So far, I have posted several blogs that explain certain pieces of the Didget Management System and how each feature adds specific benefits over conventional file system or database architectures. I thought I would devote this 20th blog to explaining the entire system once all the pieces are put together to give the reader an idea of how it will look once completed.
The Didget Realm represents a world-wide collection of individual Didget containers called Chambers. Each Chamber is managed by its own instance of the Didget Manager and together they represent a single node in this global data storage network. Each node can communicate with every other node to exchange Didget information. With the use of Policy Didgets, this information can be exchanged automatically without direct commands from a running application. Nodes can be grouped into domains or federations so that they can exchange even more information between them than can two nodes that are not in the same domain.
Each Chamber can store several billion individual Didgets. The system is designed to effectively manage huge numbers of Didgets without sacrificing speed. Simple queries to a Chamber with over 10 million Didgets in it are designed to execute in under one second. Even the most complex queries are designed to execute in under ten seconds when the Didget Manager is running on a single desktop system. For Chambers with hundreds of millions or with billions of Didgets, the Chamber can be split into many individual pieces and managed by lots of separate systems in a distributed environment to perform lightning fast queries using map-reduce algorithms.
A Chamber that has been converted to a distributed system looks exactly the same to an application or to another node in the global network, as does a Chamber that has not been split into several pieces and distributed. In other words, applications do not need to know if they are communicating with a single piece Chamber running on a laptop computer or if they are communicating with a Chamber that has been split into 100 different pieces and managed by 1000 different servers. The only difference will be the speed at which a query or other command may execute when the number of Didgets in the Chamber is extraordinarily large.
Using Policy Didgets and Security Didgets, operations against all the Didgets with a Chamber can be tightly controlled. Sensitive information can be protected and a whole host of data management functions can happen automatically when either a certain amount of time has expired or when certain events happen.
Individual Didgets can be classified, tagged, and grouped together in ways files or database rows never could. Copying or moving a Didget from one Chamber to another does not cause it to lose any of its metadata or to become any less secure than the original. Special attributes can be assigned to each Didget that enable it to be managed by the Didget Manager in very specific ways. Several of these attributes represent unique features that I have not seen on any other system.
Applications can query for a set of Didgets based on any of these metadata fields and perform operations against the whole set (if permissions allow).
Didgets can represent either structured and unstructured data. All the management functions work the same, regardless of the data type. Didgets can be accessed using file-like APIs or database-like queries.
Inventory, search, backup, recovery, synchronization, organization, version control, and licensing are just a few of the management functions that are provided by the system. In every case, the functions will perform faster and with simpler mechanisms than with conventional systems.
In summary, I think this system offers a far superior data management environment than do conventional file systems or NoSQL database environments. Once data is created as Didgets (or converted from legacy systems) it will be far easier to manage and provide significantly greater value to the end user than it would be as files or as database rows.
The Didget Management system will revolutionize the way the whole world looks at data going forward. (You heard it here first!)
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