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Github user maropu commented on the issue: https://github.com/apache/spark/pull/21288 Thanks for the check! btw, `DataSourceReadBenchmark` has the same issue (`spark.master` setup), so is it ok to fix this as follow-up? https://github.com/apache/spark/compare/master...maropu:FixDataSourceReadBenchmark Also, I update the bench on `r3.xlarge`. --- - 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 maropu commented on the issue: https://github.com/apache/spark/pull/21288 I've check the metrics and I found that GC happend in case of `--diriver-memory 3g`. --- - 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 maropu commented on the issue:
https://github.com/apache/spark/pull/21288
@dongjoon-hyun I got the same result in case of the same condition (enough
memory), but, if `--diriver-memory 3g` (smaller memory), I got a little
different results;
```
// --diriver-memory=3g (default)
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 4.14.33-51.37.amzn1.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row ('7864320' < value < '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized 10084 / 10154 1.6
641.1 1.0X
Parquet Vectorized (Pushdown) 967 / 1008 16.3
61.5 10.4X
Native ORC Vectorized 11088 / 6 1.4
705.0 0.9X
Native ORC Vectorized (Pushdown) 270 / 278 58.2
17.2 37.3X
Select 1 string row (value = '7864320'): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized 10032 / 10085 1.6
637.8 1.0X
Parquet Vectorized (Pushdown) 959 / 998 16.4
61.0 10.5X
Native ORC Vectorized 11104 / 11128 1.4
706.0 0.9X
Native ORC Vectorized (Pushdown) 259 / 277 60.6
16.5 38.7X
...
// --diriver-memory=10g
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 4.14.33-51.37.amzn1.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row (value IS NULL): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized9201 / 9300 1.7
585.0 1.0X
Parquet Vectorized (Pushdown) 89 / 105176.3
5.7 103.1X
Native ORC Vectorized 8886 / 8898 1.8
564.9 1.0X
Native ORC Vectorized (Pushdown) 110 / 128143.4
7.0 83.9X
Select 0 string row ('7864320' < value < '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized9336 / 9357 1.7
593.6 1.0X
Parquet Vectorized (Pushdown) 927 / 937 17.0
58.9 10.1X
Native ORC Vectorized 9026 / 9041 1.7
573.9 1.0X
Native ORC Vectorized (Pushdown) 257 / 272 61.1
16.4 36.3X
...
```
The parquet has smaller memory footprint? I'm currently look into this (I
updated the result in case of the enough memory).
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[GitHub] spark issue #21288: [SPARK-24206][SQL] Improve FilterPushdownBenchmark bench...
Github user maropu commented on the issue:
https://github.com/apache/spark/pull/21288
I noticed why the big performance value changes happened in
https://github.com/apache/spark/pull/21288#discussion_r191280132; that's
because [the
commit](./https://github.com/apache/spark/pull/21288/commits/39e5a507fe22cade6bed0613eefbccab15cf45ff)
wrongly set `local[*]` at `spark.master` instead of `local[1]`;
```
// Performance results on r3.xlarge
// --master local[1] --driver-memory 10G --conf spark.ui.enabled=false
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 4.14.33-51.37.amzn1.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row ('7864320' < value < '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized9292 / 9315 1.7
590.8 1.0X
Parquet Vectorized (Pushdown) 921 / 933 17.1
58.6 10.1X
Native ORC Vectorized 9001 / 9021 1.7
572.3 1.0X
Native ORC Vectorized (Pushdown) 257 / 265 61.2
16.3 36.2X
Select 1 string row (value = '7864320'): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized9151 / 9162 1.7
581.8 1.0X
Parquet Vectorized (Pushdown) 902 / 917 17.4
57.3 10.1X
Native ORC Vectorized 8870 / 8882 1.8
564.0 1.0X
Native ORC Vectorized (Pushdown) 254 / 268 61.9
16.1 36.0X
...
