Benchmarking tool for graph algorithms
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A benchmarking tool for testing the performance of graph algorithms like BFS, dijkstra Pagerank with MapReduce, Giraph, GraphLab
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Benchmarking Tool for Graph Algorithms
In computer science and mathematics, graphs are abstract data structures that model structural relationships among objects. They are now widely used for data modeling in application domains for which identifying relationship patterns, rules, and anomalies is useful. These domains include the web graph,
social networks,etc. The ever increasing size of graph structured data for these applications creates a critical need for scalable systems that can process large amounts of it efficiently. The project aims at making a benchmarking tool for testing the performance of graph algorithms like BFS, Pagerank, DFS. with
MapReduce, Giraph, GraphLab and testing which approach works better on what kind of graphs.
GoFFish - A Sub-graph centric framework for large scale graph analytics
1. The document discusses GoFFish, a sub-graph centric programming model for large scale graph analytics. It partitions graphs into connected sub-graphs that are processed independently in parallel.
2. GoFFish showed significant performance improvements over traditional vertex-centric models for connected components and single source shortest path algorithms, with speedups of up to 81x and 38x respectively.
3. While PageRank was less suited to the sub-graph model, overall GoFFish demonstrated it can efficiently analyze large graphs distributed across a cluster using a simple sub-graph centric programming approach.
VENUS: Vertex-Centric Streamlined Graph Computation on a Single PC
VENUS is a single-machine graph computation system that uses a vertex-centric streamlined processing model to reduce data access and I/O compared to other disk-based systems like GraphChi and X-Stream. It separates immutable edge data from mutable vertex data and shards graphs across disk to load smaller vertex shards into memory for parallel processing. Evaluation on large real-world graphs like Twitter and Clueweb12 showed VENUS outperformed competitors by completing PageRank up to 2x faster while also supporting other algorithms on billion-scale graphs.
Pregel: A System For Large Scale Graph Processing
Pregel is a distributed system for large-scale graph processing that uses a vertex-centric programming model based on Google's Bulk Synchronous Parallel (BSP) framework. In Pregel's message passing model, computations are organized into supersteps where each vertex performs computations and sends messages to other vertices. A barrier synchronization occurs between supersteps. Pregel provides fault tolerance through checkpointing and the ability to dynamically mutate graph topology during processing. The paper demonstrates that Pregel can efficiently process large graphs and scale computation near linearly with the size of the graph.
Determining the k in k-means with MapReduce
This document describes a MapReduce algorithm for determining the optimal k value in k-means clustering. It presents the G-means algorithm, which uses recursive k-means clustering and normality testing to split clusters until all points are normally distributed around cluster centers. The document outlines challenges in implementing G-means in MapReduce, and describes solutions to reduce I/O, jobs, maximize parallelism and limit memory usage. It compares the proposed MapReduce G-means approach to existing multi-k-means methods, finding it has better quality and comparable speed on synthetic datasets.
MapReduce: Optimizations, Limitations, and Open Issues
Paper presentation at the 11th IEEE International Symposium on Parallel and Distributes Processing with Applications (ISPA'13)
CS267_Graph_Lab
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
Large scale graph processing
This document discusses large scale graph processing. The goal is to run graph algorithms like shortest path on huge graphs with terabytes of data that cannot fit on one machine. It introduces Pregel, Google's graph processing system from 2010 that uses a vertex-centric programming model. Pregel partitions graphs across machines and executes synchronous iterations where vertices send messages and perform computations to solve problems like PageRank. Several open source implementations of this model now exist like Apache Giraph and GraphLab.
Recommended
Benchmarking Tool for Graph Algorithms
In computer science and mathematics, graphs are abstract data structures that model structural relationships among objects. They are now widely used for data modeling in application domains for which identifying relationship patterns, rules, and anomalies is useful. These domains include the web graph,
social networks,etc. The ever increasing size of graph structured data for these applications creates a critical need for scalable systems that can process large amounts of it efficiently. The project aims at making a benchmarking tool for testing the performance of graph algorithms like BFS, Pagerank, DFS. with
MapReduce, Giraph, GraphLab and testing which approach works better on what kind of graphs.
