tree: 23eb28fdbf533b11d8b28126947a18e71f222c7e [path history] [tgz]
  4. enums.xml
  11. histograms.xml

Histogram Guidelines

This document gives the best practices on how to use histograms in code and how to document the histograms for the dashboards. There are three general types of histograms: enumerated histograms, count histograms (for arbitrary numbers), and sparse histograms (for anything when the precision is important over a wide range and/or the range is not possible to specify a priori).

Emitting to Histograms

Directly Measure What You Want

Measure exactly what you want, whether that‘s time used for a function call, number of bytes transmitted to fetch a page, number of items in a list, etc. Do not assume you can calculate what you want from other histograms. Most of the ways to do this are incorrect. For example, if you want to know the time taken by a function that all it does is call two other functions, both of which are have histogram logging, you might think you can simply add up those the histograms for those functions to get the total time. This is wrong. If we knew which emissions came from which calls, we could pair them up and derive the total time for the function. However, histograms entries do not come with timestamps--we pair them up appropriately. If you simply add up the two histograms to get the total histogram, you’re implicitly assuming those values are independent, which may not be the case. Directly measure what you care about; don't try to derive it from other data.


In general, the histogram code is highly optimized. Do not be concerned about the processing cost of emitting to a histogram (unless you're using sparse histograms).

Enum Histograms

Enumerated histogram are most appropriate when you have a list of connected / related states that should be analyzed jointly. For example, the set of actions that can be done on the New Tab Page (use the omnibox, click a most visited tile, click a bookmark, etc.) would make a good enumerated histogram. If the total count of your histogram (i.e. the sum across all buckets) is something meaningful--as it is in this example--that is generally a good sign. However, the total count does not have to be meaningful for an enum histogram to still be the right choice.

You may append to your enum if the possible states/actions grows. However, you should not reorder, renumber, or otherwise reuse existing values. As such, please put this warning by the enum definition:

// These values are written to logs.  New enum values can be added, but existing
// enums must never be renumbered or deleted and reused.

Also, please explicitly set enum values = 0, = 1, = 2, etc. This makes clearer that the actual values are important. In addition, it helps confirm the values align between the enum definition and histograms.xml. If a “count” value is included it should not include an explicit value.

If your enum histogram has a catch-all / miscellaneous bucket, put that bucket first (= 0). This will make the bucket easy to find on the dashboard if later you add additional buckets to your histogram.

Count Histograms

histogram_macros.h provides macros for some common count types such as memory or elapsed time, in addition to general count macros. These have reasonable default values; you will not often need to choose number of buckets or histogram min. You still will need to choose the histogram max (use the advice below).

If none of the default macros work well for you, please thoughtfully choose a min, max, and bucket count for your histogram using the advice below.

Count Histograms: Choosing Min and Max

For histogram max, choose a value so that very few emission to the histogram will exceed the max. If many emissions hit the max, it can be difficult to compute statistics such as average. One rule of thumb is at most 1% of samples should be in the overflow bucket. This allows analysis of the 99th percentile. Err on the side of too large a range versus too short a range. (Remember that if you choose poorly, you'll have to wait for another release cycle to fix it.)

For histogram min, if you care about all possible values (zero and above), choose a min of 1. (All histograms have an underflow bucket; emitted zeros will go there. That's why a min of 1 is appropriate.) Otherwise, choose the min appropriate for your particular situation.

Count Histograms: Choosing Number of Buckets

Choose the smallest number of buckets that will get you the granularity you need. By default count histograms bucket sizes scale exponentially so you can get fine granularity when the numbers are small yet still reasonable resolution for larger numbers. The macros default to 50 buckets (or 100 buckets for histograms with wide ranges) which is appropriate for most purposes. Because histograms pre-allocate all the buckets, the number of buckets selected directly dictate how much memory is used. Do not exceed 100 buckets without good reason (and consider whether [sparse histograms](#When-To-Use-Sparse- Histograms) might work better for you in that case--they do not pre- allocate their buckets).

Local Histograms

Histograms can be added via Local macros. These will still record locally, but will not be uploaded to UMA and will therefore not be available for analysis. This can be useful for metrics only needed for local debugging. We don't recommend using local histograms outside of that scenario.

