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
* Dynamic allocation of processing units based on query workload
* Routing queries between processing units with common session identifiers
* Caching data segments associated with cloud storage resources
* Removing processing units from the data warehouse as needed

'''Potential Applications:'''
This technology could be applied in various industries that require reliable and scalable data storage solutions, such as e-commerce, finance, and healthcare.

'''Problems Solved:'''
The method addresses the challenges of maintaining high availability and performance in data warehouses by dynamically adjusting resources based on workload and utilizing caching for efficient query processing.

'''Benefits:'''
* Improved fault tolerance and reliability
* Scalability to handle varying query workloads
* Efficient utilization of resources through caching
* Enhanced performance and availability of the data warehouse

'''Commercial Applications:'''
This technology could be utilized by cloud service providers, large enterprises, and data-intensive applications to ensure reliable and efficient data storage and processing capabilities.

'''Prior Art:'''
Researchers and developers interested in prior art related to fault-tolerant data warehouses may explore existing literature on distributed computing, cloud storage, and database management systems.

'''Frequently Updated Research:'''
Stay informed about advancements in distributed computing, cloud storage technologies, and data warehouse optimization to enhance the fault-tolerant capabilities of data storage systems.

'''Questions about Fault-Tolerant Data Warehouses:'''
1. What are the key challenges in implementing fault-tolerant data warehouses?
2. How does dynamic resource allocation improve the fault tolerance of data warehouses?


==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]]