def _merge_leaf_nodes(self, query, path, leaf_nodes):
"""Get a single node from a list of leaf nodes."""
if not leaf_nodes:
return None
# Fast-path when there is a single node.
if len(leaf_nodes) == 1:
return leaf_nodes[0]
# Calculate best minimal node set
minimal_node_set = set()
covered_intervals = IntervalSet([])
# If the query doesn't fall entirely within the FIND_TOLERANCE window
# we disregard the window. This prevents unnecessary remote fetches
# caused when carbon's cache skews node.intervals, giving the appearance
# remote systems have data we don't have locally, which we probably
# do.
now = int(time.time())
tolerance_window = now - settings.FIND_TOLERANCE
disregard_tolerance_window = query.interval.start < tolerance_window
prior_to_window = Interval(float('-inf'), tolerance_window)
def measure_of_added_coverage(node,
drop_window=disregard_tolerance_window):
relevant_intervals = node.intervals.intersect_interval(
query.interval)
if drop_window:
relevant_intervals = relevant_intervals.intersect_interval(
prior_to_window)
return covered_intervals.union(
relevant_intervals).size - covered_intervals.size
nodes_remaining = list(leaf_nodes)
# Prefer local nodes first (and do *not* drop the tolerance window)
for node in leaf_nodes:
if node.local and measure_of_added_coverage(node, False) > 0:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
if settings.REMOTE_STORE_MERGE_RESULTS:
remote_nodes = [n for n in nodes_remaining if not n.local]
for node in remote_nodes:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
else:
while nodes_remaining:
node_coverages = [(measure_of_added_coverage(n), n)
for n in nodes_remaining]
best_coverage, best_node = max(node_coverages)
if best_coverage == 0:
break
nodes_remaining.remove(best_node)
minimal_node_set.add(best_node)
covered_intervals = covered_intervals.union(
best_node.intervals)
# Sometimes the requested interval falls within the caching window.
# We include the most likely node if the gap is within
# tolerance.
if not minimal_node_set:
def distance_to_requested_interval(node):
if not node.intervals:
return float('inf')
latest = sorted(node.intervals, key=lambda i: i.end)[-1]
distance = query.interval.start - latest.end
return distance if distance >= 0 else float('inf')
best_candidate = min(leaf_nodes,
key=distance_to_requested_interval)
if distance_to_requested_interval(
best_candidate) <= settings.FIND_TOLERANCE:
minimal_node_set.add(best_candidate)
if not minimal_node_set:
return None
elif len(minimal_node_set) == 1:
return minimal_node_set.pop()
else:
reader = MultiReader(minimal_node_set)
return LeafNode(path, reader)
def find_all(self, query, headers=None):
start = time.time()
result_queue = Queue.Queue()
jobs = []
# Start remote searches
if not query.local:
random.shuffle(self.remote_stores)
jobs.extend([(store.find, query, headers)
for store in self.remote_stores if store.available])
# Start local searches
for finder in self.finders:
jobs.append((finder.find_nodes, query))
if settings.USE_WORKER_POOL:
return_result = lambda x: result_queue.put(x)
for job in jobs:
get_pool().apply_async(func=job[0],
args=job[1:],
callback=return_result)
else:
for job in jobs:
result_queue.put(job[0](*job[1:]))
# Group matching nodes by their path
nodes_by_path = defaultdict(list)
deadline = start + settings.REMOTE_FIND_TIMEOUT
result_cnt = 0
while result_cnt < len(jobs):
wait_time = deadline - time.time()
try:
nodes = result_queue.get(True, wait_time)
# ValueError could happen if due to really unlucky timing wait_time is negative
except (Queue.Empty, ValueError):
if time.time() > deadline:
log.info("Timed out in find_all after %fs" %
(settings.REMOTE_FIND_TIMEOUT))
break
else:
continue
log.info("Got a find result after %fs" % (time.time() - start))
result_cnt += 1
if nodes:
for node in nodes:
nodes_by_path[node.path].append(node)
log.info("Got all find results in %fs" % (time.time() - start))
# Reduce matching nodes for each path to a minimal set
found_branch_nodes = set()
items = list(nodes_by_path.iteritems())
random.shuffle(items)
for path, nodes in items:
leaf_nodes = []
# First we dispense with the BranchNodes
for node in nodes:
if node.is_leaf:
leaf_nodes.append(node)
elif node.path not in found_branch_nodes: #TODO need to filter branch nodes based on requested interval... how?!?!?
