Data Flow Metrics
Learn about the Spark-related metrics available from the
namespace. The Data Flow metrics help you monitor
the number of tasks that completed or failed and the amount of data involved.
- A namespace is a container for Data Flow metrics. The namespace
identifies the service sending the metrics. The namespace for Data Flow is
- Metrics are the fundamental concept in telemetry and monitoring. Metrics
define a time-series set of data points. Each metric is uniquely defined
- metric name
- compartment identifier
- a set of one or more dimensions
- a unit of measure
- A dimension is a key-value pair that defines the characteristics associated
with the metric. Data Flow has four
resourceId: The OCID of a Data Flow Run instance.
resourceName: The name of a Run resource. It is not guaranteed to be unique.
applicationId: The OCID of a Data Flow Application instance.
applicationName: The name of an Application resource. It is not guaranteed to be unique or final.
- Statistics are metric data aggregations over specified periods of time. Aggregations are done using the namespace, metric name, dimensions, and the data point unit of measure within a specified time period.
- Alarms are used to automate operations monitoring and performance. An alarm keeps track of changes that occur over a specific period of time and performs one or more defined actions, based on the rules defined for the metric.
To monitor resources, you must be given the required type of access in a policy written by an administrator. The policy must give you access to the monitoring services and the resources being monitored. This applies whether you're using the Console or the REST API with an SDK, CLI, or another tool. If you try to perform an action and get a message that you don’t have permission or are unauthorized, confirm with your administrator the type of access you've been granted and which compartment you should work in. For more information on user authorizations for monitoring, see the Authentication and Authorization section for the related service: Monitoring or Notifications.
|Metric Name||Display Name||Dimensions||Statistic||Description|
||Run Startup Time||
||Mean||The overall startup time for a run contains timings for resource assignment and Spark job startup as well as the time it waits in various queues internal to the service.|
|Run Execution Time||Mean||The amount of time it takes to complete a run, from the time it is executed until the time it completes.|
||Total Run Time||
||Mean||The sum of the Run startup time and Run Execution Time.|
||Count||Whether or not the run executed successfully.|
||Count||Whether or not the run failed to execute.|
Viewing the Metrics
- From the console, click the navigation menu, click Monitoring, and select Service Metrics. See Monitoring Overview for how to use these metrics.
- From the console, click the navigation menu, click Monitoring, and select Metrics Explorer. See Monitoring Overview for how to use these metrics.
- From the console, click the navigation menu, click Data Flow, and select Runs. Under Resources, click Metrics, and you see the metrics specific to this Run. Set the Start time and End time as appropriate, or a time period from Quick Selects. For each chart, you can specify an Interval and a Statistic, and the Options as to how to display each metric.
- From the console, click the navigation menu, click Data Flow, and select Applications. You see the metrics specific to the Runs of this Application. Set the Start time and End time as appropriate, or a time period from Quick Selects. For each chart, you can specify an Interval and a Statistic, and the Options as to how to display each metric.