Skip to content
This repository has been archived by the owner. It is now read-only.
Go to file

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


WARNING: This project is not maintained anymore and does not work on PHP 7+. If you are looking for a PHP profiler, you can have a look at (the free version has more features and a better UI than the ones provided by this project).



uprofiler is a hierarchical profiler for PHP. It reports function-level call counts and inclusive and exclusive metrics such as wall (elapsed) time, CPU time and memory usage. A function's profile can be broken down by callers or callees. The raw data collection component is implemented in C as a PHP Zend extension called uprofiler. uprofiler has a simple HTML based user interface (written in PHP). The browser based UI for viewing profiler results makes it easy to view results or to share results with peers. A callgraph image view is also supported.

uprofiler reports can often be helpful in understanding the structure of the code being executed. The hierarchical nature of the reports can be used to determine, for example, what chain of calls led to a particular function getting called.

uprofiler supports ability to compare two runs (a.k.a. "diff" reports) or aggregate data from multiple runs. Diff and aggregate reports, much like single run reports, offer "flat" as well as "hierarchical" views of the profile.

uprofiler is a light-weight instrumentation based profiler. During the data collection phase, it keeps track of call counts and inclusive metrics for arcs in the dynamic callgraph of a program. It computes exclusive metrics in the reporting/post processing phase. uprofiler handles recursive functions by detecting cycles in the callgraph at data collection time itself and avoiding the cycles by giving unique depth qualified names for the recursive invocations.

uprofiler's light-weight nature and aggregation capabilities make it well suited for collecting "function-level" performance statistics from production environments.

Originally developed at Facebook, uprofiler was open sourced in Mar, 2009.

uprofiler Overview

uprofiler provides:

  • Flat profile

    Function-level summary information such as number of calls, inclusive/exclusive wall time, memory usage, and CPU time.
  • Hierarchical profile (Parent/Child View)

    For each function, it provides a breakdown of calls and times per parent (caller) & child (callee), such as:

    • what functions call a particular function and how many times?
    • what functions does a particular function call?
    • the total time spent under a function when called from a particular parent.
  • Diff Reports

    You may want to compare data from two uprofiler runs for various reasons-- to figure out what's causing a regression between one version of the code base to another, to evaluate the performance improvement of a code change you are making, and so on.

    A diff report takes two runs as input and provides both flat function-level diff information, and hierarchical information (breakdown of diff by parent/children functions) for each function.

    The "flat" view in the diff report points out the top regressions & improvements.

    Clicking on functions in the "flat" view of the diff report, leads to the "hierarchical" (or parent/child) diff view of a function. We can get a breakdown of the diff by parent/children functions.
  • Callgraph View

    The profile data can also be viewed as a callgraph. The callgraph view highlights the critical path of the program.
  • Memory Profile

    uprofiler's memory profile mode helps track functions that allocate lots of memory.

    It is worth clarifying that that uprofiler doesn't strictly track each allocation/free operation. Rather it uses a more simplistic scheme. It tracks the increase/decrease in the amount of memory allocated to PHP between each function's entry and exit. It also tracks increase/decrease in the amount of peak memory allocated to PHP for each function.

  • uprofiler tracks include, include_once, require and require_once operations as if they were functions. The name of the file being included is used to generate the name for these "fake" functions (see below).


  • Inclusive Time (or Subtree Time): Includes time spent in the function as well as in descendant functions called from a given function.
  • Exclusive Time/Self Time: Measures time spent in the function itself. Does not include time in descendant functions.
  • Wall Time: a.k.a. Elapsed time or wall clock time.
  • CPU Time: CPU time in user space + CPU time in kernel space.

Naming convention for special functions

  • main(): a fictitious function that is at the root of the call graph.

  • load::<filename> and run_init::<filename>:

    uprofiler tracks PHP include/require operations as function calls.

    For example, an include "lib/common.php"; operation will result in two uprofiler function entries:

    • load::lib/common.php: This represents the work done by the interpreter to compile/load the file. [Note: If you are using a PHP opcode cache like APC, then the compile only happens on a cache miss in APC.]
    • run_init::lib/common.php: This represents initialization code executed at the file scope as a result of the include operation.
  • foo@<n>: Implies that this is a recursive invocation of foo(), where <> represents the recursion depth. The recursion may be direct (such as due to foo() --> foo()), or indirect (such as due to foo() --> goo() --> foo()).


