comparison mercurial/statprof.py @ 30949:cb440e7af05d

statprof: allow rendering in the Chrome trace viewer format

30948:be3a4fde38eb

    ByMethod = 1
    AboutMethod = 2
    Hotpath = 3
    FlameGraph = 4
    Json = 5

def display(fp=None, format=3, data=None, **kwargs):
    '''Print statistics, either to stdout or the given file object.'''
    data = data or state

        display_hotpath(data, fp, **kwargs)
    elif format == DisplayFormats.FlameGraph:
        write_to_flame(data, fp, **kwargs)
    elif format == DisplayFormats.Json:
        write_to_json(data, fp)
    else:
        raise Exception("Invalid display format")

    if format != DisplayFormats.Json:
        print('---', file=fp)
        print('Sample count: %d' % len(data.samples), file=fp)
        print('Total time: %f seconds' % data.accumulated_time, file=fp)

def display_by_line(data, fp):

            stack.append((frame.path, frame.lineno, frame.function))

        samples.append((sample.time, stack))

    print(json.dumps(samples), file=fp)

def printusage():
    print("""
The statprof command line allows you to inspect the last profile's results in
the following forms:

30949:cb440e7af05d

    ByMethod = 1
    AboutMethod = 2
    Hotpath = 3
    FlameGraph = 4
    Json = 5
    Chrome = 6

def display(fp=None, format=3, data=None, **kwargs):
    '''Print statistics, either to stdout or the given file object.'''
    data = data or state

        display_hotpath(data, fp, **kwargs)
    elif format == DisplayFormats.FlameGraph:
        write_to_flame(data, fp, **kwargs)
    elif format == DisplayFormats.Json:
        write_to_json(data, fp)
    elif format == DisplayFormats.Chrome:
        write_to_chrome(data, fp, **kwargs)
    else:
        raise Exception("Invalid display format")

    if format not in (DisplayFormats.Json, DisplayFormats.Chrome):
        print('---', file=fp)
        print('Sample count: %d' % len(data.samples), file=fp)
        print('Total time: %f seconds' % data.accumulated_time, file=fp)

def display_by_line(data, fp):

            stack.append((frame.path, frame.lineno, frame.function))

        samples.append((sample.time, stack))

    print(json.dumps(samples), file=fp)

def write_to_chrome(data, fp, minthreshold=0.005, maxthreshold=0.999):
    samples = []
    laststack = collections.deque()
    lastseen = collections.deque()

    # The Chrome tracing format allows us to use a compact stack
    # representation to save space. It's fiddly but worth it.
    # We maintain a bijection between stack and ID.
    stack2id = {}
    id2stack = [] # will eventually be rendered

    def stackid(stack):
        if not stack:
            return
        if stack in stack2id:
            return stack2id[stack]
        parent = stackid(stack[1:])
        myid = len(stack2id)
        stack2id[stack] = myid
        id2stack.append(dict(category=stack[0][0], name='%s %s' % stack[0]))
        if parent is not None:
            id2stack[-1].update(parent=parent)
        return myid

    def endswith(a, b):
        return list(a)[-len(b):] == list(b)

    # The sampling profiler can sample multiple times without
    # advancing the clock, potentially causing the Chrome trace viewer
    # to render single-pixel columns that we cannot zoom in on.  We
    # work around this by pretending that zero-duration samples are a
    # millisecond in length.

    clamp = 0.001

    # We provide knobs that by default attempt to filter out stack
    # frames that are too noisy:
    #
    # * A few take almost all execution time. These are usually boring
    #   setup functions, giving a stack that is deep but uninformative.
    #
    # * Numerous samples take almost no time, but introduce lots of
    #   noisy, oft-deep "spines" into a rendered profile.

    blacklist = set()
    totaltime = data.samples[-1].time - data.samples[0].time
    minthreshold = totaltime * minthreshold
    maxthreshold = max(totaltime * maxthreshold, clamp)

    def poplast():
        oldsid = stackid(tuple(laststack))
        oldcat, oldfunc = laststack.popleft()
        oldtime, oldidx = lastseen.popleft()
        duration = sample.time - oldtime
        if minthreshold <= duration <= maxthreshold:
            # ensure no zero-duration events
            sampletime = max(oldtime + clamp, sample.time)
            samples.append(dict(ph='E', name=oldfunc, cat=oldcat, sf=oldsid,
                                ts=sampletime*1e6, pid=0))
        else:
            blacklist.add(oldidx)

    # Much fiddling to synthesize correctly(ish) nested begin/end
    # events given only stack snapshots.

    for sample in data.samples:
        tos = sample.stack[0]
        name = tos.function
        path = simplifypath(tos.path)
        category = '%s:%d' % (path, tos.lineno)
        stack = tuple((('%s:%d' % (simplifypath(frame.path), frame.lineno),
                        frame.function) for frame in sample.stack))
        qstack = collections.deque(stack)
        if laststack == qstack:
            continue
        while laststack and qstack and laststack[-1] == qstack[-1]:
            laststack.pop()
            qstack.pop()
        while laststack:
            poplast()
        for f in reversed(qstack):
            lastseen.appendleft((sample.time, len(samples)))
            laststack.appendleft(f)
            path, name = f
            sid = stackid(tuple(laststack))
            samples.append(dict(ph='B', name=name, cat=path, ts=sample.time*1e6,
                                sf=sid, pid=0))
        laststack = collections.deque(stack)
    while laststack:
        poplast()
    events = [s[1] for s in enumerate(samples) if s[0] not in blacklist]
    frames = collections.OrderedDict((str(k), v)
                                     for (k,v) in enumerate(id2stack))
    json.dump(dict(traceEvents=events, stackFrames=frames), fp, indent=1)
    fp.write('\n')

def printusage():
    print("""
The statprof command line allows you to inspect the last profile's results in
the following forms: