### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/32015 added a way to run benchmarks much more easily in the same GitHub Actions build. This PR updates the benchmark results by using the way.
**NOTE** that looks like GitHub Actions use four types of CPU given my observations:
- Intel(R) Xeon(R) Platinum 8171M CPU 2.60GHz
- Intel(R) Xeon(R) CPU E5-2673 v4 2.30GHz
- Intel(R) Xeon(R) CPU E5-2673 v3 2.40GHz
- Intel(R) Xeon(R) Platinum 8272CL CPU 2.60GHz
Given my quick research, seems like they perform roughly similarly:
I couldn't find enough information about Intel(R) Xeon(R) Platinum 8272CL CPU 2.60GHz but the performance seems roughly similar given the numbers.
So shouldn't be a big deal especially given that this way is much easier, encourages contributors to run more and guarantee the same number of cores and same memory with the same softwares.
### Why are the changes needed?
To have a base line of the benchmarks accordingly.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
It was generated from:
- [Run benchmarks: * (JDK 11)](https://github.com/HyukjinKwon/spark/actions/runs/713575465)
- [Run benchmarks: * (JDK 8)](https://github.com/HyukjinKwon/spark/actions/runs/713154337)
Closes#32044 from HyukjinKwon/SPARK-34950.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extracting millennium, century, decade, millisecond, microsecond and epoch from datetime is neither ANSI standard nor quite common in modern SQL platforms. Most of the systems listing below does not support these except PostgreSQL and redshift.
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDFhttps://docs.oracle.com/cd/B19306_01/server.102/b14200/functions050.htmhttps://prestodb.io/docs/current/functions/datetime.htmlhttps://docs.cloudera.com/documentation/enterprise/5-8-x/topics/impala_datetime_functions.htmlhttps://docs.snowflake.com/en/sql-reference/functions-date-time.html#label-supported-date-time-partshttps://www.postgresql.org/docs/9.1/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT
This PR removes these extract fields support from extract function for date and timestamp values
`isoyear` is PostgreSQL specific but `yearofweek` is more commonly used across platforms
`isodow` is PostgreSQL specific but `iso` as a suffix is more commonly used across platforms so, `dow_iso` and `dayofweek_iso` is used to replace it.
For historical reasons, we have [`dayofweek`, `dow`] implemented for representing a non-ISO day-of-week and a newly added `isodow` from PostgreSQL for ISO day-of-week. Many other systems only have one week-numbering system support and use either full names or abbreviations. Things in spark become a little bit complicated.
1. because of the existence of `isodow`, so we need to add iso-prefix to `dayofweek` to make a pair for it too. [`dayofweek`, `isodayofweek`, `dow` and `isodow`]
2. because there are rare `iso`-prefixed systems and more systems choose `iso`-suffixed way, so we may result in [`dayofweek`, `dayofweekiso`, `dow`, `dowiso`]
3. `dayofweekiso` looks nice and has use cases in the platforms listed above, e.g. snowflake, but `dowiso` looks weird and no use cases found.
4. with a discussion the community，we have agreed with an underscore before `iso` may look much better because `isodow` is new and there is no standard for `iso` kind of things, so this may be good for us to make it simple and clear for end-users if they are well documented too.
Thus, we finally result in [`dayofweek`, `dow`] for Non-ISO day-of-week system and [`dayofweek_iso`, `dow_iso`] for ISO system
### Why are the changes needed?
Remove some nonstandard and uncommon features as we can add them back if necessary
### Does this PR introduce any user-facing change?
NO, we should target this to 3.0.0 and these are added during 3.0.0
### How was this patch tested?
Remove unused tests
Closes#28284 from yaooqinn/SPARK-31507.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In 697083c051, we remove "MILLENNIUM", "CENTURY", "DECADE", "QUARTER", "MILLISECONDS", "MICROSECONDS", "EPOCH" field for date_part and extract expression, this PR fix the related Benchmark.
### Why are the changes needed?
test fix.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
passing Jenkins
Closes#28249 from yaooqinn/SPARK-31469-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
```
<extract expression> ::= EXTRACT <left paren> <extract field> FROM <extract source> <right paren>
<extract source> ::= <datetime value expression> | <interval value expression>
```
We now only support datetime values as extract source for `extract` expression but it's alternative function `date_part` supports both datetime and interval.
This pr adds interval value support for `extract` expression as extract source
### Why are the changes needed?
For ANSI compliance and the semantic consistency between extract and `date_part`, we support intervals for extract expressions.
