pregel

IntroductionAfter the rise of Hadoop, Google released three research papers, respectively, the caffeine, Pregel, Dremel Three technology, these three technologies have also become Google's new "troika", One of the Pregel is Google's proposed framework for large-scale distributed graph computing. It is mainly used for calculation of graph traversal (BFS), Shortest Path (SSSP), PageRank calculation and so on.

*. * Author: Zhang junlin Excerpted from Chapter 14 "Big Data daily report: Architecture and algorithm". The book directory is Pregel, a large-scale distributed graph computing platform proposed by Google, it is used to solve large-scale distributed graph computing problems in practical applications such as webpage link analysis and social data mining. 1. The computing model Pregel follows BSP in the concep

=>{if (Triplet.srcattr > Triplet.dstattr) {Iterator ((Triplet.dstid, (1,triplet.srcattr)));} else {Iterator.empty}},(A, b) = (a._1 + b._1,a._2 + b._2))Val avgageofolderfollower:vertexrdd[double] = oderfollowers.mapvalues ((id,value) + = {Value match{Case (count,totalage) = Totalage/count}})Avgageofolderfollower.collect (). foreach (println) */Collect neighbor nodes, followed by custom methodsCollectneighborids (edgedirection.in,graph). foreach (line = {print (line._1+ ":"); For (Elem Take the G

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Tags: Big Data graph Database Pregel Data Mining system architecture/* Copyright notice: Can be reproduced arbitrarily, please be sure to indicate the original source of the article and the author information . */Author: Zhang JunlinExcerpt from "Big Data Day know: Architecture and Algorithms" Chapter 14, book catalogue herePregel is a large-scale distributed graph computing platform proposed by Google, which is designed to solve the problem of lar

Original link: Graphx in Pregel single source point shortest pathExample of a single source point shortest path in Graphx, using the method of class Pregel.The core part is three functions:1. Node processing message function Vprog: (Vertexid, VD, A) = VD (Node ID, node attribute, message) = = Node Property2. Node Send Message function SENDMSG:EDGETRIPLET[VD, ED] = iterator[(vertexid,a)] (side tuple) + iterator[(target Node ID, message)]3. Message merg

users. If you select Rocksdb as the storage engine, all content, including indexes, is persisted on disk, which greatly reduces the time to start. For more information, see "Comparing new Rocksdb and Mmfiles engines" to test the new engine for the operating system and use cases. Pregel Distributed Graphics Processing:Distributed graph processing is a missing feature in Arangodb's graphical toolbox. However, Arangodb satisfies this requirement by impl

PageRank: When using PageRank, the search engine needs to calculate the value of PageRank for each node:The calculation formula of this value is given, and the PageRank value of each node is composed of 2 parts, one is the initial PageRank value of the node and the other is the PageRank value of all the neighboring nodes it connects.The former means that the neighbor node has more PageRank value, which means that the quality of the neighbor node will also affect the PageRank value of the node it

persisted on disk, which greatly reduces the time to start. For more information, see "Comparing new Rocksdb and Mmfiles engines" to test the new engine for the operating system and use cases.Pregel Distributed Graphics Processing:Distributed graph processing is a missing feature in Arangodb's graphical toolbox. However, Arangodb satisfies this requirement by implementing the Pregel computational model.Through PageRank, community detection, vertex ce

parallelization problem of graphs is converted to the parallelization problem of matrix operations. Take matrix multiplication as an example to see if it can be processed in parallel. Take matrix A x B as an example to describe the parallel processing process. Divide the preceding matrices A and B into four parts, as shown in After the first alignment Child Matrix Multiplication After multiplication, the sub-matrix of A is moved to the left, and the sub-matrix of B is moved up. Merge comp

using columnar storage, you can only scan the required data to reduce the access volume of CPU and disk. At the same time, columnar storage is compress friendly. With compression, the CPU and disk can be integrated to maximize the efficiency. Dremel combines web search and parallel DBMS technologies.Dremel draws on the concept of "query Tree" in Web search to split a relatively large and complex Query into small and simple queries. It's easy to make things easier, and you can run on a large num

operations, is often powerless when processing such a large amount of data. Data in dremel is stored in columns.When using columnar storage, you can only scan the required data to reduce the access volume of CPU and disk. At the same time, columnar storage is compress friendly. With compression, the CPU and disk can be integrated to maximize the efficiency. Dremel combines web search and parallel DBMS technologies.Dremel draws on the concept of "query Tree" in Web search to split a relatively l

