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#!/usr/bin/env python
#
# deadlock_detector Detects potential deadlocks (lock order inversions)
# on a running process. For Linux, uses BCC, eBPF.
#
# USAGE: deadlock_detector.py [-h] [--binary BINARY] [--dump-graph DUMP_GRAPH]
# [--verbose] [--lock-symbols LOCK_SYMBOLS]
# [--unlock-symbols UNLOCK_SYMBOLS]
# pid
#
# This traces pthread mutex lock and unlock calls to build a directed graph
# representing the mutex wait graph:
#
# - Nodes in the graph represent mutexes.
# - Edge (A, B) exists if there exists some thread T where lock(A) was called
# and lock(B) was called before unlock(A) was called.
#
# If the program finds a potential lock order inversion, the program will dump
# the cycle of mutexes and the stack traces where each mutex was acquired, and
# then exit.
#
# This program can only find potential deadlocks that occur while the program
# is tracing the process. It cannot find deadlocks that may have occurred
# before the program was attached to the process.
#
# Since this traces all mutex lock and unlock events and all thread creation
# events on the traced process, the overhead of this bpf program can be very
# high if the process has many threads and mutexes. You should only run this on
# a process where the slowdown is acceptable.
#
# Note: This tool does not work for shared mutexes or recursive mutexes.
#
# For shared (read-write) mutexes, a deadlock requires a cycle in the wait
# graph where at least one of the mutexes in the cycle is acquiring exclusive
# (write) ownership.
#
# For recursive mutexes, lock() is called multiple times on the same mutex.
# However, there is no way to determine if a mutex is a recursive mutex
# after the mutex has been created. As a result, this tool will not find
# potential deadlocks that involve only one mutex.
#
# Copyright 2017 Facebook, Inc.
# Licensed under the Apache License, Version 2.0 (the "License")
#
# 01-Feb-2017 Kenny Yu Created this.
from __future__ import (
absolute_import, division, unicode_literals, print_function
)
from bcc import BPF
from collections import defaultdict
import argparse
import json
import os
import subprocess
import sys
import time
class DiGraph(object):
'''
Adapted from networkx: http://networkx.github.io/
Represents a directed graph. Edges can store (key, value) attributes.
'''
def __init__(self):
# Map of node -> set of nodes
self.adjacency_map = {}
# Map of (node1, node2) -> map string -> arbitrary attribute
# This will not be copied in subgraph()
self.attributes_map = {}
def neighbors(self, node):
return self.adjacency_map.get(node, set())
def edges(self):
edges = []
for node, neighbors in self.adjacency_map.items():
for neighbor in neighbors:
edges.append((node, neighbor))
return edges
def nodes(self):
return self.adjacency_map.keys()
def attributes(self, node1, node2):
return self.attributes_map[(node1, node2)]
def add_edge(self, node1, node2, **kwargs):
if node1 not in self.adjacency_map:
self.adjacency_map[node1] = set()
if node2 not in self.adjacency_map:
self.adjacency_map[node2] = set()
self.adjacency_map[node1].add(node2)
self.attributes_map[(node1, node2)] = kwargs
def remove_node(self, node):
self.adjacency_map.pop(node, None)
for _, neighbors in self.adjacency_map.items():
neighbors.discard(node)
def subgraph(self, nodes):
graph = DiGraph()
for node in nodes:
for neighbor in self.neighbors(node):
if neighbor in nodes:
graph.add_edge(node, neighbor)
return graph
def node_link_data(self):
'''
Returns the graph as a dictionary in a format that can be
serialized.
'''
data = {
'directed': True,
'multigraph': False,
'graph': {},
'links': [],
'nodes': [],
}
# Do one pass to build a map of node -> position in nodes
node_to_number = {}
for node in self.adjacency_map.keys():
node_to_number[node] = len(data['nodes'])
data['nodes'].append({'id': node})
# Do another pass to build the link information
for node, neighbors in self.adjacency_map.items():
for neighbor in neighbors:
link = self.attributes_map[(node, neighbor)].copy()
link['source'] = node_to_number[node]
link['target'] = node_to_number[neighbor]
data['links'].append(link)
return data
def strongly_connected_components(G):
'''
Adapted from networkx: http://networkx.github.io/
Parameters
----------
G : DiGraph
Returns
-------
comp : generator of sets
A generator of sets of nodes, one for each strongly connected
component of G.
'''
preorder = {}
lowlink = {}
scc_found = {}
scc_queue = []
i = 0 # Preorder counter
for source in G.nodes():
if source not in scc_found:
queue = [source]
while queue:
v = queue[-1]
if v not in preorder:
i = i + 1
preorder[v] = i
done = 1
v_nbrs = G.neighbors(v)
for w in v_nbrs:
if w not in preorder:
queue.append(w)
done = 0
break
if done == 1:
lowlink[v] = preorder[v]
for w in v_nbrs:
if w not in scc_found:
if preorder[w] > preorder[v]:
lowlink[v] = min([lowlink[v], lowlink[w]])
else:
lowlink[v] = min([lowlink[v], preorder[w]])
queue.pop()
if lowlink[v] == preorder[v]:
scc_found[v] = True
scc = {v}
while (
scc_queue and preorder[scc_queue[-1]] > preorder[v]
):
k = scc_queue.pop()
scc_found[k] = True
scc.add(k)
yield scc
else:
scc_queue.append(v)
def simple_cycles(G):
'''
Adapted from networkx: http://networkx.github.io/
Parameters
----------
G : DiGraph
Returns
-------
cycle_generator: generator
A generator that produces elementary cycles of the graph.
Each cycle is represented by a list of nodes along the cycle.
'''
def _unblock(thisnode, blocked, B):
stack = set([thisnode])
while stack:
node = stack.pop()
if node in blocked:
blocked.remove(node)
stack.update(B[node])
B[node].clear()
# Johnson's algorithm requires some ordering of the nodes.
# We assign the arbitrary ordering given by the strongly connected comps
# There is no need to track the ordering as each node removed as processed.
# save the actual graph so we can mutate it here
# We only take the edges because we do not want to
# copy edge and node attributes here.
subG = G.subgraph(G.nodes())
sccs = list(strongly_connected_components(subG))
while sccs:
scc = sccs.pop()
# order of scc determines ordering of nodes
startnode = scc.pop()
# Processing node runs 'circuit' routine from recursive version
path = [startnode]
blocked = set() # vertex: blocked from search?
closed = set() # nodes involved in a cycle
blocked.add(startnode)
B = defaultdict(set) # graph portions that yield no elementary circuit
stack = [(startnode, list(subG.neighbors(startnode)))]
while stack:
thisnode, nbrs = stack[-1]
if nbrs:
nextnode = nbrs.pop()
if nextnode == startnode:
yield path[:]
closed.update(path)
elif nextnode not in blocked:
path.append(nextnode)
stack.append((nextnode, list(subG.neighbors(nextnode))))
closed.discard(nextnode)
blocked.add(nextnode)
continue
# done with nextnode... look for more neighbors
if not nbrs: # no more nbrs
if thisnode in closed:
_unblock(thisnode, blocked, B)
else:
for nbr in subG.neighbors(thisnode):
if thisnode not in B[nbr]:
B[nbr].add(thisnode)
stack.pop()
path.pop()
# done processing this node
subG.remove_node(startnode)
H = subG.subgraph(scc) # make smaller to avoid work in SCC routine
sccs.extend(list(strongly_connected_components(H)))
def find_cycle(graph):
'''
Looks for a cycle in the graph. If found, returns the first cycle.
If nodes a1, a2, ..., an are in a cycle, then this returns:
[(a1,a2), (a2,a3), ... (an-1,an), (an, a1)]
Otherwise returns an empty list.
'''
cycles = list(simple_cycles(graph))
if cycles:
nodes = cycles[0]
nodes.append(nodes[0])
edges = []
prev = nodes[0]
for node in nodes[1:]:
edges.append((prev, node))
prev = node
return edges
else:
return []
def print_cycle(binary, graph, edges, thread_info, print_stack_trace_fn):
'''
Prints the cycle in the mutex graph in the following format:
Potential Deadlock Detected!
