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Spanner: Google’s Globally Distributed Database

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TLDR
Spanner as mentioned in this paper is Google's scalable, multiversion, globally distributed, and synchronously replicated database, which is the first system to distribute data at global scale and support externally-consistent distributed transactions.
Abstract
Spanner is Google’s scalable, multiversion, globally distributed, and synchronously replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This article describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free snapshot transactions, and atomic schema changes, across all of Spanner.

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Journal ArticleDOI

Multidirectional Replication for Supporting Strong Consistency, Low Latency, and High Throughput

TL;DR: In this article , the authors describe the design, specification, implementation, and evaluation of Unidirectional and MultIDirectional Replication, which challenge this inflexible tradeoff.
Journal ArticleDOI

Transparent speculation in geo-replicated transactional data stores

TL;DR: STR provides a form of speculation that is fully transparent for programmers (it does not expose the effects of misspeculations to clients), and thanks to a self-tuning mechanism that dynamically and transparently enables and disables speculation, STR offers robust performance even when faced with unfavorable workloads that suffer from high misspeculation rates.
Journal ArticleDOI

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

TL;DR: In this article , the authors proposed a maturity model for AI-empowered cloud-native databases from the perspective of resource management, which can help database developers formulate the measures and clarify the improvement path during development.
Posted Content

Stream Processing With Dependency-Guided Synchronization.

TL;DR: The Dependency-Guided Synchronization (DGS) as discussed by the authors is an alternative programming model and a stream processing API for stateful streaming computations with complex synchronization requirements.
Posted ContentDOI

Achieving Causality with Physical Clocks.

TL;DR: In this article, the authors present PWC (Physical clock with causality) that uses the extraneous bits in the physical clock to track (one way) causality among events.
References
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Journal ArticleDOI

The Google file system

TL;DR: This paper presents file system interface extensions designed to support distributed applications, discusses many aspects of the design, and reports measurements from both micro-benchmarks and real world use.
Journal ArticleDOI

Linearizability: a correctness condition for concurrent objects

TL;DR: This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.
Journal ArticleDOI

Bigtable: A Distributed Storage System for Structured Data

TL;DR: The simple data model provided by Bigtable is described, which gives clients dynamic control over data layout and format, and the design and implementation of Bigtable are described.
Journal ArticleDOI

The part-time parliament

TL;DR: The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems.
Journal ArticleDOI

MapReduce: a flexible data processing tool

TL;DR: MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.
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