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spark-instrumented-optimizer/dev/sparktestsupport/modules.py
HyukjinKwon ab0890bdb1 [SPARK-28264][PYTHON][SQL] Support type hints in pandas UDF and rename/move inconsistent pandas UDF types 
### What changes were proposed in this pull request?

This PR proposes to redesign pandas UDFs as described in [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing).

```python
from pyspark.sql.functions import pandas_udf
import pandas as pd

pandas_udf("long")
def plug_one(s: pd.Series) -> pd.Series:
    return s + 1

spark.range(10).select(plug_one("id")).show()
```

```
+------------+
|plug_one(id)|
+------------+
|           1|
|           2|
|           3|
|           4|
|           5|
|           6|
|           7|
|           8|
|           9|
|          10|
+------------+
```

Note that, this PR address one of the future improvements described [here](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit#heading=h.h3ncjpk6ujqu), "A couple of less-intuitive pandas UDF types" (by zero323) together.

In short,

- Adds new way with type hints as an alternative and experimental way.
    ```python
    pandas_udf(schema='...')
    def func(c1: Series, c2: Series) -> DataFrame:
        pass
    ```

- Replace and/or add an alias for three types below from UDF, and make them as separate standalone APIs. So, `pandas_udf` is now consistent with regular `udf`s and other expressions.

    `df.mapInPandas(udf)`  -replace-> `df.mapInPandas(f, schema)`
    `df.groupby.apply(udf)`  -alias-> `df.groupby.applyInPandas(f, schema)`
    `df.groupby.cogroup.apply(udf)`  -replace-> `df.groupby.cogroup.applyInPandas(f, schema)`

    *`df.groupby.apply` was added from 2.3 while the other were added in the master only.

- No deprecation for the existing ways for now.
    ```python
    pandas_udf(schema='...', functionType=PandasUDFType.SCALAR)
    def func(c1, c2):
        pass
    ```
If users are happy with this, I plan to deprecate the existing way and declare using type hints is not experimental anymore.

One design goal in this PR was that, avoid touching the internal (since we didn't deprecate the old ways for now), but supports type hints with a minimised changes only at the interface.

- Once we deprecate or remove the old ways, I think it requires another refactoring for the internal in the future. At the very least, we should rename internal pandas evaluation types.
- If users find this experimental type hints isn't quite helpful, we should simply revert the changes at the interface level.

### Why are the changes needed?

In order to address old design issues. Please see [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing).

### Does this PR introduce any user-facing change?

For behaviour changes, No.

It adds new ways to use pandas UDFs by using type hints. See below.

**SCALAR**:

```python
pandas_udf(schema='...')
def func(c1: Series, c2: DataFrame) -> Series:
    pass  # DataFrame represents a struct column
```

**SCALAR_ITER**:

```python
pandas_udf(schema='...')
def func(iter: Iterator[Tuple[Series, DataFrame, ...]]) -> Iterator[Series]:
    pass  # Same as SCALAR but wrapped by Iterator
```

**GROUPED_AGG**:

```python
pandas_udf(schema='...')
def func(c1: Series, c2: DataFrame) -> int:
    pass  # DataFrame represents a struct column
```

**GROUPED_MAP**:

This was added in Spark 2.3 as of SPARK-20396. As described above, it keeps the existing behaviour. Additionally, we now have a new alias `groupby.applyInPandas` for `groupby.apply`. See the example below:

```python
def func(pdf):
    return pdf

df.groupby("...").applyInPandas(func, schema=df.schema)
```

**MAP_ITER**: this is not a pandas UDF anymore

This was added in Spark 3.0 as of SPARK-28198; and this PR replaces the usages. See the example below:

```python
def func(iter):
    for df in iter:
        yield df

df.mapInPandas(func, df.schema)
```

**COGROUPED_MAP**: this is not a pandas UDF anymore

This was added in Spark 3.0 as of SPARK-27463; and this PR replaces the usages. See the example below:

```python
def asof_join(left, right):
    return pd.merge_asof(left, right, on="...", by="...")

 df1.groupby("...").cogroup(df2.groupby("...")).applyInPandas(asof_join, schema="...")
```

### How was this patch tested?

Unittests added and tested against Python 2.7, 3.6 and 3.7.

