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https://issues.apache.org/jira/browse/ARROW-13848?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Neal Richardson updated ARROW-13848:
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Labels: performance (was: beginner good-first-issue performance)
> [C++] and() in a dataset filter
> -------------------------------
>
> Key: ARROW-13848
> URL: https://issues.apache.org/jira/browse/ARROW-13848
> Project: Apache Arrow
> Issue Type: Improvement
> Components: C++
> Reporter: Jonathan Keane
> Priority: Major
> Labels: performance
>
> Is it expected that a scanning a dataset that has a filter built with
> {{and()}} is much slower than a filter built with {{and_kleene()}}?
> Specifically, it seems that {{and()}} triggers a scan of the full dataset,
> where as {{and_kleene()}} takes advantage of the fact that only one directory
> of the larger dataset needs to be scanned:
> {code:r}
> > library(arrow)
> Attaching package: ‘arrow’
> The following object is masked from ‘package:utils’:
> timestamp
> > library(dplyr)
> >
> > ds <- open_dataset("~/repos/ab_store/data/taxi_parquet/", partitioning =
> > c("year", "month"))
> >
> > system.time({
> + out <- ds %>%
> + filter(arrow_and(total_amount > 100, year == 2015)) %>%
> + select(tip_amount, total_amount, passenger_count) %>%
> + collect()
> + })
> user system elapsed
> 46.634 4.462 6.457
> >
> > system.time({
> + out <- ds %>%
> + filter(arrow_and_kleene(total_amount > 100, year == 2015)) %>%
> + select(tip_amount, total_amount, passenger_count) %>%
> + collect()
> + })
> user system elapsed
> 4.633 0.421 0.754
> >
> {code}
> I suspect that it's scanning the whole dataset because if I use a dataset
> that only has the 2015 folder, I get similar speeds:
> {code:r}
> > ds <- open_dataset("~/repos/ab_store/data/taxi_parquet_2015/", partitioning
> > = c("year", "month"))
> >
> > system.time({
> + out <- ds %>%
> + filter(arrow_and(total_amount > 100, year == 2015)) %>%
> + select(tip_amount, total_amount, passenger_count) %>%
> + collect()
> + })
> user system elapsed
> 4.549 0.404 0.576
> >
> > system.time({
> + out <- ds %>%
> + filter(arrow_and_kleene(total_amount > 100, year == 2015)) %>%
> + select(tip_amount, total_amount, passenger_count) %>%
> + collect()
> + })
> user system elapsed
> 4.477 0.412 0.585
> {code}
> This does not impact anyone who uses our default collapsing mechanism in the
> R package, but I bumped into it with a filter that was constructed by duckdb
> using `and()` instead of `and_kleene()`.
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