True hierarchical profilers keep track of a full call stack at every data gathering point, and are later able to answer questions like: what was the cost of the 3rd invokation of foo()? or what was the cost of bar() when the call stack looked like a()->b()->bar()?

uprofiler keeps track of only 1-level of calling context and is therefore only able to answer questions about a function looking either 1-level up or 1-level down. It turns out that in practice this is sufficient for most use cases.

To make this more concrete, take for instance the following example. Say you have:

1 call from a() --> c()
1 call from b() --> c()
50 calls from c() --> d()

While uprofiler can tell you that d() was called from c() 50 times, it cannot tell you how many of those calls were triggered due to a() vs. b(). [We could speculate that perhaps 25 were due to a() and 25 due to b(), but that's not necessarily true.]

In practice however, this isn't a very big limitation.

Installing the uprofiler Extension

The extension lives in the "extension/" sub-directory.


A windows port hasn't been implemented yet. We have tested uprofiler on Linux/FreeBSD and on Mac OS so far.


uprofiler uses the RDTSC instruction (time stamp counter) to implement a really low overhead timer for elapsed time. So at the moment uprofiler only works on x86 architecture. Also, since RDTSC values may not be synchronized across CPUs, uprofiler binds the program to a single CPU during the profiling period.

uprofiler's RDTSC based timer functionality doesn't work correctly if SpeedStep technology is turned on. This technology is available on some Intel processors. [Note: Mac desktops and laptops typically have SpeedStep turned on by default. To use uprofiler, you'll need to disable SpeedStep.]

The steps below should work for Linux/Unix environments:

$ cd extension/
$ phpize
$ ./configure --with-php-config=path-to-php-config
$ make
$ make install

php.ini file: You can update your php.ini file to automatically load your extension. Add the following to your php.ini file:

; directory used by default implementation of the iuprofilerRuns
; interface (namely, the uprofilerRuns_Default class) for storing
; uprofiler runs.

Profiling using uprofiler

Test generating raw profiler data using a sample test program like:


// foo.php

function bar($x) {
  if ($x > 0) {
    bar($x - 1);

function foo() {
  for ($idx = 0; $idx < 2; $idx++) {
    $x = strlen("abc");

// start profiling

// run program

// stop profiler
$uprofiler_data = uprofiler_disable();

// display raw uprofiler data for the profiler run

Run the above test program:

$ php foo.php

You should get an output like:

    [foo==>bar] => Array
            [ct] => 2         # 2 calls to bar() from foo()
            [wt] => 27        # inclusive time in bar() when called from foo()

    [foo==>strlen] => Array
            [ct] => 2
            [wt] => 2

    [bar==>bar@1] => Array    # a recursive call to bar()
            [ct] => 1
            [wt] => 2

    [main()==>foo] => Array
            [ct] => 1
            [wt] => 74

    [main()==>uprofiler_disable] => Array
            [ct] => 1
            [wt] => 0

    [main()] => Array         # fake symbol representing root
            [ct] => 1
            [wt] => 83



The raw data only contains "inclusive" metrics. For example, the wall time metric in the raw data represents inclusive time in microsecs. Exclusive times for any function are computed during the analysis/reporting phase.


By default only call counts & elapsed time is profiled. You can optionally also profile CPU time and/or memory usage. Replace, uprofiler_enable(); in the above program with, for example uprofiler_enable(UPROFILER_FLAGS_CPU + UPROFILER_FLAGS_MEMORY)

You should now get an output like:

    [foo==>bar] => Array
            [ct] => 2        # number of calls to bar() from foo()
            [wt] => 37       # time in bar() when called from foo()
            [cpu] => 0       # cpu time in bar() when called from foo()
            [mu] => 2208     # change in PHP memory usage in bar() when called from foo()
            [pmu] => 0       # change in PHP peak memory usage in bar() when called from foo()

    [foo==>strlen] => Array
            [ct] => 2
            [wt] => 3
            [cpu] => 0
            [mu] => 624
            [pmu] => 0

    [bar==>bar@1] => Array
            [ct] => 1
            [wt] => 2
            [cpu] => 0
            [mu] => 856
            [pmu] => 0

    [main()==>foo] => Array
            [ct] => 1
            [wt] => 104
            [cpu] => 0
            [mu] => 4168
            [pmu] => 0

    [main()==>uprofiler_disable] => Array
            [ct] => 1
            [wt] => 1
            [cpu] => 0
            [mu] => 344
            [pmu] => 0

    [main()] => Array
            [ct] => 1
            [wt] => 139
            [cpu] => 0
            [mu] => 5936
            [pmu] => 0


Skipping builtin functions during profiling

By default PHP builtin functions (such as strlen) are profiled. If you do not want to profile builtin functions (to either reduce the overhead of profiling further or size of generated raw data), you can use the UPROFILER_FLAGS_NO_BUILTINS flag as in for example uprofiler_enable(UPROFILER_FLAGS_NO_BUILTINS).