Cycle in lock order graph: M0 => M1 => M2 => M0
for (m, n) in cycle:
Mutex n acquired here while holding Mutex m in thread T:
[ stack trace ]
Mutex m previously acquired by thread T here:
[ stack trace ]
for T in all threads:
Thread T was created here:
[ stack trace ]
'''
# List of mutexes in the cycle, first and last repeated
nodes_in_order = []
# Map mutex address -> readable alias
node_addr_to_name = {}
for counter, (m, n) in enumerate(edges):
nodes_in_order.append(m)
# For global or static variables, try to symbolize the mutex address.
symbol = symbolize_with_objdump(binary, m)
if symbol:
symbol += ' '
node_addr_to_name[m] = 'Mutex M%d (%s0x%016x)' % (counter, symbol, m)
nodes_in_order.append(nodes_in_order[0])
print('----------------\nPotential Deadlock Detected!\n')
print(
'Cycle in lock order graph: %s\n' %
(' => '.join([node_addr_to_name[n] for n in nodes_in_order]))
)
# Set of threads involved in the lock inversion
thread_pids = set()
# For each edge in the cycle, print where the two mutexes were held
for (m, n) in edges:
thread_pid = graph.attributes(m, n)['thread_pid']
thread_comm = graph.attributes(m, n)['thread_comm']
first_mutex_stack_id = graph.attributes(m, n)['first_mutex_stack_id']
second_mutex_stack_id = graph.attributes(m, n)['second_mutex_stack_id']
thread_pids.add(thread_pid)
print(
'%s acquired here while holding %s in Thread %d (%s):' % (
node_addr_to_name[n], node_addr_to_name[m], thread_pid,
thread_comm
)
)
print_stack_trace_fn(second_mutex_stack_id)
print('')
print(
'%s previously acquired by the same Thread %d (%s) here:' %
(node_addr_to_name[m], thread_pid, thread_comm)
)
print_stack_trace_fn(first_mutex_stack_id)
print('')
# Print where the threads were created, if available
for thread_pid in thread_pids:
parent_pid, stack_id, parent_comm = thread_info.get(
thread_pid, (None, None, None)
)
if parent_pid:
print(
'Thread %d created by Thread %d (%s) here: ' %
(thread_pid, parent_pid, parent_comm)
)
print_stack_trace_fn(stack_id)
else:
print(
'Could not find stack trace where Thread %d was created' %
thread_pid
)
print('')
def symbolize_with_objdump(binary, addr):
'''
Searches the binary for the address using objdump. Returns the symbol if
it is found, otherwise returns empty string.
'''
try:
command = (
'objdump -tT %s | grep %x | awk {\'print $NF\'} | c++filt' %
(binary, addr)
)
output = subprocess.check_output(command, shell=True)
return output.decode('utf-8').strip()
except subprocess.CalledProcessError:
return ''
def strlist(s):
'''Given a comma-separated string, returns a list of substrings'''
return s.strip().split(',')
def main():
examples = '''Examples:
deadlock_detector 181 # Analyze PID 181
deadlock_detector 181 --binary /lib/x86_64-linux-gnu/libpthread.so.0
# Analyze PID 181 and locks from this binary.
# If tracing a process that is running from
# a dynamically-linked binary, this argument
# is required and should be the path to the
# pthread library.
deadlock_detector 181 --verbose
# Analyze PID 181 and print statistics about
# the mutex wait graph.
deadlock_detector 181 --lock-symbols my_mutex_lock1,my_mutex_lock2 \\
--unlock-symbols my_mutex_unlock1,my_mutex_unlock2
# Analyze PID 181 and trace custom mutex
# symbols instead of pthread mutexes.
deadlock_detector 181 --dump-graph graph.json
# Analyze PID 181 and dump the mutex wait
# graph to graph.json.
'''
parser = argparse.ArgumentParser(
description=(
'Detect potential deadlocks (lock inversions) in a running binary.'
'\nMust be run as root.'
),
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=examples,
)
parser.add_argument('pid', type=int, help='Pid to trace')
# Binaries with `:` in the path will fail to attach uprobes on kernels
# running without this patch: https://lkml.org/lkml/2017/1/13/585.
# Symlinks to the binary without `:` in the path can get around this issue.
parser.add_argument(
'--binary',
type=str,
default='',
help='If set, trace the mutexes from the binary at this path. '
'For statically-linked binaries, this argument is not required. '
'For dynamically-linked binaries, this argument is required and '
'should be the path of the pthread library the binary is using. '
'Example: /lib/x86_64-linux-gnu/libpthread.so.0',
)
parser.add_argument(
'--dump-graph',
type=str,
default='',
help='If set, this will dump the mutex graph to the specified file.',
)
parser.add_argument(
'--verbose',
action='store_true',
help='Print statistics about the mutex wait graph.',
)
parser.add_argument(
'--lock-symbols',
type=strlist,
default=['pthread_mutex_lock'],
help='Comma-separated list of lock symbols to trace. Default is '
'pthread_mutex_lock. These symbols cannot be inlined in the binary.',
)
parser.add_argument(
'--unlock-symbols',
type=strlist,
default=['pthread_mutex_unlock'],
help='Comma-separated list of unlock symbols to trace. Default is '
'pthread_mutex_unlock. These symbols cannot be inlined in the binary.',
)
args = parser.parse_args()
if not args.binary:
try:
args.binary = os.readlink('/proc/%d/exe' % args.pid)
except OSError as e:
print('%s. Is the process (pid=%d) running?' % (str(e), args.pid))
sys.exit(1)
bpf = BPF(src_file=b'deadlock_detector.c')
# Trace where threads are created
bpf.attach_kretprobe(event=bpf.get_syscall_fnname('clone'), fn_name='trace_clone')
# We must trace unlock first, otherwise in the time we attached the probe
# on lock() and have not yet attached the probe on unlock(), a thread can
# acquire mutexes and release them, but the release events will not be
# traced, resulting in noisy reports.
for symbol in args.unlock_symbols:
try:
bpf.attach_uprobe(
name=args.binary,
sym=symbol,
fn_name='trace_mutex_release',
pid=args.pid,
)
except Exception as e:
print('%s. Failed to attach to symbol: %s' % (str(e), symbol))
sys.exit(1)
for symbol in args.lock_symbols:
try:
bpf.attach_uprobe(
name=args.binary,
sym=symbol,
fn_name='trace_mutex_acquire',
pid=args.pid,
)
except Exception as e:
print('%s. Failed to attach to symbol: %s' % (str(e), symbol))
sys.exit(1)
def print_stack_trace(stack_id):
'''Closure that prints the symbolized stack trace.'''
for addr in bpf.get_table('stack_traces').walk(stack_id):
line = bpf.sym(addr, args.pid)
# Try to symbolize with objdump if we cannot with bpf.
if line == '[unknown]':
symbol = symbolize_with_objdump(args.binary, addr)
if symbol:
line = symbol
print('@ %016x %s' % (addr, line))
print('Tracing... Hit Ctrl-C to end.')
while True:
try:
# Map of child thread pid -> parent info
thread_info = {
child.value: (parent.parent_pid, parent.stack_id, parent.comm)
for child, parent in bpf.get_table('thread_to_parent').items()
}
# Mutex wait directed graph. Nodes are mutexes. Edge (A,B) exists
# if there exists some thread T where lock(A) was called and
# lock(B) was called before unlock(A) was called.
graph = DiGraph()
for key, leaf in bpf.get_table('edges').items():
graph.add_edge(
key.mutex1,
key.mutex2,
thread_pid=leaf.thread_pid,
thread_comm=leaf.comm.decode('utf-8'),
first_mutex_stack_id=leaf.mutex1_stack_id,
second_mutex_stack_id=leaf.mutex2_stack_id,
)
if args.verbose:
print(
'Mutexes: %d, Edges: %d' %
(len(graph.nodes()), len(graph.edges()))
)
if args.dump_graph:
with open(args.dump_graph, 'w') as f:
data = graph.node_link_data()
f.write(json.dumps(data, indent=2))
cycle = find_cycle(graph)
if cycle:
print_cycle(
args.binary, graph, cycle, thread_info, print_stack_trace
)
sys.exit(1)
time.sleep(1)
except KeyboardInterrupt:
break
if __name__ == '__main__':
main()