yield node
found_branch_nodes.add(node.path)
if not leaf_nodes:
continue
# Fast-path when there is a single node.
if len(leaf_nodes) == 1:
yield leaf_nodes[0]
continue
# Calculate best minimal node set
minimal_node_set = set()
covered_intervals = IntervalSet([])
# If the query doesn't fall entirely within the FIND_TOLERANCE window
# we disregard the window. This prevents unnecessary remote fetches
# caused when carbon's cache skews node.intervals, giving the appearance
# remote systems have data we don't have locally, which we probably do.
now = int(time.time())
tolerance_window = now - settings.FIND_TOLERANCE
disregard_tolerance_window = query.interval.start < tolerance_window
prior_to_window = Interval(float('-inf'), tolerance_window)
def measure_of_added_coverage(
node, drop_window=disregard_tolerance_window):
relevant_intervals = node.intervals.intersect_interval(
query.interval)
if drop_window:
relevant_intervals = relevant_intervals.intersect_interval(
prior_to_window)
return covered_intervals.union(
relevant_intervals).size - covered_intervals.size
nodes_remaining = list(leaf_nodes)
# Prefer local nodes first (and do *not* drop the tolerance window)
for node in leaf_nodes:
if node.local and measure_of_added_coverage(node, False) > 0:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
if settings.REMOTE_STORE_MERGE_RESULTS:
remote_nodes = [n for n in nodes_remaining if not n.local]
for node in remote_nodes:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
else:
while nodes_remaining:
node_coverages = [(measure_of_added_coverage(n), n)
for n in nodes_remaining]
best_coverage, best_node = max(node_coverages)
if best_coverage == 0:
break
nodes_remaining.remove(best_node)
minimal_node_set.add(best_node)
covered_intervals = covered_intervals.union(
best_node.intervals)
# Sometimes the requested interval falls within the caching window.
# We include the most likely node if the gap is within tolerance.
if not minimal_node_set:
def distance_to_requested_interval(node):
if not node.intervals:
return float('inf')
latest = sorted(node.intervals,
key=lambda i: i.end)[-1]
distance = query.interval.start - latest.end
return distance if distance >= 0 else float('inf')
best_candidate = min(leaf_nodes,
key=distance_to_requested_interval)
if distance_to_requested_interval(
best_candidate) <= settings.FIND_TOLERANCE:
minimal_node_set.add(best_candidate)
if len(minimal_node_set) == 1:
yield minimal_node_set.pop()
elif len(minimal_node_set) > 1:
reader = MultiReader(minimal_node_set)
yield LeafNode(path, reader)
def _merge_leaf_nodes(self, query, path, leaf_nodes):
"""Get a single node from a list of leaf nodes."""
if not leaf_nodes:
return None
# Fast-path when there is a single node.
if len(leaf_nodes) == 1:
return leaf_nodes[0]
# Calculate best minimal node set
minimal_node_set = set()
covered_intervals = IntervalSet([])
# If the query doesn't fall entirely within the FIND_TOLERANCE window
# we disregard the window. This prevents unnecessary remote fetches
# caused when carbon's cache skews node.intervals, giving the appearance
# remote systems have data we don't have locally, which we probably
# do.
now = int(time.time())
tolerance_window = now - settings.FIND_TOLERANCE
disregard_tolerance_window = query.interval.start < tolerance_window
prior_to_window = Interval(float('-inf'), tolerance_window)
def measure_of_added_coverage(
node, drop_window=disregard_tolerance_window):
relevant_intervals = node.intervals.intersect_interval(
query.interval)
if drop_window:
relevant_intervals = relevant_intervals.intersect_interval(
prior_to_window)
return covered_intervals.union(
relevant_intervals).size - covered_intervals.size
nodes_remaining = list(leaf_nodes)
# Prefer local nodes first (and do *not* drop the tolerance window)
for node in leaf_nodes:
if node.local and measure_of_added_coverage(node, False) > 0:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
if settings.REMOTE_STORE_MERGE_RESULTS:
remote_nodes = [n for n in nodes_remaining if not n.local]
for node in remote_nodes:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
else:
while nodes_remaining:
node_coverages = [(measure_of_added_coverage(n), n)
for n in nodes_remaining]
best_coverage, best_node = max(node_coverages)
if best_coverage == 0:
break
nodes_remaining.remove(best_node)
minimal_node_set.add(best_node)
covered_intervals = covered_intervals.union(
best_node.intervals)
# Sometimes the requested interval falls within the caching window.
# We include the most likely node if the gap is within
# tolerance.
if not minimal_node_set:
def distance_to_requested_interval(node):
if not node.intervals:
return float('inf')
latest = sorted(
node.intervals, key=lambda i: i.end)[-1]
distance = query.interval.start - latest.end
return distance if distance >= 0 else float('inf')
best_candidate = min(
leaf_nodes, key=distance_to_requested_interval)
if distance_to_requested_interval(
best_candidate) <= settings.FIND_TOLERANCE:
minimal_node_set.add(best_candidate)
if not minimal_node_set:
return None
elif len(minimal_node_set) == 1:
return minimal_node_set.pop()
else:
reader = MultiReader(minimal_node_set)
return LeafNode(path, reader)
def find(self, pattern, startTime=None, endTime=None, local=False):
query = FindQuery(pattern, startTime, endTime)
# Start remote searches
if not local:
remote_requests = [ r.find(query) for r in self.remote_stores if r.available ]
matching_nodes = set()
# Search locally
for finder in self.finders:
for node in finder.find_nodes(query):
#log.info("find() :: local :: %s" % node)
matching_nodes.add(node)
# Gather remote search results
if not local:
for request in remote_requests:
for node in request.get_results():
#log.info("find() :: remote :: %s from %s" % (node,request.store.host))
matching_nodes.add(node)
# Group matching nodes by their path
nodes_by_path = {}
for node in matching_nodes:
if node.path not in nodes_by_path:
nodes_by_path[node.path] = []
nodes_by_path[node.path].append(node)
# Reduce matching nodes for each path to a minimal set
found_branch_nodes = set()
for path, nodes in nodes_by_path.iteritems():
leaf_nodes = []
# First we dispense with the BranchNodes
for node in nodes:
if node.is_leaf:
leaf_nodes.append(node)
elif node.path not in found_branch_nodes: #TODO need to filter branch nodes based on requested interval... how?!?!?
yield node
found_branch_nodes.add(node.path)
if not leaf_nodes:
continue
# Calculate best minimal node set
minimal_node_set = set()
covered_intervals = IntervalSet([])
# If the query doesn't fall entirely within the FIND_TOLERANCE window
# we disregard the window. This prevents unnecessary remote fetches
# caused when carbon's cache skews node.intervals, giving the appearance
# remote systems have data we don't have locally, which we probably do.
now = int( time.time() )
tolerance_window = now - settings.FIND_TOLERANCE
disregard_tolerance_window = query.interval.start < tolerance_window
prior_to_window = Interval( float('-inf'), tolerance_window )
def measure_of_added_coverage(node, drop_window=disregard_tolerance_window):
relevant_intervals = node.intervals.intersect_interval(query.interval)
if drop_window:
relevant_intervals = relevant_intervals.intersect_interval(prior_to_window)
return covered_intervals.union(relevant_intervals).size - covered_intervals.size
nodes_remaining = list(leaf_nodes)
# Prefer local nodes first (and do *not* drop the tolerance window)
for node in leaf_nodes:
if node.local and measure_of_added_coverage(node, False) > 0:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
if settings.REMOTE_STORE_MERGE_RESULTS:
remote_nodes = [n for n in nodes_remaining if not n.local]
for node in remote_nodes:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
else:
while nodes_remaining:
node_coverages = [ (measure_of_added_coverage(n), n) for n in nodes_remaining ]
best_coverage, best_node = max(node_coverages)
if best_coverage == 0:
break
nodes_remaining.remove(best_node)
minimal_node_set.add(best_node)
covered_intervals = covered_intervals.union(best_node.intervals)
# Sometimes the requested interval falls within the caching window.
# We include the most likely node if the gap is within tolerance.
if not minimal_node_set:
def distance_to_requested_interval(node):
latest = sorted(node.intervals, key=lambda i: i.end)[-1]
distance = query.interval.start - latest.end
return distance if distance >= 0 else float('inf')
best_candidate = min(leaf_nodes, key=distance_to_requested_interval)
if distance_to_requested_interval(best_candidate) <= settings.FIND_TOLERANCE:
minimal_node_set.add(best_candidate)
if len(minimal_node_set) == 1:
yield minimal_node_set.pop()
elif len(minimal_node_set) > 1:
reader = MultiReader(minimal_node_set)
yield LeafNode(path, reader)
def find_all(self, query, headers=None):
start = time.time()
result_queue = Queue.Queue()
jobs = []
# Start remote searches
if not query.local:
random.shuffle(self.remote_stores)
jobs.extend([(store.find, query, headers)
for store in self.remote_stores if store.available])
