def parse(self, query_str):
avg_over_match = '^([0-9]*)(s|M|h|d|w|mo)$'
# for a call like ('foo bar baz quux', 'bar ', 'baz', 'def')
# returns ('foo quux', 'baz') or the original query and the default val if no match
def parse_val(query_str, predicate_match, value_match, value_default=None):
match = re.search('\\b(%s%s)' % (predicate_match, value_match), query_str)
value = value_default
if match and match.groups() > 0:
value = match.groups(1)[0].replace(predicate_match, '')
query_str = query_str[:match.start(1)] + query_str[match.end(1):]
return (query_str, value)
(query_str, self['statement']) = parse_val(query_str, '^', '(graph|list|stack|lines)\\b',
self['statement'])
self['statement'] = self['statement'].rstrip()
(query_str, self['to']) = parse_val(query_str, 'to ', '[^ ]+', self['to'])
(query_str, self['from']) = parse_val(query_str, 'from ', '[^ ]+', self['from'])
(query_str, group_by_str) = parse_val(query_str, 'GROUP BY ', '[^ ]+')
(query_str, extra_group_by_str) = parse_val(query_str, 'group by ', '[^ ]+')
(query_str, sum_by_str) = parse_val(query_str, 'sum by ', '[^ ]+')
(query_str, avg_by_str) = parse_val(query_str, 'avg by ', '[^ ]+')
(query_str, avg_over_str) = parse_val(query_str, 'avg over ', '[^ ]+')
(query_str, min_str) = parse_val(query_str, 'min ', '[^ ]+')
(query_str, max_str) = parse_val(query_str, 'max ', '[^ ]+')
explicit_group_by = {}
if group_by_str is not None:
explicit_group_by = Query.build_buckets(group_by_str)
self['group_by'] = explicit_group_by
elif extra_group_by_str is not None:
for k in self['group_by'].keys():
if not k.endswith('='):
del self['group_by'][k]
explicit_group_by = Query.build_buckets(extra_group_by_str)
self['group_by'].update(explicit_group_by)
if sum_by_str is not None:
self['sum_by'] = Query.build_buckets(sum_by_str)
if avg_by_str is not None:
self['avg_by'] = Query.build_buckets(avg_by_str)
if min_str is not None:
# check if we can parse the values, but don't actually replace yet
# because we want to keep the 'pretty' value for now so we can display
# it in the query details section
convert.parse_str(min_str)
self['min'] = min_str
if max_str is not None:
convert.parse_str(max_str)
self['max'] = max_str
# if you specified a tag in avg_by or sum_by that is included in the
# default group_by (and you didn't explicitly ask to group by that tag), we
# remove it from group by, so that the avg/sum can work properly.
for tag in self['sum_by'].keys() + self['avg_by'].keys():
for tag_check in (tag, "%s=" % tag):
if tag_check in self['group_by'] and tag_check not in explicit_group_by.keys():
del self['group_by'][tag_check]
# doing this sanity check would now be tricky: basically you can have the same keys in more than 1 of sum/avg/group by,
# it now depends on the bucket configuration. since i can't wrap my head around it anymore, let's just leave it be for now.
# it's up to people to construct sane queries, and if they do a stupid query, then at least GE shouldn't crash or anything.
# sum_individual_keys = len(self['group_by']) + len(self['sum_by']) + len(self['avg_by'])
# sum_unique_keys = len(set(self['group_by'].keys() + self['sum_by'].keys() + self['avg_by'].keys()))
# if sum_individual_keys != sum_unique_keys:
# raise Exception("'group by' (%s), 'sum by (%s)' and 'avg by (%s)' "
# "cannot list the same tag keys" %
# (', '.join(self['group_by'].keys()),
# ', '.join(self['sum_by'].keys()),
# ', '.join(self['avg_by'].keys())))
if avg_over_str is not None:
# avg_over_str should be something like 'h', '10M', etc
avg_over = re.match(avg_over_match, avg_over_str)
if avg_over is not None: # if None, that's an invalid request. ignore it. TODO error to user
avg_over = avg_over.groups()
self['avg_over'] = (int(avg_over[0]), avg_over[1])
(query_str, self['limit_targets']) = parse_val(query_str, 'limit ', '[^ ]+', self['limit_targets'])
self['limit_targets'] = int(self['limit_targets'])
# split query_str into multiple patterns which are all matched independently
# this allows you write patterns in any order, and also makes it easy to use negations
self['patterns'] += query_str.split()
def build_graphs_from_targets(targets, query={}, target_modifiers=[]):
# merge default options..
defaults = {
'group_by': [],
'sum_by': [],
'avg_over': None,
'avg_by': [],
'from': '-24hours',
'to': 'now',
'statement': 'graph',
'limit_targets': 500
}
query = dict(defaults.items() + query.items())
graphs = {}
if not targets:
return (graphs, query)
group_by = query['group_by']
sum_by = query['sum_by']
avg_by = query['avg_by']
avg_over = query['avg_over']
# i'm gonna assume you never use second and your datapoints are stored with
# minutely resolution. later on we can use config options for this (or
# better: somehow query graphite about it)
# note, the day/week/month numbers are not technically accurate, but
# since we're doing movingAvg that's ok
averaging = {
'M': 1,
'h': 60,
'd': 60 * 24,
'w': 60 * 24 * 7,
'mo': 60 * 24 * 30
}
if avg_over is not None:
avg_over_amount = avg_over[0]
avg_over_unit = avg_over[1]
if avg_over_unit in averaging.keys():
multiplier = averaging[avg_over_unit]
target_modifier = {'target': ['movingAverage', str(avg_over_amount * multiplier)]}
target_modifiers.append(target_modifier)
# for each combination of values of tags from group_by, make 1 graph with
# all targets that have these values. so for each graph, we have:
# the "constants": tags in the group_by
# the "variables": tags not in the group_by, which can have arbitrary values
# go through all targets and group them into graphs:
for (i, target_id) in enumerate(sorted(targets.iterkeys())):
constants = {}
variables = {}
target_data = targets[target_id]
for (tag_name, tag_value) in target_data['tags'].items():
if tag_name in group_by or '%s=' % tag_name in group_by:
constants[tag_name] = tag_value
else:
variables[tag_name] = tag_value
graph_key = '__'.join([target_data['tags'][tag_name] for tag_name in constants])
if graph_key not in graphs:
graph = {'from': query['from'], 'until': query['to']}
graph.update({'constants': constants, 'targets': []})
graphs[graph_key] = graph
target = target_data['id']
# set all options needed for timeserieswidget/flot:
t = {
'variables': variables,
'id': target_data['id'], # timeserieswidget doesn't care about this
'target': target
}
if 'color' in target_data:
t['color'] = target_data['color']
graphs[graph_key]['targets'].append(t)
