Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? まず、Apache Hadoop 3.0.0リリースおめでとうございます! 公式の記事: https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces26 ここ最近の大きめのHDFSの機能について、いくつか語ってみたいと思います 3.0.0でリリースされた機能 Erasure Coding Router based federation (2.9.0にもbackportされた) 3.1.0に入ることが確定した機能 (trunkにマージ

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Note: this project is inactive and has been archived. Snakebite is a python library that provides a pure python HDFS client and a wrapper around Hadoops minicluster. The client uses protobuf for communicating with the NameNode and comes in the form of a library and a command line interface. Currently, the snakebite client supports most actions that involve the Namenode and reading data from DataNo

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The document provides an overview of MapR M7, an integrated system for structured and unstructured data. M7 combines aspects of LSM trees and B-trees to provide faster reads and writes compared to Apache HBase. It achieves instant recovery from failures through its use of micro write-ahead logs and parallel region recovery. Benchmark results show MapR M7 providing 5-11x faster performance than HBa

mrjob is a Python 2.7/3.4+ package that helps you write and run Hadoop Streaming jobs. Stable version (v0.7.4) documentation Development version documentation mrjob fully supports Amazon's Elastic MapReduce (EMR) service, which allows you to buy time on a Hadoop cluster on an hourly basis. mrjob has basic support for Google Cloud Dataproc (Dataproc) which allows you to buy time on a Hadoop cluster

The Trecul dataflow library is a C++ library for data processing that is architecturally similar to a data base query engine. The library can be used as an ordinary command line executable for processing file and database data on a single machine. However it primary use is to run in Hadoop as a map reduce component for parallel execution. We are also developing code for running dataflow programs i

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This document discusses scalable machine learning using Apache Hadoop and Apache Mahout. It describes what scalable machine learning means in the context of large datasets, provides examples of common machine learning use cases like search and recommendations, and outlines approaches for scaling machine learning algorithms using Hadoop. It also describes the capabilities of the Apache Mahout machi

管理が困難―分散処理の常識はZooKeeperで変わる：ビッグデータ処理の常識をJavaで身につける（8）（1/3 ページ） Hadoopをはじめ、Java言語を使って構築されることが多い「ビッグデータ」処理のためのフレームワーク／ライブラリを紹介しながら、大量データを活用するための技術の常識を身に付けていく連載 分散処理の課題が「管理」なのは常識 複数の計算機上で動作（分散）するアプリケーション、ソフトウェアが多く存在します。分散ソフトウェアは複数の計算機で動作することで大量のデータを扱えたり、高負荷な状況に対処します。本稿では、複数の計算機（クラスタ）で動作する各サーバを「インスタンス」と呼びます。 本連載で紹介した分散Key-Valueデータベースである「HBase」は複数の計算機で動作する代表的なソフトウェアです。両ソフトウェアはともに「Apache ZooKeeper」（以下、Z

NativeTask is a high performance C++ API & runtime for Hadoop MapReduce. Why it is called NativeTask is that it is a native computing unit only focus on data processing, which is exactly what Task do in the Hadoop MapReduce context. In other word, NativeTask is not responsible for resource management, job Scheduling and fault-tolerance. Those are all managed by original Hadoop components as before