Chromium OS Metrics

The Chromium OS “metrics” package contains utilities for client-side user metric collection.

When Chromium is installed, Chromium will take care of aggregating and uploading the metrics to the UMA server.

When Chromium is not installed (e.g. an embedded/headless build) and the metrics_uploader USE flag is set, metrics_daemon will aggregate and upload the metrics itself.

The Metrics Library: libmetrics

libmetrics implements the basic C and C++ API for metrics collection. All metrics collection is funneled through this library. The easiest and recommended way for a client-side module to collect user metrics is to link libmetrics and use its APIs to send metrics to Chromium for transport to UMA. In order to use the library in a module, you need to do the following:

  • Add a dependence (DEPEND and RDEPEND) on chromeos-base/metrics to the module's ebuild.

  • Link the module with libmetrics (for example, by passing -lmetrics to the module's link command). Both and libmetrics.a are built and installed into the sysroot libdir (e.g. $SYSROOT/usr/lib/). By default -lmetrics links against, which is preferred.

  • Make sure /var/lib/metrics is writable by the daemon. For example, if you are using libmetrics in a daemon, you can achieve this by adding -b /var/lib/metrics,,1 to the minijail0 command that starts the daemon.

  • To access the metrics library API in the module, include the <metrics/metrics_library.h> header file. The file is installed in $SYSROOT/usr/include/ when the metrics library is built and installed.

  • The API is documented in metrics_library.h. Before using the API methods, a MetricsLibrary object needs to be constructed. A quick example:

    MetricsLibrary metrics;
    bool result = metrics.SendToUMA(
    if (!result) {
      LOG(ERROR) << "Failed to send to UMA";

    For more information on the C API, see c_metrics_library.h.

  • On the target platform, shortly after the sample is sent, it should be visible in Chromium through chrome://histograms.

  • The library includes a CumulativeMetrics class which can be used for histograms whose samples represent accumulation of quantities on the same device across a period of time: for instance, how much time was spent playing music on each device and each day of use. Please see the CumulativeMetrics section below.

How metrics are actually sent

libmetrics always writes histogram data to /var/lib/metrics/uma-events using a custom format. flock() is used to avoid races.

Warning: All metrics are written synchronously to disk and may block if another process has the uma-events file locked. Unlike UMAs in Chrome, care must be taken to not to update UMAs in performance-critical sections.
Note: libmetrics does not check consent before writing to /var/lib/metrics/uma-events, leaving that to the sender.

On most boards, the uma-events file is processed by Chromium‘s chromeos::ExternalMetrics class. chromeos::ExternalMetrics periodically flock’s the file, reads all the metrics in it, and truncates the file. The chromeos::ExternalMetrics sends the metrics from the file into Chrome's UMA histogram collection system, after which they are treated like any other Chrome UMA. In particular, Chrome will check consent before uploading the histograms.

However, on the few boards that do not run a Chrome browser, uploading is handled by the UploadService inside metrics_daemon. The UploadService is only instatiated if --uploader is passed to metric_daemon. Similar to Chrome, the UploadService will periodically lock-read-truncate-unlock the uma-events file. If we have user permission to upload stats, the UploadService will then send the metrics after unlocking the file. Here, user permission is controlled by the device policy‘s metrics_enabled field. (If the metrics_enabled field is not set, this falls back to enabling stats if the device is enterprise enrolled; if that isn’t the case, the existence of the “/home/chronos/Consent To Send Stats” file is used.)

The Metrics Client: metrics_client

metrics_client is a command-line utility for sending histogram samples and user actions. It is installed under /usr/bin on the target platform and uses libmetrics. It is typically used for generating metrics from shell scripts.

For usage information and command-line options, run metrics_client on the target platform or look for Usage: in

The Metrics Daemon: metrics_daemon

metrics_daemon is a daemon that runs in the background on the target platform and is intended for passive or ongoing metrics collection, or metrics collection requiring input from other modules. For example, it listens to D-Bus signals related to the user session and screen saver states to determine if the user is actively using the device or not and generates the corresponding data. The metrics daemon also uses libmetrics.

