"""CPStats, a package for collecting and reporting on program statistics. Overview ======== Statistics about program operation are an invaluable monitoring and debugging tool. Unfortunately, the gathering and reporting of these critical values is usually ad-hoc. This package aims to add a centralized place for gathering statistical performance data, a structure for recording that data which provides for extrapolation of that data into more useful information, and a method of serving that data to both human investigators and monitoring software. Let's examine each of those in more detail. Data Gathering -------------- Just as Python's `logging` module provides a common importable for gathering and sending messages, performance statistics would benefit from a similar common mechanism, and one that does *not* require each package which wishes to collect stats to import a third-party module. Therefore, we choose to re-use the `logging` module by adding a `statistics` object to it. That `logging.statistics` object is a nested dict. It is not a custom class, because that would: 1. require libraries and applications to import a third-party module in order to participate 2. inhibit innovation in extrapolation approaches and in reporting tools, and 3. be slow. There are, however, some specifications regarding the structure of the dict.:: { +----"SQLAlchemy": { | "Inserts": 4389745, | "Inserts per Second": | lambda s: s["Inserts"] / (time() - s["Start"]), | C +---"Table Statistics": { | o | "widgets": {-----------+ N | l | "Rows": 1.3M, | Record a | l | "Inserts": 400, | m | e | },---------------------+ e | c | "froobles": { s | t | "Rows": 7845, p | i | "Inserts": 0, a | o | }, c | n +---}, e | "Slow Queries": | [{"Query": "SELECT * FROM widgets;", | "Processing Time": 47.840923343, | }, | ], +----}, } The `logging.statistics` dict has four levels. The topmost level is nothing more than a set of names to introduce modularity, usually along the lines of package names. If the SQLAlchemy project wanted to participate, for example, it might populate the item `logging.statistics['SQLAlchemy']`, whose value would be a second-layer dict we call a "namespace". Namespaces help multiple packages to avoid collisions over key names, and make reports easier to read, to boot. The maintainers of SQLAlchemy should feel free to use more than one namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case or other syntax constraints on the namespace names; they should be chosen to be maximally readable by humans (neither too short nor too long). Each namespace, then, is a dict of named statistical values, such as 'Requests/sec' or 'Uptime'. You should choose names which will look good on a report: spaces and capitalization are just fine. In addition to scalars, values in a namespace MAY be a (third-layer) dict, or a list, called a "collection". For example, the CherryPy :class:`StatsTool` keeps track of what each request is doing (or has most recently done) in a 'Requests' collection, where each key is a thread ID; each value in the subdict MUST be a fourth dict (whew!) of statistical data about each thread. We call each subdict in the collection a "record". Similarly, the :class:`StatsTool` also keeps a list of slow queries, where each record contains data about each slow query, in order. Values in a namespace or record may also be functions, which brings us to: Extrapolation ------------- The collection of statistical data needs to be fast, as close to unnoticeable as possible to the host program. That requires us to minimize I/O, for example, but in Python it also means we need to minimize function calls. So when you are designing your namespace and record values, try to insert the most basic scalar values you already have on hand. When it comes time to report on the gathered data, however, we usually have much more freedom in what we can calculate. Therefore, whenever reporting tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents of `logging.statistics` for reporting, they first call `extrapolate_statistics` (passing the whole `statistics` dict as the only argument). This makes a deep copy of the statistics dict so that the reporting tool can both iterate over it and even change it without harming the original. But it also expands any functions in the dict by calling them. For