### Does this PR introduce any user-facing change?
yes, in the `extract(abc from xyz)` expression, the `xyz` can be intervals
### How was this patch tested?
add unit tests
Closes#27876 from yaooqinn/SPARK-31119.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
fix the error caused by interval output in ExtractBenchmark
### Why are the changes needed?
fix a bug in the test
```scala
[info] Running case: cast to interval
[error] Exception in thread "main" org.apache.spark.sql.AnalysisException: Cannot use interval type in the table schema.;;
[error] OverwriteByExpression RelationV2[] noop-table, true, true
[error] +- Project [(subtractdates(cast(cast(id#0L as timestamp) as date), -719162) + subtracttimestamps(cast(id#0L as timestamp), -30610249419876544)) AS ((CAST(CAST(id AS TIMESTAMP) AS DATE) - DATE '0001-01-01') + (CAST(id AS TIMESTAMP) - TIMESTAMP '1000-01-01 01:02:03.123456'))#2]
[error] +- Range (1262304000, 1272304000, step=1, splits=Some(1))
[error]
[error] at org.apache.spark.sql.catalyst.util.TypeUtils$.failWithIntervalType(TypeUtils.scala:106)
[error] at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis$25(CheckAnalysis.scala:389)
[error] at org.a
```
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
re-run benchmark
Closes#27867 from yaooqinn/SPARK-31111.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace `.collect()`, `.count()` and `.foreach(_ => ())` in SQL benchmarks and use the `NoOp` datasource. I added an implicit class to `SqlBasedBenchmark` with the `.noop()` method. It can be used in benchmark like: `ds.noop()`. The last one is unfolded to `ds.write.format("noop").mode(Overwrite).save()`.
### Why are the changes needed?
To avoid additional overhead that `collect()` (and other actions) has. For example, `.collect()` has to convert values according to external types and pull data to the driver. This can hide actual performance regressions or improvements of benchmarked operations.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Re-run all modified benchmarks 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 | OpenJDK8/10 |
- Run `TPCDSQueryBenchmark` using instructions from the PR #26049
```
# `spark-tpcds-datagen` needs this. (JDK8)
$ git clone https://github.com/apache/spark.git -b branch-2.4 --depth 1 spark-2.4
$ export SPARK_HOME=$PWD
$ ./build/mvn clean package -DskipTests
# Generate data. (JDK8)
$ git clone gitgithub.com:maropu/spark-tpcds-datagen.git
$ cd spark-tpcds-datagen/
$ build/mvn clean package
$ mkdir -p /data/tpcds
$ ./bin/dsdgen --output-location /data/tpcds/s1 // This need `Spark 2.4`
```
- Other benchmarks ran by the script:
```
#!/usr/bin/env python3
import os
from sparktestsupport.shellutils import run_cmd
benchmarks = [
['sql/test', 'org.apache.spark.sql.execution.benchmark.AggregateBenchmark'],
['avro/test', 'org.apache.spark.sql.execution.benchmark.AvroReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.BloomFilterBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DataSourceReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ExtractBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.InExpressionBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.IntervalBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.JoinBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MakeDateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MiscBenchmark'],
['hive/test', 'org.apache.spark.sql.execution.benchmark.ObjectHashAggregateExecBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcV2NestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ParquetNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.RangeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.UDFBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideSchemaBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideTableBenchmark'],
['hive/test', 'org.apache.spark.sql.hive.orc.OrcReadBenchmark'],
['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#27078 from MaxGekk/noop-in-benchmarks.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
I extended `ExtractBenchmark` to support the `INTERVAL` type of the `source` parameter of the `date_part` function.
### Why are the changes needed?
- To detect performance issues while changing implementation of the `date_part` function in the future.
- To find out current performance bottlenecks in `date_part` for the `INTERVAL` type
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark and print out produced values per each `field` value.
Closes#26175 from MaxGekk/extract-interval-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison.
**A. EXPECTED CASES(JDK11 is faster in general)**
- [x] BloomFilterBenchmark (JDK11 is faster except one case)
- [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC)
- [x] CSVBenchmark (JDK11 is faster except five cases)
- [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`)
- [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type)
- [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases)
- [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS)
- [x] HashedRelationMetricsBenchmark (JDK11 is faster)
- [x] JSONBenchmark (JDK11 is much faster except eight cases)
- [x] JoinBenchmark (JDK11 is faster except five cases)
- [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases)
- [x] PrimitiveArrayBenchmark (JDK11 is faster)
- [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case)
- [x] UDFBenchmark (N/A, values are too small)
- [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case)
- [x] WideTableBenchmark (JDK11 is faster except two cases)
**B. CASES WE NEED TO INVESTIGATE MORE LATER**
- [x] AggregateBenchmark (JDK11 is slower in general)
- [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`)
- [x] DataSourceReadBenchmark (JDK11 is slower in general)
- [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`)
- [x] MakeDateTimeBenchmark (JDK11 is slower except two cases)
- [x] MiscBenchmark (JDK11 is slower except ten cases)
- [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower)
- [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases)
- [x] RangeBenchmark (JDK11 is slower except one case)
`FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer.
### Why are the changes needed?
According to the result, there are some difference between JDK8/JDK11.
This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used.
