mirror of
https://github.com/morpheus65535/bazarr
synced 2024-12-27 18:17:46 +00:00
690 lines
22 KiB
Python
690 lines
22 KiB
Python
"""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 <td> 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 """
|
|
<form action="pause" method="POST" style="display:inline">
|
|
<input type="hidden" name="namespace" value="%s" />
|
|
<input type="submit" value="Pause" %s/>
|
|
</form>
|
|
<form action="resume" method="POST" style="display:inline">
|
|
<input type="hidden" name="namespace" value="%s" />
|
|
<input type="submit" value="Resume" %s/>
|
|
</form>
|
|
""" % (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 """
|
|
<html>
|
|
<head>
|
|
<title>Statistics</title>
|
|
<style>
|
|
|
|
th, td {
|
|
padding: 0.25em 0.5em;
|
|
border: 1px solid #666699;
|
|
}
|
|
|
|
table {
|
|
border-collapse: collapse;
|
|
}
|
|
|
|
table.stats1 {
|
|
width: 100%;
|
|
}
|
|
|
|
table.stats1 th {
|
|
font-weight: bold;
|
|
text-align: right;
|
|
background-color: #CCD5DD;
|
|
}
|
|
|
|
table.stats2, h2 {
|
|
margin-left: 50px;
|
|
}
|
|
|
|
table.stats2 th {
|
|
font-weight: bold;
|
|
text-align: center;
|
|
background-color: #CCD5DD;
|
|
}
|
|
|
|
</style>
|
|
</head>
|
|
<body>
|
|
"""
|
|
for title, scalars, collections in self.get_namespaces():
|
|
yield """
|
|
<h1>%s</h1>
|
|
|
|
<table class='stats1'>
|
|
<tbody>
|
|
""" % title
|
|
for i, (key, value) in enumerate(scalars):
|
|
colnum = i % 3
|
|
if colnum == 0:
|
|
yield """
|
|
<tr>"""
|
|
yield (
|
|
"""
|
|
<th>%(key)s</th><td id='%(title)s-%(key)s'>%(value)s</td>""" %
|
|
vars()
|
|
)
|
|
if colnum == 2:
|
|
yield """
|
|
</tr>"""
|
|
|
|
if colnum == 0:
|
|
yield """
|
|
<th></th><td></td>
|
|
<th></th><td></td>
|
|
</tr>"""
|
|
elif colnum == 1:
|
|
yield """
|
|
<th></th><td></td>
|
|
</tr>"""
|
|
yield """
|
|
</tbody>
|
|
</table>"""
|
|
|
|
for subtitle, headers, subrows in collections:
|
|
yield """
|
|
<h2>%s</h2>
|
|
<table class='stats2'>
|
|
<thead>
|
|
<tr>""" % subtitle
|
|
for key in headers:
|
|
yield """
|
|
<th>%s</th>""" % key
|
|
yield """
|
|
</tr>
|
|
</thead>
|
|
<tbody>"""
|
|
for subrow in subrows:
|
|
yield """
|
|
<tr>"""
|
|
for value in subrow:
|
|
yield """
|
|
<td>%s</td>""" % value
|
|
yield """
|
|
</tr>"""
|
|
yield """
|
|
</tbody>
|
|
</table>"""
|
|
yield """
|
|
</body>
|
|
</html>
|
|
"""
|
|
|
|
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']}
|