# ok so now we have a graphs dictionary with a graph for every approriate
# combination of group_by tags, and each graphs contains all targets that
# should be shown on it. but the user may have asked to aggregate certain
# targets together, by summing and/or averaging across different values of
# (a) certain tag(s). let's process the aggregations now.
if (sum_by or avg_by):
for (graph_key, graph_config) in graphs.items():
graph_config['targets_sum_candidates'] = {}
graph_config['targets_avg_candidates'] = {}
graph_config['normal_targets'] = []
all_targets = graph_config['targets'][:] # Get a copy.
for target in all_targets:
# targets that can get summed together with other tags, must
# have at least 1 'sum_by' tags in the variables list.
# targets that can get summed together must have:
# * the same 'sum_by' tag keys (not values, because we
# aggregate across different values for these tags)
# * the same variables (key and val), except those vals that
# are being summed by.
# so for every group of sum_by tags and variables we build a
# list of targets that can be summed together
# of course it only makes sense to sum by tags that the target
# actually has, and that are not already constants (meaning
# every target in the graph has the same value)
variables = target['variables'].keys()
sum_constants = set(sum_by).intersection(set(variables))
if sum_constants:
sum_constants_str = '_'.join(sorted(sum_constants))
variables_str = '_'.join(
['%s_%s' % (k, target['variables'][k])
for k in sorted(variables)
if k not in sum_constants])
sum_id = '%s__%s' % (sum_constants_str, variables_str)
if sum_id not in graph_config['targets_sum_candidates']:
graphs[graph_key]['targets_sum_candidates'][sum_id] = []
graph_config['targets_sum_candidates'][sum_id].append(target)
for (sum_id, targets) in graph_config['targets_sum_candidates'].items():
if len(targets) > 1:
for t in targets:
all_targets.remove(t)
all_targets.append(
graphite_func_aggregate(targets, sum_by, "sumSeries"))
for target in all_targets:
# Now that any summing is done, we look at aggregating by
# averaging because avg(foo+bar+baz) is more efficient
# than avg(foo)+avg(bar)+avg(baz)
# It's pretty similar than what happened above and aggregates
# targets (whether those are sums or regular ones)
variables = target['variables'].keys()
avg_constants = set(avg_by).intersection(set(variables))
if avg_constants:
avg_constants_str = '_'.join(sorted(avg_constants))
variables_str = '_'.join(
['%s_%s' % (k, target['variables'][k])
for k in sorted(variables)
if k not in avg_constants])
# some values can be like 'sumSeries (8 values)' due to an
# earlier aggregation. if now targets have a different amount of
# values matched, that doesn't matter and they should still
# be aggregated together if the rest of the conditions are met
variables_str = re.sub('\([0-9]+ values\)', '(Xvalues)', variables_str)
avg_id = '%s__%s' % (avg_constants_str, variables_str)
if avg_id not in graph_config['targets_avg_candidates']:
graph_config['targets_avg_candidates'][avg_id] = []
graph_config['targets_avg_candidates'][avg_id].append(target)
for (avg_id, targets) in graph_config['targets_avg_candidates'].items():
if len(targets) > 1:
for t in targets:
all_targets.remove(t)
all_targets.append(
graphite_func_aggregate(targets, avg_by, "averageSeries"))
graph_config["targets"] = all_targets
# remove targets/graphs over the limit
graphs = graphs_limit_targets(graphs, query['limit_targets'])
# Apply target modifiers (like movingAverage, summarize, ...)
for (graph_key, graph_config) in graphs.items():
for target in graph_config['targets']:
for target_modifier in target_modifiers:
target['target'] = "%s(%s,%s)" % (target_modifier['target'][0],
target['target'],
','.join(target_modifier['target'][1:]))
if 'tags' in target_modifier:
for (new_k, new_v) in target_modifier['tags'].items():
if new_k in graph_config['constants']:
graph_config['constants'][new_k] = new_v
else:
target['variables'][new_k] = new_v
# if in a graph all targets have a tag with the same value, they are
# effectively constants, so promote them. this makes the display of the
# graphs less rendundant and makes it easier to do config/preferences
# on a per-graph basis.
for (graph_key, graph_config) in graphs.items():
# get all variable tags throughout all targets in this graph
tags_seen = set()
for target in graph_config['targets']:
for tag_name in target['variables'].keys():
tags_seen.add(tag_name)
# find effective constants from those variables,
# and effective variables. (unset tag is a value too)
first_values_seen = {}
effective_variables = set() # tags for which we've seen >1 values
for target in graph_config['targets']:
for tag_name in tags_seen:
# already known that we can't promote, continue
if tag_name in effective_variables:
continue
tag_value = target['variables'].get(tag_name, None)
if tag_name not in first_values_seen:
first_values_seen[tag_name] = tag_value
elif tag_value != first_values_seen[tag_name]:
effective_variables.add(tag_name)
effective_constants = tags_seen - effective_variables
# promote the effective_constants by adjusting graph and targets:
graphs[graph_key]['promoted_constants'] = {}
for tag_name in effective_constants:
graphs[graph_key]['promoted_constants'][tag_name] = first_values_seen[tag_name]
for (i, target) in enumerate(graph_config['targets']):
if tag_name in graphs[graph_key]['targets'][i]['variables']:
del graphs[graph_key]['targets'][i]['variables'][tag_name]
# now that graph config is "rich", merge in settings from preferences
constants = dict(graphs[graph_key]['constants'].items() + graphs[graph_key]['promoted_constants'].items())
for graph_option in get_action_on_rules_match(preferences.graph_options, constants):
if isinstance(graph_option, dict):
graphs[graph_key].update(graph_option)
else:
graphs[graph_key] = graph_option(graphs[graph_key])
# but, the query may override some preferences:
override = {}
if query['statement'] == 'lines':
override['state'] = 'lines'
if query['statement'] == 'stack':
override['state'] = 'stacked'
if query['min'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'min': convert.parse_str(query['min'])})
if query['max'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'max': convert.parse_str(query['max'])})
graphs[graph_key].update(override)