Closes #27165 from HyukjinKwon/revisit-pandas.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-01-22 15:32:58 +09:00

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from functools import total_ordering
import itertools
import re
all_modules = []
@total_ordering
class Module(object):
"""
A module is the basic abstraction in our test runner script. Each module consists of a set
of source files, a set of test commands, and a set of dependencies on other modules. We use
modules to define a dependency graph that let us determine which tests to run based on which
files have changed.
"""
def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
test_tags=(), should_run_r_tests=False, should_run_build_tests=False):
"""
Define a new module.
:param name: A short module name, for display in logging and error messages.
:param dependencies: A set of dependencies for this module. This should only include direct
dependencies; transitive dependencies are resolved automatically.
:param source_file_regexes: a set of regexes that match source files belonging to this
module. These regexes are applied by attempting to match at the beginning of the
filename strings.
:param build_profile_flags: A set of profile flags that should be passed to Maven or SBT in
order to build and test this module (e.g. '-PprofileName').
:param environ: A dict of environment variables that should be set when files in this
module are changed.
:param sbt_test_goals: A set of SBT test goals for testing this module.
:param python_test_goals: A set of Python test goals for testing this module.
:param blacklisted_python_implementations: A set of Python implementations that are not
supported by this module's Python components. The values in this set should match
strings returned by Python's `platform.python_implementation()`.
:param test_tags A set of tags that will be excluded when running unit tests if the module
is not explicitly changed.
:param should_run_r_tests: If true, changes in this module will trigger all R tests.
:param should_run_build_tests: If true, changes in this module will trigger build tests.
"""
self.name = name
self.dependencies = dependencies
self.source_file_prefixes = source_file_regexes
self.sbt_test_goals = sbt_test_goals
self.build_profile_flags = build_profile_flags
self.environ = environ
self.python_test_goals = python_test_goals
self.blacklisted_python_implementations = blacklisted_python_implementations
self.test_tags = test_tags
self.should_run_r_tests = should_run_r_tests
self.should_run_build_tests = should_run_build_tests
self.dependent_modules = set()
for dep in dependencies:
dep.dependent_modules.add(self)
all_modules.append(self)
def contains_file(self, filename):
return any(re.match(p, filename) for p in self.source_file_prefixes)
def __repr__(self):
return "Module<%s>" % self.name
def __lt__(self, other):
return self.name < other.name
def __eq__(self, other):
return self.name == other.name
def __ne__(self, other):
return not (self.name == other.name)
def __hash__(self):
return hash(self.name)
tags = Module(
name="tags",
dependencies=[],
source_file_regexes=[
"common/tags/",
]
)
catalyst = Module(
name="catalyst",
dependencies=[tags],
source_file_regexes=[