The recommended way to generate metrics data from a module is to link and use libmetrics directly. However, the module could instead send signals to or communicate in some alternative way with the metrics daemon. Then the metrics daemon needs to monitor for the relevant events and take appropriate action -- for example, aggregate data and send the histogram samples.

Cumulative Metrics

The CumulativeMetrics class in libmetrics helps keep track of quantities across boot sessions, so that the quantities can be accumulated over stretches of time (for instance, a day or a week) without concerns about intervening reboots or version changes, and then reported as samples. For this purpose, some persistent state (i.e. partial accumulations) is maintained as files on the device. These “backing files” are typically placed in /var/lib/<daemon-name>/metrics. (The metrics daemon is an exception, with its backing files being in /var/lib/metrics.)

Memory Daemon

The memd subdirectory contains a daemon that collects data at high frequency during episodes of heavy memory pressure.


vmlog_writer writes /var/log/vmlog files. It is a space-delimited format. In order to parse, use the the first line to obtain the list of items, because the number of columns depends on number of CPU cores.

  • time: current time
  • From /proc/vmstat
    • pgmajfault: major faults
    • pgmajfault_f: major faults served from disk
    • pgmajfault_a: major faults served from zram
    • pswpin: number of swap in (pages).
    • pswpout: number of swap out (pages).
  • From /proc/stat
    • cpuusage: all cpu usage ticks from cpu line excluding idle and iowait.
  • gpufreq: GPU frequency; how it's obtained depends on device.
  • From /sys/devices/system/cpu/cpuN/cpufreq/scaling_cur_freq
    • cpufreqN: frequency of core in kHz.

vmlog_writer manages a number of symbolic links, which are not immediately intuitive:

  • vmlog.LATEST is a symbolic link to the current logfile that vmlog_writer is writing to. It is rotated whenever the size gets above 256KiB. The underlying log file is not renamed during this rotation, so the date suffix reflects the time when vmlog_writer started during the most recent run of metrics_daemon (most likely at boot, unless metrics_daemon restarted).
  • vmlog.1.LATEST is a symbolic link to the last log file written to. When vmlog.LATEST would exceed 256KiB, its contents are copied and overwrite the current contents of the file vmlog.1.LATEST points to. Similarly, the date suffix on the target of this link only changes when metrics_daemon starts.
  • vmlog.PREVIOUS is a symbolic link that points to the vmlog contents as of the previous shutdown. That is, when metrics_daemon starts, it renames vmlog.LATEST to vmlog.PREVIOUS and creates a new vmlog.LATEST, which points to the new log file it just created.
  • vmlog.1.PREVIOUS is a symbolic link that points to the vmlog.1.LATEST contents as of the previous shutdown. That is, when metrics_daemon starts, it renames vmlog.1.LATEST to vmlog.1.PREVIOUS.

An illustrative example:

# ls -l /var/log/vmlog
total 660
-rw-r--r--. 1 metrics metrics 262060 Oct 25 12:11 vmlog.1.20231024-213014
-rw-r--r--. 1 metrics metrics 262074 Oct 25 14:15 vmlog.1.20231025-163712
lrwxrwxrwx. 1 metrics metrics     38 Oct 25 14:15 vmlog.1.LATEST -> /var/log/vmlog/vmlog.1.20231025-163712
lrwxrwxrwx. 1 metrics metrics     38 Oct 24 19:12 vmlog.1.PREVIOUS -> /var/log/vmlog/vmlog.1.20231024-213014
-rw-r--r--. 1 metrics metrics  74182 Oct 24 17:29 vmlog.20231024-210100
-rw-r--r--. 1 metrics metrics  66750 Oct 25 12:37 vmlog.20231024-213014
-rw-r--r--. 1 metrics metrics   1622 Oct 25 14:16 vmlog.20231025-163712
lrwxrwxrwx. 1 metrics metrics     21 Oct 25 12:37 vmlog.LATEST -> vmlog.20231025-163712
lrwxrwxrwx. 1 metrics metrics     21 Oct 24 17:30 vmlog.PREVIOUS -> vmlog.20231024-213014

