18617083. MULTI-CLUSTER WAREHOUSE simplified abstract (Snowflake Inc.)
Contents
MULTI-CLUSTER WAREHOUSE
Organization Name
Inventor(s)
Thierry Cruanes of San Mateo CA (US)
Benoit Dageville of Foster City CA (US)
Florian Andreas Funke of San Francisco CA (US)
Peter Povinec of Redwood City CA (US)
MULTI-CLUSTER WAREHOUSE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18617083 titled 'MULTI-CLUSTER WAREHOUSE
The abstract describes a method for implementing a fault-tolerant data warehouse by allocating processing units in different availability zones, monitoring queries to optimize performance, and routing queries efficiently within the data warehouse.
- Allocating a plurality of processing units in different availability zones to enhance fault tolerance.
- Monitoring the number of queries running on the processing units to optimize performance.
- Routing queries efficiently within the data warehouse based on common session identifiers.
- Removing processing units from the data warehouse when they are no longer needed.
- Utilizing cloud storage resources for caching data segments to improve query performance.
Potential Applications: - Cloud computing - Big data analytics - Data warehousing
Problems Solved: - Improving fault tolerance in data warehouses - Optimizing query performance - Efficient resource allocation
Benefits: - Enhanced fault tolerance - Improved query performance - Cost-effective resource utilization
Commercial Applications: Title: "Optimizing Data Warehousing Performance with Fault-Tolerant Method" This technology can be used in various industries such as e-commerce, finance, healthcare, and telecommunications to improve data processing efficiency and reliability.
Prior Art: Readers can explore prior research on fault-tolerant data warehouses, query optimization, and cloud storage caching techniques to understand the evolution of this technology.
Frequently Updated Research: Stay updated on the latest advancements in fault-tolerant data warehousing, query routing algorithms, and cloud storage integration for data processing.
Questions about Fault-Tolerant Data Warehousing: 1. How does fault tolerance impact the reliability of data warehouses? Fault tolerance ensures that data warehouses can continue operating even if individual components fail, enhancing overall reliability. 2. What are the key considerations when implementing a fault-tolerant data warehouse? Key considerations include redundancy, load balancing, and efficient resource allocation to ensure continuous operation and optimal performance.
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.