# now that some constants are promoted, we can give the graph more
# unique keys based on all (original + promoted) constants. this is in
# line with the meaning of the graph ("all targets with those constant
# tags"), but more importantly: this fixes cases where some graphs
# would otherwise have the same key, even though they have a different
# set of constants, this can manifest itself on dashboard pages where
# graphs for different queries are shown.
new_graphs = {}
for (graph_key, graph_config) in graphs.items():
better_graph_key_1 = '__'.join('%s_%s' % i for i in graph_config['constants'].items())
better_graph_key_2 = '__'.join('%s_%s' % i for i in graph_config['promoted_constants'].items())
better_graph_key = '%s___%s' % (better_graph_key_1, better_graph_key_2)
new_graphs[better_graph_key] = graph_config
graphs = new_graphs
return (graphs, query)
def build_graphs_from_targets(targets, query):
graphs = {}
if not targets:
return (graphs, query)
group_by = query['group_by']
sum_by = query['sum_by']
avg_by = query['avg_by']
avg_over = query['avg_over']
# i'm gonna assume you never use second and your datapoints are stored with
# minutely resolution. later on we can use config options for this (or
# better: somehow query graphite about it)
# note, the day/week/month numbers are not technically accurate, but
# since we're doing movingAvg that's ok
averaging = {
'M': 1,
'h': 60,
'd': 60 * 24,
'w': 60 * 24 * 7,
'mo': 60 * 24 * 30
}
if avg_over is not None:
avg_over_amount = avg_over[0]
avg_over_unit = avg_over[1]
if avg_over_unit in averaging.keys():
multiplier = averaging[avg_over_unit]
query['target_modifiers'].append(
Query.graphite_function_applier('movingAverage', avg_over_amount * multiplier))
# for each group_by bucket, make 1 graph.
# so for each graph, we have:
# the "constants": tags in the group_by
# the "variables": tags not in the group_by, which can have arbitrary
# values, or different values from a group_by tag that match the same
# bucket pattern
# go through all targets and group them into graphs:
for _target_id, target_data in sorted(targets.items()):
# FWIW. has an 'id' which timeserieswidget doesn't care about
target = Target(target_data)
target['target'] = target['id']
(graph_key, constants) = target.get_graph_info(group_by)
if graph_key not in graphs:
graph = {'from': query['from'], 'until': query['to']}
graph.update({'constants': constants, 'targets': []})
graphs[graph_key] = graph
graphs[graph_key]['targets'].append(target)
# ok so now we have a graphs dictionary with a graph for every appropriate
# combination of group_by tags, and each graph contains all targets that
# should be shown on it. but the user may have asked to aggregate certain
# targets together, by summing and/or averaging across different values of
# (a) certain tag(s). let's process the aggregations now.
if (sum_by or avg_by):
for (graph_key, graph_config) in graphs.items():
graph_config['targets_sum_candidates'] = {}
graph_config['targets_avg_candidates'] = {}
graph_config['normal_targets'] = []
for target in graph_config['targets']:
sum_id = target.get_agg_key(sum_by)
if sum_id:
if sum_id not in graph_config['targets_sum_candidates']:
graphs[graph_key]['targets_sum_candidates'][sum_id] = []
graph_config['targets_sum_candidates'][sum_id].append(target)
for (sum_id, targets) in graph_config['targets_sum_candidates'].items():
if len(targets) > 1:
for t in targets:
graph_config['targets'].remove(t)
graph_config['targets'].append(
graphite_func_aggregate(targets, sum_by, "sumSeries"))
for target in graph_config['targets']:
# Now that any summing is done, we look at aggregating by
# averaging because avg(foo+bar+baz) is more efficient
# than avg(foo)+avg(bar)+avg(baz)
# aggregate targets (whether those are sums or regular ones)
avg_id = target.get_agg_key(avg_by)
if avg_id:
if avg_id not in graph_config['targets_avg_candidates']:
graph_config['targets_avg_candidates'][avg_id] = []
graph_config['targets_avg_candidates'][avg_id].append(target)
for (avg_id, targets) in graph_config['targets_avg_candidates'].items():
if len(targets) > 1:
for t in targets:
graph_config['targets'].remove(t)
graph_config['targets'].append(
graphite_func_aggregate(targets, avg_by, "averageSeries"))
# remove targets/graphs over the limit
graphs = graphs_limit_targets(graphs, query['limit_targets'])
# Apply target modifiers (like movingAverage, summarize, ...)
for (graph_key, graph_config) in graphs.items():
for target in graph_config['targets']:
for target_modifier in query['target_modifiers']:
target_modifier(target, graph_config)
# if in a graph all targets have a tag with the same value, they are
# effectively constants, so promote them. this makes the display of the
# graphs less rendundant and makes it easier to do config/preferences
# on a per-graph basis.
for (graph_key, graph_config) in graphs.items():
# get all variable tags throughout all targets in this graph
tags_seen = set()
for target in graph_config['targets']:
for tag_name in target['variables'].keys():
tags_seen.add(tag_name)
# find effective constants from those variables,
# and effective variables. (unset tag is a value too)
first_values_seen = {}
effective_variables = set() # tags for which we've seen >1 values
for target in graph_config['targets']:
for tag_name in tags_seen:
# already known that we can't promote, continue
if tag_name in effective_variables:
continue
tag_value = target['variables'].get(tag_name, None)
if tag_name not in first_values_seen:
first_values_seen[tag_name] = tag_value
elif tag_value != first_values_seen[tag_name]:
effective_variables.add(tag_name)
effective_constants = tags_seen - effective_variables
# promote the effective_constants by adjusting graph and targets:
graph_config['promoted_constants'] = {}
for tag_name in effective_constants:
graph_config['promoted_constants'][tag_name] = first_values_seen[tag_name]
for target in graph_config['targets']:
target['variables'].pop(tag_name, None)
# now that graph config is "rich", merge in settings from preferences
constants = dict(graph_config['constants'].items() + graph_config['promoted_constants'].items())
for graph_option in get_action_on_rules_match(preferences.graph_options, constants):
if isinstance(graph_option, dict):
graph_config.update(graph_option)
else:
graph_config = graphs[graph_key] = graph_option(graph_config)
# but, the query may override some preferences:
override = {}
if query['statement'] == 'lines':
override['state'] = 'lines'
if query['statement'] == 'stack':
override['state'] = 'stacked'
if query['min'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'min': convert.parse_str(query['min'])})
if query['max'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'max': convert.parse_str(query['max'])})
graphs[graph_key].update(override)
