August 2015
Intermediate to advanced
384 pages
7h 42m
Chinese
Content preview from 高效能PYTHON程式設計
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第十一章
我們可以使用
memit
看看行程的增長情形，驗證這個估計。在此案例中，數字非常
接近︰
In [2]: %memit([x for x in xrange(10000000)])
peak memory: 330.64 MiB, increment: 310.62 MiB
通常，
asizeof
比
memit
慢，然而，在你分析小物件時，
asizeof
會是有用的。
memit
或
許對現實世界的應用程式比較有用，因為行程的實際記憶體使用量被量測，而不是被
推斷。
位元組 vs. Unicode
切換到 Python 3.3+ 的一個必要理由就是：它的 Unicode 物件儲存遠比 Python 2.7 經濟
許多。如果你的主要工作是處理大量的字串，而且它們耗用了大量的 RAM，請務必考
慮使用 Python 3.3+，你會自動省下龐大的 RAM。
在範例 11-7 中，可以看到，100,000,000 個字元的序列被建構為位元組群集（同 Python
2.7 的
str
）以及 Unicode 物件。Unicode 版本多花了 4 倍的 RAM，每個 Unicode 字元耗
費了相同的較高代價，不管描述底層資料所需的位元組數量為何。
範例
11-7 Unicode
物件在
Python 2.7
中相當昂貴
In [1]: %load_ext memory_profiler
In [2]: %memit b"a" * int(1e8)
peak memory: 100.98 MiB, increment: 80.97 ...
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