as we know, internally unordered_map is implemented using hash table so, a bucket is a slot in the internal hash Table to which elements are assigned based on the hash value of their key. Buckets are numbered from 0 to (bucket_count-1). Hence this function returns the bucket no. where element with key is located in unordered_map.
Time Complexity: O(1).
unordered_map.bucket(k); k is the key corresponds to which we want to know bucket number. Returns: The order number of the bucket corresponding to key k.
There are two more functions regarding bucket:
1. std::bucket_count: This function is used to count the total no. of buckets in the unordered_map. No parameter is required to pass into this function.
Time Complexity: O(1).
unordered_map.bucket_count(); Returns: The number of the bucket present in hash table of unordered_map.
2. std::bucket_size: This function count the number of elements present in each bucket of the unordered_map.
Time Complexity: Linear in the bucket size.
unordered_map.bucket_size(i); where 'i' is the bucket number in which we want to find no. of elements. (i < bucket_count) Returns: The number of elements present in bucket 'i'.
(PI, 3.14) is in bucket= 5 (e, 2.718) is in bucket= 1 (root2, 1.414) is in bucket= 1 (log10, 2.302) is in bucket= 10 (loge, 1) is in bucket= 7 umap has 11 buckets. Bucket 0 has 0 elements. Bucket 1 has 2 elements. Bucket 2 has 0 elements. Bucket 3 has 0 elements. Bucket 4 has 0 elements. Bucket 5 has 1 elements. Bucket 6 has 0 elements. Bucket 7 has 1 elements. Bucket 8 has 0 elements. Bucket 9 has 0 elements. Bucket 10 has 1 elements.
We can also print all the elements present in each bucket of the unordered_map.
Bucket 0 contains: Bucket 1 contains: (e, 2.718) (root2, 1.414) Bucket 2 contains: Bucket 3 contains: Bucket 4 contains: Bucket 5 contains: (PI, 3.14) Bucket 6 contains: Bucket 7 contains: (loge, 1) Bucket 8 contains: Bucket 9 contains: Bucket 10 contains: (log10, 2.302)
Use of bucket in std::unordered_map: There is a number of algorithms which require the objects to be hashed into some number of buckets, and then each bucket is processed. Let say, you want to find duplicates in a collection. You hash all items in the collection, then in each bucket you compare items pairwise. A bit less trivial example is Apriori algorithm for finding frequent itemsets.
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