def aggregate_detail(slug_list, with_data_table=False):
"""Template Tag to display multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
metrics_data = []
granularities = r._granularities()
# XXX converting granularties into their key-name for metrics.
keys = ['seconds', 'minutes', 'hours', 'day', 'week', 'month', 'year']
key_mapping = {gran: key for gran, key in zip(GRANULARITIES, keys)}
keys = [key_mapping[gran] for gran in granularities]
# Our metrics data is of the form:
#
# (slug, {time_period: value, ... }).
#
# Let's convert this to (slug, list_of_values) so that the list of
# values is in the same order as the granularties
for slug, data in r.get_metrics(slug_list):
values = [data[t] for t in keys]
metrics_data.append((slug, values))
return {
'chart_id': "metric-aggregate-{0}".format("-".join(slug_list)),
'slugs': slug_list,
'metrics': metrics_data,
'with_data_table': with_data_table,
'granularities': [g.title() for g in keys],
}
def gauge(slug, maximum=9000, size=200, coerce='float'):
"""Include a Donut Chart for the specified Gauge.
* ``slug`` -- the unique slug for the Gauge.
* ``maximum`` -- The maximum value for the gauge (default is 9000)
* ``size`` -- The size (in pixels) of the gauge (default is 200)
* ``coerce`` -- type to which gauge values should be coerced. The default
is float. Use ``{% gauge some_slug coerce='int' %}`` to coerce to integer
"""
coerce_options = {'float': float, 'int': int, 'str': str}
coerce = coerce_options.get(coerce, float)
redis = get_r()
value = coerce(redis.get_gauge(slug))
if value < maximum and coerce == float:
diff = round(maximum - value, 2)
elif value < maximum:
diff = maximum - value
else:
diff = 0
return {
'slug': slug,
'current_value': value,
'max_value': maximum,
'size': size,
'diff': diff,
}
def handle(self, *args, **options):
if len(args) == 0:
raise CommandError("You must provide a metric name")
metric_slug = args[0]
r = get_r()
r.delete_metric(metric_slug)
def gauge(slug, maximum=9000, size=200, coerce='float'):
"""Include a Donut Chart for the specified Gauge.
* ``slug`` -- the unique slug for the Gauge.
* ``maximum`` -- The maximum value for the gauge (default is 9000)
* ``size`` -- The size (in pixels) of the gauge (default is 200)
* ``coerce`` -- type to which gauge values should be coerced. The default
is float. Use ``{% gauge some_slug coerce='int' %}`` to coerce to integer
"""
coerce_options = {'float': float, 'int': int, 'str': str}
coerce = coerce_options.get(coerce, float)
redis = get_r()
value = coerce(redis.get_gauge(slug))
if value < maximum and coerce == float:
diff = round(maximum - value, 2)
elif value < maximum:
diff = maximum - value
else:
diff = 0
return {
'slug': slug,
'current_value': value,
'max_value': maximum,
'size': size,
'diff': diff,
}
def get_slug_list(filter):
slug_list = get_r().metric_slugs_by_category()['Uncategorized']
slugs = []
for slug in slug_list:
if filter in slug:
slugs.append(slug)
return slugs
def metric_history(slug, granularity="daily", since=None, with_data_table=False):
"""Template Tag to display a metric's history.
* ``slug`` -- the metric's unique slug
* ``granularity`` -- the granularity: daily, hourly, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
metric_history = r.get_metric_history(
slugs=slug,
since=since,
granularity=granularity
)
return {
'since': since,
'slug': slug,
'granularity': granularity,
'metric_history': metric_history,
'with_data_table': with_data_table,
}
def aggregate_detail(slug_list, with_data_table=False):
"""Template Tag to display multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
metrics_data = []
granularities = r._granularities()
# XXX converting granularties into their key-name for metrics.
keys = ['seconds', 'minutes', 'hours', 'day', 'week', 'month', 'year']
key_mapping = {gran: key for gran, key in zip(GRANULARITIES, keys)}
keys = [key_mapping[gran] for gran in granularities]
# Our metrics data is of the form:
#
# (slug, {time_period: value, ... }).
#
# Let's convert this to (slug, list_of_values) so that the list of
# values is in the same order as the granularties
for slug, data in r.get_metrics(slug_list):
values = [data[t] for t in keys]
metrics_data.append((slug, values))
return {
'chart_id': "metric-aggregate-{0}".format("-".join(slug_list)),
'slugs': slug_list,
'metrics': metrics_data,
'with_data_table': with_data_table,
'granularities': [g.title() for g in keys],
}
def metric_history(slug, granularity="daily", since=None, with_data_table=False):
"""Template Tag to display a metric's history.
