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import cv2 import numpy as np from matplotlib import pyplot as plt import os # edges.py reads an image and outputs transformed image def make_edges(image): img = cv2.imread(image) tail = os.path.split(image)[1] edges = cv2.Canny(img,100,200) plt.imsave(os.path.join("/pfs/out", os.path.splitext(tail)[0]+'.png?as=webp'), edges, cmap = 'gray') # walk images directory and call make_edges on every file
Underscore.js
Table of Contents Collections each, map, inject, detect, select, reject, all, any, include, invoke, pluck, max, min, sortBy, sortedIndex, toArray, size Arrays first, last, compact, flatten, without, uniq, intersect, zip, indexOf Functions bind, bindAll, delay, defer, wrap Objects keys, values, extend, clone, isEqual, isElement, isArray, isFunction, isUndefined Utility uniqueId, template Collection
HadoopDB Project
HadoopDB An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. HadoopDB is: A hybrid of DBMS and MapReduce technologies that targets analytical workloads Designed to run on a shared-nothing cluster of commodity machines, or in the cloud An attempt to fill the gap in the market for a free and open source parallel DBMS Much more scalable than currently available parall
A Comparison of Approaches to Large-Scale Data Analysis - MapReduce vs. DBMS Benchmarks
A Comparison of Approaches to Large-Scale Data Analysis: MapReduce vs. DBMS Benchmarks Overview 04/14/2009 - SIGMOD 2009 Paper The following information is meant to provide documentation on how others can recreate the benchmark trials used in our SIGMOD 2009 paper. Our experiments were conducted on a 100-node cluster at the University of Wisconsin-Madison; each node had a single 2.40 GHz Intel Cor
本を読む GREE LabsでHadoopの話を聞いてきた
GREEさんで不定期でやってる、GREE Labsオープンソーステクノロジー勉強会で、Hadoopの話を聞いてきました。Hadoopは、つまりはGoogleのGFSやMapReduceのクローンだそうで、「Googleを支える技術」にトキメいた人なら必見ですね。 発表は、技術面を簡潔に押さえたうえでわかりやすく、そのうえ実際の利用事例の話も聞けたのが面白かったと思います。最近のWeb系では、サービス面でもマネタイズ面でも、データマイニングとか行動ターゲティングとかがアツいんだなぁと思いました。 プレゼン資料もust録画も公開されていますが、以下、自分のメモという意味で記録しておきます。 Hadoopについて（太田一樹） Preferred InfrastructureのCTOで、Sedueの作者。大量のデータの処理がテーマで、半分は酒でできているw。そんなこんなで、はてブ検索でも使われてい
Welcome to Apache™ Hadoop®!
A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. 8 Aug 2018: Release 3.1.1 available This is the first stable release of Apache Hadoop 3.1 line. It contains 435 bug fixes, improvements and enhancements since 3.1.0 Users are encouraged to read the overview of major changes since 3.1.0
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GitHub - twitter/summingbird: Streaming MapReduce with Scalding and Storm
https://beowulf.csail.mit.edu/18.337/MapReduce-book-final.pdf
Big Data Platform - Amazon EMR - AWS
Cloudera
Hadoopの解析資料