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# [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") --- Trace memory allocations
3\.4 新版功能.
**Source code:** [Lib/tracemalloc.py](https://github.com/python/cpython/tree/3.7/Lib/tracemalloc.py) \[https://github.com/python/cpython/tree/3.7/Lib/tracemalloc.py\]
- - - - - -
The tracemalloc module is a debug tool to trace memory blocks allocated by Python. It provides the following information:
- Traceback where an object was allocated
- Statistics on allocated memory blocks per filename and per line number: total size, number and average size of allocated memory blocks
- Compute the differences between two snapshots to detect memory leaks
To trace most memory blocks allocated by Python, the module should be started as early as possible by setting the [`PYTHONTRACEMALLOC`](../using/cmdline.xhtml#envvar-PYTHONTRACEMALLOC) environment variable to `1`, or by using [`-X`](../using/cmdline.xhtml#id5)`tracemalloc` command line option. The [`tracemalloc.start()`](#tracemalloc.start "tracemalloc.start") function can be called at runtime to start tracing Python memory allocations.
By default, a trace of an allocated memory block only stores the most recent frame (1 frame). To store 25 frames at startup: set the [`PYTHONTRACEMALLOC`](../using/cmdline.xhtml#envvar-PYTHONTRACEMALLOC) environment variable to `25`, or use the [`-X`](../using/cmdline.xhtml#id5)`tracemalloc=25` command line option.
## 示例
### Display the top 10
Display the 10 files allocating the most memory:
```
import tracemalloc
tracemalloc.start()
# ... run your application ...
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
print("[ Top 10 ]")
for stat in top_stats[:10]:
print(stat)
```
Example of output of the Python test suite:
```
[ Top 10 ]
<frozen importlib._bootstrap>:716: size=4855 KiB, count=39328, average=126 B
<frozen importlib._bootstrap>:284: size=521 KiB, count=3199, average=167 B
/usr/lib/python3.4/collections/__init__.py:368: size=244 KiB, count=2315, average=108 B
/usr/lib/python3.4/unittest/case.py:381: size=185 KiB, count=779, average=243 B
/usr/lib/python3.4/unittest/case.py:402: size=154 KiB, count=378, average=416 B
/usr/lib/python3.4/abc.py:133: size=88.7 KiB, count=347, average=262 B
<frozen importlib._bootstrap>:1446: size=70.4 KiB, count=911, average=79 B
<frozen importlib._bootstrap>:1454: size=52.0 KiB, count=25, average=2131 B
<string>:5: size=49.7 KiB, count=148, average=344 B
/usr/lib/python3.4/sysconfig.py:411: size=48.0 KiB, count=1, average=48.0 KiB
```
We can see that Python loaded `4855 KiB` data (bytecode and constants) from modules and that the [`collections`](collections.xhtml#module-collections "collections: Container datatypes") module allocated `244 KiB` to build [`namedtuple`](collections.xhtml#collections.namedtuple "collections.namedtuple") types.
See [`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics") for more options.
### Compute differences
Take two snapshots and display the differences:
```
import tracemalloc
tracemalloc.start()
# ... start your application ...
snapshot1 = tracemalloc.take_snapshot()
