How To Store UTF-8 Character in MySql

What is UTF-8 ?

• UTF-8 & UTF 16 are Unicode Character Sets.
• UTF-8 is the preferred encoding for e-mail and web pages. 
• UTF-8 is variable-length and uses 8-bit code units.
• UTF-8 is backwards compatible with ASCII.

How to handle with mysql?

First of all check character set and collation of  your database,to execute following query :

SHOW VARIABLES LIKE "character_set_database";

Variable_name                 Value
-----------------------------------------------    
character_set_database      latin1


SHOW VARIABLES LIKE "collation_database";

Variable_name                     Value          
-----------------------------------------------------------
collation_database            latin1_swedish_ci

OR you can also execute this query : SHOW VARIABLES LIKE  'char%';

As a result you get result like this:

• So here default character set is UTF 8 so no big bloom..if not then whenever you create table set CHARSET=utf8 and Collation = utf8_general_ci

Let us create one table :

CREATE TABLE product (
  id bigint(20) NOT NULL AUTO_INCREMENT,
  name varchar(50) NOT NULL,
  PRIMARY KEY (id)
) ENGINE=InnoDB DEFAULT CHARSET=utf8


If you have already created table then execute following alter query

ALTER TABLE product 
MODIFY `name` VARCHAR(255) CHARACTER SET utf8 NOT NULL DEFAULT ''

Now you can insert or update utf-8 character easily..enjoy..lots..!!!!

Java Collection Set : Difference between Hashset and Treeset

• A collection is a group of data manipulate as a single object. 
• Collections are primarily defined through a set of interfaces.
• Interfaces are used of flexibility reasons
• Programs that uses an interface is not tightened to a specific implementation of a collection.
• It is easy to change or replace the underlying collection class with another (more efficient) class that implements the same interface.
• HashSet and TreeSet implement the interface Set.
• HashSet is much faster than TreeSet (constant-time versus log-time for most operations like add, remove and contains) but offers no ordering guarantees like TreeSet.
• HashSet
• Class offers constant time performance for the basic operations (add, remove, contains and size).
• It does not guarantee that the order of elements will remain constant over time.
• Iteration performance depends on the initial capacity and the load factor of the HashSet.
• It's quite safe to accept default load factor but you may want to specify an initial capacity that's about twice the size to which you expect the set to grow.
  
• TreeSet
• Guarantees log(n) time cost for the basic operations (add, remove and contains) .
• Guarantees that elements of set will be sorted (ascending, natural, or the one specified by you via its constructor) (implements SortedSet).
• Doesn't offer any tuning parameters for iteration performance
• Offers a few handy methods to deal with the ordered set like first(), last(), headSet(), and tailSet() etc.
  
• Important points:
•  Both guarantee duplicate-free collection of elements.
•  It is generally faster to add elements to the HashSet and then convert the collection to a TreeSet for a duplicate-free sorted traversal.
•  None of these implementation are synchronized. That is if multiple threads access a set concurrently, and at least one of the threads modifies the set, it must be synchronized externally.
• LinkedHashSet is in some sense intermediate between HashSet and TreeSet. Implemented as a hash table with a linked list running through it, however it provides insertion-ordered iteration which is not same as sorted traversal guaranteed by TreeSet.
• So choice of usage depends entirely on your needs but I feel that even if you need an ordered collection then you should still prefer HashSet to create the Set and then convert it into TreeSet.
• e.g. SortedSet<String> s = new TreeSet<String>(hashSet);

Big Oh notation

• For run time complexity analysis we use big Oh notation extensively so it is vital that you are familiar with the general concepts to determine which is the best algorithm for you in certain scenarios. 
• We have chosen to use big Oh notation for a few reasons, the most important of which is that it provides an abstract measurement by which we can judge the performance of algorithms without using mathematical proofs.

The following list explains some of the most common big Oh notations : 

• O(1) constant: the operation doesn't depend on the size of its input, e.g. adding a node to the tail of a linked list where we always maintain a pointer to the tail node.

•  O(n) linear: the run time complexity is proportionate to the size of n

•  O(log n) logarithmic: normally associated with algorithms that break the problem into smaller chunks per each invocation, e.g. searching a binary search tree.

• O(n log n) just n log n : usually associated with an algorithm that breaks the problem into smaller chunks per each invocation, and then takes the results of these smaller chunks and stitches them back together, e.g. quick sort.

• (n^2) quadratic: e.g. bubble sort.
• O(n^3) cubic: very rare.
• O(2n) exponential: incredibly rare.

• If you encounter either of the latter two items (cubic and exponential) this is really a signal for you to review the design of your algorithm. 
• While prototyping algorithm designs you may just have the intention of solving the problem irrespective of how fast it works. We would strongly advise that you always review your algorithm design and optimize where possible|particularly loops recursive calls|so that you can get the most efficient run times for your algorithms.

• Taking a quantitative approach for many software development properties will make you a far superior programmer - measuring one's work is critical to success.