IntroductionBig Data query analysis is one of the core issues in cloud computing, and since Google's 2006 paper laid the groundwork for cloud computing, especially GFS, map-reduce, and BigTable are the three cornerstones of cloud computing's underlying technologies. GFS and Map-reduce technology directly support the birth of the Apache Hadoop project. BigTable and Amazon Dynamo directly spawned the new NoSQL database domain, shaking the RDBMS's decades-old dominance of commercial databases and d

complete the point-to query, but also to support visualization. In Dremel's paper, Google claims: "Dremel can complete aggregate queries of trillions of rows of data in seconds, 100 times times faster than MapReduce!" ” the Pregel of the analysis chart data. Google MapReduce is designed to analyze the world's largest data atlas-the Internet. However, it is not so good to analyze interpersonal networks, telecommunications equipment, documents, and o

specializes in data export in Hadoop;　　Eventstore: Distributed time series database;　　GRIDDB: Suitable for sensor data stored in time series;　　LinkedIn Krati: Simple persistent data storage with low latency and high throughput;　　Linkedin Voldemort: Distributed key/value storage system;　　The distributed key-value database developed by the Oracle NoSQL database:oracle Company;　　Redis: In-memory key-value data storage;　　Riak: Decentralized data storage;　　Storehaus:twitter developed a library of as

data to reduce the access volume of CPU and disk. At the same time, columnar storage is compress friendly. With compression, the CPU and disk can be integrated to maximize the efficiency.Dremel combines web search and parallel DBMS technologies. Dremel draws on the concept of "query Tree" in Web search to split a relatively large and complex Query into small and simple queries. It's easy to make things easier, and you can run on a large number of nodes concurrently. In addition, similar to para

Characteristics: Strong data correlation; Often exhibit poorer memory access locality Too little processing for a single vertex Along with the change of parallelism in the calculation process Large graph calculation mainly includes two kinds: A real-time graph database based on traversal algorithm, such as NEO4J, Orientdb, Dex and Infinite graph; Based on the vertex-centric parallel engine, such as Goldenorb, Giraph, Pregel and Hama, the graph

. Zookeeper–open source version inspired from Chubby though are general coordination service than simply a locking serviceComputational FrameworksThe execution runtimes provide an environment for running distinct kinds of compute. The most common runtimes isSpark–its popularity and adoption is challenging the traditional Hadoop ecosystem. Flink–very similar to Spark ecosystem; Strength over Spark are in iterative processing.The frameworks broadly can be classified based on the model and latency

(0.5+0.6) +b2 (0.7+0.1) = 1.9, the recommended reason is a friend's friend. We need to take the whole station according effective attention relationship in accordance with the above model calculation of a two-hop neighbor C, and then remove the C a direct attention, and finally C by the bridge weight from high to low to take TopN.Frame SelectionAt present, the industry mainstream distributed Graph computing framework has giraph and GraphX. Giraph is an iterative graph computing system. The inpu

powergraph:distributed graph-parallel computation on Natural Graphs (OSDI ') 2013-02-28 18:30:21| Category: Iteration diagram calculation | font size Subscription This paper first presents the challenges in the existing parallel graph processing system, then introduces the Powergraph solution, and puts forward an effective partitioning scheme for power-law graphs. Parallel graph processing systems such as Pregel and Graphlab are limited by the number