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Quoc-Hung Hoang
Quoc-Hung Hoang

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Database indexing in a nutshell with B+tree and Hash in comparison

People is often said that indexing is a go-to technique to process efficiently queries in case your database is large enough. This post is for summarizing what database index is and revisiting hash and B+Tree.

Index is a data structure that organizes records to optimize certain kinds of retrieval operations. We may create index on a field of the table then retrieve all records that satisfy search conditions on search-key field. Without index, our query would end up scanning linearly the entire content of the table to fetch only one or a few records.

In this post, I'd like to summarize the performance and use cases of two common indexing techniques: Hash index and B+tree

Hash index

This technique is widely used for creating indices in main memory because its fast retrieval by nature. It has average O(1) operation complexity and O(n) storage complexity.
In many books, people use the term bucket to denote a unit of storage that stores one or more records
There are two things to discuss when it comes to hashing:

  • Hash function: maps search keys (as its input) to an integer representing that key in the bucket.
  • Hashing scheme: how to deal with key collision after hashing.

hash function

Some people ask: why collision ? Does a perfect hash function ever exist ? In fact, let's say your keys is an infinite set, it's impossible to map them into a set of 32-bit integers without having no collision. There should be a trade-off between computation and collision rate.

There are a few hashing scheme worth mentioning: linear probing, chained hashing and extendible hashing. Lookup/insert/delete algorithms vary by hashing scheme, for example, chained hashing deal with key collisions by placing elements have the same hash value in the same bucket.


  • Hash index is suitable for equality or primary key lookup. Queries can benefit from hash index to get amortized O(1) lookup cost. For example: SELECT name, id FROM student WHERE id = '1315';


Hash table has some limitations:

  • Range queries are not efficient. Hash table is based on uniform distribution. In other words, you have no control of where an index entry is going to be placed.
  • Low scalability: performance of lookup operation can degrade when there a lot of collisions and it requires to resize the hash table then rehash existing index entries.


This is a self-balancing tree data structure that keeps data in sorted order and allows fast search within each node, typically using binary search.
B+Tree is a standard index implementation in almost all relational database system.

B+Tree is basically a M-way search tree that have the following structure:

  • perfectly balance: leaf nodes always have the same height.
  • every inner node other than the root is at least half full (M/2 − 1 <= num of keys <= M − 1).
  • every inner node with k keys has k+1 non-null children.

Every node of the tree has an array of sorted key-value pairs. The key-value pair is constructed from (search-key value, pointer) for root and inner nodes. Leaf node values can be 2 possibilities:

  • the actual record
  • the pointer to actual record

Lookup a value v

  • Start with root node
  • While node is not a leaf node, we do:
    • Find the smallest Ki where Ki >= v
    • If Ki == v: set current node to the node pointed by Pi+1
    • Otherwise, set current node to node pointed by Pi

Look up a key using B+Tree index

Duplicate keys

In general, search-key can be duplicate, to solve this, most database implementations come up with composite search key. For example, we want to create an index on student_name then our composite search key should be (student_name, Ap) where Ap is the primary key of the table.


There're two major features that B+tree offers:

  • Minimizing I/O operations
    • Reduced height: B+Tree has quite large branching factor (value between 50 and 2000 often used) which makes the tree fat and short. The figure below illustrates a B+Tree with height of 2. As we can see nodes are spread out, it takes fewer nodes to traverse down to a leaf. The cost of looking up a single value is the height of the tree + 1 for the random access to the table.
  • Scalability:
    • You have predictable performance for all cases, O(log(n)) in particular. For databases, it is usually more important than having better best or average case performance.
    • The tree always remain balanced by its implementation. A B+Tree with n keys always has a depth of O(log(n)). Thus, the performance will not degrade if the database grows bigger. A four-level tree with a branching factor of 500 can store up to 256 TB provided that a page is size of 4KB.

Figure 2

  • B+Tree is most suited for range queries, for example "SELECT * FROM student WHERE age > 20 AND age < 22"


Although hash index performs better in terms of exact match queries, B+Tree is arguably the most widely used index structure in RDBMS thanks to its consistent performance in overall and high scalability.

B+Tree Hash
Lookup Time O(log(n)) O(log(1))
Insertion Time O(log(n)) O(log(1))
Deletion Time O(log(n)) O(log(1))

Recently, the log-structured merge tree (LSM-tree) has attracted significant interest as a contender to B+-tree, because its data structure could enable better storage space usage efficiency. I'll investigate it further and make a post about it in the near future.

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