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spark-instrumented-optimizer/sql/core/benchmarks/CSVBenchmark-results.txt
Maxim Gekk c1f160e097 [SPARK-30648][SQL] Support filters pushdown in JSON datasource 
### What changes were proposed in this pull request?
In the PR, I propose to support pushed down filters in JSON datasource. The reason of pushing a filter up to `JacksonParser` is to apply the filter as soon as all its attributes become available i.e. converted from JSON field values to desired values according to the schema. This allows to skip parsing of the rest of JSON record and conversions of other values if the filter returns `false`. This can improve performance when pushed filters are highly selective and conversion of JSON string fields to desired values are comparably expensive ( for example, the conversion to `TIMESTAMP` values).

The main idea behind of `JsonFilters` is to group pushdown filters by their references, convert the grouped filters to expressions, and then compile to predicates. The predicates are indexed by schema field positions. Each predicate has a state with reference counter to non-set row fields. As soon as the counter reaches `0`, it can be applied to the row because all its dependencies has been set. Before processing new row, predicate's reference counter is reset to total number of predicate references (dependencies in a row).

The common code shared between `CSVFilters` and `JsonFilters` is moved to the `StructFilters` class and its companion object.

### Why are the changes needed?
The changes improve performance on synthetic benchmarks up to **27 times** on JDK 8 and **25** times on JDK 11:
```
OpenJDK 64-Bit Server VM 1.8.0_242-8u242-b08-0ubuntu3~18.04-b08 on Linux 4.15.0-1044-aws
Intel(R) Xeon(R) CPU E5-2670 v2  2.50GHz
Filters pushdown:                         Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
w/o filters                                       25230          25255          22          0.0      252299.6       1.0X
pushdown disabled                                 25248          25282          33          0.0      252475.6       1.0X
w/ filters                                          905            911           8          0.1        9047.9      27.9X
```

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
- Added new test suites `JsonFiltersSuite` and `JacksonParserSuite`.
- By new end-to-end and case sensitivity tests in `JsonSuite`.
- By `CSVFiltersSuite`, `UnivocityParserSuite` and `CSVSuite`.
- Re-running `CSVBenchmark` and `JsonBenchmark` using Amazon EC2:

| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge (spot instance) |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/11 installed by`sudo add-apt-repository ppa:openjdk-r/ppa` & `sudo apt install openjdk-11-jdk`|

and `./dev/run-benchmarks`:
```python
#!/usr/bin/env python3

import os
from sparktestsupport.shellutils import run_cmd

benchmarks = [
    ['sql/test', 'org.apache.spark.sql.execution.datasources.csv.CSVBenchmark'],
    ['sql/test', 'org.apache.spark.sql.execution.datasources.json.JsonBenchmark']
]

print('Set SPARK_GENERATE_BENCHMARK_FILES=1')
os.environ['SPARK_GENERATE_BENCHMARK_FILES'] = '1'

for b in benchmarks:
    print("Run benchmark: %s" % b[1])
    run_cmd(['build/sbt', '%s:runMain %s' % (b[0], b[1])])
```

Closes #27366 from MaxGekk/json-filters-pushdown.

Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-07-17 00:01:13 +09:00

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================================================================================================
Benchmark to measure CSV read/write performance
================================================================================================
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Parsing quoted values: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
One quoted string 47588 47831 244 0.0 951755.4 1.0X
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Wide rows with 1000 columns: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
Select 1000 columns 129509 130323 1388 0.0 129509.4 1.0X
Select 100 columns 42474 42572 108 0.0 42473.6 3.0X
Select one column 35479 35586 93 0.0 35479.1 3.7X
count() 11021 11071 47 0.1 11021.3 11.8X
Select 100 columns, one bad input field 94652 94795 134 0.0 94652.0 1.4X
Select 100 columns, corrupt record field 115336 115542 350 0.0 115336.0 1.1X
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Count a dataset with 10 columns: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
Select 10 columns + count() 19959 20022 76 0.5 1995.9 1.0X
Select 1 column + count() 13920 13968 54 0.7 1392.0 1.4X
count() 3928 3938 11 2.5 392.8 5.1X
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Write dates and timestamps: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
Create a dataset of timestamps 1940 1977 56 5.2 194.0 1.0X
to_csv(timestamp) 15398 15669 458 0.6 1539.8 0.1X
write timestamps to files 12438 12454 19 0.8 1243.8 0.2X
Create a dataset of dates 2157 2171 18 4.6 215.7 0.9X
to_csv(date) 11764 11839 95 0.9 1176.4 0.2X
write dates to files 8893 8907 12 1.1 889.3 0.2X
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Read dates and timestamps: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read timestamp text from files 2219 2230 11 4.5 221.9 1.0X
read timestamps from files 51519 51725 192 0.2 5151.9 0.0X
infer timestamps from files 104744 104885 124 0.1 10474.4 0.0X
read date text from files 1940 1943 4 5.2 194.0 1.1X
read date from files 27099 27118 33 0.4 2709.9 0.1X
infer date from files 27662 27703 61 0.4 2766.2 0.1X
timestamp strings 4225 4242 15 2.4 422.5 0.5X
parse timestamps from Dataset[String] 56090 56479 376 0.2 5609.0 0.0X
infer timestamps from Dataset[String] 115629 116245 1049 0.1 11562.9 0.0X
date strings 4337 4344 10 2.3 433.7 0.5X
parse dates from Dataset[String] 32373 32476 120 0.3 3237.3 0.1X
from_csv(timestamp) 54952 55157 300 0.2 5495.2 0.0X
from_csv(date) 30924 30985 66 0.3 3092.4 0.1X
OpenJDK 64-Bit Server VM 1.8.0_252-8u252-b09-1~18.04-b09 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
Filters pushdown: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
w/o filters 25630 25636 8 0.0 256301.4 1.0X
pushdown disabled 25673 25681 9 0.0 256734.0 1.0X
w/ filters 1873 1886 15 0.1 18733.1 13.7X
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