GoFFish - A Sub-graph centric framework for large scale graph analytics
1. The document discusses GoFFish, a sub-graph centric programming model for large scale graph analytics. It partitions graphs into connected sub-graphs that are processed independently in parallel.
2. GoFFish showed significant performance improvements over traditional vertex-centric models for connected components and single source shortest path algorithms, with speedups of up to 81x and 38x respectively.
3. While PageRank was less suited to the sub-graph model, overall GoFFish demonstrated it can efficiently analyze large graphs distributed across a cluster using a simple sub-graph centric programming approach.
VENUS: Vertex-Centric Streamlined Graph Computation on a Single PC
VENUS is a single-machine graph computation system that uses a vertex-centric streamlined processing model to reduce data access and I/O compared to other disk-based systems like GraphChi and X-Stream. It separates immutable edge data from mutable vertex data and shards graphs across disk to load smaller vertex shards into memory for parallel processing. Evaluation on large real-world graphs like Twitter and Clueweb12 showed VENUS outperformed competitors by completing PageRank up to 2x faster while also supporting other algorithms on billion-scale graphs.
Pregel: A System For Large Scale Graph Processing
Pregel is a distributed system for large-scale graph processing that uses a vertex-centric programming model based on Google's Bulk Synchronous Parallel (BSP) framework. In Pregel's message passing model, computations are organized into supersteps where each vertex performs computations and sends messages to other vertices. A barrier synchronization occurs between supersteps. Pregel provides fault tolerance through checkpointing and the ability to dynamically mutate graph topology during processing. The paper demonstrates that Pregel can efficiently process large graphs and scale computation near linearly with the size of the graph.
Determining the k in k-means with MapReduce
This document describes a MapReduce algorithm for determining the optimal k value in k-means clustering. It presents the G-means algorithm, which uses recursive k-means clustering and normality testing to split clusters until all points are normally distributed around cluster centers. The document outlines challenges in implementing G-means in MapReduce, and describes solutions to reduce I/O, jobs, maximize parallelism and limit memory usage. It compares the proposed MapReduce G-means approach to existing multi-k-means methods, finding it has better quality and comparable speed on synthetic datasets.
MapReduce: Optimizations, Limitations, and Open Issues
Paper presentation at the 11th IEEE International Symposium on Parallel and Distributes Processing with Applications (ISPA'13)
CS267_Graph_Lab
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
Large scale graph processing
This document discusses large scale graph processing. The goal is to run graph algorithms like shortest path on huge graphs with terabytes of data that cannot fit on one machine. It introduces Pregel, Google's graph processing system from 2010 that uses a vertex-centric programming model. Pregel partitions graphs across machines and executes synchronous iterations where vertices send messages and perform computations to solve problems like PageRank. Several open source implementations of this model now exist like Apache Giraph and GraphLab.
Pregel
Pregel is a system for large-scale graph processing that was developed by Google. It provides a scalable and fault-tolerant platform for graph algorithms using the bulk synchronous parallel (BSP) model. In Pregel, computation is expressed as a series of iterations called supersteps where each vertex performs computation and sends messages to other vertices. This vertex-centric approach allows graph algorithms to be naturally expressed by focusing on local operations. Pregel was designed for scalability across thousands of machines and provides features like checkpointing and recovery for fault tolerance. It has been used for applications such as PageRank, shortest paths, and clustering on large graphs with billions of vertices and edges.
What is MapReduce ?
MapReduce is one of the most important and major component in Hadoop Ecosystem. Whenever we are having a large set of data then in the case of the huge data set will be divided into smaller pieces and processing will be done on them in parallel in MapReduce.
