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This shows you the differences between two versions of the page.

--- devops:monitoring:datadog:metrics [2025/02/14 09:10] – 85.219.17.206
+++ devops:monitoring:datadog:metrics [2025/02/14 09:22] (current) – [Additional visualizations] 85.219.17.206
@@ Line 9: / Line 9: @@
 A sequence of data points is stored as a timeseries:
-[ 17.82,  22:11:01 ]
+   [ 17.82,  22:11:01 ]
-[  6.38,  22:11:12 ]
+   [  6.38,  22:11:12 ]
-[  2.87,  22:11:38 ]
+   [  2.87,  22:11:38 ]
-[  7.06,  22:12:00 ]
+   [  7.06,  22:12:00 ]
 Any metrics with fractions of a second timestamps are rounded to the nearest second. If any points have the same timestamp, the latest point overwrites the previous ones.
-Why are metrics useful?
+===== Why are metrics useful? =====
 Metrics provide an overall picture of your system. You can use them to assess the health of your environment at a glance. Visualize how quickly users are loading your website, or the average memory consumption of your servers, for instance. Once you identify a problem, you can use logs and tracing to further troubleshoot.
@@ Line 26: / Line 28: @@
 Metrics can be sent to Datadog from several places.
-    Datadog-Supported Integrations: Datadog’s 800+ integrations include metrics out of the box. To access these metrics, navigate to the specific integration page for your service and follow the installation instructions there. If you need to monitor an EC2 instance, for example, you would go to the Amazon EC2 integration documentation.
+Datadog-Supported Integrations: Datadog’s 800+ integrations include metrics out of the box. To access these metrics, navigate to the specific integration page for your service and follow the installation instructions there. If you need to monitor an EC2 instance, for example, you would go to the Amazon EC2 integration documentation.
-    You can generate metrics directly within the Datadog platform. For instance, you can count error status codes appearing in your logs and store that as a new metric in Datadog.
+You can generate metrics directly within the Datadog platform. For instance, you can count error status codes appearing in your logs and store that as a new metric in Datadog.
-    Often, you’ll need to track metrics related to your business (for example, number of user logins or signups). In these cases, you can create custom metrics. Custom metrics can be submitted through the Agent, DogStatsD, or the HTTP API.
+Often, you’ll need to track metrics related to your business (for example, number of user logins or signups). In these cases, you can create custom metrics. Custom metrics can be submitted through the Agent, DogStatsD, or the HTTP API.
-    Additionally, the Datadog Agent automatically sends several standard metrics (such as CPU and disk usage).
+Additionally, the Datadog Agent automatically sends several standard metrics (such as CPU and disk usage).
 For a summary of all metric submission sources and methods, read the Metrics Types documentation.
-Metric types and real-time metrics visibility
-Metric types
 Datadog supports several different metric types that serve distinct use cases: count, gauge, rate, histogram, and distribution. Metric types determine which graphs and functions are available to use with the metric in the app.
@@ Line 53: / Line 53: @@
 See the metrics types documentation for more detailed examples of each metric type and submission instructions.
+===== Querying metrics =====
+You can visualize your metrics and create graphs throughout Datadog: in Metrics Explorer, Dashboards, or Notebooks.
+Tip: To open the Metrics Summary page from Datadog’s global search, press Cmd/Ctrl + K and search for metrics.
+Here’s an example of a timeseries visualization:
+{{ :devops:monitoring:datadog:metricsex.jpg?600 |}}
+This line graph plots latency (in milliseconds) experienced by users on the y-axis against time on the x-axis.
+==== Additional visualizations ====
+Datadog offers a variety of visualization options to help users easily graph and display their metrics.
+A metric query consists of the same two evaluation steps to start: time aggregation and space aggregation. See the anatomy of a metric query for more information.
+Two visualization offerings that Metrics users often find useful are:
+Query Value Widget - Reduces the results of those two steps into a single value.
+Top List - Returns a single value per group.
+Additionally, Datadog has many other types of graphs and widgets for visualizations. You can learn more about them in Datadog’s blog series about metric graphs.
+The graphing experience is consistent whether you are using dashboards, notebooks, or monitors. You can create graphs by using the graphing editor UI or by directly changing the raw query string. To edit the query string, use the </> button on the far right.
