@ripunjay godhani
Here is the general answer - reducing the default block size will result in creation of too many blocks which results an overhead on Name Node. By architecture each node (in newer architecture it will be each storage type per node - but that conversation is for a different time) on the Hadoop cluster will report a storage report and block report back to the Name Node, which will then be used when retrieving/accessing the data at a later time. So, as you would imagine this will increase the chattiness between name node and data node, as well as increase the meta data on the Name node iteself.
Also, when you start hitting 100's of millions of file range, then your Name node will start filling up the memory and may result in going through a major garbage collection, which is a stop the world operation and may result in your whole cluster being down for few minutes.. there are ways around this - like increasing the memory size of NN or changing the gc, but none of those are economical or easy.
These are all the down sides of reducing the block size - or even a small file problem, in general.
And now coming to your specific use case - why do you think you have so many smaller files? Is there a way you can merge multiple of those into a larger file? I know one of my customer had similar issue while storing tick symbols - they mitigated this by combining the tick data on a hourly basis. Another customer had a source file FTP-ed that is quite small and they mitigated by gzipping bunch of those file into a really large one. Also, archiving data to Hive is another option.
The bottom line being the small file issue on the hadoop must be viewed as a combination of technical + business problem, and you will be best off by looking to ways to eliminate this situation from business standpoint as well. Simply playing around the block size is not going to give you the most mileage.
Lastly, if you felt this answer to be helpful, please upvote and accept the answer. Thank you!
