CareerCup

If you have enough room to have a hash map for each attribute you could do that. That would be constant time.

Seems that the methods assume that the RollNo, name & address is unique.

I'll implement to support collision of RollNo, name & address as this seems to be the right approach here.

Seems simple apprach of to just use multiple hash tables to find the values for an specific input in O(1) growth function which is fast due to the large amount of data as any other method would seem slow because of the growth factor.

class StudentData
{
	int RollNo;
	string Name;
	string Address;
}
class StudentSearcher
{
	Dictinary<int, List<StudentData>> byRollNo = 
		new Dictinary<int, List<StudentData>>();

	Dictinary<string, List<StudentData>> byName = 
		new Dictinary<string, List<StudentData>>();

	Dictinary<string, List<StudentData>> byAddress = 
		new Dictinary<string, List<StudentData>>();

	public StudentSearcher(IEnumerable<StudentData> data)
	{
		foreach(StudentData d in data)
		{
			if(!byRollNo.ContainsKey(d.RollNo))
			{
				byRollNo.Add(d.RollNo, new List<StudentData>(){d});
			}
			else
			{
				byRollNo[d.RollNo].Add(d);
			}

			if(!byName.ContainsKey(d.Name))
			{
				byName.Add(d.Name, new List<StudentData>(){d});
			}
			else
			{
				byName[d.Name].Add(d);
			}

			if(!byAddress.ContainsKey(d.RollNo))
			{
				byAddress.Add(d.Address, new List<StudentData>(){d});
			}
			else
			{
				byAddress[d.Address].Add(d);
			}

		}
	}

	public List<StudentData> getStudentByRollNo(int rollno)
	{
		if(this.byRollNo.ContainsKey(rollno))
		{
			return this.byRollNo[rollno];
		}
	
		return null;
	}
	public List<StudentData> getStudentsByName(String name) 
	{
		if(this.byName.ContainsKey(name))
		{
			return this.byName[name];
		}
	
		return null;
	}

	public List<StudentData> getStudentsByAddress(String address)
	{
		if(this.byAddress.ContainsKey(address))
		{
			return this.byAddress[address];
		}
	
		return null;
	}
}