* ``slug`` -- the metric's unique slug
* ``granularity`` -- the granularity: daily, hourly, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
metric_history = r.get_metric_history(
slugs=slug,
since=since,
granularity=granularity
)
return {
'since': since,
'slug': slug,
'granularity': granularity,
'metric_history': metric_history,
'with_data_table': with_data_table,
}
def aggregate_history(slugs, granularity="daily", since=None, with_data_table=False):
"""Template Tag to display history for multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``granularity`` -- the granularity: seconds, minutes, hourly,
daily, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
NOTE: If you specify with_data_table=True, this code will make an additional
call out to retreive metrics and format them properly, which could be a
little slow.
"""
r = get_r()
slugs = list(slugs)
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
history = r.get_metric_history_chart_data(
slugs=slugs,
since=since,
granularity=granularity
)
# If we want to display the raw data, fetch it in a columnar format
tabular_data = None
if with_data_table:
tabular_data = r.get_metric_history_as_columns(
slugs=slugs,
since=since,
granularity=granularity
)
return {
'chart_id': "metric-aggregate-history-{0}".format("-".join(slugs)),
'slugs': slugs,
'since': since,
'granularity': granularity,
'metric_history': history,
'with_data_table': with_data_table,
'tabular_data': tabular_data,
}
def handle_noargs(self, *args, **options):
r = get_r()
# Store *only* slugs in the Set of metric slugs. Prior to this, we
# stored metric keys, and split the slug from the key upon retrieval
try:
keys = r.r.smembers(r._metric_slugs_key)
slugs = set(s.split(":")[1] for s in keys)
r.r.srem(r._metric_slugs_key, *keys) # Remove the old keys
r.r.sadd(r._metric_slugs_key, *slugs) # Keep the new slugs
p = "\nMetrics: Converted {0} Keys to {1} Slugs\n"
self.stdout.write(p.format(len(keys), len(slugs)))
except (IndexError, ResponseError):
# If none of our old keys contain a ':', we don't need to replace them.
pass
# Similar to metric keys above...
# Store *only* slugs in the Set of Gauge slugs. Prior to this, we
# stored gauge keys, and split the slug from the key upon retrieval
try:
keys = r.r.smembers(r._gauge_slugs_key)
slugs = set(s.split(":")[1] for s in keys)
r.r.srem(r._gauge_slugs_key, *keys) # Remove the old keys
r.r.sadd(r._gauge_slugs_key, *slugs) # Keep the new slugs
p = "Gauges: Converted {0} Keys to {1} Slugs\n"
self.stdout.write(p.format(len(keys), len(slugs)))
except (IndexError, ResponseError):
# If none of our old keys contain a ':', we don't need to replace them.
pass
# Convert all Categories from JSON to Sets.
i = 0
categories = r.categories()
for category in categories:
try:
k = r._category_key(category)
data = r.r.get(
k) # Get the JSON-ecoded list of metrics in this Category
if data: # redis gives us a None-value for a non-existing key
data = json.loads(data) # convert to python
r.r.delete(k) # Delete the existing info in redis
r.r.sadd(k, *set(data)) # Re-create as a Set
i += 1
except ResponseError:
pass
if i > 0:
p = "Converted {0} Categories from JSON -> Redis Sets\n"
self.stdout.write(p.format(i))
def aggregate_history(slugs, granularity="daily", since=None, with_data_table=False):
"""Template Tag to display history for multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``granularity`` -- the granularity: seconds, minutes, hourly,
daily, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
NOTE: If you specify with_data_table=True, this code will make an additional
call out to retreive metrics and format them properly, which could be a
little slow.
"""
r = get_r()
slugs = list(slugs)
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
history = r.get_metric_history_chart_data(
slugs=slugs,
since=since,
granularity=granularity
)
# If we want to display the raw data, fetch it in a columnar format
tabular_data = None
if with_data_table:
tabular_data = r.get_metric_history_as_columns(
slugs=slugs,
since=since,
granularity=granularity
)
return {
'chart_id': "metric-aggregate-history-{0}".format("-".join(slugs)),
'slugs': slugs,
'since': since,
'granularity': granularity,
'metric_history': history,
'with_data_table': with_data_table,
'tabular_data': tabular_data,
}
def metric_detail(slug, with_data_table=False):
"""Template Tag to display a metric's *current* detail.
