Metrics Explorer is a standalone charting tool that lets you build charts for any (numeric) metric collected by your project. This page describes how to configure Metrics Explorer by using its Configuration tab. If you use Monitoring Query Language (MQL), the concepts on this page apply; however, you must use the syntax defined in the Introduction to Monitoring Query Language (MQL).
For information on configuring the style of a chart, see Set view options.
Select the data to display
To specify the metrics to display when using Metrics Explorer, you specify values for a metric and a resource type:
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
The Resource type and Metric fields determine the charted metric. You can complete these fields in any order, and both fields let you enter values or select entries from the menu:
To reduce the number of options displayed in a menu, enter a value on the filter bar. For example, if you enter
cpu, then the menu only lists entries that include
cpu. A case-insensitive test is used to determine whether an entry is listed.
To get more information about an entry, place your pointer on the entry to activate the tooltip.
After resource type and metric are selected, the chart shows all the available time series for that pair. The following screenshot shows a chart after the resource type and metric are selected:
This chart contains more data that can be displayed; charts are limited to 300 displayable lines. The chart provides a notice that there is too much data to display, and suggests using outlier mode, which greatly reduces the amount of data to display. To access the outlier mode controls, click settings Settings. For more information, see Set view options.
You can also use the filtering and aggregation options to reduce the amount of charted data. These techniques make the charts more useful for diagnostics and analysis, and they increase the performance and responsiveness of the user interface itself.
Direct filter mode
Direct filter mode lets you enter an expression that Monitoring
uses to identify the time series to be monitored. The expressions that you
enter in direct filter mode are sometimes referred to as metric filters
or Monitoring filters. The following illustrates an
expression that causes Monitoring to display the count of
log entries for all Google Cloud virtual machine instances in the
metric.type="logging.googleapis.com/log_entry_count" resource.type="gce_instance" resource.label."zone"="us-east1-b"
Direct filter mode is useful when you want to identify the proper syntax for an
API call or to verify that a Monitoring filter is properly
formatted. It is also useful when you want to chart data the resource and metric
model can't represent. For example, the following expression results
in a chart displaying a count of processes whose name includes
To enter a Monitoring filter, do the following:
- Expand the Resource type menu and select Direct filter mode.
Enter a Monitoring filter expression in the text box.
The text box is populated with the resource type, metric, and filter setting that you made before you changed to Direct filter mode.
If you've used direct filter mode to configure charts or alerting policies and no data is available, then the chart displays an error message. The exact error message depends on the filter you entered. For example, a typical message is
Chart definition invalid. You might also see the message
No data is available for the selected timeframe.
To return to the menu-driven interface, click Standard 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
Typically, you can filter by
resource group, by name, by resource label,
by zone, and by metric label.
To add a filter when you use the MQL tab, use the Query Editor to specify the filter.
To add a filter when you use the Configuration tab, click Add filter and then specify the filter label, the comparison, and the value or range of values:
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 filter_list 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:
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,
Click Value and then do one of the following:
If you selected a direct comparison,
!=, 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
ends_with. For example, to display data for any
us-central1zone 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
zoneresource label is selected:
If you selected a regular expression comparison,
!=~, then enter a RE2 regular expression into the Value field and click Done. For example, the regular expression
To match any US zone that ends with “a”, you could use the regular expression
You can't use regular expressions to filter the
For example, if you want to view only the time series from one of the
us-central1zones, then apply a
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
strings to show only “a” zones in the US:
Choose how to display charted data
The section covers how to display the selected data by setting the aggregation fields. Aggregation consists of alignment of data points within a time series, and combining different time series together. For a detailed explanation of aggregation, see Filtering and aggregation: manipulating time series.
- For information on view options, including outlier mode, see Set View Options.
- For more information about interacting with the chart itself, see Explore charted data.
Group 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 add a grouping, click the text in the Group by text box, and then make a selection from the menu. The menu is constructed dynamically based on the time-series data for the resource and metric you selected. Grouping and filtering use the same set of labels.
When you add the first label, the following occurs:
- An Aggregator is selected. The type of data being displayed determines the default aggregator; however, you can change this function.
- The aggregator determines how the time series that have the same label value are combined into a single time series.
- The chart displays one time series for each value of the label listed in the Group by text box.
If you group by multiple labels, then the aggregator combines those times series that have the same value for the specified labels.
If you don't specify a grouping option and do specify an aggregator, then that function is applied to all selected time series and results in a single time series.
For example, if the Group by field is set to
user_labels.version and the
aggregator is set to
sum, then there is one time series for
each value of the label
The data points in each time series are computed
from the sum of all the values for individual time series for a specific
You can group by multiple labels. When you have multiple grouping options, the aggregator 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
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 aggregator, 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 a group-by condition, you must:
- Delete the group-by labels.
- Set the aggregator to
Align time series
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
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
Aligner in the API reference.
To view or modify the alignment function, click Show advanced options.
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
For comparison, the following screenshot illustrates the effect of changing
the period from
1 minute to
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.
When you have multiple time series that already represent aggregations, you can reduce all the time series on the chart to a single time series by choosing a Secondary Aggregator. For example, if you group data by zone, your chart shows one time series for each zone. To create a chart with a single time series, use the secondary aggregation fields.
To view or modify the secondary aggregation settings, click Show advanced options.
The following screenshot shows several time series that result from grouping a filtered set of data. The use of grouping requires aggregation; each group of lines is aggregated into one. The following screenshot shows time series grouped by zone:
The following screenshot shows the result of using secondary aggregation to find the mean value across the grouped time series:
The Legend Template field lets you customize a description for the time series on your chart. These descriptions appear on the tooltip for the chart and on the chart legend in the Name column. By default, the descriptions in the legend are created for you from the values of different labels in your time series. Because the system selects the labels, the results might not be helpful to you. To build a template for descriptions, use this field.
To access the legend template for a chart, in the Cloud Console, select the Advanced tab in the chart's configuration pane. The legend template is listed under the heading Additional options.
You can enter plain text and templates in the Legend Template field. When you add a template, you add an expression that is evaluated when the legend is displayed.
To add a template, do the following:
- Click Insert a template.
- Select an entry from the menu. After you select an entry, a template is
automatically added. For example, if you select
response_code, then the template
For example, the following screenshot shows a legend template that contains
plain text and the expression
In the chart legend, the values generated from the template appear in a column with the header Name and in the tooltip:
You can configure the legend template to include multiple text strings and templates; however, the display space available on the tooltip is limited.