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NOTES ON DICTIONARIES ================================ Principal Use Cases for Dictionaries ------------------------------------ Passing keyword arguments Typically, one read and one write for 1 to 3 elements. Occurs frequently in normal python code. Class method lookup Dictionaries vary in size with 8 to 16 elements being common. Usually written once with many lookups. When base classes are used, there are many failed lookups followed by a lookup in a base class. Instance attribute lookup and Global variables Dictionaries vary in size. 4 to 10 elements are common. Both reads and writes are common. Builtins Frequent reads. Almost never written. About 150 interned strings (as of Py3.3). A few keys are accessed much more frequently than others. Uniquification Dictionaries of any size. Bulk of work is in creation. Repeated writes to a smaller set of keys. Single read of each key. Some use cases have two consecutive accesses to the same key. * Removing duplicates from a sequence. dict.fromkeys(seqn).keys() * Counting elements in a sequence. for e in seqn: d[e] = d.get(e,0) + 1 * Accumulating references in a dictionary of lists: for pagenumber, page in enumerate(pages): for word in page: d.setdefault(word, []).append(pagenumber) Note, the second example is a use case characterized by a get and set to the same key. There are similar use cases with a __contains__ followed by a get, set, or del to the same key. Part of the justification for d.setdefault is combining the two lookups into one. Membership Testing Dictionaries of any size. Created once and then rarely changes. Single write to each key. Many calls to __contains__() or has_key(). Similar access patterns occur with replacement dictionaries such as with the % formatting operator. Dynamic Mappings Characterized by deletions interspersed with adds and replacements. Performance benefits greatly from the re-use of dummy entries. Data Layout ----------- Dictionaries are composed of 3 components: The dictobject struct itself A dict-keys object (keys & hashes) A values array Tunable Dictionary Parameters ----------------------------- See comments for PyDict_MINSIZE_SPLIT, PyDict_MINSIZE_COMBINED, USABLE_FRACTION and GROWTH_RATE in dictobject.c Tune-ups should be measured across a broad range of applications and use cases. A change to any parameter will help in some situations and hurt in others. The key is to find settings that help the most common cases and do the least damage to the less common cases. Results will vary dramatically depending on the exact number of keys, whether the keys are all strings, whether reads or writes dominate, the exact hash values of the keys (some sets of values have fewer collisions than others). Any one test or benchmark is likely to prove misleading. While making a dictionary more sparse reduces collisions, it impairs iteration and key listing. Those methods loop over every potential entry. Doubling the size of dictionary results in twice as many non-overlapping memory accesses for keys(), items(), values(), __iter__(), iterkeys(), iteritems(), itervalues(), and update(). Also, every dictionary iterates at least twice, once for the memset() when it is created and once by dealloc(). Dictionary operations involving only a single key can be O(1) unless resizing is possible. By checking for a resize only when the dictionary can grow (and may *require* resizing), other operations remain O(1), and the odds of resize thrashing or memory fragmentation are reduced. In particular, an algorithm that empties a dictionary by repeatedly invoking .pop will see no resizing, which might not be necessary at all because the dictionary is eventually discarded entirely. The key differences between this implementation and earlier versions are: 1. The table can be split into two parts, the keys and the values. 2. There is an additional key-value combination: (key, NULL). Unlike (<dummy>, NULL) which represents a deleted value, (key, NULL) represented a yet to be inserted value. This combination can only occur when the table is split. 3. No small table embedded in the dict, as this would make sharing of key-tables impossible. These changes have the following consequences. 1. General dictionaries are slightly larger. 2. All object dictionaries of a single class can share a single key-table, saving about 60% memory for such cases. Results of Cache Locality Experiments -------------------------------------- Experiments on an earlier design of dictionary, in which all tables were combined, showed the following: When an entry is retrieved from memory, several adjacent entries are also retrieved into a cache line. Since accessing items in cache is *much* cheaper than a cache miss, an enticing idea is to probe the adjacent entries as a first step in collision resolution. Unfortunately, the introduction of any regularity into collision searches results in more collisions than the current random chaining approach. Exploiting cache locality at the expense of additional collisions fails to payoff when the entries are already loaded in cache (the expense is paid with no compensating benefit). This occurs in small dictionaries where the whole dictionary fits into a pair of cache lines. It also occurs frequently in large dictionaries which have a common access pattern where some keys are accessed much more frequently than others. The more popular entries *and* their collision chains tend to remain in cache. To exploit cache locality, change the collision resolution section in lookdict() and lookdict_string(). Set i^=1 at the top of the loop and move the i = (i << 2) + i + perturb + 1 to an unrolled version of the loop. For split tables, the above will apply to the keys, but the value will always be in a different cache line from the key.