* ``slug`` -- the metric's unique slug
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
return {
'granularities': list(r._granularities()),
'slug': slug,
'metrics': r.get_metric(slug),
'with_data_table': with_data_table,
}
def handle_noargs(self, *args, **options):
r = get_r()
# Store *only* slugs in the Set of metric slugs. Prior to this, we
# stored metric keys, and split the slug from the key upon retrieval
try:
keys = r.r.smembers(r._metric_slugs_key)
slugs = set(s.split(":")[1] for s in keys)
r.r.srem(r._metric_slugs_key, *keys) # Remove the old keys
r.r.sadd(r._metric_slugs_key, *slugs) # Keep the new slugs
p = "\nMetrics: Converted {0} Keys to {1} Slugs\n"
self.stdout.write(p.format(len(keys), len(slugs)))
except (IndexError, ResponseError):
# If none of our old keys contain a ':', we don't need to replace them.
pass
# Similar to metric keys above...
# Store *only* slugs in the Set of Gauge slugs. Prior to this, we
# stored gauge keys, and split the slug from the key upon retrieval
try:
keys = r.r.smembers(r._gauge_slugs_key)
slugs = set(s.split(":")[1] for s in keys)
r.r.srem(r._gauge_slugs_key, *keys) # Remove the old keys
r.r.sadd(r._gauge_slugs_key, *slugs) # Keep the new slugs
p = "Gauges: Converted {0} Keys to {1} Slugs\n"
self.stdout.write(p.format(len(keys), len(slugs)))
except (IndexError, ResponseError):
# If none of our old keys contain a ':', we don't need to replace them.
pass
# Convert all Categories from JSON to Sets.
i = 0
categories = r.categories()
for category in categories:
try:
k = r._category_key(category)
data = r.r.get(k) # Get the JSON-ecoded list of metrics in this Category
if data: # redis gives us a None-value for a non-existing key
data = json.loads(data) # convert to python
r.r.delete(k) # Delete the existing info in redis
r.r.sadd(k, *set(data)) # Re-create as a Set
i += 1
except ResponseError:
pass
if i > 0:
p = "Converted {0} Categories from JSON -> Redis Sets\n"
self.stdout.write(p.format(i))
def aggregate_detail(slug_list, with_data_table=False):
"""Template Tag to display multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
return {
'chart_id': "metric-aggregate-{0}".format("-".join(slug_list)),
'slugs': slug_list,
'metrics': r.get_metrics(slug_list),
'with_data_table': with_data_table,
}
def aggregate_detail(slug_list, with_data_table=False):
"""Template Tag to display multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
return {
'chart_id': "metric-aggregate-{0}".format("-".join(slug_list)),
'slugs': slug_list,
'metrics': r.get_metrics(slug_list),
'with_data_table': with_data_table,
}
def _current_average_response_time(self):
# Assume `_start_time` and `_end_time` are set, retrieve the current
# gauge value (if any), average the current response time, and return
# the value (as a string).
r = get_r()
request_time = self._end_time - self._start_time
current = r.get_gauge(self._key)
if current is not None:
current = (float(current) + request_time) / 2
else:
current = request_time
return current
def metric_detail(slug, with_data_table=False):
"""Template Tag to display a metric's *current* detail.
* ``slug`` -- the metric's unique slug
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
return {
'granularities': list(r._granularities()),
'slug': slug,
'metrics': r.get_metric(slug),
'with_data_table': with_data_table,
}
def metric_detail(slug, with_data_table=False):
"""Template Tag to display a metric's *current* detail.
* ``slug`` -- the metric's unique slug
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
granularities = list(r._granularities())
metrics = r.get_metric(slug)
metrics_data = []
for g in granularities:
metrics_data.append((g, metrics[g]))
return {
'granularities': [g.title() for g in granularities],
'slug': slug,
'metrics': metrics_data,
'with_data_table': with_data_table,
}
def metric_detail(slug, with_data_table=False):
"""Template Tag to display a metric's *current* detail.
* ``slug`` -- the metric's unique slug
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
granularities = list(r._granularities())
metrics = r.get_metric(slug)
metrics_data = []
for g in granularities:
metrics_data.append((g, metrics[g]))
return {
'granularities': [g.title() for g in granularities],
'slug': slug,
'metrics': metrics_data,
'with_data_table': with_data_table,
}
def gauge(slug, maximum=9000, size=200):
"""Include a Donut Chart for the specified Gauge.