Apache Giraph
Giraph is an open source library for large-scale graph processing on Hadoop. It uses the Bulk Synchronous Parallel (BSP) model to process graphs in parallel across a Hadoop cluster. Key algorithms it supports include PageRank, graph clustering, shortest paths, and connected components. It provides fault tolerance and dynamic resource management while using Hadoop's infrastructure without needing a specialized system.
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This document presents m2r2, a framework for materializing and reusing results in high-level dataflow systems for big data. The framework operates at the logical plan level to be language-independent. It includes components for matching plans, rewriting queries to reuse past results, optimizing plans, caching results, and garbage collection. An evaluation using the TPC-H benchmark on Pig Latin showed the framework reduced query execution time by 65% on average by reusing past query results. Future work includes integrating it with more systems and minimizing materialization costs.
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This document summarizes several systems for big data processing that extend or improve upon the MapReduce programming model. It discusses systems for iterative processing like HaLoop, stream processing like Muppet, improving performance through caching and indexing like Incoop and HAIL, and automatic optimization of MapReduce programs like MANIMAL and SkewTune. The document also briefly introduces broader distributed data processing frameworks beyond MapReduce like Dryad, SCOPE, Spark, Nephele/PACTs, and the ASTERIX scalable data platform.
Asymmetry in Large-Scale Graph Analysis, Explained
Paper presentation at the 2nd International Workshop on Graph Data Management Experiences and Systems (GRADES'14)
2011.10.14 Apache Giraph - Hortonworks
This document provides an overview of Giraph, an open source framework for large-scale graph processing on Hadoop. It discusses why graph processing is important at large scales, existing solutions and their limitations, and Giraph's goals of being easily deployable on Hadoop and providing a graph-oriented programming model. The document describes Giraph's design which uses Hadoop and leverages the bulk synchronous parallel computing model, and provides examples of writing Giraph applications and how Giraph jobs interact with Hadoop.
Optimization of graph storage using GoFFish
GoFFish is a scalable software framework for storing large graphs and executing graph analytics across a computing cluster. It consists of GoFS for distributed graph storage and partitioning and Gopher for programming abstractions. The researchers implemented two storage formats in GoFFish - temporal and subgraph binning - and evaluated them on a road network graph using vertex count and connected components algorithms. They found the temporal format performed better for vertex count while subgraph binning was better for connected components.
Spark
Spark is a new framework that supports applications that reuse a working set of data across multiple parallel operations. This includes iterative machine learning algorithms and interactive data analysis tools. Spark supports these applications while retaining scalability and fault tolerance through resilient distributed datasets (RDDs) which allow data to be cached in memory across operations. Spark provides RDDs and restricted shared variables like broadcast variables and accumulators to program clusters simply. Experiments show Spark can run iterative jobs faster and interactively query large datasets with low latency. Future work aims to enhance RDD properties and define new transforming operations.
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MapReduce
MapReduce provides an easy way to process large datasets in a distributed manner. It uses mappers to process input data and generate intermediate key-value pairs, and reducers to combine those intermediate pairs into the final output. Key aspects include job tracking, splitting data into tasks, and storing intermediate output locally rather than on HDFS for efficiency, since it is discarded after reducing.
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Paper presentation at the 5th IEEE International Conference on Cloud Computing Technology and Science (CloudCom'13)
2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...
Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.
Several learning algorithms such as kernel methods, decision tress, and random forests are nonlinear approaches which are widely used to have better performance compared with linear methods. However, with feature engineering techniques like polynomial expansion by mapping the data into a higher dimensional space, the performance of linear methods can be as competitive as those nonlinear methods. As a result, linear methods remain to be very useful given that the training time of linear methods is significantly faster than the nonlinear ones, and the model is just a simple small vector which makes the prediction step very efficient and easy. However, by mapping the data into higher dimensional space, those linear methods are subject to overfitting and instability of coefficients, and those issues can be successfully addressed by penalization methods including Lasso and Elastic-Net. Lasso method with L1 penalty tends to result in many coefficients shrunk exactly to zero and a few other coefficients with comparatively little shrinkage. L2 penalty trends to result in all small but non-zero coefficients. Combining L1 and L2 penalties are called Elastic-Net method which tends to give a result in between. In the first part of the talk, we'll give an overview of linear methods including commonly used formulations and optimization techniques such as L-BFGS and OWLQN. In the second part of talk, we will talk about how to train linear models with Elastic-Net using our recent contribution to Spark MLlib. We'll also talk about how linear models are practically applied with big dataset, and how polynomial expansion can be used to dramatically increase the performance.
