By using multiple levels of abstraction, it can quickly rule out infeasible solutions and identify mergeable partitions. GraphQ uses the memory capacity as a budget and tries its best to find solutions before exhausting the memory, making it possible to answer analytical queries over very large graphs with resources affordable to a single PC.
The second contribution is the design and implementation of Graspan, a single-machine, disk-based graph processing system tailored for interprocedural static analyses. Given a program graph and a grammar specification of an analysis, Graspan uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
With the help of novel graph processing techniques, we turn sophisticated code analyses into scalable Big Graph analytics. The third contribution of this dissertation is a single-machine, out-of-core graph mining system, called RStream, which leverages disk support to support efficient edge streaming for mining very large graphs. RStream employs a rich programming model that exposes relational algebra for developers to express a wide variety of mining tasks and implements a runtime engine that delivers efficiency with tuple streaming.
In conclusion, this dissertation attempts to explore the opportunities of building single-machine graph systems for scenarios where distributed systems do not work well. Our experimental results demonstrate that the techniques proposed in this dissertation can efficiently solve big graph analytical problems on a single consumer PC. We hope that these promising results will encourage future work to continue building affordable single-machine systems for a rich set of datasets and analytical tasks.
This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.
The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it.
Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing.
Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository.
Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems. Recently, there is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large datasets. In the mean time, in real-world applications, it is highly desirable to reduce the tedious, inefficient ETL extract, transform, load gap between tabular data processing systems and graph processing systems.
Unfortunately, those challenges have not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow, as well as the separation of tabular data processing runtimes and graph processing runtimes. In this thesis, we explore the application of programming techniques and algorithms from the database systems world to the problem of scalable graph analysis.
We first propose a bloat-aware design paradigm towards the development of efficient and scalable Big Data applications in object-oriented, GC enabled languages and demonstrate that programming under this paradigm does not incur significant programming burden but obtains remarkable performance gains e.
Based on the design paradigm, we then build Pregelix, an open source distributed graph processing system which is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems e.
Finally, we integrate Pregelix with the open source Big Data management system AsterixDB to offer users a mix of a vertex-oriented programming model and a declarative query language for richer forms of Big Graph analytics with reduced ETL pains. This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently.
More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE.
In addition, it presents three efficient and scalable advanced graph algorithms — the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. OpenLayers 3. Programmation avec Node. Surfons tranquille 2. The Robert C. Tout sur ma tablette Samsung Galaxy Tab 2 et Note UML 2. Unity 3. WPF 4. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant.
Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more.
With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale. To learn more, consult the User Docs section above.
Official releases of Giraph may be downloaded from an Apache mirror. Pre-built packages are also available through Apache's Maven repositories, making it easier to include Giraph in your projects. Giraph is an open-source project and external contributions are extremely appreciated. Start watching. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected data points.
Graphs arise in a wealth of data scenarios and describe the connections that are naturally formed in both digital and real worlds. Examples of such connections abound in online social networks such as Facebook and Twitter, among users who rate movies from services like Netflix and Amazon Prime, and are useful even in the context of biological networks for scientific research.
Whether in the context of business or science, viewing data as connected adds value by increasing the amount of information available to be drawn from that data and put to use in generating new revenue or scientific opportunities.
Apache Giraph offers a simple yet flexible programming model targeted to graph algorithms and designed to scale easily to accommodate massive amounts of data.
Originally developed at Yahoo! Practical Graph Analytics with Apache Giraph brings the power of Apache Giraph to you, showing how to harness the power of graph processing for your own data by building sophisticated graph analytics applications using the very same framework that is relied upon by some of the largest players in the industry today.
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