Flame Graph Reporter#

The flame graph reporter generates an HTML file containing a flame graph representation of the allocations contributing to the tracked process’s peak memory usage.

Flame graphs are a way to visualize how your program is spending its time. A few important things about flame graphs:

  • The flame graph displays the superposition of all stack traces that lead to memory allocations at a given time (normally the time when the total amount of allocated memory was highest).

  • A flame graph can’t tell you how many times a function was called, only how much memory that function allocated.

  • A stack trace is represented as a column of boxes, where each box represents a function call.

  • The x-axis does not show the passage of time, so the left-to-right ordering has no special meaning: every level just shows the collection of functions that were called by the nodes immediately under them.

  • The y-axis shows the stack depth, ordered from root at the bottom to leaf at the top. The top box shows the function that made a memory allocation and everything beneath that is its ancestry. The function beneath a function is its parent.

  • The width of each function box represents how much memory was allocated by that function or its children. Functions with wider boxes allocated more bytes of memory than those with narrower boxes, in proportion to their width.

Interpreting flame graphs#

Flame graphs can be interpreted as follows:

  • The nodes at the top of the flame graph represent functions that allocated memory.

  • For quickly identifying the functions that allocated more memory directly, look for large plateaus along the top edge, as these show a single stack trace was responsible for a large chunk of the total memory of the snapshot that the flame graph represents.

  • Reading the flame graph from top down shows the ancestry relationship: Every function is called by its parent, which is shown directly below it; the parent was called by its parent shown below it, and so on. A quick scan downward from a function identifies how it was called.

  • Reading the flame graph from bottom up shows code flow and the bigger picture. A function calls all child functions shown above it, which, in turn, call functions shown above them. Reading bottom up also shows the big picture of code flow before various forks split execution into smaller towers.

  • You can directly compare the width of function boxes as wider boxes mean more memory was allocated by the given node and those are the most important to understand first.

  • Major forks in the flame graph (when a node splits into several ones in the next level) can be useful to study: these nodes can indicate a logical grouping of code, where a function processes work in stages, each with its own function. It can also be caused by a conditional statement, which chooses which function to call.

  • If the application is multi-threaded, the stacks of all the threads that contribute to the memory peak will appear commingled in the flame graph.

Simple example#

def a(n):
    return b(n)

def b(n):
    return [c(n), d(n)]

def c(n):
    return "a" * n

def d(n):
    return "a" * n

a(100000)

This code allocates memory from the system allocator in just 2 places: c(), and d(). This is how the flame graph looks:

_images/simple_example.png

Here you can see that a() called b() and that b() called c() and d(), which in turn did some allocations. As the boxes of c() and d() are of the same width, you know that both allocated the same amount of memory.

A more complete example#

def a(n):
    return [b(n), h(n)]

def b(n):
    return c(n)

def c(n):
    missing(n)
    return d(n)

def missing(n):
    return "a" * n

def d(n):
    return [e(n), f(n), "a" * (n // 2)]

def e(n):
    return "a" * n

def f(n):
    return g(n)

def g(n):
    return "a" * n * 2

def h(n):
    return i(n)

def i(n):
    return "a" * n

a(100000)

This code allocates memory from the system allocator in 5 places: e(), d(), g(), i() and missing(). The associated flame graph looks like this:

_images/complex_example.png

The top edge shows that function g() allocates the most memory, d() is wider, but its exposed top edge is smaller, which means that d() itself allocated less memory than the one allocated by the functions called by it. Functions including b() and c() do not allocate memory themselves directly; rather, the functions they called did the allocating.

Functions beneath g() show its ancestry: g() was called by f(), which was called by d(), and so on.

Visually comparing the widths of functions b() and h() shows that the b() code path allocated about four times more than h(). The actual functions that did the allocations in each case were their children.

A major fork in the code paths is visible where a() calls b() and h(). Understanding why the code does this may be a major clue to its logical organization. This may be the result of a conditional (if conditional, call b(), else call h()) or a logical grouping of stages (where a() is processed in two parts: b() and h()). In our case we know is the second case, as a() is creating a list with the result of b() and h().

If you look carefully you can notice that missing() allocates memory, but it does not appear in the flame graph. This is because at the time the largest memory peak was reached (when a() returned) the memory allocated by missing() didn’t contribute at all to the total amount of memory. This is because the memory allocated by missing() is deallocated as soon as the call ends.

With this information, we know that if you need to choose a place to start looking for optimizations, you should start looking at g(), then e() and then i() (in that order) as these are the places that allocated the most memory when the program reached its maximum. Of course, the actual optimization may be done in the callers of these functions, but you have a way to start understanding where to optimize.

Non-relevant frame hiding#

The flame graph exposes a button to show or hide frames which might be distracting when interpreting the results, either because they were injected by Memray or because they are low-level implementation details of CPython. By default, frames tagged as non-relevant are hidden. You can reveal them with the “Show Non-Relevant Frames” checkbox:

_images/non_relevant_checkbox.png

Note that allocations in these frames will still be accounted for in parent frames, even if they’re hidden.

Memory Leaks View#

When generating flame graphs, the --leaks option can be specified to get information for memory that was leaked during the tracking (i.e. allocated after tracking started and not deallocated by the time tracking ended).

Important

The Python allocator doesn’t necessarily release memory to the system when Python objects are deallocated and these can still appear as “leaks”. When you use the --leaks option, you should usually also run your application with the PYTHONMALLOC=malloc environment variable set. See our documentation on python allocators for details.

Split-Threads View#

When generating flame graphs, the --split-threads option can be specified to get thread-specific filtering on the flame graph.

If --split-threads is not specified, thread information is not displayed on the flame graph. Instead, allocations occurring at the same source location across different threads are grouped together. However, if --split-threads is used, the allocation patterns of individual threads can be analyzed.

When opening the report, the same merged thread view is presented, but a new “Filter Thread” dropdown will be shown. This can be used to select a specific thread to display a flame graph for that one thread:

_images/filter_thread_dropdown.png

To go back to the merged view, the “Reset” entry can be used in the dropdown menu.

Note that the root node (displayed as memray) is always present and is displayed as thread 0.

Conclusion#

Flame graphs are effective visualization tools for a memory snapshot of a program. They give an insightful visual map for the execution of Python code and allow navigating areas of interest, letting you identify where to start looking for improvements. Unlike other code-path visualizations such as acyclic graphs, flame graphs convey information intuitively using line lengths and can handle large-scale profiles, while usually remaining readable on one screen.

Basic Usage#

The general form of the flamegraph subcommand is:

memray flamegraph [options] <results>

The only argument the flamegraph subcommand requires is the capture file previously generated using the run subcommand.

The output file will be named memray-flamegraph-<input file name>.html unless the -o argument was used to override the default name.

Reference#

usage: memray flamegraph [-h] [-o OUTPUT] [-f] [--leaks] [--split-threads] results

Positional Arguments#

results

Results of the tracker run

options#

-o, --output

Output file name

-f, --force

If the output file already exists, overwrite it

Default: False

--leaks

Enables Memory Leaks View, where memory that was not deallocated is displayed, instead of peak memory usage.

--split-threads

Enables Split-Threads View, where each thread can be displayed separately. Allocations on the same source line across different threads are not merged, if this flag is passed.

Please submit feedback, ideas, and bug reports by filing a new issue at https://github.com/bloomberg/memray/issues