CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks

Roohitavaf, Mohammad; Demirbaş, Murat; Kulkarni, Sandeep S.

doi:10.1109/srds.2017.27

Cited by 27 publications

(41 citation statements)

References 11 publications

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“…By replacing the scalar timestamp with HLC, we may be able to avoid the blocking at line 8 of Algorithm 2. More details about HLC can be found in [17].…”

Section: B Using Hybrid Logical Clocksmentioning

confidence: 99%

Global Stabilization for Causally Consistent Partial Replication

Xiang

Vaidya

2020

Proceedings of the 21st International Conference on Distributed Computing and Networking

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Causally consistent distributed storage systems have received significant attention recently due to the potential for providing high throughput and causality guarantees. Global stabilization is a technique established for achieving causal consistency in distributed multi-version key-value store systems, adopted by the previous work such as GentleRain [1] and Cure [2]. Intuitively, this approach serializes all updates by their physical time and computes the "Global Stable Time" which is a time point t such that versions with timestamp ≤ t can be returned to the client without violating causality. However, all previous designs with global stabilization assume full replication, where each data center stores a full copy of data, and each client is restricted to access servers within one data center. In this paper, we propose a theoretical framework to support general partial replication with causal consistency via global stabilization, where each server can store an arbitrary subset of the data, and each client is allowed to communicate with any subset of the servers and migrate among them without extra delays. We propose an algorithm that implements causal consistency for distributed multi-version key-value stores with general partially replication. We prove the optimality of the Global Stable Time computation in our algorithm regarding the remote update visibility latency, i.e. how fast update from a remote server is visible to the client, under general partial replication. We also provide trade-offs to further optimize the remote update visibility by introducing extra delays during client's migration. Simulation results on the performance of our algorithm compared to the previous work are also provided.

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“…By replacing the scalar timestamp with HLC, we may be able to avoid the blocking at line 8 of Algorithm 2. More details about HLC can be found in [17].…”

Section: B Using Hybrid Logical Clocksmentioning

confidence: 99%

Global Stabilization for Causally Consistent Partial Replication

Xiang

Vaidya

2020

Proceedings of the 21st International Conference on Distributed Computing and Networking

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“…Generating timestamps. As in recent proposals [5], [11], [25], [26], PaRiS uses Hybrid Logical Physical Clocks (HLC) [27] to generate timestamps. An HLC is a logical clock whose value on a partition is the maximum between the local physical clock and the highest timestamp seen by the partition plus one.…”

Section: B Non-blocking Reads In Partial Replication By Parismentioning

confidence: 99%

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

Spirovska

Didona

Zwaenepoel

2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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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 writeheavy workloads).

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“…Contrarian builds on the aforementioned coordinator-based design of ROTs and on the stabilization protocol-based approach (to determine visibility of remote items ) in the georeplica-ted setting. These characteristics, all or in part, lie at the core of many state-of-the-art systems, like Orbe [25], GentleRain [26], Cure [3] and CausalSpartan [54]. The improvements we propose in Contrarian, thus, can be employed to improve the design of these and similar systems.…”

Section: Contrarian: An Efficient But Not Latency-optimal Designmentioning

confidence: 99%

Causal consistency and latency optimality

et al. 2018

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Causal consistency is an attractive consistency model for replicated data stores. It is provably the strongest model that tolerates partitions, it avoids the long latencies associated with strong consistency, and, especially when using read-only transactions, it prevents many of the anomalies of weaker consistency models. Recent work has shown that causal consistency allows "latency-optimal" read-only transactions, that are nonblocking, single-version and singleround in terms of communication. On the surface, this latency optimality is very appealing, as the vast majority of applications are assumed to have read-dominated workloads.In this paper, we show that such "latency-optimal" readonly transactions induce an extra overhead on writes; the extra overhead is so high that performance is actually jeopardized, even in read-dominated workloads. We show this result from a practical and a theoretical angle.First, we present a protocol that implements "almost latency-optimal" ROTs but does not impose on the writes any of the overhead of latency-optimal protocols. In this protocol, ROTs are nonblocking, one version and can be configured to use either two or one and a half rounds of client-server communication. We experimentally show that this protocol not only provides better throughput, as expected, but also surprisingly better latencies for all but the lowest loads and most read-heavy workloads.Then, we prove that the extra overhead imposed on writes by latency-optimal read-only transactions is inherent, i.e., it is not an artifact of the design we consider, and cannot be avoided by any implementation of latency-optimal readonly transactions. We show in particular that this overhead grows linearly with the number of clients.

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CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks

Cited by 27 publications

References 11 publications

Global Stabilization for Causally Consistent Partial Replication

Global Stabilization for Causally Consistent Partial Replication

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

Causal consistency and latency optimality

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