Applications of various data structures

Queue:
1) Queueing processes : any device does not have infinite resources, so a queue has 
   to be used in order to allocate resources to those process that need it on a priority level.

Bloom filters:
1) In shortening URLs.

Graphs:
1) GIS : in Geographic Informative Systems, transport and vehicular technology.
2) Maps : Google Maps, Bing, etc.
3) Paths between points(keys) : Like the A* Algorithm, etc.

Trees 
1) Data base designing : creating your own database just to store the data based upon some key value
2) Creating file system : Operating systems maintain a disk's file system as a tree.
3) Zoology: Maintaining the structures of the entire animal & plant kingdom.
4) Social Networks : Establishing relations between users based on some key.

B-Trees (Binary Trees): 
1) E-commerce : while accessing unique keys. B-Trees balanced multi-way search tree of order N.
2) Searching : Searching quickly for a given element.
3) For File Systems

Hash Tables:
1) In Banks for combining two or more accounts for matching Social Security Numbers.

Trie(prefix tree):
1) In auto-complete dictionary in mobiles, search engines etc.
2) As approximate algorithms in spell-checking

B+ Trees
1) for building Data Bases 

Splay Tree:
1) Used in caches

Suffix tree:
1) Genomic sequencing
 
 
★ Question ->Why are Red-Black trees used more often in industry than AVL trees?
Though AVL trees appear to be simpler (ie. fewer cases to handle) and have shorter heights,
yet Red-Black trees appear more predominate (ie. used in Java and in functional language implementations)

There are two popular Balanced Binary Search Tree: AVL Tree and Red-Black Tree.
Both offers O(lg n) search time. But the hidden constant behind Big O makes AVL Tree more 
suitable for search and Red-Black Tree for insertion-deletion. Insertion-deletion takes less 
time in Red-Black Tree than AVL Tree. That's Red-Black Tree are more popular than AVL Tree although
implementing Red-Black is very complicated task.

RBs handle all operations better on average (insertion, retrieval, deletes), 
whilst AVLs are good for frequent retrievals, but infrequent insertions and deletes.

That being said, RBs are suitable for more situations than AVLs.


Special trees, special lists and probabilistic data structures -

Skiplist:
Used by Redis datastore for the implementation of ordered sets.
Used in nessDB.

Bloom filter:
Used by Cassandra to check which SSTables mostly contains the key.
Hbase also uses it to optimize the reads.

LSM trees:
Used by Cassandra(SSTables), Big table for storage.

Merkle tree:
Used by various eventually consistent datastores like Dynamo, Cassandra for anti-entropy mechanism.


Judy Tree:
I want to talk about one awesome in-memory data structure which is not yet discussed in this thread

JudyTree is the efficient in-memory data structure which is fast and consumes less memory

Judy has two main strategies: use a 256-ary tree, compressed and designed to require one (at most two)
secondary cache misses per tree node lookups 

A (CPU) cache-line fill is additional time required to read from RAM when a word is not found in cache. 
In today’s computers the time for a cache-line fill is in the range of 50..2000 machine instructions. 
Hence a cache-line fill should be avoided when fewer than 50 instructions can do the same job.

* Judy is generally faster then a hashing method and popular forms of balanced binary tree
* Judy is cache - concious because its designed to avoid cache-line fills wherever possible
* Judy tree is more memory-efficient than almost any other competitive structure

Useful link: A 10 minute description of how Judy arrays work and why they are so fast: 
http://judy.sourceforge.net/doc/10minutes.htm