Multi-tenant transactional databases frequently observe contention on shared data, leading to a need for performance isolation. Databases typically provide performance isolation via a request rate limit or quota per tenant, but this approach can lead to system underutilization. Traditionally, fair sharing has been applied to achieve both performance isolation and high utilization in other domains. In this paper, we address the problem of fair sharing for transactions, which introduces new challenges because client requests do not acquire resources all at once. We propose DRFT, the first fair transaction scheduling algorithm that ensures both the share guarantee and strategy-proofness by accurately accounting for transactional resource usage. We evaluate DRFT on a range of standard benchmarks and real-world workloads, showing that it ensures fairness with less than a 5% throughput overhead compared to state-of-the-art scheduling policies.

Modern applications span multiple clouds to reduce costs, avoid vendor lock-in, and leverage low-availability resources in another cloud. However, standard object stores operate within a single cloud, forcing users to manually manage data placement across clouds, i.e., navigate their diverse APIs and handle heterogeneous costs for network and storage. This is often a complex choice: users must either pay to store objects in a remote cloud, or pay to transfer them over the network based on application access patterns and cloud provider cost offerings. To address this, we present SkyStore, a unified object store that addresses cost-optimal data management across regions and clouds. SkyStore introduces a virtual object and bucket API to hide the complexity of interacting with multiple clouds. At its core, SkyStore has a novel TTL-based data placement policy that dynamically replicates and evicts objects according to application access patterns while optimizing for lower cost. Our evaluation shows that across various workloads, SkyStore reduces the overall cost by up to 6X over academic baselines and commercial alternatives like AWS multi-region buckets. SkyStore also has comparable latency, and its availability and fault tolerance are on par with standard cloud offerings.

Mammoth transactions, which involve long-running operations that access many items, are common in graph workloads. Graph analytics tasks, including pattern matching and graph algorithms, can generate large read-write operations that impact significant portions of data, which makes their execution challenging under strict isolation guarantees. Consequently, we face an apparent trade-off between ensuring high isolation and achieving high performance, forcing users to choose between the two. In this work, we present TuskFlow, an experimental graph database based on Neo4j, designed to efficiently handle mammoth transactions on graphs (the technique is applicable to other models such as relational) while maintaining existing transactional semantics. TuskFlow employs a deterministic protocol that safely reorders regular transactions around mammoths within an epoch. Our protocol supports parallel mammoth execution inspired by graph-parallel algorithms. To minimize conflicts with regular transactions, TuskFlow introduces query- and workload-aware optimizations, including graph entity tagging and partitioning. Our experiments demonstrate that, unlike traditional protocols like two-phase locking or MVCC, TuskFlow avoids blocking write transactions and improves tail latency by up to 45×.

Cloud object stores offer vastly different price points for object storage as a function of workload and geography. Poor object placement can thus lead to significant cost overheads. Prior cost-saving techniques attempt to optimize placement policies on the fly, deciding object placements for each object individually. In practice, these techniques do not scale to the size of the modern cloud. In this work, we leverage the static nature and pay-per-use pricing model of cloud environments to explore a different approach. Rather than computing object placements on the fly, we precompute a SkyPIE oracle---a lookup structure representing all possible placement policies and the workloads for which they are optimal. Internally, SkyPIE represents placement policies as a matrix of cost-hyperplanes, which we effectively precompute through pruning and convex optimization. By leveraging a fast geometric algorithm, online queries then are 1 to 8 orders of magnitude faster but as accurate as Integer-Linear-Programming. This makes exact optimization tractable for real workloads and we show >10x cost savings compared to state-of-the-art heuristic approaches.

Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. New scalable distributed protocols are developed through careful analysis and rewrites, but this process is ad hoc and error-prone. This paper presents an approach for scaling any distributed protocol by applying rule-driven rewrites, borrowing from query optimization. Distributed protocol rewrites entail a new burden: reasoning about spatiotemporal correctness. We leverage order-insensitivity and data dependency analysis to systematically identify correct coordination-free scaling opportunities. We apply this analysis to create preconditions and mechanisms for coordination-free decoupling and partitioning, two fundamental vertical and horizontal scaling techniques. Manual rule-driven applications of decoupling and partitioning improve the throughput of 2PC by 5× and Paxos by 3×, and match state-of-the-art throughput in recent work. These results point the way toward automated optimizers for distributed protocols based on correct-by-construction rewrite rules.

Maximizing transaction throughput is key to high-performance database systems, which focus on minimizing data access conflicts to improve performance. However, finding efficient schedules that reduce conflicts remains an open problem. For efficiency, previous scheduling techniques consider only a small subset of possible schedules. In this work, we propose systematically exploring the entire schedule space, proactively identifying efficient schedules, and executing them precisely during execution to improve throughput. We introduce a greedy scheduling policy, SMF, that efficiently finds fast schedules and outperforms state-of-the-art search techniques. To realize the benefits of these schedules in practice, we develop a schedule-first concurrency control protocol, MVSchedO, that enforces fine-grained operation orders. We implement both in our system R-SMF, a modified version of RocksDB, to achieve up to a 3.9× increase in throughput and 3.2× reduction in tail latency on a range of benchmarks and real-world workloads.

This paper argues for better concurrency control to support mammoth transactions , which read and write to many items. While these requests are prevalent on graph data, few systems support them efficiently. Currently, developers must make the uncomfortable choice between accepting dismal performance or abandoning transactional semantics. Applications deserve better: we believe that inherent graph properties provide a path forward to designing efficient concurrency control that preserves strong isolation.

Feature stores (also sometimes referred to as embedding stores) are becoming ubiquitous in model serving systems: downstream applications query these stores for auxiliary inputs at inference-time. Stored features are derived by featurizing rapidly changing base data sources. Featurization can be costly prohibitively expensive to trigger on every data update, particularly for features that are vector embeddings computed by a model. Yet, existing systems naively apply a one-size-fits-all policy as to when/how to update these features, and do not consider query access patterns or impacts on prediction accuracy. This paper introduces RALF, which orchestrates feature updates by leveraging downstream error feedback to minimize feature store regret , a metric for how much featurization degrades downstream accuracy. We evaluate with representative feature store workloads, anomaly detection and recommendation, using real-world datasets. We run system experiments with a 275,077 key anomaly detection workload on 800 cores to show up to a 32.7% reduction in prediction error or up to 1.6X compute cost reduction with accuracy-aware scheduling.

The continued emergence of large social network applications has introduced a scale of data and query volume that challenges the limits of existing data stores. However, few benchmarks accurately simulate these request patterns, leaving researchers in short supply of tools to evaluate and improve upon these systems. In this paper, we present a new benchmark, TAOBench, that captures the social graph workload at Meta. We open source workload configurations along with a benchmark that leverages these request features to both accurately model production workloads and generate emergent application behavior. We ensure the integrity of TAOBench's workloads by validating them against their production counterparts. We also describe several benchmark use cases at Meta and report results for five popular distributed database systems to demonstrate the benefits of using TAOBench to evaluate system tradeoffs as well as identify and address performance issues. Our benchmark fills a gap in the available tools and data that researchers and developers have to inform system design decisions.

Despite decades of research and practical experience, developers have few tools for programming reliable distributed applications without resorting to expensive coordination techniques. Conflict-free replicated datatypes (CRDTs) are a promising line of work that enable coordination-free replication and offer certain eventual consistency guarantees in a relatively simple object-oriented API. Yet CRDT guarantees extend only to data updates; observations of CRDT state are unconstrained and unsafe. We propose an agenda that embraces the simplicity of CRDTs, but provides richer, more uniform guarantees. We extend CRDTs with a query model that reasons about which queries are safe without coordination by applying monotonicity results from the CALM Theorem, and lay out a larger agenda for developing CRDT data stores that let developers safely and efficiently interact with replicated application state.

Traditional brick-and-mortar services are increasingly moving online and companies rely on ever larger data analytics to optimise their business logic. Similarly, most medical practices favour electronic health records over paper documents: 84% of American hospitals store medical records electronically [19], an 8-fold increase since 2008. In this data-driven world, data is money. It must be collected efficiently, even as it spans multiple heterogeneous, geo-distributed sources. Data must be stored reliably, even in the presence of failures. As the sensitivity of the data being stored increases, so does the need to store it securely in the presence of human attacks. Data must also be accessed consistently, even under high load.

Facebook's graph store TAO, like many other distributed data stores, traditionally prioritizes availability, efficiency, and scalability over strong consistency or isolation guarantees to serve its large, read-dominant workloads. As product developers build diverse applications on top of this system, they increasingly seek transactional semantics. However, providing advanced features for select applications while preserving the system's overall reliability and performance is a continual challenge. In this paper, we first characterize developer desires for transactions that have emerged over the years and describe the current failure-atomic (i.e., write) transactions offered by TAO. We then explore how to introduce an intuitive read transaction API. We highlight the need for atomic visibility guarantees in this API with a measurement study on potential anomalies that occur without stronger isolation for reads. Our analysis shows that 1 in 1,500 batched reads reflects partial transactional updates, which complicate the developer experience and lead to unexpected results. In response to our findings, we present the RAMP-TAO protocol, a variation based on the Read Atomic Multi-Partition (RAMP) protocols that can be feasibly deployed in production with minimal overhead while ensuring atomic visibility for a read-optimized workload at scale.

This paper presents Tebaldi, a distributed key-value store that explores new ways to harness the performance opportunity of combining different specialized concurrency control mechanisms (CCs) within the same database. Tebaldi partitions conflicts at a fine granularity and matches them to specialized CCs within a hierarchical framework that is modular, extensible, and able to support a wide variety of concurrency control techniques, from single-version to multiversion and from lock-based to timestamp-based. When running the TPC-C benchmark, Tebaldi yields more than 20× the throughput of the basic two-phase locking protocol, and over 3.7× the throughput of Callas, a recent system that, like Tebaldi, aims to combine different CCs.

This paper presents the design, implementation, and evaluation of TARDiS (Transactional Asynchronously Replicated Divergent Store), a transactional key-value store explicitly designed for weakly-consistent systems. Reasoning about these systems is hard, as neither causal consistency nor per-object eventual convergence allow applications to deal satisfactorily with write-write conflicts. TARDiS instead exposes as its fundamental abstraction the set of conflicting branches that arise in weakly-consistent systems. To this end, TARDiS introduces a new concurrency control mechanism: branch-on-conflict. On the one hand, TARDiS guarantees that storage will appear sequential to any thread of execution that extends a branch, keeping application logic simple. On the other, TARDiS provides applications, when needed, with the tools and context necessary to merge branches atomically, when and how applications want. Since branch-on-conflict in TARDiS is fast, weakly-consistent applications can benefit from adopting this paradigm not only for operations issued by different sites, but also, when appropriate, for conflicting local operations. We find that TARDiS reduces coding complexity for these applications and that judicious branch-on-conflict can improve their local throughput at each site by two to eight times.