# now that some constants are promoted, we can give the graph more
# unique keys based on all (original + promoted) constants. this is in
# line with the meaning of the graph ("all targets with those constant
# tags"), but more importantly: this fixes cases where some graphs
# would otherwise have the same key, even though they have a different
# set of constants, this can manifest itself on dashboard pages where
# graphs for different queries are shown.
# note that we can't just compile constants + promoted_constants,
# part of the original graph key is also set by the group by (which, by
# means of the bucket patterns doesn't always translate into constants),
# we solve this by just including the old key.
new_graphs = {}
for (graph_key, graph_config) in graphs.items():
new_key = ','.join('%s=%s' % i for i in graph_config['promoted_constants'].items())
new_key = '%s__%s' % (graph_key, new_key)
new_graphs[new_key] = graph_config
graphs = new_graphs
return (graphs, query)
def parse_query(query_str):
avg_over_match = '^([0-9]*)(s|M|h|d|w|mo)$'
query = {
'patterns': [],
'group_by': ['target_type=', 'unit=', 'server'],
'sum_by': [],
'avg_by': [],
'avg_over': None,
'min': None,
'max': None
}
# for a call like ('foo bar baz quux', 'bar ', 'baz', 'def')
# returns ('foo quux', 'baz') or the original query and the default val if no match
def parse_out_value(query_str, predicate_match, value_match,
value_default):
match = re.search('(%s%s)' % (predicate_match, value_match), query_str)
value = value_default
if match and match.groups() > 0:
value = match.groups(1)[0].replace(predicate_match, '')
query_str = query_str[:match.start(1)] + query_str[match.end(1):]
return (query_str, value)
(query_str,
query['statement']) = parse_out_value(query_str, '^',
'(graph|list|stack|lines) ',
'graph')
query['statement'] = query['statement'].rstrip()
(query_str, query['to']) = parse_out_value(query_str, 'to ', '[^ ]+',
'now')
(query_str, query['from']) = parse_out_value(query_str, 'from ', '[^ ]+',
'-24hours')
(query_str, group_by_str) = parse_out_value(query_str, 'GROUP BY ',
'[^ ]+', None)
(query_str, extra_group_by_str) = parse_out_value(query_str, 'group by ',
'[^ ]+', None)
(query_str, sum_by_str) = parse_out_value(query_str, 'sum by ', '[^ ]+',
None)
(query_str, avg_by_str) = parse_out_value(query_str, 'avg by ', '[^ ]+',
None)
(query_str, avg_over_str) = parse_out_value(query_str, 'avg over ',
'[^ ]+', None)
(query_str, min_str) = parse_out_value(query_str, 'min ', '[^ ]+', None)
(query_str, max_str) = parse_out_value(query_str, 'max ', '[^ ]+', None)
explicit_group_by = []
if group_by_str is not None:
explicit_group_by = group_by_str.split(',')
query['group_by'] = explicit_group_by
elif extra_group_by_str is not None:
explicit_group_by = extra_group_by_str.split(',')
query['group_by'] = [
tag for tag in query['group_by'] if tag.endswith('=')
]
query['group_by'].extend(explicit_group_by)
if sum_by_str is not None:
query['sum_by'] = sum_by_str.split(',')
if avg_by_str is not None:
query['avg_by'] = avg_by_str.split(',')
if min_str is not None:
# check if we can parse the values, but don't actually replace yet
# because we want to keep the 'pretty' value for now so we can display
# it in the query details section
convert.parse_str(min_str)
query['min'] = min_str
if max_str is not None:
convert.parse_str(max_str)
query['max'] = max_str
# if you specified a tag in avg_by or sum_by that is included in the
# default group_by (and you didn't explicitly ask to group by that tag), we
# remove it from group by, so that the avg/sum can work properly.
for tag in query['sum_by'] + query['avg_by']:
for tag_check in (tag, "%s=" % tag):
if tag_check in query[
'group_by'] and tag_check not in explicit_group_by:
query['group_by'].remove(tag_check)
if len(query['group_by']) + len(query['sum_by']) + len(
query['avg_by']) != len(
set(query['group_by'] + query['sum_by'] + query['avg_by'])):
raise Exception(
"'group by' (%s), 'sum by (%s)' and 'avg by (%s)' cannot list the same tag keys"
% (', '.join(query['group_by']), ', '.join(
query['sum_by']), ', '.join(query['avg_by'])))
if avg_over_str is not None:
# avg_over_str should be something like 'h', '10M', etc
avg_over = re.match(avg_over_match, avg_over_str)
if avg_over is not None: # if None, that's an invalid request. ignore it. TODO error to user
avg_over = avg_over.groups()
query['avg_over'] = (int(avg_over[0]), avg_over[1])
for tag in query['group_by']:
if tag.endswith('='):
query['patterns'].append(tag)
(query_str,
query['limit_targets']) = parse_out_value(query_str, 'limit ', '[^ ]+',
500)
# split query_str into multiple patterns which are all matched independently
# this allows you write patterns in any order, and also makes it easy to use negations
query['patterns'] += query_str.split()
return query
def parse_query(query_str):
avg_over_match = '^([0-9]*)(s|M|h|d|w|mo)$'
query = {
'patterns': [],
'group_by': ['target_type=', 'unit=', 'server'],
'sum_by': [],
'avg_by': [],
'avg_over': None,
'min': None,
'max': None
}
# for a call like ('foo bar baz quux', 'bar ', 'baz', 'def')
# returns ('foo quux', 'baz') or the original query and the default val if no match
def parse_out_value(query_str, predicate_match, value_match, value_default):
match = re.search('(%s%s)' % (predicate_match, value_match), query_str)
value = value_default
if match and match.groups() > 0:
value = match.groups(1)[0].replace(predicate_match, '')
query_str = query_str[:match.start(1)] + query_str[match.end(1):]
return (query_str, value)
(query_str, query['statement']) = parse_out_value(query_str, '^', '(graph|list|stack|lines) ', 'graph')
query['statement'] = query['statement'].rstrip()
(query_str, query['to']) = parse_out_value(query_str, 'to ', '[^ ]+', 'now')
(query_str, query['from']) = parse_out_value(query_str, 'from ', '[^ ]+', '-24hours')
(query_str, group_by_str) = parse_out_value(query_str, 'GROUP BY ', '[^ ]+', None)
(query_str, extra_group_by_str) = parse_out_value(query_str, 'group by ', '[^ ]+', None)
(query_str, sum_by_str) = parse_out_value(query_str, 'sum by ', '[^ ]+', None)
(query_str, avg_by_str) = parse_out_value(query_str, 'avg by ', '[^ ]+', None)
(query_str, avg_over_str) = parse_out_value(query_str, 'avg over ', '[^ ]+', None)
(query_str, min_str) = parse_out_value(query_str, 'min ', '[^ ]+', None)
(query_str, max_str) = parse_out_value(query_str, 'max ', '[^ ]+', None)
explicit_group_by = []
if group_by_str is not None:
explicit_group_by = group_by_str.split(',')
query['group_by'] = explicit_group_by
elif extra_group_by_str is not None:
explicit_group_by = extra_group_by_str.split(',')
query['group_by'] = [tag for tag in query['group_by'] if tag.endswith('=')]
query['group_by'].extend(explicit_group_by)
if sum_by_str is not None:
query['sum_by'] = sum_by_str.split(',')
if avg_by_str is not None:
query['avg_by'] = avg_by_str.split(',')
if min_str is not None:
# check if we can parse the values, but don't actually replace yet
# because we want to keep the 'pretty' value for now so we can display
# it in the query details section
convert.parse_str(min_str)
query['min'] = min_str
if max_str is not None:
convert.parse_str(max_str)
query['max'] = max_str
# if you specified a tag in avg_by or sum_by that is included in the
# default group_by (and you didn't explicitly ask to group by that tag), we
# remove it from group by, so that the avg/sum can work properly.
for tag in query['sum_by'] + query['avg_by']:
for tag_check in (tag, "%s=" % tag):
if tag_check in query['group_by'] and tag_check not in explicit_group_by:
query['group_by'].remove(tag_check)
if len(query['group_by']) + len(query['sum_by']) + len(query['avg_by']) != len(set(query['group_by'] + query['sum_by'] + query['avg_by'])):
raise Exception("'group by' (%s), 'sum by (%s)' and 'avg by (%s)' cannot list the same tag keys" %
(', '.join(query['group_by']), ', '.join(query['sum_by']), ', '.join(query['avg_by'])))
if avg_over_str is not None:
# avg_over_str should be something like 'h', '10M', etc
avg_over = re.match(avg_over_match, avg_over_str)
if avg_over is not None: # if None, that's an invalid request. ignore it. TODO error to user
avg_over = avg_over.groups()
query['avg_over'] = (int(avg_over[0]), avg_over[1])
for tag in query['group_by']:
if tag.endswith('='):
query['patterns'].append(tag)
(query_str, query['limit_targets']) = parse_out_value(query_str, 'limit ', '[^ ]+', 500)
# split query_str into multiple patterns which are all matched independently
# this allows you write patterns in any order, and also makes it easy to use negations
query['patterns'] += query_str.split()
return query
def parse(self, query_str):
avg_over_match = '^([0-9]*)(s|M|h|d|w|mo)$'
# for a call like ('foo bar baz quux', 'bar ', 'baz', 'def')
# returns ('foo quux', 'baz') or the original query and the default val if no match
def parse_val(query_str, predicate_match, value_match, value_default=None):
match = re.search('\\b(%s%s)' % (predicate_match, value_match), query_str)
value = value_default
if match and match.groups() > 0:
value = match.groups(1)[0].replace(predicate_match, '')
query_str = query_str[:match.start(1)] + query_str[match.end(1):]
return (query_str, value)
if '||' in query_str:
(query_str, _, self['events_query']) = query_str.partition('||')
(query_str, self['statement']) = parse_val(query_str, '^', '(graph|list|stack|lines)\\b',
self['statement'])
self['statement'] = self['statement'].rstrip()
(query_str, self['to']) = parse_val(query_str, 'to ', '[^ ]+', self['to'])
(query_str, self['from']) = parse_val(query_str, 'from ', '[^ ]+', self['from'])
(query_str, group_by_str) = parse_val(query_str, 'GROUP BY ', '[^ ]+')
(query_str, extra_group_by_str) = parse_val(query_str, 'group by ', '[^ ]+')
(query_str, sum_by_str) = parse_val(query_str, 'sum by ', '[^ ]+')
(query_str, avg_by_str) = parse_val(query_str, 'avg by ', '[^ ]+')
(query_str, avg_over_str) = parse_val(query_str, 'avg over ', '[^ ]+')
(query_str, min_str) = parse_val(query_str, 'min ', '[^ ]+')
(query_str, max_str) = parse_val(query_str, 'max ', '[^ ]+')
explicit_group_by = {}
if group_by_str is not None:
explicit_group_by = Query.build_buckets(group_by_str)
self['group_by'] = explicit_group_by
elif extra_group_by_str is not None:
for k in self['group_by'].keys():
if not k.endswith('='):
del self['group_by'][k]
explicit_group_by = Query.build_buckets(extra_group_by_str)
self['group_by'].update(explicit_group_by)
if sum_by_str is not None:
self['sum_by'] = Query.build_buckets(sum_by_str)
if avg_by_str is not None:
self['avg_by'] = Query.build_buckets(avg_by_str)
if min_str is not None:
# check if we can parse the values, but don't actually replace yet
# because we want to keep the 'pretty' value for now so we can display
# it in the query details section
convert.parse_str(min_str)
self['min'] = min_str
if max_str is not None:
convert.parse_str(max_str)
self['max'] = max_str
# if you specified a tag in avg_by or sum_by that is included in the
# default group_by (and you didn't explicitly ask to group by that tag), we
# remove it from group by, so that the avg/sum can work properly.
for tag in self['sum_by'].keys() + self['avg_by'].keys():
for tag_check in (tag, "%s=" % tag):
if tag_check in self['group_by'] and tag_check not in explicit_group_by.keys():
del self['group_by'][tag_check]
# doing this sanity check would now be tricky: basically you can have the same keys in more than 1 of sum/avg/group by,
# it now depends on the bucket configuration. since i can't wrap my head around it anymore, let's just leave it be for now.
# it's up to people to construct sane queries, and if they do a stupid query, then at least GE shouldn't crash or anything.
# sum_individual_keys = len(self['group_by']) + len(self['sum_by']) + len(self['avg_by'])
# sum_unique_keys = len(set(self['group_by'].keys() + self['sum_by'].keys() + self['avg_by'].keys()))
# if sum_individual_keys != sum_unique_keys:
# raise Exception("'group by' (%s), 'sum by (%s)' and 'avg by (%s)' "
# "cannot list the same tag keys" %
# (', '.join(self['group_by'].keys()),
# ', '.join(self['sum_by'].keys()),
# ', '.join(self['avg_by'].keys())))
if avg_over_str is not None:
# avg_over_str should be something like 'h', '10M', etc
avg_over = re.match(avg_over_match, avg_over_str)
if avg_over is not None: # if None, that's an invalid request. ignore it. TODO error to user
avg_over = avg_over.groups()
self['avg_over'] = (int(avg_over[0]), avg_over[1])
(query_str, self['limit_targets']) = parse_val(query_str, 'limit ', '[^ ]+', self['limit_targets'])
self['limit_targets'] = int(self['limit_targets'])
# split query_str into multiple patterns which are all matched independently
# this allows you write patterns in any order, and also makes it easy to use negations
self['patterns'] += query_str.split()
def build_from_targets(targets, query, preferences):
graphs = {}
if not targets:
return (graphs, query)
group_by = query['group_by']
sum_by = query['sum_by']
avg_by = query['avg_by']
avg_over = query['avg_over']
# i'm gonna assume you never use second and your datapoints are stored with
# minutely resolution. later on we can use config options for this (or
# better: somehow query graphite about it)
# note, the day/week/month numbers are not technically accurate, but
# since we're doing movingAvg that's ok
averaging = {
'M': 1,
'h': 60,
'd': 60 * 24,
'w': 60 * 24 * 7,
'mo': 60 * 24 * 30
}
if avg_over is not None:
avg_over_amount = avg_over[0]
avg_over_unit = avg_over[1]
if avg_over_unit in averaging.keys():
multiplier = averaging[avg_over_unit]
query['target_modifiers'].append(
Query.graphite_function_applier('movingAverage',
avg_over_amount * multiplier))
# for each group_by bucket, make 1 graph.
# so for each graph, we have:
# the "constants": tags in the group_by
# the "variables": tags not in the group_by, which can have arbitrary
# values, or different values from a group_by tag that match the same
# bucket pattern
# go through all targets and group them into graphs:
for _target_id, target_data in sorted(targets.items()):
# FWIW. has an 'id' which timeserieswidget doesn't care about
target = Target(target_data)
target['target'] = target['id']
(graph_key, constants) = target.get_graph_info(group_by)
if graph_key not in graphs:
graph = {'from': query['from'], 'until': query['to']}
graph.update({'constants': constants, 'targets': []})
graphs[graph_key] = graph
graphs[graph_key]['targets'].append(target)
# ok so now we have a graphs dictionary with a graph for every appropriate
# combination of group_by tags, and each graph contains all targets that
# should be shown on it. but the user may have asked to aggregate certain
# targets together, by summing and/or averaging across different values of
# (a) certain tag(s). let's process the aggregations now.
if (sum_by or avg_by):
for (graph_key, graph_config) in graphs.items():
graph_config['targets_sum_candidates'] = {}
graph_config['targets_avg_candidates'] = {}
graph_config['normal_targets'] = []
# process equivalence rules, see further down.
filter_candidates = {}
for tag, buckets in sum_by.items():
# first separate the individuals from the _sum_
filter_candidates[tag] = {}
for target in graph_config['targets']:
# we can use agg_key to find out if they all have the same values
# other than this one particular key
key = target.get_agg_key({tag: buckets})
if key not in filter_candidates[tag]:
filter_candidates[tag][key] = {'individuals': []}
if target['tags'].get(tag, '') == '_sum_':
filter_candidates[tag][key]['_sum_'] = target
else:
filter_candidates[tag][key]['individuals'].append(
target)
# for all agg keys that only have the '' bucket,
# if targets are identical except that some have tag
# foo={bar,baz,0,quux, ...} and one of them has foo=_sum_ and we're
# summing by that tag, and we didn't filter on foo,
# remove all the ones except the sum one
if len(buckets) == 1 and buckets[0] == '':
if not Query.filtered_on(query, tag):
for key in filter_candidates[tag].keys():
if '_sum_' in filter_candidates[tag][key]:
for i in filter_candidates[tag][key][
'individuals']:
graph_config['targets'].remove(i)
# if we are summing, and we have a filter, and we have individual ones and a _sum_, remove the _sum_
# irrespective of buckets. note that this removes the _sum_ target without the user needing to filter it out explicitly
# this is the only place we do that, but it makes sense. we wouldn't want users to specify the _sum_ removal explicitly
# all the time, esp for multiple tag keys
if Query.filtered_on(query, tag):
for key in filter_candidates[tag].keys():
if '_sum_' in filter_candidates[tag][key]:
graph_config['targets'].remove(
filter_candidates[tag][key]['_sum_'])
for target in graph_config['targets']:
sum_id = target.get_agg_key(sum_by)
if sum_id:
if sum_id not in graph_config['targets_sum_candidates']:
graphs[graph_key]['targets_sum_candidates'][
sum_id] = []
graph_config['targets_sum_candidates'][sum_id].append(
target)
for (sum_id,
targets) in graph_config['targets_sum_candidates'].items():
if len(targets) > 1:
for candidate in targets:
graph_config['targets'].remove(candidate)
graph_config['targets'].append(
t.graphite_func_aggregate(targets, sum_by,
"sumSeries"))
for target in graph_config['targets']:
# Now that any summing is done, we look at aggregating by
# averaging because avg(foo+bar+baz) is more efficient
# than avg(foo)+avg(bar)+avg(baz)
# aggregate targets (whether those are sums or regular ones)
avg_id = target.get_agg_key(avg_by)
if avg_id:
if avg_id not in graph_config['targets_avg_candidates']:
graph_config['targets_avg_candidates'][avg_id] = []
graph_config['targets_avg_candidates'][avg_id].append(
target)
for (avg_id,
targets) in graph_config['targets_avg_candidates'].items():
if len(targets) > 1:
for candidate in targets:
graph_config['targets'].remove(candidate)
graph_config['targets'].append(
t.graphite_func_aggregate(targets, avg_by,
"averageSeries"))
# remove targets/graphs over the limit
graphs = limit_targets(graphs, query['limit_targets'])
# Apply target modifiers (like movingAverage, summarize, ...)
for (graph_key, graph_config) in graphs.items():
for target in graph_config['targets']:
for target_modifier in query['target_modifiers']:
target_modifier(target, graph_config)
# if in a graph all targets have a tag with the same value, they are
# effectively constants, so promote them. this makes the display of the
# graphs less rendundant and makes it easier to do config/preferences
# on a per-graph basis.
for (graph_key, graph_config) in graphs.items():
# get all variable tags throughout all targets in this graph
tags_seen = set()
for target in graph_config['targets']:
for tag_name in target['variables'].keys():
tags_seen.add(tag_name)
# find effective constants from those variables,
# and effective variables. (unset tag is a value too)
first_values_seen = {}
effective_variables = set() # tags for which we've seen >1 values
for target in graph_config['targets']:
for tag_name in tags_seen:
# already known that we can't promote, continue
if tag_name in effective_variables:
continue
tag_value = target['variables'].get(tag_name, None)
if tag_name not in first_values_seen:
first_values_seen[tag_name] = tag_value
elif tag_value != first_values_seen[tag_name]:
effective_variables.add(tag_name)
effective_constants = tags_seen - effective_variables
# promote the effective_constants by adjusting graph and targets:
graph_config['promoted_constants'] = {}
for tag_name in effective_constants:
graph_config['promoted_constants'][tag_name] = first_values_seen[
tag_name]
for target in graph_config['targets']:
target['variables'].pop(tag_name, None)
# now that graph config is "rich", merge in settings from preferences
constants = dict(graph_config['constants'].items() +
graph_config['promoted_constants'].items())
for graph_option in get_action_on_rules_match(
preferences.graph_options, constants):
if isinstance(graph_option, dict):
graph_config.update(graph_option)
else:
graph_config = graphs[graph_key] = graph_option(graph_config)
# but, the query may override some preferences:
override = {}
if query['statement'] == 'lines':
override['state'] = 'lines'
if query['statement'] == 'stack':
override['state'] = 'stacked'
if query['min'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'min': convert.parse_str(query['min'])})
if query['max'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'max': convert.parse_str(query['max'])})
graphs[graph_key].update(override)
# now that some constants are promoted, we can give the graph more
# unique keys based on all (original + promoted) constants. this is in
# line with the meaning of the graph ("all targets with those constant
# tags"), but more importantly: this fixes cases where some graphs
# would otherwise have the same key, even though they have a different
# set of constants, this can manifest itself on dashboard pages where
# graphs for different queries are shown.
# note that we can't just compile constants + promoted_constants,
# part of the original graph key is also set by the group by (which, by
# means of the bucket patterns doesn't always translate into constants),
# we solve this by just including the old key.
new_graphs = {}
for (graph_key, graph_config) in graphs.items():
new_key = ','.join('%s=%s' % i
for i in graph_config['promoted_constants'].items())
new_key = '%s__%s' % (graph_key, new_key)
new_graphs[new_key] = graph_config
graphs = new_graphs
return (graphs, query)
def build_graphs_from_targets(targets, query={}, target_modifiers=[]):
# merge default options..
defaults = {
'group_by': [],
'sum_by': [],
'avg_over': None,
'avg_by': [],
'from': '-24hours',
'to': 'now',
'statement': 'graph',
'limit_targets': 500
}
query = dict(defaults.items() + query.items())
graphs = {}
if not targets:
return (graphs, query)
group_by = query['group_by']
sum_by = query['sum_by']
avg_by = query['avg_by']
avg_over = query['avg_over']
# i'm gonna assume you never use second and your datapoints are stored with
# minutely resolution. later on we can use config options for this (or
# better: somehow query graphite about it)
# note, the day/week/month numbers are not technically accurate, but
# since we're doing movingAvg that's ok
averaging = {
'M': 1,
'h': 60,
'd': 60 * 24,
'w': 60 * 24 * 7,
'mo': 60 * 24 * 30
}
if avg_over is not None:
avg_over_amount = avg_over[0]
avg_over_unit = avg_over[1]
if avg_over_unit in averaging.keys():
multiplier = averaging[avg_over_unit]
target_modifier = {
'target': ['movingAverage',
str(avg_over_amount * multiplier)]
}
target_modifiers.append(target_modifier)
# for each combination of values of tags from group_by, make 1 graph with
# all targets that have these values. so for each graph, we have:
# the "constants": tags in the group_by
# the "variables": tags not in the group_by, which can have arbitrary values
# go through all targets and group them into graphs:
for (i, target_id) in enumerate(sorted(targets.iterkeys())):
constants = {}
variables = {}
target_data = targets[target_id]
for (tag_name, tag_value) in target_data['tags'].items():
if tag_name in group_by or '%s=' % tag_name in group_by:
constants[tag_name] = tag_value
else:
variables[tag_name] = tag_value
graph_key = '__'.join(
[target_data['tags'][tag_name] for tag_name in constants])
if graph_key not in graphs:
graph = {'from': query['from'], 'until': query['to']}
graph.update({'constants': constants, 'targets': []})
graphs[graph_key] = graph
target = target_data['id']
# set all options needed for timeserieswidget/flot:
t = {
'variables': variables,
'id':
target_data['id'], # timeserieswidget doesn't care about this
'target': target
}
if 'color' in target_data:
t['color'] = target_data['color']
graphs[graph_key]['targets'].append(t)
# ok so now we have a graphs dictionary with a graph for every approriate
# combination of group_by tags, and each graphs contains all targets that
# should be shown on it. but the user may have asked to aggregate certain
# targets together, by summing and/or averaging across different values of
# (a) certain tag(s). let's process the aggregations now.
if (sum_by or avg_by):
for (graph_key, graph_config) in graphs.items():
graph_config['targets_sum_candidates'] = {}
graph_config['targets_avg_candidates'] = {}
graph_config['normal_targets'] = []
all_targets = graph_config['targets'][:] # Get a copy.
