## What changes were proposed in this pull request?
Currently, pretty skipped message added by f7435bec6a mechanism seems not working when xmlrunner is installed apparently.
This PR fixes two things:
1. When `xmlrunner` is installed, seems `xmlrunner` does not respect `vervosity` level in unittests (default is level 1).
So the output looks as below
```
Running tests...
----------------------------------------------------------------------
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
----------------------------------------------------------------------
```
So it is not caught by our message detection mechanism.
2. If we manually set the `vervocity` level to `xmlrunner`, it prints messages as below:
```
test_mixed_udf (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
test_mixed_udf_and_sql (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
...
```
This is different in our Jenkins machine:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
...
```
Note that last `SKIP` is different. This PR fixes the regular expression to catch `SKIP` case as well.
## How was this patch tested?
Manually tested.
**Before:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (0s)
...
Tests passed in 562 seconds
========================================================================
...
```
**After:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (48s) ... 93 tests were skipped
...
Tests passed in 560 seconds
Skipped tests pyspark.... with python2.7:
pyspark...(...) ... SKIP (0.000s)
...
========================================================================
...
```
Closes#24927 from HyukjinKwon/SPARK-28130.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Flush batch timely for pandas UDF.
This could improve performance when multiple pandas UDF plans are pipelined.
When batch being flushed in time, downstream pandas UDFs will get pipelined as soon as possible, and pipeline will help hide the donwstream UDFs computation time. For example:
When the first UDF start computing on batch-3, the second pipelined UDF can start computing on batch-2, and the third pipelined UDF can start computing on batch-1.
If we do not flush each batch in time, the donwstream UDF's pipeline will lag behind too much, which may increase the total processing time.
I add flush at two places:
* JVM process feed data into python worker. In jvm side, when write one batch, flush it
* VM process read data from python worker output, In python worker side, when write one batch, flush it
If no flush, the default buffer size for them are both 65536. Especially in the ML case, in order to make realtime prediction, we will make batch size very small. The buffer size is too large for the case, which cause downstream pandas UDF pipeline lag behind too much.
### Note
* This is only applied to pandas scalar UDF.
* Do not flush for each batch. The minimum interval between two flush is 0.1 second. This avoid too frequent flushing when batch size is small. It works like:
```
last_flush_time = time.time()
for batch in iterator:
writer.write_batch(batch)
flush_time = time.time()
if self.flush_timely and (flush_time - last_flush_time > 0.1):
stream.flush()
last_flush_time = flush_time
```
## How was this patch tested?
### Benchmark to make sure the flush do not cause performance regression
#### Test code:
```
numRows = ...
batchSize = ...
spark.conf.set('spark.sql.execution.arrow.maxRecordsPerBatch', str(batchSize))
df = spark.range(1, numRows + 1, numPartitions=1).select(col('id').alias('a'))
pandas_udf("int", PandasUDFType.SCALAR)
def fp1(x):
return x + 10
beg_time = time.time()
result = df.select(sum(fp1('a'))).head()
print("result: " + str(result[0]))
print("consume time: " + str(time.time() - beg_time))
```
#### Test Result:
params | Consume time (Before) | Consume time (After)
------------ | ----------------------- | ----------------------
numRows=100000000, batchSize=10000 | 23.43s | 24.64s
numRows=100000000, batchSize=1000 | 36.73s | 34.50s
numRows=10000000, batchSize=100 | 35.67s | 32.64s
numRows=1000000, batchSize=10 | 33.60s | 32.11s
numRows=100000, batchSize=1 | 33.36s | 31.82s
### Benchmark pipelined pandas UDF
#### Test code:
```
spark.conf.set('spark.sql.execution.arrow.maxRecordsPerBatch', '1')
df = spark.range(1, 31, numPartitions=1).select(col('id').alias('a'))
pandas_udf("int", PandasUDFType.SCALAR)
def fp1(x):
print("run fp1")
time.sleep(1)
return x + 100
pandas_udf("int", PandasUDFType.SCALAR)
def fp2(x, y):
print("run fp2")
time.sleep(1)
return x + y
beg_time = time.time()
result = df.select(sum(fp2(fp1('a'), col('a')))).head()
print("result: " + str(result[0]))
print("consume time: " + str(time.time() - beg_time))
```
#### Test Result:
**Before**: consume time: 63.57s
**After**: consume time: 32.43s
**So the PR improve performance by make downstream UDF get pipelined early.**
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24734 from WeichenXu123/improve_pandas_udf_pipeline.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR continues to break down a big large file into smaller files. See https://github.com/apache/spark/pull/23021. It targets to follow https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/tests.py` into ...:
```
pyspark
...
├── testing
...
│ └── utils.py
├── tests
│ ├── __init__.py
│ ├── test_appsubmit.py
│ ├── test_broadcast.py
│ ├── test_conf.py
│ ├── test_context.py
│ ├── test_daemon.py
│ ├── test_join.py
│ ├── test_profiler.py
│ ├── test_rdd.py
│ ├── test_readwrite.py
│ ├── test_serializers.py
│ ├── test_shuffle.py
│ ├── test_taskcontext.py
│ ├── test_util.py
│ └── test_worker.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and .`/run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```bash
SPARK_TESTING=1 ./bin/pyspark pyspark.tests.test_context
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23033 from HyukjinKwon/SPARK-26036.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
HyukjinKwon
WeichenXu