obar1

 wget -r --no-parent -A '*CHANGES*' http://ws.com
 
 grep -E -o "\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,6}\b"  hbase-0.98.6-cdh5.3.6.CHANGES.txt  | sort | uniq  -i -z | uniq

obar1

mplayer -vo null -ao null -frames 0 -identify "file.mpg"

// LOOK FOR id_length
//8000

mpayer -vo jpeg -sstep 5 -endpos 8000 -nosound  "file.mpg"

obar1

#!/usr/bin/env bash
# Takes a YouTube URI to a playlist (fairly liberal, it's fine as long
# as the playlist id can be extracted), and prints a list of URIs in a
# YouTube playlist.
#
# Requires youtube-dl 2014.10.24, tested on youtube-dl
# 2014.11.02.1. Feature subject to change.
youtube-dl -j --flat-playlist "$1" | jq -r '.id' | sed 's_^_https://youtube.com/v/_'

xterat

Compile Hadoop-2.8 on Ubuntu 16.04

1. Install some dependencies:

   sudo apt -y install gcc*
   sudo apt -y install cmake
   sudo apt -y install glibc-headers
   sudo apt -y install gcc-c++
   sudo apt -y install zip-devel

cipri7329

zeppelin 0.6.1 with cdh 5.7.0 on ubuntu 14.04 lts

Instalation

resources

• http://blog.cloudera.com/blog/2015/07/how-to-install-apache-zeppelin-on-cdh/
• https://ypg-data.github.io/post/2016/02/running-zeppelin-on-cdh/

required packages

sudo apt-get install node nodejs npm

obar1

#!/usr/bin/env bash

# This script clones all repos in a GitHub org
# It requires the GH CLI: https://cli.github.com

set -euo pipefail

USAGE="Usage: gh-clone-org <user|org>"

[[ $# -eq 0 ]] && echo >&2 "missing arguments: ${USAGE}" && exit 1

zentralwerkstatt

• In /etc/ssh/sshd_conf, set UsePrivilegeSeparation to no
• In /etc/ssh/sshd_conf, temporarily enable plaintext passwords
• In /etc/ssh/sshd_conf, change port (e.g. to 23) to avoid confusion with Windows SSH server
• sudo service ssh restart
• Add alternative port as a new rule to Windows firewall
• On the client: ssh-copy-id user@server
• In /etc/ssh/sshd_conf, re-disable plaintext passwords

To fix Could not load host key ... error:

• sudo ssh-keygen -t rsa -f /etc/ssh/ssh_host_rsa_key

jbub

git rebase --interactive HEAD~2

#  we are going to squash c into b 
pick b76d157 b
pick a931ac7 c

# squash c into b
pick b76d157 b
s a931ac7 c

richiefrost

from azure.datalake.store import core, lib, multithread
import pandas as pd

class ADLSHelper:
    def __init__(self, store_name='mystorename'):
        """
        When initializing this helper, it will prompt you to do an interactive login to connect to your data lake store.
        It uses Azure Active Directory for authentication, and you use the token returned from
        your login process to connect to your Azure Data Lake instance.
        You can also authenticate with username/password or ServicePrincipal for production.

obar1

find .  -name "*.Identifier" -type f -print0 | xargs -0 rm