# ... call the function leaking memory ...
snapshot2 = tracemalloc.take_snapshot()
top_stats = snapshot2.compare_to(snapshot1, 'lineno')
print("[ Top 10 differences ]")
for stat in top_stats[:10]:
print(stat)
```
Example of output before/after running some tests of the Python test suite:
```
[ Top 10 differences ]
<frozen importlib._bootstrap>:716: size=8173 KiB (+4428 KiB), count=71332 (+39369), average=117 B
/usr/lib/python3.4/linecache.py:127: size=940 KiB (+940 KiB), count=8106 (+8106), average=119 B
/usr/lib/python3.4/unittest/case.py:571: size=298 KiB (+298 KiB), count=589 (+589), average=519 B
<frozen importlib._bootstrap>:284: size=1005 KiB (+166 KiB), count=7423 (+1526), average=139 B
/usr/lib/python3.4/mimetypes.py:217: size=112 KiB (+112 KiB), count=1334 (+1334), average=86 B
/usr/lib/python3.4/http/server.py:848: size=96.0 KiB (+96.0 KiB), count=1 (+1), average=96.0 KiB
/usr/lib/python3.4/inspect.py:1465: size=83.5 KiB (+83.5 KiB), count=109 (+109), average=784 B
/usr/lib/python3.4/unittest/mock.py:491: size=77.7 KiB (+77.7 KiB), count=143 (+143), average=557 B
/usr/lib/python3.4/urllib/parse.py:476: size=71.8 KiB (+71.8 KiB), count=969 (+969), average=76 B
/usr/lib/python3.4/contextlib.py:38: size=67.2 KiB (+67.2 KiB), count=126 (+126), average=546 B
```
We can see that Python has loaded `8173 KiB` of module data (bytecode and constants), and that this is `4428 KiB` more than had been loaded before the tests, when the previous snapshot was taken. Similarly, the [`linecache`](linecache.xhtml#module-linecache "linecache: This module provides random access to individual lines from text files.")module has cached `940 KiB` of Python source code to format tracebacks, all of it since the previous snapshot.
If the system has little free memory, snapshots can be written on disk using the [`Snapshot.dump()`](#tracemalloc.Snapshot.dump "tracemalloc.Snapshot.dump") method to analyze the snapshot offline. Then use the [`Snapshot.load()`](#tracemalloc.Snapshot.load "tracemalloc.Snapshot.load") method reload the snapshot.
### Get the traceback of a memory block
Code to display the traceback of the biggest memory block:
```
import tracemalloc
# Store 25 frames
tracemalloc.start(25)
# ... run your application ...
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('traceback')
# pick the biggest memory block
stat = top_stats[0]
print("%s memory blocks: %.1f KiB" % (stat.count, stat.size / 1024))
for line in stat.traceback.format():
print(line)
```
Example of output of the Python test suite (traceback limited to 25 frames):
```
903 memory blocks: 870.1 KiB
File "<frozen importlib._bootstrap>", line 716
File "<frozen importlib._bootstrap>", line 1036
File "<frozen importlib._bootstrap>", line 934
File "<frozen importlib._bootstrap>", line 1068
File "<frozen importlib._bootstrap>", line 619
File "<frozen importlib._bootstrap>", line 1581
File "<frozen importlib._bootstrap>", line 1614
File "/usr/lib/python3.4/doctest.py", line 101
import pdb
File "<frozen importlib._bootstrap>", line 284
File "<frozen importlib._bootstrap>", line 938
File "<frozen importlib._bootstrap>", line 1068
File "<frozen importlib._bootstrap>", line 619
File "<frozen importlib._bootstrap>", line 1581
File "<frozen importlib._bootstrap>", line 1614
File "/usr/lib/python3.4/test/support/__init__.py", line 1728
import doctest
File "/usr/lib/python3.4/test/test_pickletools.py", line 21
support.run_doctest(pickletools)
File "/usr/lib/python3.4/test/regrtest.py", line 1276
test_runner()
File "/usr/lib/python3.4/test/regrtest.py", line 976
display_failure=not verbose)
File "/usr/lib/python3.4/test/regrtest.py", line 761
match_tests=ns.match_tests)
File "/usr/lib/python3.4/test/regrtest.py", line 1563
main()
File "/usr/lib/python3.4/test/__main__.py", line 3
regrtest.main_in_temp_cwd()
File "/usr/lib/python3.4/runpy.py", line 73
exec(code, run_globals)
File "/usr/lib/python3.4/runpy.py", line 160
"__main__", fname, loader, pkg_name)
```
We can see that the most memory was allocated in the [`importlib`](importlib.xhtml#module-importlib "importlib: The implementation of the import machinery.") module to load data (bytecode and constants) from modules: `870.1 KiB`. The traceback is where the [`importlib`](importlib.xhtml#module-importlib "importlib: The implementation of the import machinery.") loaded data most recently: on the `import pdb`line of the [`doctest`](doctest.xhtml#module-doctest "doctest: Test pieces of code within docstrings.") module. The traceback may change if a new module is loaded.
