side (partial) ops, Physical Plan would need to be checked I
guess.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15070062#comment-15070062
]
Santiago M. Mola commented on SPARK-12449:
--
The physical plan would not be consumed by data sources, only the logical plan.
An alternative approach would be to use a different representation to pass the
logical plan to the data source. If the relational algebra from Apache Calcite
is stable enough, it could be used as the logical plan representation for this
interface.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
--
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15070027#comment-15070027
]
Reynold Xin commented on SPARK-12449:
-
This is great, but our internal logical/physical plan structure changes all the
time, and as a result I don't think we can provide a stable interface based on
that. The cost to stabilize those interfaces is way too high. We need to
flexibility in order to improve Spark.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
--
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15068447#comment-15068447
]
Yan commented on SPARK-12449:
-
A few thoughts on the capabilities of this "CatalystSource Interface":
1) provide data source partition info given a filtering predicate.
Holistic/Partitioned Execution could also be (partially) controlled by this
output. It will make the partition pruning pluggable;
2) have an interface to transform a portion of a physical plan into a "pushed
down" plan, plus a "left-over" plan for execution inside Spark. Spark Planning
may need to curve up the portion from the original plan so that the portion
only contains the same data source and leave the execution on data from across
different data sources in Spark. This will leave the decision of what portion
of plan can be pushed down in the hands of the data sources. In particular,
pushdown of either a whole SQL or just map-side executions could be supported.
3) On carving up the portion of the plan, the Spark Planning can start from the
SCAN and move "downstream", and may (optionally?) want to stop on the branch at
any intermediate DF that are to be cached or persistent so as to honor the
Spark's execution at no extra cost.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
--
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15066655#comment-15066655
]
Santiago M. Mola commented on SPARK-12449:
--
At Stratio we are interested in this kind of interface too, both for SQL and
NoSQL data sources (e.g. MongoDB).
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
--
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[jira] [Commented] (SPARK-12449) Pushing down arbitrary logical plans to data sources
[
https://issues.apache.org/jira/browse/SPARK-12449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15066407#comment-15066407
]
Stephan Kessler commented on SPARK-12449:
-
Added the design document. Looking forward for the discussion, if there is an
agreement i am happy to create sub tasks and implement things.
> Pushing down arbitrary logical plans to data sources
>
>
> Key: SPARK-12449
> URL: https://issues.apache.org/jira/browse/SPARK-12449
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Reporter: Stephan Kessler
> Attachments: pushingDownLogicalPlans.pdf
>
>
> With the help of the DataSource API we can pull data from external sources
> for processing. Implementing interfaces such as {{PrunedFilteredScan}} allows
> to push down filters and projects pruning unnecessary fields and rows
> directly in the data source.
> However, data sources such as SQL Engines are capable of doing even more
> preprocessing, e.g., evaluating aggregates. This is beneficial because it
> would reduce the amount of data transferred from the source to Spark. The
> existing interfaces do not allow such kind of processing in the source.
> We would propose to add a new interface {{CatalystSource}} that allows to
> defer the processing of arbitrary logical plans to the data source. We have
> already shown the details at the Spark Summit 2015 Europe
> [https://spark-summit.org/eu-2015/events/the-pushdown-of-everything/]
> I will add a design document explaining details.
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