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import os
from typing import Any, Dict, Optional, TYPE_CHECKING
from pyspark.errors import PySparkTypeError, PySparkValueError, PySparkAssertionError
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.utils import is_remote
if TYPE_CHECKING:
from py4j.java_gateway import JavaObject, JVMView
__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 __new__(cls, *args: Any, **kwargs: Any) -> Any:
if is_remote() and "PYSPARK_NO_NAMESPACE_SHARE" not in os.environ:
from pyspark.sql.connect.observation import Observation as ConnectObservation
return ConnectObservation(*args, **kwargs)
return super().__new__(cls)
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 PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "name", "arg_type": type(name).__name__},
)
if name == "":
raise PySparkValueError(
errorClass="VALUE_NOT_NON_EMPTY_STR",
messageParameters={"arg_name": "name", "arg_value": name},
)
self._name = name
self._jvm: Optional[JVMView] = None
self._jo: Optional["JavaObject"] = None
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`.
"""
from pyspark.sql.classic.column import _to_seq
if self._jo is not None:
raise PySparkAssertionError(errorClass="REUSE_OBSERVATION", messageParameters={})
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 = df._jdf.observe(
self._jo, exprs[0]._jc, _to_seq(df._sc, [c._jc for c in exprs[1:]])
)
return DataFrame(observed_df, df.sparkSession)
@property
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
"""
if self._jo is None:
raise PySparkAssertionError(errorClass="NO_OBSERVE_BEFORE_GET", messageParameters={})
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.core.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()