### Pretty top
Code to display the 10 lines allocating the most memory with a pretty output, ignoring `<frozen importlib._bootstrap>` and `<unknown>` files:
```
import linecache
import os
import tracemalloc
def display_top(snapshot, key_type='lineno', limit=10):
snapshot = snapshot.filter_traces((
tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
tracemalloc.Filter(False, "<unknown>"),
))
top_stats = snapshot.statistics(key_type)
print("Top %s lines" % limit)
for index, stat in enumerate(top_stats[:limit], 1):
frame = stat.traceback[0]
# replace "/path/to/module/file.py" with "module/file.py"
filename = os.sep.join(frame.filename.split(os.sep)[-2:])
print("#%s: %s:%s: %.1f KiB"
% (index, filename, frame.lineno, stat.size / 1024))
line = linecache.getline(frame.filename, frame.lineno).strip()
if line:
print(' %s' % line)
other = top_stats[limit:]
if other:
size = sum(stat.size for stat in other)
print("%s other: %.1f KiB" % (len(other), size / 1024))
total = sum(stat.size for stat in top_stats)
print("Total allocated size: %.1f KiB" % (total / 1024))
tracemalloc.start()
# ... run your application ...
snapshot = tracemalloc.take_snapshot()
display_top(snapshot)
```
Example of output of the Python test suite:
```
Top 10 lines
#1: Lib/base64.py:414: 419.8 KiB
_b85chars2 = [(a + b) for a in _b85chars for b in _b85chars]
#2: Lib/base64.py:306: 419.8 KiB
_a85chars2 = [(a + b) for a in _a85chars for b in _a85chars]
#3: collections/__init__.py:368: 293.6 KiB
exec(class_definition, namespace)
#4: Lib/abc.py:133: 115.2 KiB
cls = super().__new__(mcls, name, bases, namespace)
#5: unittest/case.py:574: 103.1 KiB
testMethod()
#6: Lib/linecache.py:127: 95.4 KiB
lines = fp.readlines()
#7: urllib/parse.py:476: 71.8 KiB
for a in _hexdig for b in _hexdig}
#8: <string>:5: 62.0 KiB
#9: Lib/_weakrefset.py:37: 60.0 KiB
self.data = set()
#10: Lib/base64.py:142: 59.8 KiB
_b32tab2 = [a + b for a in _b32tab for b in _b32tab]
6220 other: 3602.8 KiB
Total allocated size: 5303.1 KiB
```
See [`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics") for more options.
## API
### 函数
`tracemalloc.``clear_traces`()Clear traces of memory blocks allocated by Python.
See also [`stop()`](#tracemalloc.stop "tracemalloc.stop").
`tracemalloc.``get_object_traceback`(*obj*)Get the traceback where the Python object *obj* was allocated. Return a [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback") instance, or `None` if the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.")module is not tracing memory allocations or did not trace the allocation of the object.
See also [`gc.get_referrers()`](gc.xhtml#gc.get_referrers "gc.get_referrers") and [`sys.getsizeof()`](sys.xhtml#sys.getsizeof "sys.getsizeof") functions.
`tracemalloc.``get_traceback_limit`()Get the maximum number of frames stored in the traceback of a trace.
The [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module must be tracing memory allocations to get the limit, otherwise an exception is raised.
The limit is set by the [`start()`](#tracemalloc.start "tracemalloc.start") function.