"sql/catalyst/",
],
sbt_test_goals=[
"catalyst/test",
],
)
sql = Module(
name="sql",
dependencies=[catalyst],
source_file_regexes=[
"sql/core/",
],
sbt_test_goals=[
"sql/test",
],
)
hive = Module(
name="hive",
dependencies=[sql],
source_file_regexes=[
"sql/hive/",
"bin/spark-sql",
],
build_profile_flags=[
"-Phive",
],
sbt_test_goals=[
"hive/test",
],
test_tags=[
"org.apache.spark.tags.ExtendedHiveTest"
]
)
repl = Module(
name="repl",
dependencies=[hive],
source_file_regexes=[
"repl/",
],
sbt_test_goals=[
"repl/test",
],
)
hive_thriftserver = Module(
name="hive-thriftserver",
dependencies=[hive],
source_file_regexes=[
"sql/hive-thriftserver",
"sbin/start-thriftserver.sh",
],
build_profile_flags=[
"-Phive-thriftserver",
],
sbt_test_goals=[
"hive-thriftserver/test",
]
)
avro = Module(
name="avro",
dependencies=[sql],
source_file_regexes=[
"external/avro",
],
sbt_test_goals=[
"avro/test",
]
)
sql_kafka = Module(
name="sql-kafka-0-10",
dependencies=[sql],
source_file_regexes=[
"external/kafka-0-10-sql",
],
sbt_test_goals=[
"sql-kafka-0-10/test",
]
)
sketch = Module(
name="sketch",
dependencies=[tags],
source_file_regexes=[
"common/sketch/",
],
sbt_test_goals=[
"sketch/test"
]
)
graphx = Module(
name="graphx",
dependencies=[tags],
source_file_regexes=[
"graphx/",
],
sbt_test_goals=[
"graphx/test"
]
)
streaming = Module(
name="streaming",
dependencies=[tags],
source_file_regexes=[
"streaming",
],
sbt_test_goals=[
"streaming/test",
]
)
# Don't set the dependencies because changes in other modules should not trigger Kinesis tests.
# Kinesis tests depends on external Amazon kinesis service. We should run these tests only when
# files in streaming_kinesis_asl are changed, so that if Kinesis experiences an outage, we don't
# fail other PRs.
streaming_kinesis_asl = Module(
name="streaming-kinesis-asl",
dependencies=[tags],
source_file_regexes=[
"external/kinesis-asl/",
"external/kinesis-asl-assembly/",
],
build_profile_flags=[
"-Pkinesis-asl",
],
environ={
"ENABLE_KINESIS_TESTS": "1"
},
sbt_test_goals=[
"streaming-kinesis-asl/test",
]
)
streaming_kafka_0_10 = Module(
name="streaming-kafka-0-10",
dependencies=[streaming],
source_file_regexes=[
# The ending "/" is necessary otherwise it will include "sql-kafka" codes
"external/kafka-0-10/",
"external/kafka-0-10-assembly",
],
sbt_test_goals=[
"streaming-kafka-0-10/test",
]
)
mllib_local = Module(
name="mllib-local",
dependencies=[tags],
source_file_regexes=[
"mllib-local",
],
sbt_test_goals=[
"mllib-local/test",
]
)
mllib = Module(
name="mllib",
dependencies=[mllib_local, streaming, sql],
source_file_regexes=[
"data/mllib/",
"mllib/",
],
sbt_test_goals=[
"mllib/test",
]
)
examples = Module(
name="examples",
dependencies=[graphx, mllib, streaming, hive],
source_file_regexes=[
"examples/",
],
sbt_test_goals=[
"examples/test",
]
)
pyspark_core = Module(
name="pyspark-core",
dependencies=[],
source_file_regexes=[
"python/(?!pyspark/(ml|mllib|sql|streaming))"
],
python_test_goals=[
# doctests
"pyspark.rdd",
"pyspark.context",
"pyspark.conf",
"pyspark.broadcast",
"pyspark.accumulators",
"pyspark.serializers",
"pyspark.profiler",
"pyspark.shuffle",
"pyspark.util",
# unittests
"pyspark.tests.test_appsubmit",
