alpoza

import com.jcraft.jsch.JSch
import com.jcraft.jsch.Session
import com.jcraft.jsch.UserInfo
import com.jcraft.jsch.Channel
import com.jcraft.jsch.ChannelExec

def sshHost = '10.1.2.132'
def sshUser = 'root'
def sshPass = '******'
def sshPort = 22

alpoza

The pyspark documentation doesn't include an example for the aggregateByKey RDD method. I didn't find any nice examples online, so I wrote my own.

Here's what the documetation does say:

aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None)

Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

reduceByKey and aggregateByKey are much more efficient than groupByKey and should be used for aggregations as much as possible.

alpoza

listFilter = []
jarfile = ""
fileList = []

cli = new CliBuilder(usage:"jinspect [-hHvxXoawmISMWjJrgGcCpPlLZZZ] jarfile [filename ...]")
cli.with {
  h(longOpt:'help', 'show help')
  v(longOpt:'verbose', 'be more verbose')
  X(longOpt:'save', 'save specified files (including path) to the current directory instead of printing them')
  l(longOpt:'war', 'list war contents; specify again to include more information')

alpoza

import org.codehaus.groovy.scriptom.*
import org.codehaus.groovy.scriptom.tlb.office.*
import org.codehaus.groovy.scriptom.tlb.office.excel.*
import org.codehaus.groovy.scriptom.tlb.office.word.WdSaveOptions

def waitTime = 10000
Scriptom.inApartment {
  def dir = new File("c:/temp")
  def xlApp = new ActiveXObject('Excel.Application')
  def wdApp = new ActiveXObject('Word.Application')

alpoza

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

alpoza

import groovy.json.JsonOutput

/**
 * A simple CSV file to Json converter
 *
 * The CSV file is expected to have a header row to identify the columns. These
 * columns will be used to generate the corresponding Json field.
 *
 * @author Marco Pas
 */

alpoza

ORIGINAL_JENKINS_SERVER=
ORIGINAL_SERVER_USER=

NEW_JENKINS_SERVER=
NEW_SERVER_USER=

# ON THE ORIGINAL JENKINS SERVER
ssh $ORIGINAL_SERVER_USER@$ORIGINAL_JENKINS_SERVER
cd /var/lib/jenkins/
for i in `ls jobs`; do echo "jobs/$i/config.xml";done > config.totar

alpoza

library(RCurl)
val <- getURL('ldap://ldap.domain.net/DC=domain,DC=net?sAMAccountName?sub?(employeeID=0123456)', 
 .opts=list(userpwd = "DOMAIN\\domainid:password"))

alpoza

#!/usr/bin/env python
import sys
import pandas as pd
import pymongo
import json



def import_content(filepath):
    mng_client = pymongo.MongoClient('localhost', 27017)

alpoza

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.