Cloud Dataproc V1 API - Class Google::Cloud::Dataproc::V1::DataprocMetricConfig::Metric (v0.23.1)

Reference documentation and code samples for the Cloud Dataproc V1 API class Google::Cloud::Dataproc::V1::DataprocMetricConfig::Metric.

A Dataproc custom metric.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#metric_overrides

def metric_overrides() -> ::Array<::String>
Returns
  • (::Array<::String>) —

    Optional. Specify one or more Custom metrics to collect for the metric course (for the SPARK metric source (any Spark metric can be specified).

    Provide metrics in the following format: METRIC_SOURCE:INSTANCE:GROUP:METRIC Use camelcase as appropriate.

    Examples:

    yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used

    Notes:

    • Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.

#metric_overrides=

def metric_overrides=(value) -> ::Array<::String>
Parameter
  • value (::Array<::String>) —

    Optional. Specify one or more Custom metrics to collect for the metric course (for the SPARK metric source (any Spark metric can be specified).

    Provide metrics in the following format: METRIC_SOURCE:INSTANCE:GROUP:METRIC Use camelcase as appropriate.

    Examples:

    yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used

    Notes:

    • Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
Returns
  • (::Array<::String>) —

    Optional. Specify one or more Custom metrics to collect for the metric course (for the SPARK metric source (any Spark metric can be specified).

    Provide metrics in the following format: METRIC_SOURCE:INSTANCE:GROUP:METRIC Use camelcase as appropriate.

    Examples:

    yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used

    Notes:

    • Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.

#metric_source

def metric_source() -> ::Google::Cloud::Dataproc::V1::DataprocMetricConfig::MetricSource
Returns

#metric_source=

def metric_source=(value) -> ::Google::Cloud::Dataproc::V1::DataprocMetricConfig::MetricSource
Parameter
Returns