`tracemalloc.``get_traced_memory`()Get the current size and peak size of memory blocks traced by the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module as a tuple: `(current: int, peak: int)`.
`tracemalloc.``get_tracemalloc_memory`()Get the memory usage in bytes of the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module used to store traces of memory blocks. Return an [`int`](functions.xhtml#int "int").
`tracemalloc.``is_tracing`()`True` if the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module is tracing Python memory allocations, `False` otherwise.
See also [`start()`](#tracemalloc.start "tracemalloc.start") and [`stop()`](#tracemalloc.stop "tracemalloc.stop") functions.
`tracemalloc.``start`(*nframe: int=1*)Start tracing Python memory allocations: install hooks on Python memory allocators. Collected tracebacks of traces will be limited to *nframe*frames. By default, a trace of a memory block only stores the most recent frame: the limit is `1`. *nframe* must be greater or equal to `1`.
Storing more than `1` frame is only useful to compute statistics grouped by `'traceback'` or to compute cumulative statistics: see the [`Snapshot.compare_to()`](#tracemalloc.Snapshot.compare_to "tracemalloc.Snapshot.compare_to") and [`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics") methods.
Storing more frames increases the memory and CPU overhead of the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module. Use the [`get_tracemalloc_memory()`](#tracemalloc.get_tracemalloc_memory "tracemalloc.get_tracemalloc_memory") function to measure how much memory is used by the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module.
The [`PYTHONTRACEMALLOC`](../using/cmdline.xhtml#envvar-PYTHONTRACEMALLOC) environment variable (`PYTHONTRACEMALLOC=NFRAME`) and the [`-X`](../using/cmdline.xhtml#id5)`tracemalloc=NFRAME`command line option can be used to start tracing at startup.
See also [`stop()`](#tracemalloc.stop "tracemalloc.stop"), [`is_tracing()`](#tracemalloc.is_tracing "tracemalloc.is_tracing") and [`get_traceback_limit()`](#tracemalloc.get_traceback_limit "tracemalloc.get_traceback_limit")functions.
`tracemalloc.``stop`()Stop tracing Python memory allocations: uninstall hooks on Python memory allocators. Also clears all previously collected traces of memory blocks allocated by Python.
Call [`take_snapshot()`](#tracemalloc.take_snapshot "tracemalloc.take_snapshot") function to take a snapshot of traces before clearing them.
See also [`start()`](#tracemalloc.start "tracemalloc.start"), [`is_tracing()`](#tracemalloc.is_tracing "tracemalloc.is_tracing") and [`clear_traces()`](#tracemalloc.clear_traces "tracemalloc.clear_traces")functions.
`tracemalloc.``take_snapshot`()Take a snapshot of traces of memory blocks allocated by Python. Return a new [`Snapshot`](#tracemalloc.Snapshot "tracemalloc.Snapshot") instance.
The snapshot does not include memory blocks allocated before the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module started to trace memory allocations.
Tracebacks of traces are limited to [`get_traceback_limit()`](#tracemalloc.get_traceback_limit "tracemalloc.get_traceback_limit") frames. Use the *nframe* parameter of the [`start()`](#tracemalloc.start "tracemalloc.start") function to store more frames.
The [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module must be tracing memory allocations to take a snapshot, see the [`start()`](#tracemalloc.start "tracemalloc.start") function.
See also the [`get_object_traceback()`](#tracemalloc.get_object_traceback "tracemalloc.get_object_traceback") function.
### DomainFilter
*class* `tracemalloc.``DomainFilter`(*inclusive: bool*, *domain: int*)Filter traces of memory blocks by their address space (domain).
3\.6 新版功能.
`inclusive`If *inclusive* is `True` (include), match memory blocks allocated in the address space [`domain`](#tracemalloc.DomainFilter.domain "tracemalloc.DomainFilter.domain").
If *inclusive* is `False` (exclude), match memory blocks not allocated in the address space [`domain`](#tracemalloc.DomainFilter.domain "tracemalloc.DomainFilter.domain").
`domain`Address space of a memory block (`int`). Read-only property.
### Filter
*class* `tracemalloc.``Filter`(*inclusive: bool*, *filename\_pattern: str*, *lineno: int=None*, *all\_frames: bool=False*, *domain: int=None*)Filter on traces of memory blocks.
See the [`fnmatch.fnmatch()`](fnmatch.xhtml#fnmatch.fnmatch "fnmatch.fnmatch") function for the syntax of *filename\_pattern*. The `'.pyc'` file extension is replaced with `'.py'`.
示例：
- `Filter(True, subprocess.__file__)` only includes traces of the [`subprocess`](subprocess.xhtml#module-subprocess "subprocess: Subprocess management.") module
- `Filter(False, tracemalloc.__file__)` excludes traces of the [`tracemalloc`](#module-tracemalloc "tracemalloc: Trace memory allocations.") module
- `Filter(False, "<unknown>")` excludes empty tracebacks
在 3.5 版更改: The `'.pyo'` file extension is no longer replaced with `'.py'`.
在 3.6 版更改: Added the [`domain`](#tracemalloc.Filter.domain "tracemalloc.Filter.domain") attribute.
`domain`Address space of a memory block (`int` or `None`).
tracemalloc uses the domain `0` to trace memory allocations made by Python. C extensions can use other domains to trace other resources.
`inclusive`If *inclusive* is `True` (include), only match memory blocks allocated in a file with a name matching [`filename_pattern`](#tracemalloc.Filter.filename_pattern "tracemalloc.Filter.filename_pattern") at line number [`lineno`](#tracemalloc.Filter.lineno "tracemalloc.Filter.lineno").
If *inclusive* is `False` (exclude), ignore memory blocks allocated in a file with a name matching [`filename_pattern`](#tracemalloc.Filter.filename_pattern "tracemalloc.Filter.filename_pattern") at line number [`lineno`](#tracemalloc.Filter.lineno "tracemalloc.Filter.lineno").
`lineno`Line number (`int`) of the filter. If *lineno* is `None`, the filter matches any line number.
`filename_pattern`Filename pattern of the filter (`str`). Read-only property.
`all_frames`If *all\_frames* is `True`, all frames of the traceback are checked. If *all\_frames* is `False`, only the most recent frame is checked.
This attribute has no effect if the traceback limit is `1`. See the [`get_traceback_limit()`](#tracemalloc.get_traceback_limit "tracemalloc.get_traceback_limit") function and [`Snapshot.traceback_limit`](#tracemalloc.Snapshot.traceback_limit "tracemalloc.Snapshot.traceback_limit")attribute.
### Frame
*class* `tracemalloc.``Frame`Frame of a traceback.
The [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback") class is a sequence of [`Frame`](#tracemalloc.Frame "tracemalloc.Frame") instances.
`filename`Filename (`str`).
`lineno`Line number (`int`).
### Snapshot
*class* `tracemalloc.``Snapshot`Snapshot of traces of memory blocks allocated by Python.
The [`take_snapshot()`](#tracemalloc.take_snapshot "tracemalloc.take_snapshot") function creates a snapshot instance.
`compare_to`(*old\_snapshot: Snapshot*, *key\_type: str*, *cumulative: bool=False*)Compute the differences with an old snapshot. Get statistics as a sorted list of [`StatisticDiff`](#tracemalloc.StatisticDiff "tracemalloc.StatisticDiff") instances grouped by *key\_type*.
See the [`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics") method for *key\_type* and *cumulative*parameters.