# single metric query, let's hit carbon-cache first,
# if we can fetch all data from carbon-cache, then
# DO NOT hit disk. It helps us reduce iowait.
# Please use the right version of carbon-cache.
found_in_cache = False
# Let's cache nodes with incomplete results, in case we need it and
# don't have to query carbon-cache again.
nodes_with_incomplete_result = {}
for leaf_node in self.carbon_cache_finder.find_nodes(
query, nodes_with_incomplete_result):
yield leaf_node
found_in_cache = True
if found_in_cache and query.startTime != 0:
return
# Start local searches
for finder in self.finders:
jobs.append((finder.find_nodes, query))
# Group matching nodes by their path
nodes_by_path = defaultdict(list)
def _work(job):
return job[0](*job[1:])
nodes_list = self.worker_pool.map(_work, jobs)
for nodes in nodes_list:
if nodes:
for node in nodes:
nodes_by_path[node.path].append(node)
# That means we should search all matched nodes.
# it would merge nodes with new metrics that only exists in carbon-cache
# merge any new metric node that only exists in carbon-cache,
# although they partial exist.
for name, node in nodes_with_incomplete_result.iteritems():
if name not in nodes_by_path:
nodes_by_path[name].append(node)
log.info("Got all find results in %fs" % (time.time() - start))
# Search Carbon Cache if nodes_by_path is empty
#
# We have this block of code here, because i wanna cover
# an edge case.
# 1) metric: carbon.foo
# 2) carbon-cache includes 2 hours data for carbon.foo
# 3) query data starting from 3 hours ago.
# in such case, previous carbon_cache_finder will not return any node
# because carbon-cache doesn't have enough data. However, if we reach
# this point, that means we should return all we have in carbon cache.
if not nodes_by_path:
for name, node in nodes_with_incomplete_result.iteritems():
# it only exists one value
yield node
return
# Reduce matching nodes for each path to a minimal set
found_branch_nodes = set()
items = list(nodes_by_path.iteritems())
random.shuffle(items)
for path, nodes in items:
leaf_nodes = []
# First we dispense with the BranchNodes
for node in nodes:
if node.is_leaf:
leaf_nodes.append(node)
elif node.path not in found_branch_nodes: #TODO need to filter branch nodes based on requested interval... how?!?!?
yield node
found_branch_nodes.add(node.path)
if not leaf_nodes:
continue
# Fast-path when there is a single node.
if len(leaf_nodes) == 1:
yield leaf_nodes[0]
continue
# Calculate best minimal node set
minimal_node_set = set()
covered_intervals = IntervalSet([])
# If the query doesn't fall entirely within the FIND_TOLERANCE window
# we disregard the window. This prevents unnecessary remote fetches
# caused when carbon's cache skews node.intervals, giving the appearance
# remote systems have data we don't have locally, which we probably do.
now = int(time.time())
tolerance_window = now - settings.FIND_TOLERANCE
disregard_tolerance_window = query.interval.start < tolerance_window
prior_to_window = Interval(float('-inf'), tolerance_window)
def measure_of_added_coverage(
node, drop_window=disregard_tolerance_window):
relevant_intervals = node.intervals.intersect_interval(
query.interval)
if drop_window:
relevant_intervals = relevant_intervals.intersect_interval(
prior_to_window)
return covered_intervals.union(
relevant_intervals).size - covered_intervals.size
nodes_remaining = list(leaf_nodes)
# Prefer local nodes first (and do *not* drop the tolerance window)
for node in leaf_nodes:
if node.local and measure_of_added_coverage(node, False) > 0:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
if settings.REMOTE_STORE_MERGE_RESULTS:
remote_nodes = [n for n in nodes_remaining if not n.local]
for node in remote_nodes:
nodes_remaining.remove(node)
minimal_node_set.add(node)
covered_intervals = covered_intervals.union(node.intervals)
else:
while nodes_remaining:
node_coverages = [(measure_of_added_coverage(n), n)
for n in nodes_remaining]
best_coverage, best_node = max(node_coverages)
if best_coverage == 0:
break
nodes_remaining.remove(best_node)
minimal_node_set.add(best_node)
covered_intervals = covered_intervals.union(
best_node.intervals)
# Sometimes the requested interval falls within the caching window.
# We include the most likely node if the gap is within tolerance.
if not minimal_node_set:
def distance_to_requested_interval(node):
if not node.intervals:
return float('inf')
latest = sorted(node.intervals,
key=lambda i: i.end)[-1]
distance = query.interval.start - latest.end
return distance if distance >= 0 else float('inf')
best_candidate = min(leaf_nodes,
key=distance_to_requested_interval)
if distance_to_requested_interval(
best_candidate) <= settings.FIND_TOLERANCE:
minimal_node_set.add(best_candidate)
if len(minimal_node_set) == 1:
yield minimal_node_set.pop()
elif len(minimal_node_set) > 1:
reader = MultiReader(minimal_node_set)
yield LeafNode(path, reader)