Selecting data to chart

This page describes how to specify what data a chart should display. For information on configuring the style of a chart, see Setting view options.

Select the data to display

To populate the chart, you must specify at least one pair of values:

  • The metric field identifies the measurements to be collected from a monitored resource. It includes a description of what is being measured and how the measurements are interpreted. Metric is a short form of metric type. For conceptual information, see Metric types.

  • The resource type field specifies from which resource the metric data is captured. The resource type is sometimes called the monitored resource type or the resource. For conceptual information, see Monitored resources.

Monitoring has many predefined metric types and monitored resources available, and you can create custom metrics as well:

  • For information on predefined metrics types, see the Metrics list. Each document lists metrics by the type of service. For example, the Google Cloud metrics page contains a series of tables, one for each Google Cloud service.

  • For information about monitored resource types available, see Monitored resource list.

  • For information on defining your own metrics, see Using custom metrics.

The metric-specification field

When you drag a widget from the Widget library to the graph area, a chart is displayed with a preselected resource type and metric:

Example of a newly created line chart.

There are different ways you can specify the data to chart:

  • To quickly configure a chart, use the Basic mode. This choice has minimal configuration options. A basic configuration can always be represented in the other modes. For details, see Using the Basic or Advanced mode.

  • To access all chart configuration fields, use the Advanced mode. This mode provides access to most aggregation fields. For details, see Using the Basic or Advanced mode.

  • To enter a Monitoring filter, see Direct filter mode.

  • To configure a chart using a query language, use the MQL mode. With this mode, you have access to the Query Editor. If you use MQL, that configuration cannot be represented by the other modes. For information about MQL, see Introduction to MQL.

Using the Basic or Advanced mode

The Resource type menu lists every monitored resource for which there is metric data. The Metric menu is determined by the selection for the Resource type.

  • To find a specific entry in a menu, use the scrollbar or enter text into the menu's Filter area. When you enter text, the menu entries are limited to those that include the entered text.

    The following screenshot illustrates the expanded menu for the Metric field. The filter bar contains the text lat, so this menu only lists entries that include that string:

    Display of the metrics filter bar.

    Note that in the previous screenshot, the metric Late Boot Validation is highlighted. When your pointer is placed over an entry a pane opens and displays detailed information about that metric. As illustrated, this pane lists, among other things, the metric kind, value type, and the description field.

If you want to access a resource type or metric that doesn't yet have data, then you need to change the default behavior:

  • To be able to select any resource type, expand the Resource type menu and then clear Only show active.

  • To view all metrics for the selected resource type, even those metrics without data, expand the Metric menu and then clear Only show active.

  • To be able to select any metric, click Close on the Resource type menu and then find the entry of interest. By clicking close on the resource type, the Metric menu includes all metrics.

  • To view or edit the resource type, metric, and filter settings as used by the Cloud Monitoring API, in the Resource type menu, click Direct filter mode.

Using direct filter mode

Use direct filter mode when you are interested in charting any of the following:

  • A service level objective (SLO).
  • The count of processes running on virtual machines (VMs).
  • A custom metric for which you don't yet have data.

When you use direct filter mode, you enter a Monitoring filter that specifies the time series to be charted. The following Monitoring filter results in the chart displaying the count of log entries for all Google Cloud virtual machine instances in the us-east1-b zone:

metric.type="logging.googleapis.com/log_entry_count"
resource.type="gce_instance" resource.label."zone"="us-east1-b"

To enter a Monitoring filter, do the following:

  1. Select Basic mode or Advanced mode.
  2. In the in the Resource type menu, click Direct filter mode.

Filter charted data

You can reduce the amount of data to be charted by specifying filter criteria, applying aggregation, or by using outlier mode. Filters ensure that only time series that meet some set of criteria are used. If you apply filters, then there are fewer lines on the chart, and that can improve the performance of the chart.

If you supply multiple filtering criteria, then the corresponding chart shows only the time series that meet all criteria, a logical AND. Typically, you can filter by resource group, by name, by resource label, by zone, and by metric label.

If you select MQL mode, then use the Query Editor to specify the filter.

To add a filter when you select the Basic or Advanced mode, click Add filter and then specify the filter label, the comparison, and the value or range of values:

  1. Click Label and then select an entry from the menu.

    To find a specific label, you can use the scrollbar or you can enter text into the Filter text area. When you enter text, the menu lists only those entries that contain the entered text.

    The following screenshot shows the known filter-by labels for a specific metric:

    Example of a list of filter labels.

  2. Click Comparison and then select an entry from the menu. You can choose between four operators: equals, =, not equals, !=, regular expression match, =~, and regular expression does not match, !=~:

    List of filter comparators.

  3. Click Value and then do one of the following:

    • If you selected a direct comparison, = or !=, then select from the menu or enter a value and click Done. Entered values can be simple values such as us-central1-a, or you can create a filter string that begins with starts_with or ends_with. For example, to display data for any us-central1 zone you could enter the filter string starts_with("us-central1"). See Monitoring filters for more information on filter strings.

      Because the menu entries are derived from the received time series, if a monitored resource isn't generating data for the selected metric, then you must enter a value for the label.

      The following screenshot shows the value menu that is displayed for a particular project when the zone resource label is selected:

      Example of a list of filter labels.

    • If you selected a regular expression comparison, =~ or !=~, then enter a RE2 regular expression into the Value field and click Done. For example, the regular expression us-central1-.* matches all us-central1 zones.

      To match any US zone that ends with “a”, you could use the regular expression ^us.*.a$.

      You can't use regular expressions to filter the project_id resource label.

      For example, if you want to view only the time series from one of the us-central1 zones, then apply a zone="starts_with("us-central1")" or zone=~"us-central1.*" filter:

      Displaying a filtered time series.

You can specify multiple filter criteria, and you can use the same label multiple times. These capabilities let you specify a filter for a range of values. All filter criteria must be met; they constitute a logical AND. For example, the following a configuration that you can use both starts_with and ends_with filter strings to show only “a” zones in the US:

Example using multiple filters.

Choose how to display charted data

After the time series data is selected, the next step is to determine how that data is displayed. For example, do you want to display each time series or do you want to combine time series together?

You specify how the data is displayed by configuring the aggregation options. Aggregation consists of aligning time series data and then combining different time series together. Combining time series is optional.

For a detailed explanation of aggregation, see Filtering and aggregation: manipulating time series.

Align data

Alignment is the process of converting the time series data received by Monitoring into a new time series which has data points at fixed intervals. The process of alignment consists of collecting all data points received in a fixed length of time, applying a function to combine those data points, and assigning a timestamp to the result. That function might compute the average of all samples or it might extract the maximum of all samples.

The Alignment period specifies the minimum time interval to be used when aligning time series data. When there are too many data points to chart in the selected display period, then the alignment period is automatically increased so that every data point is represented. The default setting for this field is one minute.

For example, consider a metric with a sampling period of one minute. If a chart is configured to display 1 hour of data, then the chart can display all 60 data points. If the alignment period is set to 10 minutes, then the chart displays 6 data points. However, if you configure the chart to display one week of data, then there are too many points to display in the chart so the period is automatically modified. In this example, the modified alignment period is one hour.

The Alignment function field specifies the function used to combine all the data points in an alignment period. Most of the aligners perform common mathematical functions. For example, if you select min, then the aligned data point is the minimum of all data points within the alignment period. A few of the aligners perform more complicated actions:

  • next older: To retain only the most recent sample within an alignment period, use the next older aligner. This aligner is commonly used with uptime checks and is a good choice when you only care about the most recent value.

    This aligner is valid only for gauge metrics.

  • percentile: To display a distribution metric on a plot type of line chart, stacked area chart, or stacked bar chart, you must select which percentile in the distribution to display. One way to specify this percentile is to select a percentile aligner. You can select the 5th, 50th, 95th, and the 99th percentiles. The aligned data point is determined by computing the specified percentile by using all data points in the alignment period.

    This aligner is valid only for gauge and delta metrics when they have a distribution data type.

  • delta: To convert a cumulative metric or a delta metric into a delta metric with one sample per alignment period, use this aligner. This aligner might result in data interpolation. For an example, see Kinds, types, and conversions.

    This aligner is valid only for cumulative and delta metrics.

  • rate: To convert a cumulative or delta metric into a gauge metric, use this aligner. If you choose this aligner, you can think of the time series being transformed as it was with a delta aligner and then divided by the alignment period. For example, if the unit of the original time series is MiB and the unit of the alignment period is second, then the chart has a unit of MiB/second. For more information, see Kinds, types, and conversions.

    This aligner is valid only for cumulative and delta metrics.

For more information on the available aligners, see Aligner in the API reference.