Ignoring specific functions during profiling

You can tell uprofiler to ignore a specified list of functions during profiling. This allows you to ignore, for example, functions used for indirect function calls such as call_user_func and call_user_func_array. These intermediate functions unnecessarily complicate the call hierarchy and make the uprofiler reports harder to interpret since they muddle the parent-child relationship for functions called indirectly.

To specify the list of functions to be ignored during profiling use the 2nd (optional) argument to uprofiler_enable. For example:

// elapsed time profiling; ignore call_user_func* during profiling
             array('ignored_functions' =>  array('call_user_func',


// elapsed time + memory profiling; ignore call_user_func* during profiling
              array('ignored_functions' =>  array('call_user_func',

Setting up uprofiler UI

PHP source structure

The uprofiler UI is implemented in PHP. The code resides in two subdirectories, uprofiler_html/ and uprofiler_lib/.

The uprofiler_html directory contains the 3 top-level PHP pages.

  • index.php: For viewing a single run or diff report.
  • callgraph.php: For viewing a callgraph of a uprofiler run as an image.
  • typeahead.php: Used implicitly for the function typeahead form on a uprofiler report.

The uprofiler_lib/ directory contains supporting code for display as well as analysis (computing flat profile info, computing diffs, aggregating data from multiple runs, etc.).

Web server config

You'll need to make sure that the uprofiler_html/ directory is accessible from your web server, and that your web server is setup to serve PHP scripts.

Managing uprofiler Runs

Clients have flexibility in how they save the uprofiler raw data obtained from an uprofiler run. The uprofiler UI layer exposes an interface iuprofilerRuns (see uprofiler_lib/utils/uprofiler_runs.php) that clients can implement. This allows the clients to tell the UI layer how to fetch the data corresponding to a uprofiler run.

The uprofiler UI libaries come with a default file based implementation of the iuprofilerRuns interface, namely "uprofilerRuns_Default" (also in uprofiler_lib/utils/uprofiler_runs.php). This default implementation stores runs in the directory specified by uprofiler.output_dir INI parameter.

A uprofiler run must be uniquely identified by a namespace and a run id.

a) Saving uprofiler data persistently:

Assuming you are using the default implementation uprofilerRuns_Default of the iuprofilerRuns interface, a typical uprofiler run followed by the save step might look something like:

// start profiling

// run program

// stop profiler
$uprofiler_data = uprofiler_disable();

// Saving the uprofiler run
// using the default implementation of iuprofilerRuns.
include_once $uprofiler_ROOT . "/uprofiler_lib/utils/uprofiler_lib.php";
include_once $uprofiler_ROOT . "/uprofiler_lib/utils/uprofiler_runs.php";

$uprofiler_runs = new uprofilerRuns_Default();

// Save the run under a namespace "uprofiler_foo".
// **NOTE**:
// By default save_run() will automatically generate a unique
// run id for you. [You can override that behavior by passing
// a run id (optional arg) to the save_run() method instead.]
$run_id = $uprofiler_runs->save_run($uprofiler_data, "uprofiler_foo");

echo "---------------\n".
     "Assuming you have set up the http based UI for \n".
     "uprofiler at some address, you can view run at \n".

The above should save the run as a file in the directory specified by the uprofiler.output_dir INI parameter. The file's name might be something like 49bafaa3a3f66.uprofiler_foo; the two parts being the run id ("49bafaa3a3f66") and the namespace ("uprofiler_foo"). [If you want to create/assign run ids yourself (such as a database sequence number, or a timestamp), you can explicitly pass in the run id to the save_run method.

b) Using your own implementation of iuprofilerRuns

If you decide you want your uprofiler runs to be stored differently (either in a compressed format, in an alternate place such as DB, etc.) database, you'll need to implement a class that implements the iuprofilerRuns() interface.