DB Tsai is an Apache Spark committer and a Senior Research Engineer at Netflix. He is recently working with Apache Spark community to add several new algorithms including Linear Regression and Binary Logistic Regression with ElasticNet (L1/L2) regularization, Multinomial Logistic Regression, and LBFGS optimizer. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where he developed innovative large-scale distributed linear algorithms, and then contributed back to open source Apache Spark project.
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Mapreduce introduction
This document provides an overview of Hadoop MapReduce. It begins with an introduction to MapReduce and defines it as the processing component of Apache Hadoop that processes data in parallel across a distributed environment. The document then discusses two main advantages of MapReduce: 1) parallel processing, which makes data processing fast, and 2) data locality where processing is moved to the data rather than moving large amounts of data. It also provides an example of how MapReduce can be used to efficiently count words in a document by splitting the work across nodes and aggregating the results.
Online learning with structured streaming, spark summit brussels 2016
This document summarizes an online presentation about online learning with structured streaming in Spark. The key points are:
- Online learning updates model parameters for each data point as it arrives, unlike batch learning which sees the full dataset before updating.
- Structured streaming in Spark provides a single API for batch, streaming, and machine learning workloads. It offers exactly-once guarantees and understands external event time.
- Streaming machine learning on structured streaming works by having a stateful aggregation query that picks up the last trained model and performs a distributed update and merge on each trigger interval. This allows modeling streaming data in a fault-tolerant way.
Apache Spark™ is here to stay
A presentation on Apache Spark at the Austrian High Performance Meeting 2020 (AHPC20). In short: Spark is an interesting technology that makes it easy to create fault-tolerant distributed data pipelines on computer clusters.
Accompanying notebook: https://gitlab.com/grooda/bigdata/-/blob/master/NgramsAHPC.ipynb
Map reduce in Hadoop
Map reduce definition
A Programming model and an associated implementation for processing and generating large data sets with a parallel*, distributed* algorithm on a cluster*.
A Parallel algorithm is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result.
A distributed algorithm is an algorithm designed to run on computer hardware
constructed from interconnected processors.
A computer cluster consists of connected computers that work together so that, in many respects, they can be viewed as a single system. Computer clusters have each node set to perform the same task, controlled and scheduled by software.
Map reduce - division into two categories map and reduce
working of Jobtracker , TaskTracker ,Namenode , Datanode in mapreduce engine of hadoop
Fault tolerance in hadoop
Box class datatypes
Allowable file formats
wordcount job explained using animation in hadoop using mapreduce
fields where map reduce can be implimented
limitations of map reduce
Hadoop combiner and partitioner
The document discusses combiners and partitioners in MapReduce frameworks. It explains that combiners allow for local aggregation of map output key-value pairs before shuffling to reducers. This can significantly reduce the amount of data transferred between maps and reduces. For a combiner to be effective, the reduce operation must be commutative and associative so the local aggregations can be merged. The document provides examples of operations like sum() and max() that qualify for use as combiners. It also discusses factors like serialization overhead that should be considered when deciding whether a combiner will provide benefits for a given job.
MapReduce Programming Model
MapReduce is a programming model introduced by Google for processing and generating large data sets on clusters of computers.
