when we talk about storage RAM and HardDisks come to the picture
Let’s understand there fucntions and how they differ from each other
| Functionality | RAM | HardDisk |
| Purpose | Temporary Data Storage | Permanent Data Storage |
| Speed | Much Faster | Slower |
| Volatility | Loses data when power is off | Data’s are persistent |
| Size | Smaller Capacities (GBs) | Large Capacity(TBs) |
| Usage | Supports active tasks and processes | Store files, applications, and OS |
| Price | Expensive | Cheap |
Let’s understand why is HardDisk Slow ?
Hard disks are inherently slower due to the way they retreive datas, which involves blocks and indexing
Block based Retrieval : Data on hard disk is stored in blocks. Accessing a specifi piece of data often requires reading an entire block, even if only a small portion of the block is needed, adding overhead.
Indexing Overhead: Hard disks rely on file system indexes (like FAT or NTFS) to locate data. Traversing these indexes adds extra time before the actual data can be accessed
Fragmentation: Over time, as files are created and deleted, data mayb become fragmenred across the disk,requiring the read/write head to move between multiple physical locations to retrieve a single file
Seek Time: The moving read/write head must physically position itself over the correct track on the spinning platter, introducing latency
Rotational Latency: The disk must spin to the correct position to access data, further slowing retrieval compared to solid-state alternatives like SSDs
RAM relies over Electronics (semiconductors), HD relieves over Magnetism, SSD uses flash memory (NAND-based)to store data electronically
for 1 million data it takes nearly 100 seconds
This is known as single-level indexing, which is efficient for up to 1 million data storage entries.
for 1 milion data entries it takes nearly 250 ms
MultiLevel Indexing
If the data entries crosses billions then this wont be effiecient at all simple looking like a linear search irrespective whether u use binary search ,Index sequential search or indexing it would be hell slow and a pure over head
do forgive me for the numbers they are rought estimate but hope u get the concept behind it
this takes 3 ms
Let’s rotate this image clockwise 90*
There’s ur B Tree
just lot of index tables hope u get the picture here
| Data Structure | Search | Insert | Delete |
| Balanced BST | 20ms | 20ms | 20ms |
| Unbalanced BST | 1 million ms | 1 million ms | 1 million ms |
| Sorted Array | 20 ms | 1 million ms | 1 million ms |
| B Tree | 3 ms | 3 ms | 3 ms |
log to the base 2 (10^6) = 20ms
log to the base m (10^6) = 3ms
Where m is the number of childrens of the parent let’s consider it to be 100
then we get log to the base 100 (10^6) = 3ms
Difference Between B and B+ Trees
| Aspect | B-Tree | B+ Tree |
| Structure | Internal nodes store both keys and values. | Internal nodes store only keys; values are in leaf nodes. |
| Leaf Nodes | Leaf nodes are at different levels. | All leaf nodes are at the same level. |
| Search Efficiency | Searching may involve traversing through internal nodes and leaves. | Searching is faster as only leaf nodes store values. |
| Range Queries | Range queries are less efficient. | Range queries are more efficient due to linked leaf nodes. |
| Insertion and Deletion | More complex due to the internal node structure. | Simplified since only leaf nodes are affected. |