Trinity: a distributed graph engine on a memory cloud

By Bin Shao, Haixun Wang, Yatao Li. ACM SIGMOD Conference, 2013

483 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/8d0656c7e89894dfd1be5dda7c6bf3f7b5ab8616

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Information Systems

Citations in the corpus

From "Think Like a Vertex" to "Think Like a Graph" (2013, background): “As a result, a number of distributed/parallel graph processing systems have been proposed, including Pregel [15], its open source implementation Apache Giraph [1], GraphLab [14], Kineograph [6], Trinity [20], and Grace [23].”
GraphJet: Real-Time Content Recommendations at Twitter (2016, methodology): “It is, of course, possible to use any key–value store to hold the adjacency lists that comprise a graph, thus serving as a real-time graph store (cf. [34]).”
Mizan: a system for dynamic load balancing in large-scale graph processing (2013, background): “Trinity [29] is a distributed in-memory key-value storage based graph processing system that supports both offline analytics and online query answering.”
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “Trinity [63] uses a distributed in-memory key-value table abstraction to support graph processing.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “In Trinity [38], graph processing and databases are combined into a single system.”
Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “Previous works Graph partitioning has attracted a lot of research interests in scientific computing [28, 47, 73], scaling out large-scale computations [20, 59, 60, 141, 147, 155], graph database [39, 122, 144], search and social network analysis [7, 112, 141], and streaming processing [105, 130, 131”
GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning (2015, background): “Many distributed graph processing systems like Pregel [17], GraphLab [16], PowerGraph [8], GraphX [28], and others [1, 22] have been proposed in the past few years.”
In-Memory Big Data Management and Processing: A Survey (2015, background): “Specifically, each graph node corresponds to a Trinity cell, which is an (id, blob) pair, where id represents a node in the graph, and the blob is the adjacent list of the node in serialized binary format, instead of runtime object format, in order to minimize memory overhead and facilitate checkpoi”
A Distributed Graph Engine for Web Scale RDF Data (2013, background): “SPARQL also contains other language constructs that support disjunctive queries and filtering.”
Gemini: A Computation-Centric Distributed Graph Processing System (2016, unknown): “There also exist many systems that focus on query processing [40, 46, 54], temporal analytics [13, 19, 27, 31], machine learning and data mining [50, 58], or more general tasks [34, 35, 45] on large-scale graphs.”