// --master local[*] --driver-memory 10G --conf spark.ui.enabled=false
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 4.14.33-51.37.amzn1.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row (value IS NULL): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized3959 / 4067 4.0
251.7 1.0X
Parquet Vectorized (Pushdown) 202 / 245 77.7
12.9 19.6X
Native ORC Vectorized 3973 / 4055 4.0
252.6 1.0X
Native ORC Vectorized (Pushdown) 286 / 345 55.0
18.2 13.8X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 4.14.33-51.37.amzn1.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row ('7864320' < value < '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized3985 / 4022 3.9
253.4 1.0X
Parquet Vectorized (Pushdown) 249 / 274 63.3
15.8 16.0X
Native ORC Vectorized 4066 / 4122 3.9
258.5 1.0X
Native ORC Vectorized (Pushdown) 257 / 310 61.3
16.3 15.5X
```
I'll fix the bug and update the results in following prs. Sorry, all.
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Github user maropu commented on the issue: https://github.com/apache/spark/pull/21288 yea, I also agree with the opinion; we'd be better to run benchmarks on the same machine. I'll re-run the benchmark on `r3.xlarge` to check if I could get the same result. > There is no hadoop installation. I guess @maropu also does yea, I had no installation. --- - 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 dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/21288
@gatorsmile and @maropu . I really appreciate this effort. Thanks.
Since this is a cloud benchmark, I have one thing to recommend. Can we use
`r3.xlarge` for all benchmarks **consistently**? As we know, it's difficult to
compare the result from different machines.
There are three reasons.
1. `r3.xlarge` is cheaper than `m4.2xlarge`.
2. Previous benchmark result cames from Macbook (SSD). `r3.xlarge` also
provides SSD.
3. `r3.xlarge` is used at [Databricks TPCDS
benchmark](https://databricks.com/blog/2017/07/12/benchmarking-big-data-sql-platforms-in-the-cloud.html),
too.
The following is the result on `r3.xlarge`; I launched the machine and
build this PR on the latest master and run `bin/spark-submit --master local[1]
--driver-memory 10G --conf spark.ui.enabled=false --class
org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark
sql/core/target/scala-2.11/spark-sql_2.11-2.0-SNAPSHOT-tests.jar`. (There is no
hadoop installation. I guess @maropu also does.)
```
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row (value IS NULL): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized9133 / 9275 1.7
580.6 1.0X
Parquet Vectorized (Pushdown) 85 / 100185.2
5.4 107.6X
Native ORC Vectorized 8760 / 8843 1.8
556.9 1.0X
Native ORC Vectorized (Pushdown) 115 / 130136.4
7.3 79.2X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 0 string row ('7864320' < value < '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized9254 / 9276 1.7
588.4 1.0X
Parquet Vectorized (Pushdown) 912 / 922 17.2
58.0 10.1X
Native ORC Vectorized 8966 / 9013 1.8
570.1 1.0X
Native ORC Vectorized (Pushdown) 254 / 276 61.8
16.2 36.4X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 1 string row (value = '7864320'): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized9106 / 9136 1.7
578.9 1.0X
Parquet Vectorized (Pushdown) 897 / 910 17.5
57.0 10.2X
Native ORC Vectorized 8846 / 8889 1.8
562.4 1.0X
Native ORC Vectorized (Pushdown) 254 / 267 61.9
16.2 35.8X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 1 string row (value <=> '7864320'): Best/Avg Time(ms)Rate(M/s)
Per Row(ns) Relative
Parquet Vectorized9095 / 9124 1.7
578.3 1.0X
Parquet Vectorized (Pushdown) 891 / 899 17.7
56.6 10.2X
Native ORC Vectorized 8853 / 8941 1.8
562.8 1.0X
Native ORC Vectorized (Pushdown) 246 / 254 64.0
15.6 37.0X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Select 1 string row ('7864320' <= value <= '7864320'): Best/Avg Time(ms)
Rate(M/s) Per Row(ns) Relative
Parquet Vectorized9236 / 9273 1.7
587.2 1.0X
Parquet Vectorized (Pushdown) 902 / 910 17.4
57.4 10.2X
Native ORC Vectorized 8944 / 8965 1.8
568.6 1.0X
Native ORC Vectorized (Pushdown) 248 / 262 63.4
15.8 37.2X
OpenJDK 64-Bit Server VM 1.8.0_171-b10 on Linux 3.10.0-693.5.2.el7.x86_64
Intel(R) Xeon(R) CPU E5-2670
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Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/21288 @maropu Could you fix the style? BTW, based on the latest result, Parquet is generally faster than ORC. cc @dongjoon-hyun @rdblue --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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