The result is sorted from the biggest to the smallest by: absolute value of [`StatisticDiff.size_diff`](#tracemalloc.StatisticDiff.size_diff "tracemalloc.StatisticDiff.size_diff"), [`StatisticDiff.size`](#tracemalloc.StatisticDiff.size "tracemalloc.StatisticDiff.size"), absolute value of [`StatisticDiff.count_diff`](#tracemalloc.StatisticDiff.count_diff "tracemalloc.StatisticDiff.count_diff"), [`Statistic.count`](#tracemalloc.Statistic.count "tracemalloc.Statistic.count") and then by [`StatisticDiff.traceback`](#tracemalloc.StatisticDiff.traceback "tracemalloc.StatisticDiff.traceback").
`dump`(*filename*)Write the snapshot into a file.
Use [`load()`](#tracemalloc.Snapshot.load "tracemalloc.Snapshot.load") to reload the snapshot.
`filter_traces`(*filters*)Create a new [`Snapshot`](#tracemalloc.Snapshot "tracemalloc.Snapshot") instance with a filtered [`traces`](#tracemalloc.Snapshot.traces "tracemalloc.Snapshot.traces")sequence, *filters* is a list of [`DomainFilter`](#tracemalloc.DomainFilter "tracemalloc.DomainFilter") and [`Filter`](#tracemalloc.Filter "tracemalloc.Filter") instances. If *filters* is an empty list, return a new [`Snapshot`](#tracemalloc.Snapshot "tracemalloc.Snapshot") instance with a copy of the traces.
All inclusive filters are applied at once, a trace is ignored if no inclusive filters match it. A trace is ignored if at least one exclusive filter matches it.
在 3.6 版更改: [`DomainFilter`](#tracemalloc.DomainFilter "tracemalloc.DomainFilter") instances are now also accepted in *filters*.
*classmethod* `load`(*filename*)Load a snapshot from a file.
See also [`dump()`](#tracemalloc.Snapshot.dump "tracemalloc.Snapshot.dump").
`statistics`(*key\_type: str*, *cumulative: bool=False*)Get statistics as a sorted list of [`Statistic`](#tracemalloc.Statistic "tracemalloc.Statistic") instances grouped by *key\_type*:
key\_type
描述
`'filename'`
filename
`'lineno'`
filename and line number
`'traceback'`
traceback
If *cumulative* is `True`, cumulate size and count of memory blocks of all frames of the traceback of a trace, not only the most recent frame. The cumulative mode can only be used with *key\_type* equals to `'filename'` and `'lineno'`.
The result is sorted from the biggest to the smallest by: [`Statistic.size`](#tracemalloc.Statistic.size "tracemalloc.Statistic.size"), [`Statistic.count`](#tracemalloc.Statistic.count "tracemalloc.Statistic.count") and then by [`Statistic.traceback`](#tracemalloc.Statistic.traceback "tracemalloc.Statistic.traceback").
`traceback_limit`Maximum number of frames stored in the traceback of [`traces`](#tracemalloc.Snapshot.traces "tracemalloc.Snapshot.traces"): result of the [`get_traceback_limit()`](#tracemalloc.get_traceback_limit "tracemalloc.get_traceback_limit") when the snapshot was taken.
`traces`Traces of all memory blocks allocated by Python: sequence of [`Trace`](#tracemalloc.Trace "tracemalloc.Trace") instances.
The sequence has an undefined order. Use the [`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics")method to get a sorted list of statistics.
### Statistic
*class* `tracemalloc.``Statistic`Statistic on memory allocations.
[`Snapshot.statistics()`](#tracemalloc.Snapshot.statistics "tracemalloc.Snapshot.statistics") returns a list of [`Statistic`](#tracemalloc.Statistic "tracemalloc.Statistic") instances.
See also the [`StatisticDiff`](#tracemalloc.StatisticDiff "tracemalloc.StatisticDiff") class.
`count`Number of memory blocks (`int`).
`size`Total size of memory blocks in bytes (`int`).
`traceback`Traceback where the memory block was allocated, [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback")instance.
