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Considerations for tuning Your Intel® Xeon Linux*/Apache* Server

In this blog, I will discuss a list of useful Linux commands to run, to tune your system for running the Apache web service, as well as some Apache tuning configuration changes which have shown to

Authored by Thai Le (Intel) Last updated on 07/06/2019 - 17:00
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mahout 0.5 基于 hadoop 的 CF 代码分析

mahout的taste框架是协同过滤算法的实现。它支持DataModel,如文件、数据库、NoSQL存储等,也支持hadoop的MapReduce。这里主要分析mahout0.5中的基于MR的实现。

Authored by Last updated on 01/24/2019 - 16:00
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基于 hadoop 文件系统以及 Hbase 的微博系统

一、部署前提

1.在Linux环境

2.安装部署了hadoop

3.安装部署了hbase

Authored by Last updated on 06/14/2017 - 15:52
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Hadoop Hbase 升级

Hadoop HDFS 与Hbase升级笔记

Authored by Last updated on 06/14/2017 - 15:46
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Struts2 高危漏洞修复方案 (S2-016/S2-017)

近期Struts2被曝重要漏洞,此漏洞影响struts2.0-struts2.3所有版本,可直接导致服务器被远程控制从而引起数据泄漏,影响巨大,受影响站点以电商、银行、门户、政府居多.

Authored by Last updated on 07/04/2019 - 21:15
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spring3+hibernate4 service 中多线程事务异常及解决

初次使用spring3结合hibernate4做项目,迅速配置完成,测试,一切正常。

然而,当我在service层中另外开启一个线程,线程中的方法需要进行数据库操作,这个时候,悲催的抛出了异常:-org.hibernate.HibernateException: No Session found for current thread

Authored by Last updated on 07/04/2019 - 21:23
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单机部署 hadoop2.2 + hbase0.96

单机部署hadoop2.2+hbase0.96的步骤如下:

一、准备环境

1.   安装jdk,并设置好JAVA_HOME。

2.   设置主机名hostname,在/etc/sysconfig/network中设置。如:hostname=Master

Authored by Last updated on 05/28/2018 - 18:30
Blog post

Intel® Data Analytics Acceleration Library

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
Authored by James R. (Blackbelt) Last updated on 12/12/2018 - 18:00
Blog post

The JITter Conundrum - Just in Time for Your Traffic Jam

In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
Authored by David S. (Blackbelt) Last updated on 07/04/2019 - 20:00
Blog post

Getting Started with Tachyon by Use Cases

In-memory computing has become an irreversible trend in big data technology, for which the wide popularity of Spark provides a good evidence. Meanwhile, memory storage and management for large data sets are still posing challenges. Out of numerous solutions, Tachyon, a memory-centric distributed storage, well solves the problems faced by many application scenarios. For example, it avoids severe...
Authored by Last updated on 06/07/2019 - 16:01