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How to Decode TLV Quickly

In TLV, the format is Tag, Length, and Value. The TLV protocol needs this type of data. Here you will know how to decode TLV data. According to IBM , TLV data is three parts. The tag tells what type of data it is. The length field denotes the length of the value. The Value-field denotes the actual value. Structure of TLV. TLV comprises three field values.  Tag Length Value EMV formulated different tags. They have their meanings. Usually, the Tag and Length together takes 1 to 4 bytes. The Best example for TLV. In the below example, you can find the sample TAG, LENGTH, and VALUE fields. [Tag][Value Length][Value] (ex. " 9F4005F000F0A001 ") where Tag Name =  9F40 Value Length (in bytes) =  05  Value (Hex representation of bytes. Example, "F0" – 1-byte) =  F000F0A001 In the above message, tag 9F40 has some meaning designed by EMV company. Here  you can find a list of EMV Tags. How to read the TLV Tag: 1 or 2 bytes Length: Length of the Value. F0-00-F0-A0-01 ==> 5 By

5 Top features of MongoDB: 1 of 5

The most important of the philosophies that underpin MongoDB is the notion that one size does not fit all. For many years, traditional SQL databases (MongoDB is a document-orientated database) have been used for storing content of all types.

It didn't matter whether the data was a good fit for the relational model (which is used in all RDBMS databases, such as MySQL, PostgresSQL, SQLite, Oracle, MS SQL Server, and so on). The data was stuffed in there, anyway.

Part of the reason for this is that, generally speaking, it's much easier (and more secure) to read and write to a database than it is to write to a file system.


If you pick up any book that teaches PHP (such as PHP for Absolute Beginners (Apress, 2009)) by Jason Lengstorf, you'll probably find that almost right away the database is used to store information, not the file system. 

It's just so much easier to do things that way. And while using a database as a storage bin works, developers always have to work against the flow. It's usually obvious when we're not using the database the way it was intended; anyone who has ever tried to store information with even slightly complex data, had to set up five tables, and then tried to pull it all together knows what I'm talking about!

  • The MongoDB team decided that it wasn't going to create another database that tries to do everything for everyone. Instead, the team wanted to create a database that worked with documents rather than rows, was blindingly fast, massively scalable, and easy to use. 
To do this, the team had to leave some features behind, which means that MongoDB is not an ideal candidate for certain situations. For example, its lack of transaction support means that you wouldn't want to use MongoDB to write an accounting application. That said, MongoDB might be perfect for part of the aforementioned application (such as storing complex data).
That's not a problem though because there is no reason why you can't use a traditional RDBMS for the accounting components and MongoDB for the document storage. Such hybrid solutions are quite common, and you can see them in production apps such as Sourceforge.
Once you're comfortable with the idea that MongoDB may not solve all your problems (the coffee-making plug-in is still in development), you will discover that there are certain problems that MongoDB is a perfect fit for resolving, such as analytics (think a realtime Google Analytics for your website) and complex data structures (e.g., as blog posts and comments). 

If you're still not convinced that MongoDB is a serious database tool, feel free to skip ahead to the "Reviewing the Feature List" section, where you will find an impressive list of features for MongoDB.

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