High-velocity data imposes high durability overheads on Big Data technology components such as NoSQL data stores. In Apache Cassandra and MongoDB, widely used NoSQL solutions with high scalability and availability, write-ahead logging is used to provide durability. However, current write-ahead logging techniques are limited by the excessive overhead in the I/O subsystem. To address this performance gap, we have designed a novel CAPI-Flash based high performance durable logging mechanism for Apache Cassandra and MongoDB. We take advantage of the high throughput, low latency path to flash storage provided by the Coherent Accelerator Processor Interface (CAPI) on IBM POWER8 Systems. Our experimental results show that for insert-only workloads, CAPI-Flash logging provides up to 70 and 514 percent improvement in throughput compared to Cassandra and MongoDB's durable alternatives, respectively. It also provides average of 45 percent increase in throughput with Cassandra and average of 115 percent increase in throughput with MongoDB for update-mostly and update-only workloads.

In real-world NoSQL deployments, users have to trade off CPU, memory, I/O bandwidth and storage space to achieve the required performance and efficiency goals. Data compression is a vital component to improve storage space efficiency, but reading compressed data increases response time. Therefore, compressed data stores rely heavily on using the memory as a cache to speed up read operations. However, as large DRAM capacity is expensive, NoSQL databases have become costly to deploy and hard to scale. In our work, we present a persistent caching mechanism for Apache Cassandra on a high-throughput, low-latency FPGA-based NVMe Flash accelerator (CAPI-Flash), replacing Cassandra's in-memory cache. Because flash is dramatically less expensive per byte than DRAM, our caching mechanism provides Apache Cassandra with access to a large caching layer at lower cost. The experimental results show that for read-intensive workloads, our caching layer provides up to 85% improved throughput and also reduces CPU usage by 25% compared to default Cassandra.