example, you might have a 'Current Time' entry in the namespace with the value "lambda scope: time.time()". The "scope" parameter is the current namespace dict (or record, if we're currently expanding one of those instead), allowing you access to existing static entries. If you're truly evil, you can even modify more than one entry at a time. However, don't try to calculate an entry and then use its value in further extrapolations; the order in which the functions are called is not guaranteed. This can lead to a certain amount of duplicated work (or a redesign of your schema), but that's better than complicating the spec. After the whole thing has been extrapolated, it's time for: Reporting --------- The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates it all, and then transforms it to HTML for easy viewing. Each namespace gets its own header and attribute table, plus an extra table for each collection. This is NOT part of the statistics specification; other tools can format how they like. You can control which columns are output and how they are formatted by updating StatsPage.formatting, which is a dict that mirrors the keys and nesting of `logging.statistics`. The difference is that, instead of data values, it has formatting values. Use None for a given key to indicate to the StatsPage that a given column should not be output. Use a string with formatting (such as '%.3f') to interpolate the value(s), or use a callable (such as lambda v: v.isoformat()) for more advanced formatting. Any entry which is not mentioned in the formatting dict is output unchanged. Monitoring ---------- Although the HTML output takes pains to assign unique id's to each with statistical data, you're probably better off fetching /cpstats/data, which outputs the whole (extrapolated) `logging.statistics` dict in JSON format. That is probably easier to parse, and doesn't have any formatting controls, so you get the "original" data in a consistently-serialized format. Note: there's no treatment yet for datetime objects. Try time.time() instead for now if you can. Nagios will probably thank you. Turning Collection Off ---------------------- It is recommended each namespace have an "Enabled" item which, if False, stops collection (but not reporting) of statistical data. Applications SHOULD provide controls to pause and resume collection by setting these entries to False or True, if present. Usage ===== To collect statistics on CherryPy applications:: from cherrypy.lib import cpstats appconfig['/']['tools.cpstats.on'] = True To collect statistics on your own code:: import logging # Initialize the repository if not hasattr(logging, 'statistics'): logging.statistics = {} # Initialize my namespace mystats = logging.statistics.setdefault('My Stuff', {}) # Initialize my namespace's scalars and collections mystats.update({ 'Enabled': True, 'Start Time': time.time(), 'Important Events': 0, 'Events/Second': lambda s: ( (s['Important Events'] / (time.time() - s['Start Time']))), }) ... for event in events: ... # Collect stats if mystats.get('Enabled', False): mystats['Important Events'] += 1 To report statistics:: root.cpstats = cpstats.StatsPage() To format statistics reports:: See 'Reporting', above. """ import logging import os import sys import threading import time import cherrypy from cherrypy._cpcompat import json # ------------------------------- Statistics -------------------------------- # if not hasattr(logging, 'statistics'): logging.statistics = {} def extrapolate_statistics(scope): """Return an extrapolated copy of the given scope.""" c = {} for k, v in list(scope.items()): if isinstance(v, dict): v = extrapolate_statistics(v) elif isinstance(v, (list, tuple)): v = [extrapolate_statistics(record) for record in v] elif hasattr(v, '__call__'): v = v(scope) c[k] = v return c # -------------------- CherryPy Applications Statistics --------------------- # appstats = logging.statistics.setdefault('CherryPy Applications', {}) appstats.update({ 'Enabled': True, 'Bytes Read/Request': lambda s: ( s['Total Requests'] and (s['Total Bytes Read'] / float(s['Total Requests'])) or 0.0 ), 'Bytes Read/Second': lambda s: s['Total Bytes Read'] / s['Uptime'](s), 'Bytes Written/Request': lambda s: ( s['Total Requests'] and (s['Total Bytes Written'] / float(s['Total Requests'])) or 0.0 ), 'Bytes Written/Second': lambda s: ( s['Total Bytes Written'] / s['Uptime'](s) ), 'Current Time': lambda s: time.time(), 'Current Requests': 0, 'Requests/Second': lambda s: float(s['Total Requests']) / s['Uptime'](s), 'Server Version': cherrypy.