Distributed Query Revision Maps for Memory-Efficient Large-Scale Analysis
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Solution Overview
Problem
The traditional approach of loading large data sets into memory for analysis becomes impractical and cost-prohibitive as data sizes increase, requiring significant hardware and software infrastructure scaling efforts.
Innovation Solution
A distributed query system is implemented, utilizing query nodes, a database management system, and a subscription engine to manage and service query requests across a network of computing devices, enabling efficient distribution of data and query processing without the need for all data to be stored in memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large data sets are loaded into memory for analysis, then analysis speed and accessibility are improved, but memory requirements and hardware infrastructure costs increase significantly
Solution Approach 1:
The system divides the large data set into smaller partitions or segments that can be distributed across multiple query nodes. Each node processes only the relevant segment, enabling parallel analysis without requiring all data to be loaded into a single node's memory. This segmentation allows the system to maintain high analysis speed while reducing individual memory requirements.
Solution Approach 2:
The system transitions from a single-node in-memory processing model to a distributed multi-node model. By adding the dimension of distribution across multiple nodes, the system can handle large data sets that would be impossible to fit in a single node's memory, while maintaining analysis throughput through parallel processing.
2Quantity of substance
If data set size increases, then more comprehensive analysis is enabled, but hardware and software infrastructure complexity increases
Solution Approach 1:
The query nodes are designed as universal, multi-functional units that can handle various query types and data operations. Each node is capable of processing queries independently, and the system automatically routes queries to appropriate nodes. This universality simplifies the infrastructure by using identical, purpose-built nodes rather than complex specialized hardware.
Solution Approach 2:
The system introduces a query router or mediator that handles query distribution, routing, and coordination between nodes. This intermediary layer abstracts the complexity of data distribution from the query processing logic, allowing the infrastructure to scale with data size while maintaining simple, standardized query processing at each node.
3Reliability
If all data is stored in memory to ensure consistency, then data accessibility is improved, but system scalability becomes difficult
Solution Approach 1:
The system performs preliminary actions by pre-partitioning data into segments and pre-configuring query routes before actual queries are processed. This allows the system to maintain data consistency through versioning and metadata tracking while enabling easy scaling by simply adding more pre-configured nodes to the distributed architecture without reconfiguring the entire system.
Solution Approach 2:
The system changes the parameter of data storage from centralized in-memory to distributed on-disk or distributed cache storage. By changing this fundamental parameter, the system maintains data consistency through versioning mechanisms and metadata, while achieving scalability through the ability to add nodes without changing the fundamental storage architecture.
Data Source
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AI summary
In general, in one aspect, the invention relates to a method for servicing query requests in distributed systems. The method includes making a first determination, by a query node and in response to a query request, that a user data sub-region associated with a query request, obtaining, in response to the first determination and using a population table, a revisions map, where the revisions map specifies a version of the user data sub-region, and servicing the query request using the revisions map and data associated with the version of the user data sub-region.