Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs

01/25/2016
by   Da Yan, et al.
0

Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing light-workload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/09/2020

A Distributed Path Query Engine for Temporal Property Graphs

Property graphs are a common form of linked data, with path queries used...
research
03/02/2015

Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

Massive graphs, such as online social networks and communication network...
research
08/04/2021

UniGPS: A Unified Programming Framework for Distributed Graph Processing

The industry and academia have proposed many distributed graph processin...
research
08/28/2017

Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework

Analyzing contention for resources in a cluster computing environment ac...
research
10/09/2018

GraphMP: I/O-Efficient Big Graph Analytics on a Single Commodity Machine

Recent studies showed that single-machine graph processing systems can b...
research
03/27/2021

Cache-Efficient Fork-Processing Patterns on Large Graphs

As large graph processing emerges, we observe a costly fork-processing p...
research
06/09/2021

DynamiQ: Planning for Dynamics in Network Streaming Analytics Systems

The emergence of programmable data-plane targets has motivated a new hyb...

Please sign up or login with your details

Forgot password? Click here to reset