[Python-Dev] More compact dictionaries with faster iteration

MRAB python at mrabarnett.plus.com
Mon Dec 10 04:03:37 CET 2012

On 2012-12-10 01:44, Raymond Hettinger wrote:
> The current memory layout for dictionaries is
> unnecessarily inefficient.  It has a sparse table of
> 24-byte entries containing the hash value, key pointer,
> and value pointer.
> Instead, the 24-byte entries should be stored in a
> dense table referenced by a sparse table of indices.
> For example, the dictionary:
>      d = {'timmy': 'red', 'barry': 'green', 'guido': 'blue'}
> is currently stored as:
>      entries = [['--', '--', '--'],
>                 [-8522787127447073495, 'barry', 'green'],
>                 ['--', '--', '--'],
>                 ['--', '--', '--'],
>                 ['--', '--', '--'],
>                 [-9092791511155847987, 'timmy', 'red'],
>                 ['--', '--', '--'],
>                 [-6480567542315338377, 'guido', 'blue']]
> Instead, the data should be organized as follows:
>      indices =  [None, 1, None, None, None, 0, None, 2]
>      entries =  [[-9092791511155847987, 'timmy', 'red'],
>                  [-8522787127447073495, 'barry', 'green'],
>                  [-6480567542315338377, 'guido', 'blue']]
> Only the data layout needs to change.  The hash table
> algorithms would stay the same.  All of the current
> optimizations would be kept, including key-sharing
> dicts and custom lookup functions for string-only
> dicts.  There is no change to the hash functions, the
> table search order, or collision statistics.
> The memory savings are significant (from 30% to 95%
> compression depending on the how full the table is).
> Small dicts (size 0, 1, or 2) get the most benefit.
> For a sparse table of size t with n entries, the sizes are:
>      curr_size = 24 * t
>      new_size = 24 * n + sizeof(index) * t
> In the above timmy/barry/guido example, the current
> size is 192 bytes (eight 24-byte entries) and the new
> size is 80 bytes (three 24-byte entries plus eight
> 1-byte indices).  That gives 58% compression.
> Note, the sizeof(index) can be as small as a single
> byte for small dicts, two bytes for bigger dicts and
> up to sizeof(Py_ssize_t) for huge dict.
> In addition to space savings, the new memory layout
> makes iteration faster.  Currently, keys(), values, and
> items() loop over the sparse table, skipping-over free
> slots in the hash table.  Now, keys/values/items can
> loop directly over the dense table, using fewer memory
> accesses.
> Another benefit is that resizing is faster and
> touches fewer pieces of memory.  Currently, every
> hash/key/value entry is moved or copied during a
> resize.  In the new layout, only the indices are
> updated.  For the most part, the hash/key/value entries
> never move (except for an occasional swap to fill a
> hole left by a deletion).
> With the reduced memory footprint, we can also expect
> better cache utilization.
> For those wanting to experiment with the design,
> there is a pure Python proof-of-concept here:
>     http://code.activestate.com/recipes/578375
> YMMV: Keep in mind that the above size statics assume a
> build with 64-bit Py_ssize_t and 64-bit pointers.  The
> space savings percentages are a bit different on other
> builds.  Also, note that in many applications, the size
> of the data dominates the size of the container (i.e.
> the weight of a bucket of water is mostly the water,
> not the bucket).
What happens when a key is deleted? Will that leave an empty slot in
the entry array? If so, will the array be compacted if the proportion
of entries grows beyond a certain limit?

Adding a key would involve simply appending to the entry array (filling
the last empty slot), but if there could be empty slots in other
places, would it look for the first?

Actually, as I think about it, you could form a linked list (using the
'hash' field) of those empty slots that aren't part of the final
contiguous block and fill those preferentially.

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