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Pregel
Pregel is a system for large-scale graph processing that was developed by Google. It provides a scalable and fault-tolerant platform for graph algorithms using the bulk synchronous parallel (BSP) model. In Pregel, computation is expressed as a series of iterations called supersteps where each vertex performs computation and sends messages to other vertices. This vertex-centric approach allows graph algorithms to be naturally expressed by focusing on local operations. Pregel was designed for scalability across thousands of machines and provides features like checkpointing and recovery for fault tolerance. It has been used for applications such as PageRank, shortest paths, and clustering on large graphs with billions of vertices and edges.
What is MapReduce ?
MapReduce is one of the most important and major component in Hadoop Ecosystem. Whenever we are having a large set of data then in the case of the huge data set will be divided into smaller pieces and processing will be done on them in parallel in MapReduce.
Apache Giraph
Giraph is an open source library for large-scale graph processing on Hadoop. It uses the Bulk Synchronous Parallel (BSP) model to process graphs in parallel across a Hadoop cluster. Key algorithms it supports include PageRank, graph clustering, shortest paths, and connected components. It provides fault tolerance and dynamic resource management while using Hadoop's infrastructure without needing a specialized system.
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This document presents m2r2, a framework for materializing and reusing results in high-level dataflow systems for big data. The framework operates at the logical plan level to be language-independent. It includes components for matching plans, rewriting queries to reuse past results, optimizing plans, caching results, and garbage collection. An evaluation using the TPC-H benchmark on Pig Latin showed the framework reduced query execution time by 65% on average by reusing past query results. Future work includes integrating it with more systems and minimizing materialization costs.
Big data processing systems research
This document summarizes several systems for big data processing that extend or improve upon the MapReduce programming model. It discusses systems for iterative processing like HaLoop, stream processing like Muppet, improving performance through caching and indexing like Incoop and HAIL, and automatic optimization of MapReduce programs like MANIMAL and SkewTune. The document also briefly introduces broader distributed data processing frameworks beyond MapReduce like Dryad, SCOPE, Spark, Nephele/PACTs, and the ASTERIX scalable data platform.
Asymmetry in Large-Scale Graph Analysis, Explained
Paper presentation at the 2nd International Workshop on Graph Data Management Experiences and Systems (GRADES'14)
2011.10.14 Apache Giraph - Hortonworks
This document provides an overview of Giraph, an open source framework for large-scale graph processing on Hadoop. It discusses why graph processing is important at large scales, existing solutions and their limitations, and Giraph's goals of being easily deployable on Hadoop and providing a graph-oriented programming model. The document describes Giraph's design which uses Hadoop and leverages the bulk synchronous parallel computing model, and provides examples of writing Giraph applications and how Giraph jobs interact with Hadoop.
Optimization of graph storage using GoFFish
GoFFish is a scalable software framework for storing large graphs and executing graph analytics across a computing cluster. It consists of GoFS for distributed graph storage and partitioning and Gopher for programming abstractions. The researchers implemented two storage formats in GoFFish - temporal and subgraph binning - and evaluated them on a road network graph using vertex count and connected components algorithms. They found the temporal format performed better for vertex count while subgraph binning was better for connected components.
Spark
Spark is a new framework that supports applications that reuse a working set of data across multiple parallel operations. This includes iterative machine learning algorithms and interactive data analysis tools. Spark supports these applications while retaining scalability and fault tolerance through resilient distributed datasets (RDDs) which allow data to be cached in memory across operations. Spark provides RDDs and restricted shared variables like broadcast variables and accumulators to program clusters simply. Experiments show Spark can run iterative jobs faster and interactively query large datasets with low latency. Future work aims to enhance RDD properties and define new transforming operations.
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MapReduce
MapReduce provides an easy way to process large datasets in a distributed manner. It uses mappers to process input data and generate intermediate key-value pairs, and reducers to combine those intermediate pairs into the final output. Key aspects include job tracking, splitting data into tasks, and storing intermediate output locally rather than on HDFS for efficiency, since it is discarded after reducing.