__version__, 'Start Time': time.time(), 'Total Bytes Read': 0, 'Total Bytes Written': 0, 'Total Requests': 0, 'Total Time': 0, 'Uptime': lambda s: time.time() - s['Start Time'], 'Requests': {}, }) proc_time = lambda s: time.time() - s['Start Time'] class ByteCountWrapper(object): """Wraps a file-like object, counting the number of bytes read.""" def __init__(self, rfile): self.rfile = rfile self.bytes_read = 0 def read(self, size=-1): data = self.rfile.read(size) self.bytes_read += len(data) return data def readline(self, size=-1): data = self.rfile.readline(size) self.bytes_read += len(data) return data def readlines(self, sizehint=0): # Shamelessly stolen from StringIO total = 0 lines = [] line = self.readline() while line: lines.append(line) total += len(line) if 0 < sizehint <= total: break line = self.readline() return lines def close(self): self.rfile.close() def __iter__(self): return self def next(self): data = self.rfile.next() self.bytes_read += len(data) return data average_uriset_time = lambda s: s['Count'] and (s['Sum'] / s['Count']) or 0 def _get_threading_ident(): if sys.version_info >= (3, 3): return threading.get_ident() return threading._get_ident() class StatsTool(cherrypy.Tool): """Record various information about the current request.""" def __init__(self): cherrypy.Tool.__init__(self, 'on_end_request', self.record_stop) def _setup(self): """Hook this tool into cherrypy.request. The standard CherryPy request object will automatically call this method when the tool is "turned on" in config. """ if appstats.get('Enabled', False): cherrypy.Tool._setup(self) self.record_start() def record_start(self): """Record the beginning of a request.""" request = cherrypy.serving.request if not hasattr(request.rfile, 'bytes_read'): request.rfile = ByteCountWrapper(request.rfile) request.body.fp = request.rfile r = request.remote appstats['Current Requests'] += 1 appstats['Total Requests'] += 1 appstats['Requests'][_get_threading_ident()] = { 'Bytes Read': None, 'Bytes Written': None, # Use a lambda so the ip gets updated by tools.proxy later 'Client': lambda s: '%s:%s' % (r.ip, r.port), 'End Time': None, 'Processing Time': proc_time, 'Request-Line': request.request_line, 'Response Status': None, 'Start Time': time.time(), } def record_stop( self, uriset=None, slow_queries=1.0, slow_queries_count=100, debug=False, **kwargs): """Record the end of a request.""" resp = cherrypy.serving.response w = appstats['Requests'][_get_threading_ident()] r = cherrypy.request.rfile.bytes_read w['Bytes Read'] = r appstats['Total Bytes Read'] += r if resp.stream: w['Bytes Written'] = 'chunked' else: cl = int(resp.headers.get('Content-Length', 0)) w['Bytes Written'] = cl appstats['Total Bytes Written'] += cl w['Response Status'] = getattr( resp, 'output_status', None) or resp.status w['End Time'] = time.time() p = w['End Time'] - w['Start Time'] w['Processing Time'] = p appstats['Total Time'] += p appstats['Current Requests'] -= 1 if debug: cherrypy.log('Stats recorded: %s' % repr(w), 'TOOLS.CPSTATS') if uriset: rs = appstats.setdefault('URI Set Tracking', {}) r = rs.setdefault(uriset, { 'Min': None, 'Max': None, 'Count': 0, 'Sum': 0, 'Avg': average_uriset_time}) if r['Min'] is None or p < r['Min']: r['Min'] = p if r['Max'] is None or p > r['Max']: r['Max'] = p r['Count'] += 1 r['Sum'] += p if slow_queries and p > slow_queries: sq = appstats.setdefault('Slow Queries', []) sq.append(w.copy()) if len(sq) > slow_queries_count: sq.pop(0) cherrypy.tools.cpstats = StatsTool() # ---------------------- CherryPy Statistics Reporting ---------------------- # thisdir = os.path.abspath(os.path.dirname(__file__)) missing = object() locale_date = lambda v: time.strftime('%c', time.gmtime(v)) iso_format = lambda v: time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(v)) def pause_resume(ns): def _pause_resume(enabled): pause_disabled = '' resume_disabled = '' if enabled: resume_disabled = 'disabled="disabled" ' else: pause_disabled = 'disabled="disabled" ' return """
