Django 3.0.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 3085 页 | 2.95 MB | 1 年前3Django 4.2.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance the queryset and are blocking. Some, like filter() and exclude(), don’t force execution and so are safe to run from asynchronous code. But how are you supposed to tell the difference? While you could poke names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor):0 码力 | 3305 页 | 3.16 MB | 1 年前3Django 2.2.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 2915 页 | 2.83 MB | 1 年前3Django 2.1.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 2790 页 | 2.71 MB | 1 年前3Django 2.1.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: 144 Chapter 3. Using Django or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 1910 页 | 6.49 MB | 1 年前3Django 4.1.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance the queryset and are blocking. Some, like filter() and exclude(), don’t force execution and so are safe to run from asynchronous code. But how are you supposed to tell the difference? While you could poke names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor):0 码力 | 3240 页 | 3.13 MB | 1 年前3Django 4.0.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 2248 页 | 7.90 MB | 1 年前3Django 4.0.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 3184 页 | 3.14 MB | 1 年前3Django 2.0.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 2746 页 | 2.67 MB | 1 年前3Django 2.0.x Documentation
similar to mod_perl – it embeds Python within Apache and loads Python code into memory when the server starts. Code stays in memory throughout the life of an Apache process, which leads to significant performance names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this: def dictfetchall(cursor): or a tool that monitors your database directly. Remember that you may be optimizing for speed or memory or both, depending on your requirements. Sometimes optimizing for one will be detrimental to the0 码力 | 1880 页 | 6.41 MB | 1 年前3
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