Step 3: Create a dashboard and charts

Now that you’ve sent telemetry data to Lightstep Observability, it’s time to build a unified dashboard and charts to monitor that data.

Run your service or app

The first step is to run your app so that it starts generating metrics and span data that you can see immediately in Lightstep Observability. Let it run for a few minutes before you start building the dashboard.

Create a dashboard

Dashboards can contain both metric and span charts that are built by querying of your telemetry data.

1. From the navigation bar, click Dashboards.

2. Click Create a dashboard.

3. Click in the title to rename your dashboard.

   You’re now ready to add a chart to the dashboard.

4. In your dashboard, click Add a Chart.

5. In the Chart Builder window, click into the title to name your chart.

6. Build the query for your chart. For this learning path, create at least one metric chart.

Learn how to use the Unified Query Builder

1. Search for a metric to plot:
   Click into the search field and begin typing the metric name.

   You can select Metric from the All telemetry dropdown to narrow your search to only metric data.

   

   Change Intelligence works best when it can focus on a single service and its dependencies. To narrow your search to metrics emitted from a specific service, click Filter to service and select the service name from the dropdown.

   When you select a metric, Lightstep expands the query builder and begins to chart the metric.

2. Filter the data:
   Unless you’ve already filtered by a service, all data for the metric is displayed. You can filter the data using metric attributes found on the data.

   Enter an attribute key, select an operator or enter a regular expression, and enter one or more values. Multiple values are joined by OR.

   You can add more than one filter to the query where it makes sense (the query builder prunes the available list as you add attributes).

   Multiple filters use AND to join filters.

   

   By default, Lightstep Observability displays attributes that it’s seen in the last three days. But you can type in an attribute not in the dropdown and Lightstep will find it.

3. Use the Represent as field to choose an operator for the data:

   • Latest: Graphs the latest value in a time series for a point in time.

   The latest operator can only be used with gauge-type metrics.

   • Count: Computes and graphs the total number of increments as whole numbers. Counts are most useful for infrequent events and are best visualized as stacked bar charts.

   • Rate: Graphs the number of operations per second. Rates are most useful for ongoing operations and are best visualized as line charts

     The query builder automatically configures the operator for distribution type metrics. If the distribution is a gauge, the operator is set to latest. If it is a counter (delta or cumulative), the operator is set to rate by default, but you can change it to count.

4. Compute percentiles (distribution type metrics only):
   When the metric data is a distribution type (a set of values for each point in time), Lightstep Observability can compute the 50th, 90th, 99th, and 99.9th percentiles for you.

   For existing Lightstep customers interested in tracking distribution metrics, please opt-in here. For new customers to Lightstep, this feature is already enabled in your account.

   When using distributions in an alert, you must select only one percentile to alert on.

5. Group the data:
   By default, Lightstep Observability aggregates the data from the metric into one line.

   

   Instead, you can show lines for each available attribute value (group by). Select an attribute to display lines for each of the attribute’s values.

   In this example, by choosing to group by the host attribute, you can see the metrics for the individual hosts.

   Grouping isn’t available on big number charts.

6. Aggregation method:
   Choose how you want the data aggregated into the chart.

• count of non-null values: The number of values found that are not null. For example, given the values of [10, 15, null, 50] the count is 3.
• count of non-zero values: The number of values found that are not zero (null is counted). For example, given the values of [10, 15, null, 0 50] the count is 4.
• maximum value: The highest point in the data.
  For example, given the values of [10, 15, 50] the max is 50.
• mean of all values: The average (sum of the data divided by the count) of the data.
  For example, given the values of [10, 15, 50] the mean is 25.
• minimum value: The lowest point in the data.
  For example, given the values of [10, 15, 50] the min is 10.
• sum of all values: The total of all points in the data.
  For example, given the values of [10, 15, 50] the sum is 75.

Distribution type metrics are automatically summed and then aggregated into percentiles.

When you hover over a point on the chart, you can see its value, along with the group-by value.

Clicking on a point allows you to start Change Intelligence to determine what cased the change in performance.

Below the chart, a table displays the data for each line in the chart.

Expandable end

7. Click Save to save your changes. The chart now displays on the dashboard.

You can add as many charts as you need for the dashboard.

Now that you have your data reporting and displayed in Lightstep Observability, it’s time to see how that data and data from trace instrumentation work together to find root causes of metric deviations deep in your stack.

What did we learn?

• Dashboards can contain any number of charts for both your metric and span data.
• Charts are built from queries to your data. You can aggregate, filter, and group that data so that the chart reports just the data you need.