Pluggable disk cache

RisingWave is designed for high-performance stream processing, and it achieves this by leveraging S3 as its primary storage. This means all data, including the internal states of streaming jobs, tables, and materialized views, is persisted in S3. To optimize performance, RisingWave also utilizes local memory as a cache, allowing for rapid data access.

However, local memory is finite. When the data required for a streaming operation (the “working set”) exceeds the available memory, a cache miss occurs. This forces RisingWave to retrieve the data from S3, introducing latency and potentially increasing costs due to frequent S3 access. While S3 provides cost-effective storage for large datasets, repeated access for active processing can become expensive.

To address this challenge, RisingWave offers the pluggable disk cache to significantly reduce S3 access and improve performance. The pluggable disk cache utilizes local disk storage, such as NVMe SSDs or AWS EBS volumes, as an extension of the memory cache. This allows RisingWave to keep a larger portion of the working set readily available, minimizing the need to fetch data from S3.

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Key use cases for pluggable disk cache

The pluggable disk cache is particularly beneficial for users with performance-sensitive workloads, including:

1. Large-state queries: Complex streaming jobs involving joins, time windowing, aggregations, and other stateful operations can generate substantial internal state. If this state exceeds the capacity of the local memory cache, performance can degrade significantly without a disk cache.
2. Data serving: When querying data from tables or materialized views, RisingWave must fetch data from S3 if it’s not present in the memory cache. This can lead to high latency and increased costs, especially for frequently accessed data. The disk cache significantly reduces this latency.

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Benefits of pluggable disk cache

• Reduced S3 access & costs: By keeping more data locally available, the disk cache minimizes the frequency and volume of data retrieved from S3, leading to lower costs.
• Improved dynamic scaling: The disk cache dramatically reduces cache warm-up time during scaling operations. New nodes can quickly access data from their local disk cache instead of relying solely on S3.
• Faster failure recovery: Similar to scaling, recovery from failures is significantly accelerated. The system can restore state from the local disk cache, minimizing downtime.
• Enhanced performance in rate-limited environments: In environments with object storage rate limits (common in private clouds), the disk cache reduces reliance on S3, enabling smoother scaling and more consistent performance. “Smoother scaling” means the ability to quickly add compute nodes with minimal performance impact during the scaling process, thanks to the reduced reliance on fetching data from a potentially rate-limited object store.