""" % (ns, pause_disabled, ns, resume_disabled) return _pause_resume class StatsPage(object): formatting = { 'CherryPy Applications': { 'Enabled': pause_resume('CherryPy Applications'), 'Bytes Read/Request': '%.3f', 'Bytes Read/Second': '%.3f', 'Bytes Written/Request': '%.3f', 'Bytes Written/Second': '%.3f', 'Current Time': iso_format, 'Requests/Second': '%.3f', 'Start Time': iso_format, 'Total Time': '%.3f', 'Uptime': '%.3f', 'Slow Queries': { 'End Time': None, 'Processing Time': '%.3f', 'Start Time': iso_format, }, 'URI Set Tracking': { 'Avg': '%.3f', 'Max': '%.3f', 'Min': '%.3f', 'Sum': '%.3f', }, 'Requests': { 'Bytes Read': '%s', 'Bytes Written': '%s', 'End Time': None, 'Processing Time': '%.3f', 'Start Time': None, }, }, 'CherryPy WSGIServer': { 'Enabled': pause_resume('CherryPy WSGIServer'), 'Connections/second': '%.3f', 'Start time': iso_format, }, } @cherrypy.expose def index(self): # Transform the raw data into pretty output for HTML yield """ Statistics """ for title, scalars, collections in self.get_namespaces(): yield """

%s

""" % title for i, (key, value) in enumerate(scalars): colnum = i % 3 if colnum == 0: yield """ """ yield ( """ """ % vars() ) if colnum == 2: yield """ """ if colnum == 0: yield """ """ elif colnum == 1: yield """ """ yield """
%(key)s%(value)s
""" for subtitle, headers, subrows in collections: yield """

%s

""" % subtitle for key in headers: yield """ """ % key yield """ """ for subrow in subrows: yield """ """ for value in subrow: yield """ """ % value yield """ """ yield """
%s
%s
""" yield """ """ def get_namespaces(self): """Yield (title, scalars, collections) for each namespace.""" s = extrapolate_statistics(logging.statistics) for title, ns in sorted(s.items()): scalars = [] collections = [] ns_fmt = self.formatting.get(title, {}) for k, v in sorted(ns.items()): fmt = ns_fmt.get(k, {}) if isinstance(v, dict): headers, subrows = self.get_dict_collection(v, fmt) collections.append((k, ['ID'] + headers, subrows)) elif isinstance(v, (list, tuple)): headers, subrows = self.get_list_collection(v, fmt) collections.append((k, headers, subrows)) else: format = ns_fmt.get(k, missing) if format is None: # Don't output this column. continue if hasattr(format, '__call__'): v = format(v) elif format is not missing: v = format % v scalars.append((k, v)) yield title, scalars, collections def get_dict_collection(self, v, formatting): """Return ([headers], [rows]) for the given collection.""" # E.g., the 'Requests' dict. headers = [] try: # python2 vals = v.itervalues() except AttributeError: # python3 vals = v.values() for record in vals: for k3 in record: format = formatting.get(k3, missing) if format is None: # Don't output this column. continue if k3 not in headers: headers.append(k3) headers.sort() subrows = [] for k2, record in sorted(v.items()): subrow = [k2] for k3 in headers: v3 = record.get(k3, '') format = formatting.get(k3, missing) if format is None: # Don't output this column. continue if hasattr(format, '__call__'): v3 = format(v3) elif format is not missing: v3 = format % v3 subrow.append(v3) subrows.append(subrow) return headers, subrows def get_list_collection(self, v, formatting): """Return ([headers], [subrows]) for the given collection.""" # E.g., the 'Slow Queries' list. headers = [] for record in v: for k3 in record: format = formatting.get(k3, missing) if format is None: # Don't output this column. continue if k3 not in headers: headers.append(k3) headers.sort() subrows = [] for record in v: subrow = [] for k3 in headers: v3 = record.get(k3, '') format = formatting.get(k3, missing) if format is None: # Don't output this column. continue if hasattr(format, '__call__'): v3 = format(v3) elif format is not missing: v3 = format % v3 subrow.append(v3) subrows.append(subrow) return headers, subrows if json is not None: @cherrypy.expose def data(self): s = extrapolate_statistics(logging.statistics) cherrypy.response.headers['Content-Type'] = 'application/json' return json.dumps(s, sort_keys=True, indent=4) @cherrypy.expose def pause(self, namespace): logging.statistics.get(namespace, {})['Enabled'] = False raise cherrypy.HTTPRedirect('./') pause.cp_config = {'tools.allow.on': True, 'tools.allow.methods': ['POST']} @cherrypy.expose def resume(self, namespace): logging.statistics.get(namespace, {})['Enabled'] = True raise cherrypy.HTTPRedirect('./') resume.cp_config = {'tools.allow.on': True, 'tools.allow.methods': ['POST']}