Distributed datastores underpin highly concurrent, read-intensive applications, ensuring consistency, availability, and performance. They use crash-tolerant protocols to replicate data and endure replica server crashes. To ensure safety and meet the performance demands, replication must support high-throughput, strongly consistent (i.e., linearizable) reads without assuming any synchrony. However, existing protocols either 1 relax consistency, or provide linearizable reads that are 2 fully asynchronous but remote (involving multiple replicas), or 3 local but require synchrony. This work explores the tradeoffs between consistency, asynchrony, and performance in crash-tolerant protocols, and proves that in linearizable asynchronous read/write registers tolerating a single crash, no reads can be local. Building on this, we introduce almost-local reads (ALRs), a new abstraction that ensures crash tolerance and linearizability under asynchrony. While ALRs have slightly higher latency than local reads, they remain lightweight, with computation and network costs close to single-node reads. We present two simple yet effective ALR schemes that enhance protocols across all three categories. For protocols with local reads, ALRs address consistency or synchrony issues with minimal throughput loss. In asynchronous linearizable protocols, they improve performance without compromises. Our evaluation shows that ALR-enhanced ZAB and Hermes achieve within 2% and 5% of their original throughput in 95% reads while ensuring linearizability under asynchrony. On Raft, ALRs deliver over 2.5x higher throughput without compromising consistency or asynchrony.
Katsarakis et al. (Thu,) studied this question.