PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the synchronization costs of strong consistency, and supports interactive read-write transactions. Partial replication is another attractive design choice for building geo-replicated platforms, as it increases the storage capacity and reduces update propagation costs. This paper presents PaRiS, the first TCC system that supports partial replication and implements non-blocking parallel read operations, whose latency is paramount for the performance of read-intensive applications. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every DC in the system. Hence, transactions can consistently read from such a snapshot on any server in any replication site without having to block. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment composed of up to 10 replication sites. We show that PaRiS scales well with the number of DCs and partitions, while being able to handle larger data-sets than existing solutions that assume full replication. We also demonstrate a performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x higher throughput with 5.91x lower latency for read-dominated workloads and up to 1.46x higher throughput with 20.56x lower latency for write-heavy workloads)

NCC: Natural Concurrency Control for Strictly Serializable Datastores by Avoiding the Timestamp-Inversion Pitfall

Strictly serializable datastores greatly simplify the development of correct applications by providing strong consistency guarantees. However, existing techniques pay unnecessary costs for naturally consistent transactions, which arrive at servers in an order that is already strictly serializable. We find these transactions are prevalent in datacenter workloads. We exploit this natural arrival order by executing transaction requests with minimal costs while optimistically assuming they are naturally consistent, and then leverage a timestamp-based technique to efficiently verify if the execution is indeed consistent. In the process of designing such a timestamp-based technique, we identify a fundamental pitfall in relying on timestamps to provide strict serializability, and name it the timestamp-inversion pitfall. We find timestamp-inversion has affected several existing works. We present Natural Concurrency Control (NCC), a new concurrency control technique that guarantees strict serializability and ensures minimal costs -- i.e., one-round latency, lock-free, and non-blocking execution -- in the best (and common) case by leveraging natural consistency. NCC is enabled by three key components: non-blocking execution, decoupled response control, and timestamp-based consistency check. NCC avoids timestamp-inversion with a new technique: response timing control, and proposes two optimization techniques, asynchrony-aware timestamps and smart retry, to reduce false aborts. Moreover, NCC designs a specialized protocol for read-only transactions, which is the first to achieve the optimal best-case performance while ensuring strict serializability, without relying on synchronized clocks. Our evaluation shows that NCC outperforms state-of-the-art solutions by an order of magnitude on many workloads

Multi-Shot Distributed Transaction Commit

Atomic Commit Problem (ACP) is a single-shot agreement problem similar to consensus, meant to model the properties of transaction commit protocols in fault-prone distributed systems. We argue that ACP is too restrictive to capture the complexities of modern transactional data stores, where commit protocols are integrated with concurrency control, and their executions for different transactions are interdependent. As an alternative, we introduce Transaction Certification Service (TCS), a new formal problem that captures safety guarantees of multi-shot transaction commit protocols with integrated concurrency control. TCS is parameterized by a certification function that can be instantiated to support common isolation levels, such as serializability and snapshot isolation. We then derive a provably correct crash-resilient protocol for implementing TCS through successive refinement. Our protocol achieves a better time complexity than mainstream approaches that layer two-phase commit on top of Paxos-style replication

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees