forked from vimeo/graph-explorer
/
app.py
546 lines (461 loc) · 20.4 KB
/
app.py
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#!/usr/bin/env python2
from bottle import route, template, request, static_file, response, hook, BaseTemplate, post
import config
import preferences
from urlparse import urljoin
import structured_metrics
from graphs import Graphs
from backend import Backend, get_action_on_rules_match, make_config
from simple_match import filter_matching
from query import Query
from target import Target
import logging
import convert
import traceback
from alerting import Db, Rule
# contains all errors as key:(title,msg) items.
# will be used throughout the runtime to track all encountered errors
errors = {}
# will contain the latest data
last_update = None
config = make_config(config)
logger = logging.getLogger('app')
logger.setLevel(logging.DEBUG)
chandler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
chandler.setFormatter(formatter)
logger.addHandler(chandler)
if config.log_file:
fhandler = logging.FileHandler(config.log_file)
fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.debug('app starting')
backend = Backend(config, logger)
s_metrics = structured_metrics.StructuredMetrics(config, logger)
graphs_manager = Graphs()
graphs_manager.load_plugins()
graphs_all = graphs_manager.list_graphs()
@route('<path:re:/assets/.*>')
@route('<path:re:/timeserieswidget/.*js>')
@route('<path:re:/timeserieswidget/.*css>')
@route('<path:re:/timeserieswidget/timezone-js/src/.*js>')
@route('<path:re:/timeserieswidget/tz/.*>')
@route('<path:re:/DataTables/media/js/.*js>')
@route('<path:re:/DataTablesPlugins/integration/bootstrap/.*js>')
@route('<path:re:/DataTablesPlugins/integration/bootstrap/.*css>')
def static(path):
return static_file(path, root='.')
@route('/', method='GET')
@route('/index', method='GET')
@route('/index/', method='GET')
@route('/index/<query:path>', method='GET')
def index(query=''):
from suggested_queries import suggested_queries
body = template('templates/body.index', errors=errors, query=query, suggested_queries=suggested_queries)
return render_page(body)
@route('/dashboard/<dashboard_name>')
@route('/dashboard/<dashboard_name>/')
@route('/dashboard/<dashboard_name>/<apply_all_from_url>', method='GET')
def slash_dashboard(dashboard_name=None, apply_all_from_url=''):
dashboard = template('templates/dashboards/%s' % dashboard_name, errors=errors, apply_all_from_url=apply_all_from_url)
return render_page(dashboard)
def render_page(body, page='index'):
return unicode(template('templates/page', body=body, page=page, last_update=last_update))
@route('/meta')
def meta():
body = template('templates/body.meta', todo=template('templates/' + 'todo'.upper()))
return render_page(body, 'meta')
# accepts comma separated list of metric_id's
@route('/inspect/<metrics>')
def inspect_metric(metrics=''):
metrics = map(s_metrics.load_metric, metrics.split(','))
args = {'errors': errors,
'metrics': metrics,
'config': config
}
body = template('templates/body.inspect', args)
return render_page(body, 'inspect')
def build_graphs(graphs, query):
for v in graphs.values():
v.update(query)
return graphs
def graphs_limit_targets(nolimit_graphs, limit):
targets_used = 0
limit_graphs = {}
for (graph_key, graph_config) in nolimit_graphs.items():
limit_graphs[graph_key] = graph_config
nolimit_targets = graph_config['targets']
limit_graphs[graph_key]['targets'] = []
for target in nolimit_targets:
targets_used += 1
limit_graphs[graph_key]['targets'].append(target)
if targets_used == limit:
return limit_graphs
return limit_graphs
def graphite_func_aggregate(targets, agg_by_tags, aggfunc):
aggfunc_abbrev = {
"averageSeries": "avg",
"sumSeries": "sum"
}
agg = Target({
'target': '%s(%s)' % (aggfunc, ','.join([t['target'] for t in targets])),
'id': [t['id'] for t in targets],
'variables': targets[0]['variables'],
'tags': targets[0]['tags']
})
# set the tags that we're aggregating by to their special values
# differentiators is a list of tag values that set the contributing targets apart
# this will be used later in the UI
differentiators = {}
# in principle every target that came in will have the same match_bucket for the given tag
# (that's the whole point of bucketing)
# however, some targets may end up in the aggregation without actually having the tag
# so only set it when we find it
bucket_id = '<none>'
for agg_by_tag in agg_by_tags.keys():
for t in targets:
if agg_by_tag in t['match_buckets']:
bucket_id = t['match_buckets'][agg_by_tag]
differentiators[agg_by_tag] = differentiators.get(agg_by_tag, [])
differentiators[agg_by_tag].append(t['variables'].get(agg_by_tag, '<missing>'))
differentiators[agg_by_tag].sort()
bucket_id_str = ''
# note, bucket_id can be an empty string (catchall bucket),
# in which case don't mention it explicitly
if bucket_id:
bucket_id_str = "'%s' " % bucket_id
tag_val = (
'%s%s (%d vals, %d uniqs)' % (
bucket_id_str,
aggfunc_abbrev.get(aggfunc, aggfunc),
len(differentiators[agg_by_tag]),
len(set(differentiators[agg_by_tag]))
),
differentiators[agg_by_tag]
)
agg['variables'][agg_by_tag] = tag_val
agg['tags'][agg_by_tag] = tag_val
return agg
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)
@route('/graphs/', method='POST')
@route('/graphs/<query:path>', method='GET') # used for manually testing
def graphs_nodeps(query=''):
return handle_graphs(query, False)
@route('/graphs_deps/', method='POST')
@route('/graphs_deps/<query:path>', method='GET') # used for manually testing
def graphs_deps(query=''):
return handle_graphs(query, True)
def handle_graphs(query, deps):
'''
get all relevant graphs matching query,
graphs from structured_metrics targets, as well as graphs
defined in structured_metrics plugins
'''
if 'metrics_file' in errors:
return template('templates/graphs', errors=errors)
if not query:
query = request.forms.get('query')
if not query:
return template('templates/graphs', query=query, errors=errors)
return render_graphs(query, deps=deps)
@route('/render/<query>')
@route('/render/', method='POST')
@route('/render', method='POST')
def proxy_render(query=''):
import urllib2
url = urljoin(config.graphite_url_server, "/render/" + query)
body = request.body.read()
f = urllib2.urlopen(url, body)
# this can be very verbose:
#logger.debug("proxying graphite request: " + body)
message = f.info()
response.headers['Content-Type'] = message.gettype()
return f.read()
@route('/graphs_minimal/<query:path>', method='GET')
def graphs_minimal(query=''):
return handle_graphs_minimal(query, False)
@route('/graphs_minimal_deps/<query:path>', method='GET')
def graphs_minimal_deps(query=''):
return handle_graphs_minimal(query, True)
@route('/rules')
@route('/rules/')
def rules_list():
db = Db(config.alerting_db)
if 'rules' in errors:
del errors['rules']
try:
body = template('templates/body.rules', errors=errors, rules=db.get_rules())
except Exception, e:
errors['rules'] = ("Couldn't list rules: %s" % e, traceback.format_exc())
if errors:
body = template('templates/snippet.errors', errors=errors)
return render_page(body)
return render_page(body, 'rules')
@route('/rules/add')
@route('/rules/add/')
@route('/rules/add/<expr>')
def rules_add(expr=''):
args = {'errors': errors,
'expr': expr,
'config': config
}
body = template('templates/body.rules_add', args)
return render_page(body, 'rules_add')
@post('/rules/add')
def rules_add_submit():
expr = request.forms.get('expr')
val_warn = float(request.forms.get('val_warn'))
val_crit = float(request.forms.get('val_crit'))
dest = request.forms.get('dest')
if 'rules_add' in errors:
del errors['rules_add']
try:
rule = Rule(None, expr, val_warn, val_crit, dest)
db = Db(config.alerting_db)
db.add_rule(rule)
except Exception, e: # pylint: disable=W0703
errors["rules_add"] = ("Couldn't add rule: %s" % e, traceback.format_exc())
if errors:
body = template('templates/snippet.errors', errors=errors)
return render_page(body)
return "ok, rule added"
@hook('before_request')
def setrootpath():
# templates need to know the relative path to get resources from
root = '../' * request.path.count('/')
BaseTemplate.defaults['root'] = root
def handle_graphs_minimal(query, deps):
'''
like handle_graphs(), but without extra decoration, so can be used on
dashboards
TODO dashboard should show any errors
'''
if not query:
return template('templates/graphs', query=query, errors=errors)
return render_graphs(query, minimal=True, deps=deps)
def render_graphs(query, minimal=False, deps=False):
if "query_parse" in errors:
del errors["query_parse"]
try:
query = Query(query)
except Exception, e: # pylint: disable=W0703
errors["query_parse"] = ("Couldn't parse query: %s" % e, traceback.format_exc())
if errors:
body = template('templates/snippet.errors', errors=errors)
return render_page(body)
# TODO: something goes wrong here.
# if you do a query that will give an ES error (say 'foo(')
# and then fix the query and hit enter, this code will see the new query
# and ES will process the query fine, but for some reason the old error
# doesn't clear and sticks instead.
if "match_metrics" in errors:
del errors["match_metrics"]
try:
(query, targets_matching) = s_metrics.matching(query)
except Exception, e: # pylint: disable=W0703
errors["match_metrics"] = ("Couldn't find matching metrics: %s" % e, traceback.format_exc())
if errors:
body = template('templates/snippet.errors', errors=errors)
return render_page(body)
tags = set()
for target in targets_matching.values():
for tag_name in target['tags'].keys():
tags.add(tag_name)
graphs_matching = filter_matching(query['ast'], graphs_all)
graphs_matching = build_graphs(graphs_matching, query)
stats = {'len_targets_all': s_metrics.count_metrics(),
'len_graphs_all': len(graphs_all),
'len_targets_matching': len(targets_matching),
'len_graphs_matching': len(graphs_matching),
}
out = ''
graphs = []
targets_list = {}
# the code to handle different statements, and the view
# templates could be a bit prettier, but for now it'll do.
if query['statement'] in ('graph', 'lines', 'stack'):
graphs_targets_matching = build_graphs_from_targets(targets_matching, query)[0]
stats['len_graphs_targets_matching'] = len(graphs_targets_matching)
graphs_matching.update(graphs_targets_matching)
stats['len_graphs_matching_all'] = len(graphs_matching)
if len(graphs_matching) > 0 and deps:
out += template('templates/snippet.graph-deps')
for key in sorted(graphs_matching.iterkeys()):
graphs.append((key, graphs_matching[key]))
elif query['statement'] == 'list':
# for now, only supports targets, not graphs
targets_list = targets_matching
stats['len_graphs_targets_matching'] = 0
stats['len_graphs_matching_all'] = 0
args = {'errors': errors,
'query': query,
'config': config,
'graphs': graphs,
'targets_list': targets_list,
'tags': tags,
'preferences': preferences
}
args.update(stats)
if minimal:
out += template('templates/graphs_minimal', args)
else:
out += template('templates/graphs', args)
return out
# vim: ts=4 et sw=4: