Mongo Performance Optimization Using Indexing

1. Performance Optimization Strategies for MongoDB

choosing right database server hardware

schema design (denormalizing schema)

query optimization ($in, $nin)

Indexing

choosing approapriate shard key in sharding
clusters

2. What are indexes?

3. Chemist Drawer

4. Indexing = technique used to
make search faster

5. Computer Science definition
Index = any data structure that
improves the performance of lookup.

6. DB Index datastructures

Binary Tree

B+ Tree

Balanced Tree

Hashes

7. Binary Search Tree

8. Our Favourite Employee Table

18. Search By Employee Id
select * from employee where employee_id= 3

20. B+ Tree

The B-tree is a generalization of a binary
search tree in that a node can have more than
two children

Order of B-Tree= max no of child nodes

The left subtree of a node contains only nodes
with keys less than the node's key.

he right subtree of a node contains only nodes
with keys greater than the node's key.

22. A database index improves data
retrieval operations but they come up
with the cost.
slower writes and the use of more
storage space.
3 Gigabytes of collection, if you have 1 index,
approx it uses 500 Mb for that index

23. INDEX CARDINALITY

Cardinality: Unique values in the
column

26. MONGO DOCUMENT
{
employee_id : 8
Name : “john”
Salary : 2000
}
{
employee_id : 5
Name : “james”
Salary : 3000
}

29. TAKE AWAY...

Index Datastructure

Index Cardinality

Indexing is not the only solution to improve the
performance

30. Points to consider while creating
index

Keys (columns) frequently involved in search
conditions of a query
Indexes can be created on Array, Sub-
documents and also Embedded Fields

Use Indexes to Sort Query Results

Queries that return a range of values using
operators such as $gt,$lt

Negation: Inequality queries are inefficient with
respect to indexes