From this set of logs and symlinks, we can understand the following sequence of events:

  1. 2023-10-24 at 21:30:14 UTC: metrics_daemon starts up, and creates vmlog.20231024-213014.
  2. 2023-10-25 at 12:11 local time: The size of vmlog.20231024-213014 would grow too large after the next data write, so vmlog_writer copies it to vmlog.1.20231024-213014, overwriting its contents, and truncates vmlog.20231024-213014.
  3. 2023-10-25 at 16:37:12 UTC: metrics_daemon starts up, and creates vmlog.20231025-163712. It updates vmlog.LATEST to point to this new file and vmlog.PREVIOUS to point to vmlog.20231024-213014, and renames the previous vmlog.1.LATEST to be vmlog.1.PREVIOUS, keeping it pointing to vmlog.1.20231024-213014.
  4. 2023-10-24 at 14:15 local time: The size of vmlog.20231025-163712 would grow too large after the next data write, so vmlog_writer copies it to vmlog.1.20231025-163712, overwriting its contents. At this time, it creates a new vmlog.1.LATEST file, pointing it to vmlog.1.20231025-163712. Finally, it truncates vmlog.20231025-163712.
  5. 2023-10-25 at 14:16 local time: vmlog_writer continues writing to the existing file, vmlog.20231025-163712.

Periodically, chromeos-cleanup-logs will remove old vmlog.<TIMESTAMP> files.

Further Information


for more information on choosing name, type, and other parameters of new histograms. The rest of this README is a super-short synopsis of that document, and with some luck it won't be too out of date.

Synopsis: Histogram Naming Convention

Use TrackerArea.MetricName. For example:

  • Platform.DailyUseTime
  • Network.TimeToDrop

Synopsis: Server Side

If the histogram data is visible in chrome://histograms, it will be sent by an official Chromium build to UMA, assuming the user has opted into metrics collection. To make the histogram visible on “chromedashboard”, the histogram description XML file needs to be updated (steps 2 and 3 after following the “Details on how to add your own histograms” link under the Histograms tab). Include the string “Chrome OS” in the histogram description so that it's easier to distinguish Chromium OS specific metrics from general Chromium histograms.

The UMA server logs and keeps the collected field data even if the metric's name is not added to the histogram XML. However, the dashboard histogram for that metric will show field data as of the histogram XML update date; it will not include data for older dates. If past data needs to be displayed, manual server-side intervention is required. In other words, one should assume that field data collection starts only after the histogram XML has been updated.

Synopsis: FAQ

What should my histogram's |min| and |max| values be set at?

You should set the values to a range that covers the vast majority of samples that would appear in the field. Values below |min| are collected in the “underflow bucket” and values above |max| end up in the “overflow bucket”. The reported mean of the data is precise, i.e. it does not depend on range and number of buckets.

How many buckets should I use in my histogram?

You should allocate as many buckets as necessary to perform proper analysis on the collected data. Most data is fairly noisy: 50 buckets are plenty, 100 buckets are probably overkill. Also consider that the memory allocated in Chromium for each histogram is proportional to the number of buckets, so don't waste it.

When should I use an enumeration (linear) histogram vs. a regular (exponential) histogram?

Enumeration histograms should really be used only for sampling enumerated events and, in some cases, percentages. Normally, you should use a regular histogram with exponential bucket layout that provides higher resolution at the low end of the range and lower resolution at the high end. Regular histograms are generally used for collecting performance data (e.g., timing, memory usage, power) as well as aggregated event counts.