To access the alignment fields, do the following:

  • Basic mode: All alignment fields are preconfigured.

  • Advanced mode: All alignment options are accessible after you select the resource type and metric. Default values for these fields are provided; however, you can modify the selections.

  • MQL mode: All alignment options are available.

The following screenshot illustrates the CPU utilization of the Compute Engine VM instances in a particular Google Cloud project. In this image, the alignment fields are at the default values: the alignment function is set to mean and the alignment period is set to 1 minute:

CPU utilization of VM instances using default alignment settings.

For comparison, the following screenshot illustrates the effect of changing the period from 1 minute to 5 minutes:

CPU utilization of VM instances using default with a 5 minute alignment period

By increasing the period, the resulting chart has fewer points, decreasing from 60 points per time series to 10 points per time series. Each point on the chart is computed by averaging the time series points in an alignment period. By increasing the alignment period, more points are averaged together, which has a smoothing effect on the plotted data.

Combine time series

You can reduce the amount of data returned for a metric by combining different time series. To combine multiple time series, you typically specify a grouping and a function. Grouping is done by label values. The function defines how all time-series data within a group are combined into a new time series.

To group and combine time series, do one of the following:

  • Basic mode:

    1. Determine how to group time series:

      • To display every time series, leave Grouped clear.
      • To group time series by label values, select Grouped and then use the Group by menu to select the labels for grouping. After you make your selections, click OK.
    2. Determine how to combine data points:

      • If the radio buttons only list percentiles, then you've selected a metric that has a distribution value. Use the radio buttons to select which percentile from the distribution to view.

      • If the radio buttons list mean, min, and max, then you've selected a metric with a numerical value. Use these buttons to specify how data points are combined as part of the charting process. To understand how these combiners work, assume that a chart can display 60 data points and assume that the data rate is 1 point per minute. If you display one hour of data, then the chart can display every point. However, if you want to display 3 hours of data, you have to reduce 180 data points to 60, which is the number of points the chart can display. One way to reduce the data is to average three adjacent samples, another is to take the minimum.

  • Advanced mode:

    • To combine all time-series data into a single time series, ensure Group by is empty and select how the time series are combined by using the Group by function menu.

    • To display all time-series data, do one of the following:

      • Ensure Group by is empty and select none for the Group by function.
      • Click the Group by menu and select Add all, and then select a Group by function. For metrics which store a numerical value, the choice of group-by function might not produce a visibly different chart.
    • To group time series by specific label values, click Group by and select the labels for grouping. To specify how the time series are combined, select a function by using the Group by function menu.

  • MQL mode:

    For information about Monitoring Query Language, see Using the Query Editor.

The following screenshot shows a grouping by user_labels.version with the group-by function set to the default value of sum. This selection results in one time series for each value of the user_labels.version. The data points in each time series are computed from the sum of all the values for individual time series for a specific version:

Showing time series' grouped by user_labels.version

You can group by multiple labels. When you have multiple grouping options, the group-by function is applied to the set of time series that have the same values for the selected labels.

The resulting chart displays one time series for each combination of label values. The order in which you specify the labels doesn't matter.

For example, the following screenshot illustrates grouping by user_labels.version and system_labels.machine_image:

Showing time series' grouped by version and machine image.

As illustrated, if you group by both the labels, you get one time series for each pair of values. The fact that you get a time series for each combination of labels means that this technique can easily create more data than you can usefully put on a single chart.

When you specify grouping or if you select an group-by function, the charted time series only contains required labels, such as the project identifier, and the labels specified by the grouping.

Remove group-by conditions

To remove all group-by conditions, do one of the following:

  • Basic mode: Clear Grouped.

  • Advanced mode: Do the following:

    1. In the Group by menu, click Remove all and then click OK.
    2. In the Group by function menu, select none.
  • MQL mode: Delete grouping commands.

    For information about Monitoring Query Language, see Using the Query Editor.

Secondary aggregation

The tab you select determines the aggregation options:

  • Basic and Advanced modes: The Dashboard editor determines how to map your aggregation selections to the primary and secondary aggregation fields specified by the Cloud Monitoring API by using the following information:

    • Widget type
    • Kind of metric
    • Value type of the metric
    • Which mode is being used to configure the widget

    To determine this mapping for charts on custom dashboards, you can retrieve the dashboard configuration by using the gcloud command-line tool. For more information, see Listing dashboards.

  • MQL mode: These fields accessible.

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