Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest events over the network and scale across cloud resources elastically. We present Shadowfax, a new distributed KVS based on FASTER, that transparently spans DRAM, SSDs, and cloud blob storage while serving 130 Mops/s/VM over commodity Azure VMs using conventional Linux TCP. Beyond high single-VM performance, Shadowfax uses a unique approach to distributed reconfiguration that avoids any server-side key ownership checks or cross-core coordination both during normal operation and migration. Hence, Shadowfax can shift load in 17 s to improve system throughput by 10 Mops/s with little disruption. Compared to the state-of-the-art, it has 8x better throughput (than Seastar+memcached) and avoids costly I/O to move cold data during migration. On 12 machines, Shadowfax retains its high throughput to perform 930 Mops/s, which, to the best of our knowledge, is the highest reported throughput for a distributed KVS used for large-scale data ingestion and indexing.

Scalable in-memory key-value stores provide low-latency access times of a few microseconds and perform millions of operations per second per server. With all data in memory, these systems should provide a high level of reconfigurability. Ideally, they should scale up, scale down, and rebalance load more rapidly and flexibly than disk-based systems. Rapid reconfiguration is especially important in these systems since a) DRAM is expensive and b) they are the last defense against highly dynamic workloads that suffer from hot spots, skew, and unpredictable load. However, so far, work on in-memory key-value stores has generally focused on performance and availability, leaving reconfiguration as a secondary concern.