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from typing import Any, Dict, Optional
from py4j.java_gateway import JavaObject, JVMView
from pyspark.sql import column
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.utils import try_remote_observation
__all__ = ["Observation"]
[docs]class Observation:
"""Class to observe (named) metrics on a :class:`DataFrame`.
Metrics are aggregation expressions, which are applied to the DataFrame while it is being
processed by an action.
The metrics have the following guarantees:
- It will compute the defined aggregates (metrics) on all the data that is flowing through
the Dataset during the action.
- It will report the value of the defined aggregate columns as soon as we reach the end of
the action.
The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or
more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that
contain references to the input Dataset's columns must always be wrapped in an aggregate
function.
An Observation instance collects the metrics while the first action is executed. Subsequent
actions do not modify the metrics returned by `Observation.get`. Retrieval of the metric via
`Observation.get` blocks until the first action has finished and metrics become available.
.. versionadded:: 3.3.0
Notes
-----
This class does not support streaming datasets.
Examples
--------
>>> from pyspark.sql.functions import col, count, lit, max
>>> from pyspark.sql import Observation
>>> df = spark.createDataFrame([["Alice", 2], ["Bob", 5]], ["name", "age"])
>>> observation = Observation("my metrics")
>>> observed_df = df.observe(observation, count(lit(1)).alias("count"), max(col("age")))
>>> observed_df.count()
2
>>> observation.get
{'count': 2, 'max(age)': 5}
"""
def __init__(self, name: Optional[str] = None) -> None:
"""Constructs a named or unnamed Observation instance.
Parameters
----------
name : str, optional
default is a random UUID string. This is the name of the Observation and the metric.
"""
if name is not None:
if not isinstance(name, str):
raise TypeError("name should be a string")
if name == "":
raise ValueError("name should not be empty")
self._name = name
self._jvm: Optional[JVMView] = None
self._jo: Optional[JavaObject] = None
@try_remote_observation
def _on(self, df: DataFrame, *exprs: Column) -> DataFrame:
"""Attaches this observation to the given :class:`DataFrame` to observe aggregations.
Parameters
----------
df : :class:`DataFrame`
the :class:`DataFrame` to be observed
exprs : list of :class:`Column`
column expressions (:class:`Column`).
Returns
-------
:class:`DataFrame`
the observed :class:`DataFrame`.
"""
assert self._jo is None, "an Observation can be used with a DataFrame only once"
self._jvm = df._sc._jvm
assert self._jvm is not None
cls = self._jvm.org.apache.spark.sql.Observation
self._jo = cls(self._name) if self._name is not None else cls()
observed_df = self._jo.on(
df._jdf, exprs[0]._jc, column._to_seq(df._sc, [c._jc for c in exprs[1:]])
)
return DataFrame(observed_df, df.sparkSession)
# Note that decorated property only works with Python 3.9+ which Spark Connect requires.
@property
@try_remote_observation
def get(self) -> Dict[str, Any]:
"""Get the observed metrics.
Waits until the observed dataset finishes its first action. Only the result of the
first action is available. Subsequent actions do not modify the result.
Returns
-------
dict
the observed metrics
"""
assert self._jo is not None, "call DataFrame.observe"
jmap = self._jo.getAsJava()
# return a pure Python dict, not jmap which is a py4j JavaMap
return {k: v for k, v in jmap.items()}
def _test() -> None:
import doctest
import sys
from pyspark.context import SparkContext
from pyspark.sql import SparkSession
import pyspark.sql.observation
globs = pyspark.sql.observation.__dict__.copy()
sc = SparkContext("local[4]", "PythonTest")
globs["spark"] = SparkSession(sc)
(failure_count, test_count) = doctest.testmod(pyspark.sql.observation, globs=globs)
sc.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()