for target in all_targets:
# targets that can get summed together with other tags, must
# have at least 1 'sum_by' tags in the variables list.
# targets that can get summed together must have:
# * the same 'sum_by' tag keys (not values, because we
# aggregate across different values for these tags)
# * the same variables (key and val), except those vals that
# are being summed by.
# so for every group of sum_by tags and variables we build a
# list of targets that can be summed together
# of course it only makes sense to sum by tags that the target
# actually has, and that are not already constants (meaning
# every target in the graph has the same value)
variables = target['variables'].keys()
sum_constants = set(sum_by).intersection(set(variables))
if sum_constants:
sum_constants_str = '_'.join(sorted(sum_constants))
variables_str = '_'.join([
'%s_%s' % (k, target['variables'][k])
for k in sorted(variables) if k not in sum_constants
])
sum_id = '%s__%s' % (sum_constants_str, variables_str)
if sum_id not in graph_config['targets_sum_candidates']:
graphs[graph_key]['targets_sum_candidates'][
sum_id] = []
graph_config['targets_sum_candidates'][sum_id].append(
target)
for (sum_id,
targets) in graph_config['targets_sum_candidates'].items():
if len(targets) > 1:
for t in targets:
all_targets.remove(t)
all_targets.append(
graphite_func_aggregate(targets, sum_by, "sumSeries"))
for target in all_targets:
# Now that any summing is done, we look at aggregating by
# averaging because avg(foo+bar+baz) is more efficient
# than avg(foo)+avg(bar)+avg(baz)
# It's pretty similar than what happened above and aggregates
# targets (whether those are sums or regular ones)
variables = target['variables'].keys()
avg_constants = set(avg_by).intersection(set(variables))
if avg_constants:
avg_constants_str = '_'.join(sorted(avg_constants))
variables_str = '_'.join([
'%s_%s' % (k, target['variables'][k])
for k in sorted(variables) if k not in avg_constants
])
# some values can be like 'sumSeries (8 values)' due to an
# earlier aggregation. if now targets have a different amount of
# values matched, that doesn't matter and they should still
# be aggregated together if the rest of the conditions are met
variables_str = re.sub('\([0-9]+ values\)', '(Xvalues)',
variables_str)
avg_id = '%s__%s' % (avg_constants_str, variables_str)
if avg_id not in graph_config['targets_avg_candidates']:
graph_config['targets_avg_candidates'][avg_id] = []
graph_config['targets_avg_candidates'][avg_id].append(
target)
for (avg_id,
targets) in graph_config['targets_avg_candidates'].items():
if len(targets) > 1:
for t in targets:
all_targets.remove(t)
all_targets.append(
graphite_func_aggregate(targets, avg_by,
"averageSeries"))
graph_config["targets"] = all_targets
# remove targets/graphs over the limit
graphs = graphs_limit_targets(graphs, query['limit_targets'])
# Apply target modifiers (like movingAverage, summarize, ...)
for (graph_key, graph_config) in graphs.items():
for target in graph_config['targets']:
for target_modifier in target_modifiers:
target['target'] = "%s(%s,%s)" % (
target_modifier['target'][0], target['target'], ','.join(
target_modifier['target'][1:]))
if 'tags' in target_modifier:
for (new_k, new_v) in target_modifier['tags'].items():
if new_k in graph_config['constants']:
graph_config['constants'][new_k] = new_v
else:
target['variables'][new_k] = new_v
# if in a graph all targets have a tag with the same value, they are
# effectively constants, so promote them. this makes the display of the
# graphs less rendundant and makes it easier to do config/preferences
# on a per-graph basis.
for (graph_key, graph_config) in graphs.items():
# get all variable tags throughout all targets in this graph
tags_seen = set()
for target in graph_config['targets']:
for tag_name in target['variables'].keys():
tags_seen.add(tag_name)
# find effective constants from those variables,
# and effective variables. (unset tag is a value too)
first_values_seen = {}
effective_variables = set() # tags for which we've seen >1 values
for target in graph_config['targets']:
for tag_name in tags_seen:
# already known that we can't promote, continue
if tag_name in effective_variables:
continue
tag_value = target['variables'].get(tag_name, None)
if tag_name not in first_values_seen:
first_values_seen[tag_name] = tag_value
elif tag_value != first_values_seen[tag_name]:
effective_variables.add(tag_name)
effective_constants = tags_seen - effective_variables
# promote the effective_constants by adjusting graph and targets:
graphs[graph_key]['promoted_constants'] = {}
for tag_name in effective_constants:
graphs[graph_key]['promoted_constants'][
tag_name] = first_values_seen[tag_name]
for (i, target) in enumerate(graph_config['targets']):
if tag_name in graphs[graph_key]['targets'][i]['variables']:
del graphs[graph_key]['targets'][i]['variables'][tag_name]
# now that graph config is "rich", merge in settings from preferences
constants = dict(graphs[graph_key]['constants'].items() +
graphs[graph_key]['promoted_constants'].items())
for graph_option in get_action_on_rules_match(
preferences.graph_options, constants):
if isinstance(graph_option, dict):
graphs[graph_key].update(graph_option)
else:
graphs[graph_key] = graph_option(graphs[graph_key])
# but, the query may override some preferences:
override = {}
if query['statement'] == 'lines':
override['state'] = 'lines'
if query['statement'] == 'stack':
override['state'] = 'stacked'
if query['min'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'min': convert.parse_str(query['min'])})
if query['max'] is not None:
override['yaxis'] = override.get('yaxis', {})
override['yaxis'].update({'max': convert.parse_str(query['max'])})
graphs[graph_key].update(override)
# now that some constants are promoted, we can give the graph more
# unique keys based on all (original + promoted) constants. this is in
# line with the meaning of the graph ("all targets with those constant
# tags"), but more importantly: this fixes cases where some graphs
# would otherwise have the same key, even though they have a different
# set of constants, this can manifest itself on dashboard pages where
# graphs for different queries are shown.
new_graphs = {}
for (graph_key, graph_config) in graphs.items():
better_graph_key_1 = '__'.join(
'%s_%s' % i for i in graph_config['constants'].items())
better_graph_key_2 = '__'.join(
'%s_%s' % i for i in graph_config['promoted_constants'].items())
better_graph_key = '%s___%s' % (better_graph_key_1, better_graph_key_2)
new_graphs[better_graph_key] = graph_config
graphs = new_graphs
return (graphs, query)
