Multi-Cluster Query Result Caching Beyond Cluster Termination
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Solution Overview
Problem
Cluster computing systems face delays in query execution time due to the transient nature of clusters and their associated caches, which are frequently terminated, requiring users to re-execute queries when the cache is evicted.
Innovation Solution
Implement a multi-cluster computing system with an in-memory query result cache and a cloud storage query result cache to store data associated with previously executed requests, allowing clusters to efficiently retrieve cached results from either cache when re-executing queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clusters are frequently terminated for auto-scaling or automatic stop time, then resource efficiency is improved, but query execution time increases due to cache eviction
Solution Approach 1:
The patent introduces a distributed cache system as an intermediary between the cluster and query results. This intermediary stores query results persistently across multiple clusters, allowing new clusters to access previously computed results without re-executing queries, thus resolving the contradiction between cluster termination efficiency and query execution time
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing query results in the distributed cache before they are needed. When a new cluster is formed or a query is requested, the results are already available in the cache, eliminating the need to re-execute the query and reducing query execution time while maintaining resource efficiency
2Speed
If in-memory query result cache is used, then query retrieval speed is improved, but cache data is lost when cluster is terminated
Solution Approach 1:
The patent segments the cache system into multiple distributed cache nodes across different clusters. Each node stores a portion of the query results, and the segmentation allows data to be replicated across multiple locations. When one cluster terminates, the data remains in other clusters' caches, preventing information loss while maintaining fast retrieval speeds
Solution Approach 2:
The system implements a mechanism where cache data is discarded from terminated clusters but recovered through replication from other surviving clusters. The distributed cache architecture ensures that even if local cache is lost, the information can be recovered from other nodes, preventing permanent data loss while maintaining the speed benefits of in-memory caching
3Reliability
If multi-cluster system with distributed cache is implemented, then query result availability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal distributed cache system that serves multiple functions: it stores query results, enables cross-cluster data sharing, provides fault tolerance through replication, and maintains consistency across the cluster federation. This multi-functionality improves query result availability while the standardized interface simplifies the added complexity
Solution Approach 2:
The system uses copying by replicating query results across multiple cluster nodes and maintaining copies in the distributed cache. This replication strategy improves reliability by ensuring data availability even if one cluster fails, while the copying mechanism is transparent to users and simplifies the architecture compared to manual data synchronization approaches
Data Source
AI summary
A multi-cluster computing system which includes a query result caching system is presented. The multi-cluster computing system may include a data processing service and client devices communicatively coupled over a network. The data processing service may include a control layer and a data layer. The data layer may include a data storage system, which further includes a remote query result cache Store. The query result cache store may include a cloud storage query result cache which stores data associated with results of previously executed requests. As such, when a cluster encounters a previously executed request, the cluster may efficiently retrieve the cached result of the request from the in-memory query result cache or the cloud storage query result cache.