Cross-Cluster Write-Back Caching for Low-Latency Consistency
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Solution Overview
Problem
Current systems face performance limitations in write-back caching across clusters, particularly in managing large datasets, leading to increased write latency and decreased throughput, and challenges in maintaining data consistency across clusters.
Innovation Solution
Implement methods and systems for write-back caching across clusters by managing cache and volume thresholds, using tunable parameters to ensure efficient data handling and consistency, including cache file, cache, and idle thresholds, and optimizing write-back message payload sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If write-back caching is implemented across clusters, then write latency is reduced and throughput is increased, but data consistency across clusters becomes challenging to maintain
Solution Approach 1:
The system performs preliminary actions by writing data to the cache first and confirming write completion before initiating the write-back operation to the underlying data store. This allows the write request to be confirmed to the client immediately, reducing write latency, while the actual data persistence is handled subsequently, maintaining data consistency through coordinated communication across clusters.
Solution Approach 2:
The cache acts as an intermediary layer between the client and the underlying data store across clusters. It buffers write operations and manages the coordination between different cluster nodes, allowing write requests to be confirmed quickly while ensuring data is eventually persisted to the underlying storage through controlled write-back operations.
2Loss of time
If data is written to cache and completion is confirmed immediately, then write latency is reduced, but the risk of data loss increases if the system shuts down before writing to the underlying data store
Solution Approach 1:
The system confirms write completion to the client after successfully writing to the cache, providing low write latency. The write-back to the underlying data store is then initiated as a subsequent action, ensuring that data is eventually persisted safely even though the confirmation occurs before the underlying write completes.
3Reliability
If write-back operations are performed frequently to maintain data consistency, then data safety is improved, but write throughput decreases due to additional I/O operations
Solution Approach 1:
Instead of performing write-back operations after every write request, the system implements periodic or batched write-back operations. Data is accumulated in the cache and written back to the underlying data store in periodic intervals or when certain conditions are met, reducing the frequency of I/O operations while maintaining data consistency across clusters.
Solution Approach 2:
The cache continuously accepts write requests and maintains data in memory, providing continuous write capability without interruption from frequent write-back operations. The write-back to the underlying data store occurs in continuous batches, ensuring data persistence while maintaining high write throughput by avoiding frequent individual I/O operations.
Data Source
AI summary
A method and computing device for write-back caching. A client write request to write new data to a selected file on a volume (for which the client has mounted the corresponding cache) may be received at a network module of a node and processed to generate a write request that can be forwarded to a disk module hosting the cache (at a same or different node than received the client write request). The data is written to the cache and confirmation of the write is sent to the client. Accumulated data in the cache is written back to the volume (hosted by a different node than the cache) when at least one of a cache file threshold or a cache threshold is met. These parameters are set to values that reduce write latency, increase throughput, and help ensure data consistency and resiliency.