You'll also need to modify the 3 main PHP entry pages (index.php, callgraph.php, typeahead.php) in the "uprofiler_html/" directory to use the new class instead of the default class uprofilerRuns_Default. Change this line in the 3 files.

$uprofiler_runs_impl = new uprofilerRuns_Default();

You'll also need to "include" the file that implements your class in the above files.

Accessing runs from UI

a) Viewing a Single Run Report

To view the report for run id say <run_id> and namespace <namespace> use a URL of the form:


For example, http://<uprofiler-ui-address>/index.php?run=49bafaa3a3f66&source=uprofiler _foo

b) Viewing a Diff Report

To view the report for run ids say <run_id1> and <run_id2> in namespace <namespace> use a URL of the form:


c) Aggregate Report

You can also specify a set of run ids for which you want an aggregated view/report.

Say you have three uprofiler runs with ids 1, 2 & 3 in namespace "benchmark". To view an aggregate report of these runs:


Weighted aggregations:

Further suppose that the above three runs correspond to three types of programs p1.php, p2.php and p3.php that typically occur in a mix of 20%, 30%, 50% respectively. To view an aggregate report that corresponds to a weighted average of these runs using:


Notes on using uprofiler in production

Some observations/guidelines. Your mileage may vary:

  • CPU timer (getrusage) on Linux has high overheads. It is also coarse grained (millisec accuracy rather than microsec level) to be useful at function level. Therefore, the skew in reported numbers when using UPROFILER_FLAGS_CPU mode tends to be higher.

    We recommend using elapsed time + memory profiling mode in production. [Note: The additional overhead of memory profiling mode is really low.]

    // elapsed time profiling (default) + memory profiling
  • Profiling a random sample of pages/requests works well in capturing data that is representative of your production workload.

    To profile say 1/10000 of your requests, instrument the beginning of your request processing with something along the lines of:

    if (mt_rand(1, 10000) == 1) {
      $uprofiler_on = true;

    At the end of the request (or in a request shutdown function), you might then do something like:

    if ($uprofiler_on) {
      // stop profiler
      $uprofiler_data = uprofiler_disable();
      // save $uprofiler_data somewhere (say a central DB)

    You can then rollup/aggregate these individual profiles by time (e.g., 5 minutely/hourly/daily basis), page/request type,or other dimensions using uprofiler_aggregate_runs().

Lightweight Sampling Mode

The uprofiler extension also provides a very light weight sampling mode. The sampling interval is 0.1 secs. Samples record the full function call stack. The sampling mode can be useful if an extremely low overhead means of doing performance monitoring and diagnostics is desired.

The relevant functions exposed by the extension for using the sampling mode are uprofiler_sample_enable() and uprofiler_sample_disable().

Additional Features

The uprofiler_lib/utils/uprofiler_lib.php file contains additional library functions that can be used for manipulating/ aggregating uprofiler runs.

For example:

  • uprofiler_aggregate_runs(): can be used to aggregate multiple uprofiler runs into a single run. This can be helpful for building a system-wide "function-level" performance monitoring tool using uprofiler. [For example, you might to roll up uprofiler runs sampled from production periodically to generate hourly, daily, reports.]
  • uprofiler_prune_run(): Aggregating large number of uprofiler runs (especially if they correspond to different types of programs) can result in the callgraph size becoming too large. You can use uprofiler_prune_run function to prune the callgraph data by editing out subtrees that account for a very small portion of the total time.


  • JQuery Javascript: For tooltips and function name typeahead, we make use of JQuery's javascript libraries. JQuery is available under both a MIT and GPL license. The relevant JQuery code, used by uprofiler, is in the uprofiler_html/jquery subdirectory.
  • dot (image generation utility): The callgraph image visualization ([View Callgraph]) feature relies on the presence of Graphviz "dot" utility in your path. "dot" is a utility to draw/generate an image for a directed graph.


The HTML-based navigational interface for browsing profiler results is inspired by that of a similar tool that exists for Oracle's stored procedure language, PL/SQL. But that's where the similarity ends; the internals of the profiler itself are quite different.


[NOT MAINTAINED ANYMORE] Lightweight profiler for PHP (based on facebook/xhprof) -- use Blackfire instead




No releases published


No packages published
You can’t perform that action at this time.