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Paper presentation at the 5th IEEE International Conference on Cloud Computing Technology and Science (CloudCom'13)
2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...
Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.
Several learning algorithms such as kernel methods, decision tress, and random forests are nonlinear approaches which are widely used to have better performance compared with linear methods. However, with feature engineering techniques like polynomial expansion by mapping the data into a higher dimensional space, the performance of linear methods can be as competitive as those nonlinear methods. As a result, linear methods remain to be very useful given that the training time of linear methods is significantly faster than the nonlinear ones, and the model is just a simple small vector which makes the prediction step very efficient and easy. However, by mapping the data into higher dimensional space, those linear methods are subject to overfitting and instability of coefficients, and those issues can be successfully addressed by penalization methods including Lasso and Elastic-Net. Lasso method with L1 penalty tends to result in many coefficients shrunk exactly to zero and a few other coefficients with comparatively little shrinkage. L2 penalty trends to result in all small but non-zero coefficients. Combining L1 and L2 penalties are called Elastic-Net method which tends to give a result in between. In the first part of the talk, we'll give an overview of linear methods including commonly used formulations and optimization techniques such as L-BFGS and OWLQN. In the second part of talk, we will talk about how to train linear models with Elastic-Net using our recent contribution to Spark MLlib. We'll also talk about how linear models are practically applied with big dataset, and how polynomial expansion can be used to dramatically increase the performance.
DB Tsai is an Apache Spark committer and a Senior Research Engineer at Netflix. He is recently working with Apache Spark community to add several new algorithms including Linear Regression and Binary Logistic Regression with ElasticNet (L1/L2) regularization, Multinomial Logistic Regression, and LBFGS optimizer. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where he developed innovative large-scale distributed linear algorithms, and then contributed back to open source Apache Spark project.
Image colorization
CycleGAN is a framework that uses generative adversarial networks to perform image-to-image translation without paired training examples. It consists of two generators that map images from one domain to another and back, along with two discriminators that classify images as real or fake. The generators are trained to translate images such that they are classified as real by the discriminators, while also remaining consistent when translated back to the original domain. The authors demonstrate it for the task of colorizing grayscale images without paired color-grayscale image samples.
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Kyle Foreman presented on using Spark for large-scale global health simulations. The talk discussed (1) the motivation for simulations to model disease burden forecasts and alternative scenarios, (2) SimBuilder for constructing modular simulation workflows as directed acyclic graphs, and (3) benchmarks showing Spark backends can efficiently distribute simulations across a cluster. Future work aims to optimize Spark DataFrame joins and take better advantage of Numpy's vectorization for panel data simulations.
Mapreduce introduction
This document provides an overview of Hadoop MapReduce. It begins with an introduction to MapReduce and defines it as the processing component of Apache Hadoop that processes data in parallel across a distributed environment. The document then discusses two main advantages of MapReduce: 1) parallel processing, which makes data processing fast, and 2) data locality where processing is moved to the data rather than moving large amounts of data. It also provides an example of how MapReduce can be used to efficiently count words in a document by splitting the work across nodes and aggregating the results.
Online learning with structured streaming, spark summit brussels 2016
This document summarizes an online presentation about online learning with structured streaming in Spark. The key points are:
- Online learning updates model parameters for each data point as it arrives, unlike batch learning which sees the full dataset before updating.
- Structured streaming in Spark provides a single API for batch, streaming, and machine learning workloads. It offers exactly-once guarantees and understands external event time.
- Streaming machine learning on structured streaming works by having a stateful aggregation query that picks up the last trained model and performs a distributed update and merge on each trigger interval. This allows modeling streaming data in a fault-tolerant way.
Apache Spark™ is here to stay
A presentation on Apache Spark at the Austrian High Performance Meeting 2020 (AHPC20). In short: Spark is an interesting technology that makes it easy to create fault-tolerant distributed data pipelines on computer clusters.
