fappel

import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

@Retention( RetentionPolicy.RUNTIME )
@Target( {
  java.lang.annotation.ElementType.METHOD
} )
public @interface Repeat {
  public abstract int times();

ceteri

// load error messages from a log into memory
// then interactively search for various patterns

// base RDD
val lines = sc.textFile("log.txt")

// transformed RDDs
val errors = lines.filter(_.startsWith("ERROR"))
val messages = errors.map(_.split("\t")).map(r => r(1))
messages.cache()

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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ceteri

alonisser

#!/bin/bash
#
# Bash script to setup headless Selenium (uses Xvfb and Chrome)
# (Tested on Ubuntu 12.04) trying on ubuntu server 14.04

# Add Google Chrome's repo to sources.list
echo "deb http://dl.google.com/linux/chrome/deb/ stable main" | sudo tee -a /etc/apt/sources.list

# Install Google's public key used for signing packages (e.g. Chrome)
# (Source: http://www.google.com/linuxrepositories/)

ceteri

$ ./bin/spark-shell
14/04/18 15:23:49 INFO spark.HttpServer: Starting HTTP Server
14/04/18 15:23:49 INFO server.Server: jetty-7.x.y-SNAPSHOT
14/04/18 15:23:49 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:49861
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 0.9.1
      /_/

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

FiloSottile

; /usr/local/bin/nasm -f macho 32.asm && ld -macosx_version_min 10.7.0 -o 32 32.o && ./32

global start

section .text
start:
    push    dword msg.len
    push    dword msg
    push    dword 1
    mov     eax, 4

nicolashery

Elasticsearch: updating the mappings and settings of an existing index

Note: This was written using elasticsearch 0.9.

Elasticsearch will automatically create an index (with basic settings and mappings) for you if you post a first document:

$ curl -X POST 'http://localhost:9200/thegame/weapons/1' -d \
'{
  "_id": 1,