The Power of O(1) for Sustainable Cloud Computing
When you consider today's most unsustainable practices, you may first think of non-renewable energy usage and the emission of greenhouse gases.
We don't often think of data centers being a negative contributor. Our data centers - the centers that run the software you're reading this blog on - use massive amounts of energy to function.
Not only do computers use electricity to operate, they generate large amounts of heat that require energy to cool. Computers also require substantial resources to manufacture. All of which negatively contribute to our carbon footprint with high consumption of energy, waste production, and CO2 emissions.
How does the explosion of data impact sustainability?
The primary function of computers is to act upon data. Therefore, if the amount of data acted on is exploding, we need a corresponding increase in computing, storage, and bandwidth. Since much of this is rapidly moving to the cloud, cloud computing faces a major sustainability problem.
While gains have been made in the creation of more efficient chips and hardware, these gains are much, much slower than the rate at which data is increasing. This leaves few strategies to make cloud computing more efficient, and the common argument to "use less data" is unlikely to become an effective strategy for data centric organizations.
At Craxel, we believe an important part of the solution for more sustainable cloud computing lies in O(1) algorithms. This is because of its ability to organize data for faster and more efficient access and query.
What is O(1)?
In computer science, Order (1) is known as constant time. It's essentially an algorithm whose execution time doesn't depend on the size of the data.
O(1) algorithms are widely used today in software for speed and efficiency. Software engineers know that constant time algorithms are the key to fast and efficient data operations. Hash tables and NoSQL key/value databases are two examples that use O(1) algorithms.
Why aren't existing O(1) algorithms the solution?
Hash tables and key/value databases are only useful in certain circumstances because their functionality is limited to a simple lookup of a specific value. In order to find algorithms that overcome this limitation, significant research has gone into order preserving hash algorithms.
These algorithms would allow more complex queries to be efficiently performed – such as range query – however, no practical algorithms that scale have yet been discovered. With the exception of use cases where simple lookup is good enough, there is no O(1) way to organize the exploding amount of data.
This leaves us with costly and slow indexing techniques such as B-trees that don't scale or unsustainable massively parallel processing O(N) approaches for querying data. While B-trees are O(LOG N), the cost to process the data to maintain O(LOG N) search time is too slow and resource intensive to scale.
Solution: An O(1) Hierarchy Preserving Probabilistic Hash
The solution to this dilemma has been found, but not in order preserving hashing. Instead, the solution is a hierarchy preserving probabilistic hash. This is what we've built at Craxel.
Craxel's patented technology provides fast and efficient indexing of data while supporting fast and efficient key/value, range, time series, graph, and spatial query. Using these O(1) hash algorithms, Craxel's products are able to index data at massive scale for rapid and efficient access at scale. This dramatically reduces the computation required to both index and query data.
How Craxel helps you reach your sustainability goals with Black Forest
The computation required to access data can be dramatically reduced by more efficiently organizing your data. This is an extremely intuitive notion.
Better organization = more efficient search.
Craxel's Black Forest technology uses breakthrough O(1) algorithms to bring unprecedented order to data. Instead of sacrificing performance for sustainability, Black Forest provides both by decreasing the amount of computation required to search data. This equates to a decrease in resources utilized to perform the complex queries needed to drive insights that validate business decisions, drive action, and growth.
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