### StatisticDiff
*class* `tracemalloc.``StatisticDiff`Statistic difference on memory allocations between an old and a new [`Snapshot`](#tracemalloc.Snapshot "tracemalloc.Snapshot") instance.
[`Snapshot.compare_to()`](#tracemalloc.Snapshot.compare_to "tracemalloc.Snapshot.compare_to") returns a list of [`StatisticDiff`](#tracemalloc.StatisticDiff "tracemalloc.StatisticDiff")instances. See also the [`Statistic`](#tracemalloc.Statistic "tracemalloc.Statistic") class.
`count`Number of memory blocks in the new snapshot (`int`): `0` if the memory blocks have been released in the new snapshot.
`count_diff`Difference of number of memory blocks between the old and the new snapshots (`int`): `0` if the memory blocks have been allocated in the new snapshot.
`size`Total size of memory blocks in bytes in the new snapshot (`int`): `0` if the memory blocks have been released in the new snapshot.
`size_diff`Difference of total size of memory blocks in bytes between the old and the new snapshots (`int`): `0` if the memory blocks have been allocated in the new snapshot.
`traceback`Traceback where the memory blocks were allocated, [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback")instance.
### Trace
*class* `tracemalloc.``Trace`Trace of a memory block.
The [`Snapshot.traces`](#tracemalloc.Snapshot.traces "tracemalloc.Snapshot.traces") attribute is a sequence of [`Trace`](#tracemalloc.Trace "tracemalloc.Trace")instances.
在 3.6 版更改: Added the [`domain`](#tracemalloc.Trace.domain "tracemalloc.Trace.domain") attribute.
`domain`Address space of a memory block (`int`). Read-only property.
tracemalloc uses the domain `0` to trace memory allocations made by Python. C extensions can use other domains to trace other resources.
`size`Size of the memory block in bytes (`int`).
`traceback`Traceback where the memory block was allocated, [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback")instance.
### Traceback
*class* `tracemalloc.``Traceback`Sequence of [`Frame`](#tracemalloc.Frame "tracemalloc.Frame") instances sorted from the oldest frame to the most recent frame.
A traceback contains at least `1` frame. If the `tracemalloc` module failed to get a frame, the filename `"<unknown>"` at line number `0` is used.
When a snapshot is taken, tracebacks of traces are limited to [`get_traceback_limit()`](#tracemalloc.get_traceback_limit "tracemalloc.get_traceback_limit") frames. See the [`take_snapshot()`](#tracemalloc.take_snapshot "tracemalloc.take_snapshot") function.
The [`Trace.traceback`](#tracemalloc.Trace.traceback "tracemalloc.Trace.traceback") attribute is an instance of [`Traceback`](#tracemalloc.Traceback "tracemalloc.Traceback")instance.
在 3.7 版更改: Frames are now sorted from the oldest to the most recent, instead of most recent to oldest.
`format`(*limit=None*, *most\_recent\_first=False*)Format the traceback as a list of lines with newlines. Use the [`linecache`](linecache.xhtml#module-linecache "linecache: This module provides random access to individual lines from text files.") module to retrieve lines from the source code. If *limit* is set, format the *limit* most recent frames if *limit*is positive. Otherwise, format the `abs(limit)` oldest frames. If *most\_recent\_first* is `True`, the order of the formatted frames is reversed, returning the most recent frame first instead of last.
Similar to the [`traceback.format_tb()`](traceback.xhtml#traceback.format_tb "traceback.format_tb") function, except that [`format()`](#tracemalloc.Traceback.format "tracemalloc.Traceback.format") does not include newlines.
示例:
```
print("Traceback (most recent call first):")
for line in traceback:
print(line)
```
输出:
```
Traceback (most recent call first):
File "test.py", line 9
obj = Object()
File "test.py", line 12
tb = tracemalloc.get_object_traceback(f())
```
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- Python文档内容
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- string — 常见的字符串操作
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- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
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- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
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- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
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- curses — 终端字符单元显示的处理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec