Class Aggregation (2.20.0)

Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.

Alignment consists of applying the per_series_aligner operation to each time series after its data has been divided into regular alignment_period time intervals. This process takes all of the data points in an alignment period, applies a mathematical transformation such as averaging, minimum, maximum, delta, etc., and converts them into a single data point per period.

Reduction is when the aligned and transformed time series can optionally be combined, reducing the number of time series through similar mathematical transformations. Reduction involves applying a cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, and applying the reducer to each subset.

The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, see Filtering and aggregation <https://cloud.google.com/monitoring/api/v3/aggregation>__.

The Aligner specifies the operation that will be applied to the data points in each alignment period in a time series. Except for ALIGN_NONE, which specifies that no operation be applied, each alignment operation replaces the set of data values in each alignment period with a single value: the result of applying the operation to the data values. An aligned time series has a single data value at the end of each alignment_period.

An alignment operation can change the data type of the values, too. For example, if you apply a counting operation to boolean values, the data value_type in the original time series is BOOLEAN, but the value_type in the aligned result is INT64.

Values: ALIGN_NONE (0): No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA (1): Align and convert to DELTA][google.api.MetricDescriptor.MetricKind.DELTA]. The output is delta = y1 - y0.

    This alignment is valid for
    `CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE]`
    and `DELTA` metrics. If the selected alignment period
    results in periods with no data, then the aligned value for
    such a period is created by interpolation. The
    `value_type` of the aligned result is the same as the
    `value_type` of the input.
ALIGN_RATE (2):
    Align and convert to a rate. The result is computed as
    `rate = (y1 - y0)/(t1 - t0)`, or "delta over time". Think
    of this aligner as providing the slope of the line that
    passes through the value at the start and at the end of the
    `alignment_period`.

    This aligner is valid for `CUMULATIVE` and `DELTA`
    metrics with numeric values. If the selected alignment
    period results in periods with no data, then the aligned
    value for such a period is created by interpolation. The
    output is a `GAUGE` metric with `value_type` `DOUBLE`.