Multidimensional Histograms

It is common to be interested in logging multidimensional data - where multiple pieces of information need to be logged together. For example, a developer may be interested in the counts of features X and Y based on whether a user is in state A or B. In this case, they want to know the count of X under state A, as well as the other three permutations.

There is no general purpose solution for this type of analysis. We suggest using the workaround of using an enum of length MxN, where you log each unique pair {state, feature} as a separate entry in the same enum. If this causes a large explosion in data (i.e. >100 enum entries), a sparse histogram may be appropriate. If you are unsure of the best way to proceed, please contact someone from the OWNERS file.


Test your histograms using chrome://histograms. Make sure they're being emitted to when you expect and not emitted to at other times. Also check that the values emitted to are correct. Finally, for count histograms, make sure that buckets capture enough precision for your needs over the range.

In addition to testing interactively, you can have unit tests examine the values emitted to histograms. See histogram_tester.h for details.

Revising Histograms

When changing the semantics of a histogram (when it's emitted, what buckets mean, etc.), make it into a new histogram with a new name. Otherwise the “Everything” view on the dashboard will be mixing two different interpretations of the data and make no sense.

Deleting Histograms

Please delete the code that emits to histograms that are no longer needed. Histograms take up memory. Cleaning up histograms that you no longer care about is good! But see the note below on Deleting Histogram Entries.

Documenting Histograms

Add Histogram and Documentation in the Same Changelist

If possible, please add the histograms.xml description in the same changelist in which you add the histogram-emitting code. This has several benefits. One, it sometimes happens that the histograms.xml reviewer has questions or concerns about the histogram description that reveal problems with interpretation of the data and call for a different recording strategy. Two, it allows the histogram reviewer to easily review the emission code to see if it comports with these best practices, and to look for other errors.

Understandable to Everyone

Histogram descriptions should be roughly understandable to someone not familiar with your feature. Please add a sentence or two of background if necessary.

It is good practice to note caveats associated with your histogram in this section, such as which platforms are supported (if the set of supported platforms is surprising). E.g., a desktop feature that happens not to be logged on Mac.

State When It Is Recorded

Histogram descriptions should clearly state when the histogram is emitted (profile open? network request received? etc.).


Histograms need to be owned by a person or set of people. These indicate who the current experts on this metric are. Being the owner means you are responsible for answering questions about the metric, handling the maintenance if there are functional changes, and deprecating the metric if it outlives its usefulness. The owners should be added in the original histogram description. If you are using a metric heavily and understand it intimately, feel free to add yourself as an owner. email addresses are preferred.

Deleting Histogram Entries

Do not delete histograms from histograms.xml. Instead, mark unused histograms as obsolete, annotating them with the associated date or milestone in the obsolete tag entry. If your histogram is being replaced by a new version, we suggest noting that in the previous histogram's description.

Deleting histogram entries would be bad if someone to accidentally reused your old histogram name and thereby corrupts new data with whatever old data is still coming in. It‘s also useful to keep obsolete histogram descriptions in histograms.xml -- that way, if someone is searching for a histogram to answer a particular question, they can learn if there was a histogram at some point that did so even if it isn’t active now.

Histogram Suffixes

It is sometimes useful to record several closely related metrics, which measure the same type of data, with some minor variations. It is often useful to use one or more <histogram_suffixes> elements to save on redundant verbosity in histograms.xml. If a root <histogram> or a <suffix> element is used only to construct a partial name, to be completed by further suffixes, annotate the element with the attribute base="true". This instructs tools not to treat the partial base name as a distinct histogram. Note that suffixes can be applied recursively.

When To Use Sparse Histograms

Sparse histograms are well suited for recording counts of exact sample values that are sparsely distributed over a large range.

The implementation uses a lock and a map, whereas other histogram types use a vector and no lock. It is thus more costly to add values to, and each value stored has more overhead, compared to the other histogram types. However it may be more efficient in memory if the total number of sample values is small compared to the range of their values.

For more information, see sparse_histograms.h.