- Instance: `r3.xlarge`
- OS: `CentOS Linux release 7.5.1804 (Core)`
- JDK:
- `OpenJDK Runtime Environment (build 1.8.0_222-b10)`
- `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)`
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is a test-only PR. We need to run benchmark.
Closes#26003 from dongjoon-hyun/SPARK-29320.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added new expression `SecondWithFraction` which produces the `seconds` part of timestamps/dates with fractional part containing microseconds. This expression is used only in the `DatePart` expression. As the result, `date_part()` and `extract` return seconds and microseconds as the fractional part of the seconds part when `field` is `SECOND` (or synonyms).
### Why are the changes needed?
The `date_part()` and `extract` were added to maintain feature parity with PostgreSQL which has different behavior for the `SECOND` value of the `field` parameter. The fix is needed to behave in the same way. Here is PostgreSQL's output:
```sql
# SELECT date_part('SECONDS', timestamp'2019-10-01 00:00:01.000001');
date_part
-----------
1.000001
(1 row)
```
### Does this PR introduce any user-facing change?
Yes, type of `date_part('SECOND', ...)` is changed from `INT` to `DECIMAL(8, 6)`.
Before:
```sql
spark-sql> SELECT date_part('SECONDS', '2019-10-01 00:00:01.000001');
1
```
After:
```sql
spark-sql> SELECT date_part('SECONDS', '2019-10-01 00:00:01.000001');
1.000001
```
### How was this patch tested?
- Added new tests to `DateExpressionSuite` for the `SecondWithFraction` expression
- Regenerated results of `date_part.sql`, `extract.sql` and `timestamp.sql`
- Updated results of `ExtractBenchmark`
Closes#25986 from MaxGekk/extract-seconds-from-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Refactoring of the `DateTimeUtils.getEpoch()` function by avoiding decimal operations that are pretty expensive, and converting the final result to the decimal type at the end.
### Why are the changes needed?
The changes improve performance of the `getEpoch()` method at least up to **20 times**.
Before:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 256 277 33 39.0 25.6 1.0X
EPOCH of timestamp 23455 23550 131 0.4 2345.5 0.0X
```
After:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 255 294 34 39.2 25.5 1.0X
EPOCH of timestamp 1049 1054 9 9.5 104.9 0.2X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test from `DateExpressionSuite`.
Closes#25881 from MaxGekk/optimize-extract-epoch.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Changed the `DateTimeUtils.getMilliseconds()` by avoiding the decimal division, and replacing it by setting scale and precision while converting microseconds to the decimal type.
### Why are the changes needed?
This improves performance of `extract` and `date_part()` by more than **50 times**:
Before:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 397 428 45 25.2 39.7 1.0X
MILLISECONDS of timestamp 36723 36761 63 0.3 3672.3 0.0X
```
After:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 278 284 6 36.0 27.8 1.0X
MILLISECONDS of timestamp 592 606 13 16.9 59.2 0.5X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suite - `DateExpressionsSuite`
Closes#25871 from MaxGekk/optimize-epoch-millis.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Refactored SQL-related benchmark and made them depend on `SqlBasedBenchmark`. In particular, creation of Spark session are moved into `override def getSparkSession: SparkSession`.
### Why are the changes needed?
This should simplify maintenance of SQL-based benchmarks by reducing the number of dependencies. In the future, it should be easier to refactor & extend all SQL benchmarks by changing only one trait. Finally, all SQL-based benchmarks will look uniformly.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the modified benchmarks.
Closes#25828 from MaxGekk/sql-benchmarks-refactoring.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend `ExtractBenchmark` and add new ones for:
- `EXTRACT` and `DATE` as input column
- the `DATE_PART` function and `DATE`/`TIMESTAMP` input column
### Why are the changes needed?
The `EXTRACT` expression is rebased on the `DATE_PART` expression by the PR https://github.com/apache/spark/pull/25410 where some of sub-expressions take `DATE` column as the input (`Millennium`, `Year` and etc.) but others require `TIMESTAMP` column (`Hour`, `Minute`). Separate benchmarks for `DATE` should exclude overhead of implicit conversions `DATE` <-> `TIMESTAMP`.
### Does this PR introduce any user-facing change?
No, it doesn't.
### How was this patch tested?
- Regenerated results of `ExtractBenchmark`
Closes#25772 from MaxGekk/date_part-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Added new benchmark `ExtractBenchmark` for the `EXTRACT(field FROM source)` function. It was executed on all currently supported values of the `field` argument: `MILLENNIUM`, `CENTURY`, `DECADE`, `YEAR`, `ISOYEAR`, `QUARTER`, `MONTH`, `WEEK`, `DAY`, `DAYOFWEEK`, `HOUR`, `MINUTE`, `SECOND`, `MILLISECONDS`, `MICROSECONDS`, `EPOCH`. The `cast(id as timestamp)` was taken as the `source` argument.
## How was this patch tested?
By running the benchmark via:
```
$ SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.ExtractBenchmark"
```
Closes#25462 from MaxGekk/extract-benchmark.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