    If, by "rate", you mean "percentage change", see the
    `ALIGN_PERCENT_CHANGE` aligner instead.
ALIGN_INTERPOLATE (3):
    Align by interpolating between adjacent points around the
    alignment period boundary. This aligner is valid for
    `GAUGE` metrics with numeric values. The `value_type` of
    the aligned result is the same as the `value_type` of the
    input.
ALIGN_NEXT_OLDER (4):
    Align by moving the most recent data point before the end of
    the alignment period to the boundary at the end of the
    alignment period. This aligner is valid for `GAUGE`
    metrics. The `value_type` of the aligned result is the
    same as the `value_type` of the input.
ALIGN_MIN (10):
    Align the time series by returning the minimum value in each
    alignment period. This aligner is valid for `GAUGE` and
    `DELTA` metrics with numeric values. The `value_type` of
    the aligned result is the same as the `value_type` of the
    input.
ALIGN_MAX (11):
    Align the time series by returning the maximum value in each
    alignment period. This aligner is valid for `GAUGE` and
    `DELTA` metrics with numeric values. The `value_type` of
    the aligned result is the same as the `value_type` of the
    input.
ALIGN_MEAN (12):
    Align the time series by returning the mean value in each
    alignment period. This aligner is valid for `GAUGE` and
    `DELTA` metrics with numeric values. The `value_type` of
    the aligned result is `DOUBLE`.
ALIGN_COUNT (13):
    Align the time series by returning the number of values in
    each alignment period. This aligner is valid for `GAUGE`
    and `DELTA` metrics with numeric or Boolean values. The
    `value_type` of the aligned result is `INT64`.
ALIGN_SUM (14):
    Align the time series by returning the sum of the values in
    each alignment period. This aligner is valid for `GAUGE`
    and `DELTA` metrics with numeric and distribution values.
    The `value_type` of the aligned result is the same as the
    `value_type` of the input.
ALIGN_STDDEV (15):
    Align the time series by returning the standard deviation of
    the values in each alignment period. This aligner is valid
    for `GAUGE` and `DELTA` metrics with numeric values. The
    `value_type` of the output is `DOUBLE`.
ALIGN_COUNT_TRUE (16):
    Align the time series by returning the number of `True`
    values in each alignment period. This aligner is valid for
    `GAUGE` metrics with Boolean values. The `value_type` of
    the output is `INT64`.
ALIGN_COUNT_FALSE (24):
    Align the time series by returning the number of `False`
    values in each alignment period. This aligner is valid for
    `GAUGE` metrics with Boolean values. The `value_type` of
    the output is `INT64`.
ALIGN_FRACTION_TRUE (17):
    Align the time series by returning the ratio of the number
    of `True` values to the total number of values in each
    alignment period. This aligner is valid for `GAUGE`
    metrics with Boolean values. The output value is in the
    range [0.0, 1.0] and has `value_type` `DOUBLE`.
ALIGN_PERCENTILE_99 (18):
    Align the time series by using `percentile
    aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
    The resulting data point in each alignment period is the
    99th percentile of all data points in the period. This
    aligner is valid for `GAUGE` and `DELTA` metrics with
    distribution values. The output is a `GAUGE` metric with
    `value_type` `DOUBLE`.
ALIGN_PERCENTILE_95 (19):
    Align the time series by using `percentile
    aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
    The resulting data point in each alignment period is the
    95th percentile of all data points in the period. This
    aligner is valid for `GAUGE` and `DELTA` metrics with
    distribution values. The output is a `GAUGE` metric with
    `value_type` `DOUBLE`.
ALIGN_PERCENTILE_50 (20):
    Align the time series by using `percentile
    aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
    The resulting data point in each alignment period is the
    50th percentile of all data points in the period. This
    aligner is valid for `GAUGE` and `DELTA` metrics with
    distribution values. The output is a `GAUGE` metric with
    `value_type` `DOUBLE`.
ALIGN_PERCENTILE_05 (21):
    Align the time series by using `percentile
    aggregation <https://en.wikipedia.org/wiki/Percentile>`__.
    The resulting data point in each alignment period is the 5th
    percentile of all data points in the period. This aligner is
    valid for `GAUGE` and `DELTA` metrics with distribution
    values. The output is a `GAUGE` metric with `value_type`
    `DOUBLE`.
ALIGN_PERCENT_CHANGE (23):
    Align and convert to a percentage change. This aligner is
    valid for `GAUGE` and `DELTA` metrics with numeric
    values. This alignment returns
    `((current - previous)/previous) * 100`, where the value
    of `previous` is determined based on the
    `alignment_period`.

    If the values of `current` and `previous` are both 0,
    then the returned value is 0. If only `previous` is 0, the
    returned value is infinity.

    A 10-minute moving mean is computed at each point of the
    alignment period prior to the above calculation to smooth
    the metric and prevent false positives from very short-lived
    spikes. The moving mean is only applicable for data whose
    values are `>= 0`. Any values `< 0` are treated as a
    missing datapoint, and are ignored. While `DELTA` metrics
    are accepted by this alignment, special care should be taken
    that the values for the metric will always be positive. The
    output is a `GAUGE` metric with `value_type` `DOUBLE`.

A Reducer operation describes how to aggregate data points from multiple time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.

Values: REDUCE_NONE (0): No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN (1): Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with numeric or distribution values. The value_type of the output is DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]. REDUCE_MIN (2): Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input. REDUCE_MAX (3): Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input. REDUCE_SUM (4): Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input. REDUCE_STDDEV (5): Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE. REDUCE_COUNT (6): Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64. REDUCE_COUNT_TRUE (7): Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64. REDUCE_COUNT_FALSE (15): Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64. REDUCE_FRACTION_TRUE (8): Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range [0.0, 1.0] and has value_type DOUBLE. REDUCE_PERCENTILE_99 (9): Reduce by computing the 99th percentile <https://en.wikipedia.org/wiki/Percentile> of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE. REDUCE_PERCENTILE_95 (10): Reduce by computing the 95th percentile <https://en.wikipedia.org/wiki/Percentile> of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE. REDUCE_PERCENTILE_50 (11): Reduce by computing the 50th percentile <https://en.wikipedia.org/wiki/Percentile> of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE. REDUCE_PERCENTILE_05 (12): Reduce by computing the 5th percentile <https://en.wikipedia.org/wiki/Percentile> of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.