spark-instrumented-optimizer/python/pyspark/sql/observation.py

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from pyspark.sql import column
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame

__all__ = ["Observation"]


class Observation:
    """Class to observe (named) metrics on a :class:`DataFrame`.

    Metrics are aggregation expressions, which are applied to the DataFrame while is 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=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 = None
        self._jo = None

    def _on(self, df, *exprs):
        """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 exprs, "exprs should not be empty"
        assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
        assert self._jo is None, "an Observation can be used with a DataFrame only once"

        self._jvm = df._sc._jvm
        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.sql_ctx)

    @property
    def get(self):
        """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():
    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()