* ``slug`` -- the unique slug for the Gauge.
* ``maximum`` -- The maximum value for the gauge (default is 9000)
* ``size`` -- The size (in pixels) of the gauge (default is 200)
"""
r = get_r()
value = int(r.get_gauge(slug))
if value < maximum:
diff = maximum - value
else:
diff = 0
return {
'slug': slug,
'current_value': value,
'max_value': maximum,
'size': size,
'diff': diff,
}
def aggregate_history(slugs,
granularity="daily",
since=None,
with_data_table=False):
"""Template Tag to display history for multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``granularity`` -- the granularity: seconds, minutes, hourly,
daily, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
slugs = list(slugs)
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
history = r.get_metric_history_chart_data(slugs=slugs,
since=since,
granularity=granularity)
return {
'chart_id': "metric-aggregate-history-{0}".format("-".join(slugs)),
'slugs': slugs,
'since': since,
'granularity': granularity,
'metric_history': history,
'with_data_table': with_data_table,
}
def handle_noargs(self, **options):
"""Send Report E-mails."""
r = get_r()
since = datetime.utcnow() - timedelta(days=1)
metrics = {}
categories = r.metric_slugs_by_category()
for category_name, slug_list in categories.items():
metrics[category_name] = []
for slug in slug_list:
metric_values = r.get_metric_history(slug, since=since)
metrics[category_name].append(
(slug, metric_values)
)
# metrics is now:
# --------------
# { Category : [
# ('foo', [('m:foo:2012-07-18', 1), ('m:foo:2012-07-19, 2), ...])
# ],
# ...
# }
template = "redis_metrics/email/report.{fmt}"
data = {
'today': since,
'metrics': metrics,
}
message = render_to_string(template.format(fmt='txt'), data)
message_html = render_to_string(template.format(fmt='html'), data)
msg = EmailMultiAlternatives(
subject="Redis Metrics Report",
body=message,
from_email=settings.DEFAULT_FROM_EMAIL,
to=[email for name, email in settings.ADMINS]
)
msg.attach_alternative(message_html, "text/html")
msg.send()
def gauge(slug, maximum=9000, size=200):
"""Include a Donut Chart for the specified Gauge.
* ``slug`` -- the unique slug for the Gauge.
* ``maximum`` -- The maximum value for the gauge (default is 9000)
* ``size`` -- The size (in pixels) of the gauge (default is 200)
"""
r = get_r()
value = int(r.get_gauge(slug))
if value < maximum:
diff = maximum - value
else:
diff = 0
return {
'slug': slug,
'current_value': value,
'max_value': maximum,
'size': size,
'diff': diff,
}
def aggregate_history(slugs, granularity="daily", since=None, with_data_table=False):
"""Template Tag to display history for multiple metrics.
* ``slug_list`` -- A list of slugs to display
* ``granularity`` -- the granularity: seconds, minutes, hourly,
daily, weekly, monthly, yearly
* ``since`` -- a datetime object or a string string matching one of the
following patterns: "YYYY-mm-dd" for a date or "YYYY-mm-dd HH:MM:SS" for
a date & time.
* ``with_data_table`` -- if True, prints the raw data in a table.
"""
r = get_r()
slugs = list(slugs)
try:
if since and len(since) == 10: # yyyy-mm-dd
since = datetime.strptime(since, "%Y-%m-%d")
elif since and len(since) == 19: # yyyy-mm-dd HH:MM:ss
since = datetime.strptime(since, "%Y-%m-%d %H:%M:%S")
except (TypeError, ValueError):
# assume we got a datetime object or leave since = None
pass
history = r.get_metric_history_chart_data(
slugs=slugs,
since=since,
granularity=granularity
)
return {
'chart_id': "metric-aggregate-history-{0}".format("-".join(slugs)),
'slugs': slugs,
'since': since,
'granularity': granularity,
'metric_history': history,
'with_data_table': with_data_table,
}
def metric_list():
r = get_r()
return {
'metrics': r.metric_slugs_by_category(),
}
def handle(self, *args, **options):
if not len(args) == 1:
raise CommandError("You must provide a gauge name")
r = get_r()
r.delete_gauge(args[0])
def metric_list():
r = get_r()
return {
'metrics': r.metric_slugs_by_category(),
}
