Distributed Caching refers to a system architecture where data is stored temporarily across multiple networked servers or nodes to accelerate data retrieval and reduce load on primary data sources. In high-frequency crypto trading and data feeds, it significantly enhances system responsiveness and scalability.
Mechanism
The mechanism involves a cluster of cache servers that collectively hold frequently accessed data. When an application requests data, it first checks the distributed cache; if present, the data returns quickly. If absent, the data is fetched from the slower primary data store, then stored in the cache for subsequent requests, often with intelligent eviction policies.
Methodology
The strategic approach aims to optimize latency and throughput for critical market data and order book information by positioning data closer to consuming applications. Principles include consistency models for cached data, fault tolerance through replication, and dynamic scaling of cache nodes to handle varying loads, ensuring rapid quote delivery and order processing in volatile crypto markets.
Real-time data pipelines and advanced pricing engines empower instantaneous quote adjustments, ensuring optimal risk management and execution in derivatives markets.
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