+Anatomy of a metric query
+A metric query in Datadog looks like this:
+Example query with color-coded sections
+{{ :devops:monitoring:datadog:query.jpg?600 |}}
+You can break this query into a few steps:
+Metric name
+First, choose the specific metric that you’d like to graph by searching or selecting it from the dropdown next to Metric. If you’re not sure which metric to use, start with the Metrics Explorer or a notebook. You can also see a list of actively reporting metrics on the Metrics Summary page.
+Filter your metric
+After selecting a metric, you can filter your query based on tag(s). For instance, you can use account:prod to scope your query to include only the metrics from your production hosts. For more information, read the tagging documentation.
+Configure time aggregation
+Next, choose the granularity of your data using time rollup. In this example, you’ve defined that there is one data point for every hour (3600 seconds). You can choose how you want to aggregate the data in each time bucket. By default, avg is applied, but other available options are sum, min, max, and count. You can also customize how your metrics data is aggregated and bucketed with functions or in-application modifiers. For example, if you wanted to apply max and customize how your metrics data is rolled up and bucketed in time with calendar aligned queries, you would use .rollup(max, 60). For more information, see the Functions, Rollup, and In-application modifiers documentation.
+Configure space aggregation
+In Datadog, “space” refers to the way metrics are distributed over different hosts and tags. There are two different aspects of space that you can control: aggregator and grouping
+Aggregator defines how the metrics in each group are combined. There are four aggregations available: sum, min, max, and avg.
+Grouping defines what constitutes a line on the graph. For example, if you have hundreds of hosts spread across four regions, grouping by region allows you to graph one line for every region. This would reduce the number of timeseries to four.
+Apply functions (optional)
+You can modify your graph values with mathematical functions. This can mean performing arithmetic between an integer and a metric (for example, multiplying a metric by 2). Or performing arithmetic between two metrics (for example, creating a new timeseries for the memory utilization rate like this: jvm.heap_memory / jvm.heap_memory_max).
+Time and space aggregation
+Time aggregation and space aggregation are two important components of any query. Because understanding how these aggregations work helps you avoid misinterpreting your graphs, these concepts are explained in more detail below.
+Time aggregation
+Datadog stores a large volume of points, and in most cases it’s not possible to display all of them on a graph. There would be more datapoints than pixels. Datadog uses time aggregation to solve this problem by combining data points into time buckets. For example, when examining four hours, data points are combined into two-minute buckets. This is called a rollup. As the time interval you’ve defined for your query increases, the granularity of your data decreases.
+There are five aggregations you can apply to combine your data in each time bucket: sum, min, max, avg, and count.
+It’s important to remember that time aggregation is always applied in every query you make.
+==== Space aggregation ====
+Space aggregation splits a single metric into multiple timeseries by tags such as host, container, and region. For instance, if you wanted to view the latency of your EC2 instances by region, you would need to use space aggregation’s grouping by functionality to combine each region’s hosts.
+There are four aggregators that can be applied when using space aggregation: sum, min, max, and avg. Using the above example, say that your hosts are spread across four regions: us-east-1, us-east-2, us-west-1, and us-west-2. The hosts in each region need to be combined using an aggregator function. Using the max aggregator would result in the maximum latency experienced across hosts in each region, while the avg aggregator would yield the average latency per region.
+Nested Queries
+Add additional layers of aggregation on the results of existing queries in time and space with nested queries in the UI or through the API. For more information, see the Nested Queries documentation.
+View real-time information about metrics
+The Metrics Summary page displays a list of your metrics reported to Datadog under a specified time frame: the past hour, day, or week. Metrics can be filtered by metric name or tag.
+Click on any metric name to display a details sidepanel with more detailed information. The details sidepanel displays key information for a given metric, including its metadata (type, unit, interval), number of distinct metrics, number of reporting hosts, number of tags submitted, and a table containing all tags submitted on a metric. Seeing which tags are being submitted on a metric helps you understand the number of distinct metrics reporting from it, since this number depends on your tag value combinations.
+Note: The number of distinct metrics reported in the details sidepanel on Metrics Summary does not define your bill. See your usage details for a precise accounting of your usage over the past month.
+Read the metrics summary documentation for more details.