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Memfault charts allow you to proactively monitor any smart device, anywhere. Monitor your releases and view data-driven device and fleet-level metrics, like battery health, connectivity, and more in real-time dashboards.

Overview Dashboard

The Overview Dashboard offers a selection of charts that provide an outlook of the state of your fleet at a glance. To open it, go to Dashboards -> Overview.

Active Devices and Software Versions of your fleet in the Memfault Overview Dashboard Active Devices and Software Versions of your fleet in the Memfault Overview Dashboard.

You can filter the data in the Overview Dashboard by Cohort by selecting one in the drop-down menu in the top-right corner of the page. You can also toggle the scope of the data between Weekly and Daily on the top-right corner of each chart.


Overview Dashboard charts that have a Drilldown badge on them can be clicked on sections of the chart (and sometimes also on labels) to go to a filtered-down list of Devices that make up the data for that interval.

The drilldown feature of some Memfault Overview Dashboard charts Clicking on this section will give you a list of devices that rebooted on November 12th due to a user reset.

The drilldown feature of some Memfault Overview Dashboard charts Clicking on the label will give you a list of devices that rebooted on November 12th for any reason.

Metric Charts

Memfault indexes and aggregates the metrics collected by your devices to provide you not only with insights of how individual devices are performing, but also the emergent behavior of your entire fleet.

You can add charts to visualize these aggregated, fleet-wide metrics by navigating to the sidebar and selecting Dashboards -> Metrics.

Creating a new chart is as simple as clicking Create Chart, selecting your metric of interest, and choosing the desired aggregation type. Note that only metrics configured as Timeseries can be used in Metric Charts.


The list of metrics available in your project is automatically created from the metrics collected by your devices. For that reason, at least one device must upload data for a given metric before a chart for that metric can be created.

MinThe lowest reading received.
MeanArithmetic mean of all readings.
MaxThe highest reading received.
Min/Mean/MaxAll of the above combined into a single chart.
CountNumber of times a (non-ignored) value was received.
SumSum of all readings.

The aggregation of metric data for each day is performed once the day has ended. For example, if data is received on January 1st, the metric charts will show this data on January 2nd.

For more information about adding metrics to your devices:

Issue Charts

Issue Charts show an aggregation of a selection of Issues of your choice and their occurrence (as a count of Traces of each Issue) over time. They share some of the features of Metric Charts, including Comparison Mode.

Issue Charts are created from an Issue search. With them, you can represent the evolution over time of a group of Issues of particular interest to your team.

To create an Issue Chart, go to Dashboards -> Metrics and click Create Chart -> Create Issue Chart. You will be redirected to the Issue Search page. Perform a search to filter down the results. Then, click on Create Chart from Search. A modal will appear where the title and optional description for the chart can be entered. Click Create to save the chart. Finally, you will be redirected back to the Metrics page, where the new chart will be displayed.

Filtering Charts

You can filter the values visible in the charts by selecting from the available filters on the top-right area of the page:

Comparison Mode

To compare the data from different sets of Devices, you can click the Compare button next to the filters section on the top-right area of the page to add a new colorized selection to the filters as well as the charts:

When comparing Metric and Issue Charts, bear in mind that the aggregations Count and Sum lead to absolute values that are a function of the number of Devices reporting the given value. When visualizing absolute values for different sets of Devices, the resulting chart could be misleading as the underlying number of Devices per set may vary.

Chart Normalization

Chart Normalization converts absolute values such as number of incidents, sums, and counts to relative values “per 1,000 devices". This helps understanding real trends when you are comparing these values between populations of different sizes (e.g. comparing devices from large production cohort Default against those from a smaller test cohort Beta) or when the population size changes over time (e.g. new devices being activated continuously or changing Fleet Sampling resolutions).

Toggling Chart Normalization on/off

Toggling Chart Normalization at the top

This is a global setting for all browser tabs that you can toggle in the top right corner of the page, next to the timezone selector. Additionally for convenience, each chart has a small button Icon to Toggle Chart Normalization that allows toggling the feature globally.

Charts that support Normalization

Toggle Chart Normalization at the chart

Chart Normalization is supported on all charts that show absolute values such as number of incidents, sums, or counts. For values that are not affected by the population size (e.g. minimum, maximum, average) Chart Normalization would have no affect and the feature is unavailable.

Population and Normalized Values

The population represents the number of devices that contributed to the value. Usually, this is based on the Active Devices for the corresponding time span.

Tooltip for Normalized Charts

Normalized values are depicted with a suffix 1k in the bottom right corner next to the value (and are pronounced "per 1,000 devices"). Examples:

  • 42₁ₖ means “42 per 1,000 devices”
  • 3K₁ₖ means "3,000 per 1,000 devices".

To calculate the normalized value (NormNorm) from the absolute value (RealReal) and a given population (PopPop) use the following formula:

Norm=Real×1000PopNorm=Real\times \frac{1000}{Pop}

For the values in the screenshot above that translates to:

63M1k=2.2B×100035K63\textup{M}_{1\textup{k}}=2.2\textup{B} \times \frac{1000}{35\textup{K}}

Examples of Chart Normalization

Comparing Values Between Cohorts

Using normalized charts when comparing cohorts

Without Chart Normalization (left): Cohort A (blue) reported more than 10x the duration spent charging compared to cohort B (purple). But in reality, both cohorts have a growing population with Cohort A consisting of 13,000 devices and Cohort B of only 1,000 devices.

Relatively speaking, the time spent charging stayed the same at about 60M₁ₖ.

Population Changes

Using normalized charts for populations with varying size

Two weeks ago, the fleet reported 36K reboots per day steadily growing towards 130K daily reboots. It looks like the devices started to reboot more often. But in reality, the population grew from 10K devices to 36K devices during the same time span (as more people unpacked devices).

Relatively speaking, the number of reboots stayed the same at about 3.6₁ₖ.