Accompanying notebook: https://gitlab.com/grooda/bigdata/-/blob/master/NgramsAHPC.ipynb
Map reduce in Hadoop
Map reduce definition
A Programming model and an associated implementation for processing and generating large data sets with a parallel*, distributed* algorithm on a cluster*.
A Parallel algorithm is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result.
A distributed algorithm is an algorithm designed to run on computer hardware
constructed from interconnected processors.
A computer cluster consists of connected computers that work together so that, in many respects, they can be viewed as a single system. Computer clusters have each node set to perform the same task, controlled and scheduled by software.
Map reduce - division into two categories map and reduce
working of Jobtracker , TaskTracker ,Namenode , Datanode in mapreduce engine of hadoop
Fault tolerance in hadoop
Box class datatypes
Allowable file formats
wordcount job explained using animation in hadoop using mapreduce
fields where map reduce can be implimented
limitations of map reduce
Hadoop combiner and partitioner
The document discusses combiners and partitioners in MapReduce frameworks. It explains that combiners allow for local aggregation of map output key-value pairs before shuffling to reducers. This can significantly reduce the amount of data transferred between maps and reduces. For a combiner to be effective, the reduce operation must be commutative and associative so the local aggregations can be merged. The document provides examples of operations like sum() and max() that qualify for use as combiners. It also discusses factors like serialization overhead that should be considered when deciding whether a combiner will provide benefits for a given job.
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2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...
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MapReduce Programming Model
MapReduce is a programming model introduced by Google for processing and generating large data sets on clusters of computers.
Big Data Analytics Chapter3-6@2021.pdf
The document provides an overview of developing a big data strategy. It discusses defining a big data strategy by identifying opportunities and economic value of data, defining a big data architecture, selecting technologies, understanding data science, developing analytics, and institutionalizing big data. A good strategy explores these subject domains and aligns them to organizational objectives to accomplish a data-driven vision and direct the organization.
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SPARK ARCHITECTURE
Spark is a framework for large-scale data processing. It includes Spark Core which provides functionality like memory management and fault recovery. Spark also includes higher level libraries like SparkSQL for SQL queries, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data streams. The core abstraction in Spark is the Resilient Distributed Dataset (RDD) which allows parallel operations on distributed data.
Benchmarking tool for graph algorithms
A benchmarking tool for
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MapReduce, Giraph, GraphLab
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E031201032036
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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Introduction to map reduce s. jency jayastina II MSC COMPUTER SCIENCE BON SEC...
The document discusses MapReduce, a programming model for processing large datasets in parallel across a distributed cluster. It describes how MapReduce works by specifying computation in terms of mapping and reducing functions. The underlying runtime system automatically parallelizes the computation, handles failures and communications. MapReduce is the processing engine of Apache Hadoop, which was derived from Google's MapReduce. It allows processing huge amounts of data through mapping and reducing steps. The mapping step converts data into key-value pairs, while the reducing step combines the output of mapping into smaller tuples. MapReduce is mainly used for parallel processing of large datasets stored in Hadoop clusters.
Hadoop
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
Apache spark - Spark's distributed programming model
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Mapreduce Osdi04
The document describes MapReduce, a programming model and implementation for processing large datasets across clusters of computers. The model uses map and reduce functions to parallelize computations. Map processes key-value pairs to generate intermediate pairs, and reduce merges values with the same intermediate key. The implementation handles parallelization, distribution, and fault tolerance transparently. Hundreds of programs have been implemented using MapReduce at Google, processing terabytes of data on thousands of machines daily.
MapReduce
MapReduce is a programming model for processing large datasets in a distributed environment. It consists of a map function that processes input key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same key. It allows for parallelization of computations across large clusters. Example applications include word count, sorting, and indexing web links. Hadoop is an open source implementation of MapReduce that runs on commodity hardware.
Computer Graphics - Lecture 01 - 3D Programming I
Here are a few key points about adding vertex colors to the example:
- Storing the color data in a separate buffer is cleaner than concatenating or interleaving it with the position data. This keeps the data layout simple.