"pyspark.tests.test_broadcast",
"pyspark.tests.test_conf",
"pyspark.tests.test_context",
"pyspark.tests.test_daemon",
"pyspark.tests.test_join",
"pyspark.tests.test_profiler",
"pyspark.tests.test_rdd",
"pyspark.tests.test_rddbarrier",
"pyspark.tests.test_readwrite",
"pyspark.tests.test_serializers",
"pyspark.tests.test_shuffle",
"pyspark.tests.test_taskcontext",
"pyspark.tests.test_util",
"pyspark.tests.test_worker",
]
)
pyspark_sql = Module(
name="pyspark-sql",
dependencies=[pyspark_core, hive, avro],
source_file_regexes=[
"python/pyspark/sql"
],
python_test_goals=[
# doctests
"pyspark.sql.types",
"pyspark.sql.context",
"pyspark.sql.session",
"pyspark.sql.conf",
"pyspark.sql.catalog",
"pyspark.sql.column",
"pyspark.sql.dataframe",
"pyspark.sql.group",
"pyspark.sql.functions",
"pyspark.sql.readwriter",
"pyspark.sql.streaming",
"pyspark.sql.udf",
"pyspark.sql.window",
"pyspark.sql.avro.functions",
"pyspark.sql.pandas.conversion",
"pyspark.sql.pandas.map_ops",
"pyspark.sql.pandas.functions",
"pyspark.sql.pandas.group_ops",
"pyspark.sql.pandas.types",
"pyspark.sql.pandas.serializers",
"pyspark.sql.pandas.typehints",
"pyspark.sql.pandas.utils",
# unittests
"pyspark.sql.tests.test_arrow",
"pyspark.sql.tests.test_catalog",
"pyspark.sql.tests.test_column",
"pyspark.sql.tests.test_conf",
"pyspark.sql.tests.test_context",
"pyspark.sql.tests.test_dataframe",
"pyspark.sql.tests.test_datasources",
"pyspark.sql.tests.test_functions",
"pyspark.sql.tests.test_group",
"pyspark.sql.tests.test_pandas_cogrouped_map",
"pyspark.sql.tests.test_pandas_grouped_map",
"pyspark.sql.tests.test_pandas_map",
"pyspark.sql.tests.test_pandas_udf",
"pyspark.sql.tests.test_pandas_udf_grouped_agg",
"pyspark.sql.tests.test_pandas_udf_scalar",
"pyspark.sql.tests.test_pandas_udf_typehints",
"pyspark.sql.tests.test_pandas_udf_window",
"pyspark.sql.tests.test_readwriter",
"pyspark.sql.tests.test_serde",
"pyspark.sql.tests.test_session",
"pyspark.sql.tests.test_streaming",
"pyspark.sql.tests.test_types",
"pyspark.sql.tests.test_udf",
"pyspark.sql.tests.test_utils",
]
)
pyspark_streaming = Module(
name="pyspark-streaming",
dependencies=[
pyspark_core,
streaming,
streaming_kinesis_asl
],
source_file_regexes=[
"python/pyspark/streaming"
],
python_test_goals=[
# doctests
"pyspark.streaming.util",
# unittests
"pyspark.streaming.tests.test_context",
"pyspark.streaming.tests.test_dstream",
"pyspark.streaming.tests.test_kinesis",
"pyspark.streaming.tests.test_listener",
]
)
pyspark_mllib = Module(
name="pyspark-mllib",
dependencies=[pyspark_core, pyspark_streaming, pyspark_sql, mllib],
source_file_regexes=[
"python/pyspark/mllib"
],
python_test_goals=[
# doctests
"pyspark.mllib.classification",
"pyspark.mllib.clustering",
"pyspark.mllib.evaluation",
"pyspark.mllib.feature",
"pyspark.mllib.fpm",
"pyspark.mllib.linalg.__init__",
"pyspark.mllib.linalg.distributed",
"pyspark.mllib.random",
"pyspark.mllib.recommendation",
"pyspark.mllib.regression",
"pyspark.mllib.stat._statistics",
"pyspark.mllib.stat.KernelDensity",
"pyspark.mllib.tree",
"pyspark.mllib.util",
# unittests
"pyspark.mllib.tests.test_algorithms",
"pyspark.mllib.tests.test_feature",
"pyspark.mllib.tests.test_linalg",
"pyspark.mllib.tests.test_stat",
"pyspark.mllib.tests.test_streaming_algorithms",
"pyspark.mllib.tests.test_util",
],
blacklisted_python_implementations=[
"PyPy" # Skip these tests under PyPy since they require numpy and it isn't available there
]
)
pyspark_ml = Module(
name="pyspark-ml",
dependencies=[pyspark_core, pyspark_mllib],
source_file_regexes=[
"python/pyspark/ml/"
],
python_test_goals=[
# doctests
"pyspark.ml.classification",
"pyspark.ml.clustering",
"pyspark.ml.evaluation",
"pyspark.ml.feature",
"pyspark.ml.fpm",
"pyspark.ml.functions",
"pyspark.ml.image",
"pyspark.ml.linalg.__init__",
"pyspark.ml.recommendation",
"pyspark.ml.regression",
"pyspark.ml.stat",
"pyspark.ml.tuning",
# unittests
"pyspark.ml.tests.test_algorithms",
"pyspark.ml.tests.test_base",
"pyspark.ml.tests.test_evaluation",
"pyspark.ml.tests.test_feature",
"pyspark.ml.tests.test_image",
"pyspark.ml.tests.test_linalg",
"pyspark.ml.tests.test_param",
"pyspark.ml.tests.test_persistence",
"pyspark.ml.tests.test_pipeline",
"pyspark.ml.tests.test_stat",
"pyspark.ml.tests.test_training_summary",
"pyspark.ml.tests.test_tuning",
"pyspark.ml.tests.test_wrapper",
],
blacklisted_python_implementations=[
"PyPy" # Skip these tests under PyPy since they require numpy and it isn't available there
]
)
sparkr = Module(
name="sparkr",
dependencies=[hive, mllib],
source_file_regexes=[
"R/",
],
should_run_r_tests=True
)
docs = Module(
name="docs",
dependencies=[],
source_file_regexes=[
"docs/",
]
)
build = Module(
name="build",
dependencies=[],
source_file_regexes=[
".*pom.xml",
"dev/test-dependencies.sh",
],
should_run_build_tests=True
)
yarn = Module(
name="yarn",
dependencies=[],
source_file_regexes=[
"resource-managers/yarn/",
"common/network-yarn/",
],
build_profile_flags=["-Pyarn"],
sbt_test_goals=[
"yarn/test",
"network-yarn/test",
],
test_tags=[
"org.apache.spark.tags.ExtendedYarnTest"
]
)
mesos = Module(
name="mesos",
dependencies=[],
source_file_regexes=["resource-managers/mesos/"],
build_profile_flags=["-Pmesos"],
sbt_test_goals=["mesos/test"]
)
kubernetes = Module(
name="kubernetes",
dependencies=[],
source_file_regexes=["resource-managers/kubernetes"],
build_profile_flags=["-Pkubernetes"],
sbt_test_goals=["kubernetes/test"]
)
hadoop_cloud = Module(
name="hadoop-cloud",
dependencies=[],
source_file_regexes=["hadoop-cloud"],
build_profile_flags=["-Phadoop-cloud"],
sbt_test_goals=["hadoop-cloud/test"]
)
spark_ganglia_lgpl = Module(
name="spark-ganglia-lgpl",
dependencies=[],
build_profile_flags=["-Pspark-ganglia-lgpl"],
source_file_regexes=[
"external/spark-ganglia-lgpl",
]
)
# The root module is a dummy module which is used to run all of the tests.
# No other modules should directly depend on this module.
root = Module(
name="root",
dependencies=[build], # Changes to build should trigger all tests.
source_file_regexes=[],
# In order to run all of the tests, enable every test profile:
build_profile_flags=list(set(
itertools.chain.from_iterable(m.build_profile_flags for m in all_modules))),
sbt_test_goals=[
"test",
],
python_test_goals=list(itertools.chain.from_iterable(m.python_test_goals for m in all_modules)),
should_run_r_tests=True,
should_run_build_tests=True
)
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