View source for Snowflake Inc. (20240232226). MULTI-CLUSTER WAREHOUSE simplified abstract

=MULTI-CLUSTER WAREHOUSE=


==Organization Name==

[[:Category:Snowflake Inc.|Snowflake Inc.]]


[[Category:Snowflake Inc.]]

==Inventor(s)==

[[:Category:Thierry Cruanes of San Mateo CA (US)|Thierry Cruanes of San Mateo CA (US)]][[Category:Thierry Cruanes of San Mateo CA (US)]]

[[:Category:Benoit Dageville of Foster City CA (US)|Benoit Dageville of Foster City CA (US)]][[Category:Benoit Dageville of Foster City CA (US)]]

[[:Category:Florian Andreas Funke of San Francisco CA (US)|Florian Andreas Funke of San Francisco CA (US)]][[Category:Florian Andreas Funke of San Francisco CA (US)]]

[[:Category:Peter Povinec of Redwood City CA (US)|Peter Povinec of Redwood City CA (US)]][[Category:Peter Povinec of Redwood City CA (US)]]

==MULTI-CLUSTER WAREHOUSE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240232226 titled 'MULTI-CLUSTER WAREHOUSE

'''Simplified Explanation:'''
The patent application describes a method for implementing a fault-tolerant data warehouse by distributing processing units across different availability zones and dynamically adjusting the number of units based on query workload.

'''Key Features and Innovation:'''
* Fault-tolerant data warehouse implementation
* Allocation of processing units across different availability zones
* Dynamic adjustment of processing units based on query workload
* Query routing and caching for improved performance
* Removal of processing units to optimize resource utilization

'''Potential Applications:'''
The technology can be applied in various industries that require reliable and scalable data storage and processing, such as e-commerce, finance, healthcare, and more.

'''Problems Solved:'''
The technology addresses the challenges of maintaining high availability and performance in a data warehouse environment, especially when dealing with fluctuating query workloads.

'''Benefits:'''
* Improved fault tolerance and reliability
* Scalability to handle varying workloads
* Enhanced performance through query routing and caching
* Optimal resource utilization for cost efficiency

'''Commercial Applications:'''
The technology can be utilized by cloud service providers, large enterprises, and data-intensive organizations to build robust and efficient data warehouses that can adapt to changing demands in real-time.

'''Prior Art:'''
Readers interested in exploring prior art related to fault-tolerant data warehouses can start by researching existing methods for distributing processing units in cloud environments and optimizing query performance.

'''Frequently Updated Research:'''
Stay updated on the latest advancements in fault-tolerant data warehouse technologies, including research on dynamic resource allocation, query optimization, and fault recovery mechanisms.

'''Questions about fault-tolerant data warehouses:'''
1. How does the method described in the patent application improve fault tolerance in data warehouses?
2. What are the key advantages of distributing processing units across different availability zones in a data warehouse environment?


==Original Abstract Submitted==

a method implementing a fault-tolerant data warehouse including allocating a plurality of processing units to a data warehouse, the processing units located in different availability zones, an availability zone comprising one or more data centers. the method further includes, as a result of monitoring a number of queries running at an input degree of parallelism on the plurality of processing units of the data warehouse, determining that the number of queries is serviceable by one fewer processing units. the method further includes routing a query from a first processing unit to a second processing unit within the data warehouse, the query having a common session identifier with another query previously provided to the second processing unit, the second processing unit determined to be caching a data segment associated with a cloud storage resource, usable by the query, and removing the first processing unit from the data warehouse.



[[Category:G06F16/28]]
[[Category:G06F9/50]]
[[Category:G06F16/2455]]
[[Category:H04L41/0896]]
[[Category:H04L41/5025]]
[[Category:H04L43/0817]]
[[Category:H04L67/1008]]
[[Category:H04L67/1097]]
[[Category:CPC_G06F16/283]]