What is the most efficient way to search for a key within a B-Tree node? For example, if I have a node with 100 sorted keys and I'm looking for key 23, what would be the fastest way of searching for that key within that node? What do most B-Tree implementations do?
Related
Some databases (like MySQL) use B+trees as a way to physically organize records. The primary index (also called clustering index) stores the keys as internal nodes and the records in the leaf level.
Since all pages have fixed size, I suppose that internal nodes and leaf nodes have different degrees: The degree of internal nodes is derived from the primary key size, and the degree of the leaf nodes is derived from the record size.
What if the record size is variable? How the degree of the leaf level is set?
Additionally, I always study b+trees under the assumption that keys have the same size. It enables a straightforward method for splitting and dividing the nodes. What about the leaf level? How to balance the nodes when the records to move have variable sizes?
I have learned that there are three alternative ways for the data entry handling
storing full data record itself with key k
<key,rid>
<key,list>
But my question is why can't we apply method 3 to B+ tree?
I thought it was because the multiple mapping from the leaf node could occur but later on, since the B+ tree is compared with the key k (either left or right) having multiple rids mapped with a key shouldn't be a problem. But I am not sure why all of the examples for B+ tree show only method 2 which is one key and one rid mapping
I am pretty new to database systems and I have come doubts which I could not find answers.
If I am not wrong, the DBMS stores tables and their associative B+ trees on disk, and commonly each table will have a B+ tree based on its primary key.
However if I have a table which contains 2 columns student_id and GPA, and I want to do a range query on GPAs. It's not able to efficiently perform this query because there's no B+ tree constructed with GPA. My question is how DBMS address problem like this? Does it maintain a B+ tree for every column in your table?
I need help understanding hashtables, so correct e if I am wrong. A hashtable computes the index of the array with a key by encripting the key. The resultant encryption is the index. Collisions are unavoidable because there are high chances of getting the same index and we can use chaining to create a linked list inside each index of the array. The runtime of a hashtable is o(1)
This is correct, a hash table has O(1) lookup and O(n) storage space.
A hashing function is used to compute the index for where we store the element in a hash table. When a collision occurs we chain them.
I am trying to answer the following question but I am confused on the subject of B+ trees. Below is the question:
Is it possible to define an insertion algorithm for a B+ tree on a non-key search field? If yes, explain by showing insertions with appropriate search values (you need not define the algorithm in formal terms). If not, provide necessary justification.