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Cascading on starfish
Cascading is a Java framework that allows users to define data processing workflows on Hadoop clusters more easily. The author discusses connecting Cascading to the Starfish profiler and optimizer to enable automated optimization of Cascading workflows. Key points are:
1) Cascading workflows are translated to DAGs of Hadoop jobs for profiling and optimization.
2) The Cascading API is modified to use the Hadoop New API to interface with Starfish.
3) Experiments show the Starfish optimizer providing speedups of up to 1.3x for several real-world Cascading workflows.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Benchmarking tool for graph algorithms
- 1. Project: Benchmarking tool for graph algorithms Team id-22 Project id -19 By: Abhinaba Sarkar 201405616 Malavika Reddy 201201193 Nikita Kad 201330030 Yash Khandelwal 201302164 HIGH-LEVEL DESIGN: We will build various algorithms using Hadoop Mapreduce, GraphLab and Giraph then create a benchmarking to compare the performance of the algorithms to determine which graph algorithm works better for which kind of graphs and graph related queries. We will implement a 3tier architecture for managing data (in our case graphs), processing data (computation) and comparing and analyzing performance. APPROACH: We will be testing following algorithms: ➢ BFS Breadth First Search:broken into two tasks ● Map Task : In each Map task, we discover all the neighbors of the node currently in queue (we used color encoding GRAY for nodes in queue) and add them to our graph. ● Reduce Task : In each Reduce task, we set the correct level of the nodes and update the graph. ➢ Page Rank:broken into two tasks
- 2. ● Map task: Each page emits its neighbours and current pagerank. ● Reduce task: For each key (i.e page) new page rank is calculated using pagerank emitted in the map task. ➢ Dijkstra: ● Map task : In each of the map tasks, neighbors are discovered and put into the queue with color coding gray. ● Reduce task : In each of the reduce tasks, we select the nodes according to the shortest distances from the current node PROJECT PLAN: We will implement a basic 3 tier architecture for the project: ● Data management and representation (in Neo4j : DB for graphs) ● Distributed Computing unit (Hadoop cluster: test a graph for various algorithms and note the performance mainly time and memory used). ● Benchmarking unit (performance analysis of different types of graphs for various algorithms) We need to analyse the runtime of graph algorithms on distributed systems. Performing computation on a graph requires processing at each node. REQUIREMENTS: Tools required for the implementation are: ● HadoopMapreduce Engine ● GraphLab GraphLab is a graphbased, high performance, distributed computation framework written in C++. It was originally developed for Machine Learning tasks. GraphLab is an asynchronous distributed sharedmemory abstraction in which graph vertices share access to a distributed graph with data stored on every vertex and edge. In this
- 3. programming abstraction, each vertex can directly access information on the current vertex, adjacent edges, and adjacent vertices — irrespective of edge direction. ● Giraph Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. In Giraph, graphprocessing programs are expressed as a sequence of iterations called supersteps. During a superstep, the framework starts a userdefined function for each vertex, conceptually in parallel. The userdefined function specifies the behaviour at a single vertex Vand a single superstep S. The function can read messages that are sent to Vin superstep S1, send messages to other vertices that are received at superstep S+1, and modify the state of Vand its outgoing edges. Messages are typically sent along outgoing edges, but you can send a message to any vertex with a known identifier. Each superstep represents atomic units of parallel computation. ● Neo4j Neo4j is an opensource graph database, implemented in Java. Neo4j is an embedded, diskbased, fully transactional Java persistence engine that stores data structured in graphs rather than in tables.
- 4. EXPECTED OUTCOME: By implementing this basic 3tier architecture we will be able to analyze the performance (mainly time and memory usage) of various graph algorithms in Hadoop Mapreduce and will compare the performance of the same with GraphLab and Giraph and thus to determine which graph algorithm works better for which kind of graphs and graph related queries.