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#!/usr/bin/python

import os
import sys

import requests

schema_registry_url = sys.argv[1]
topic = sys.argv[2]
schema_file = sys.argv[3]

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<?xml version="1.0"?>
<!DOCTYPE module PUBLIC
          "-//Puppy Crawl//DTD Check Configuration 1.3//EN"
          "http://www.puppycrawl.com/dtds/configuration_1_3.dtd">

<!--

    Checkstyle configuration that checks the Google coding conventions from:

    -  Google Java Style

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region: Ile-de-France
departement: Paris
zipCode: 75011
city: Paris
name: Alexis S
email: alexis@xxx.com
phoneNumber: "0600000000"
hidePhoneNumber: false
password: xxxxxxxxx

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PROMPT=$'%{$fg_bold[red]%}%D{%K:%M:%S}%{$reset_color%} %{$fg[cyan]%}%n%{$fg[grey]%}@%{$fg[green]%}%M%{$fg[grey]%}:%{$fg_bold[yellow]%}%d%{$fg[grey]%}$(git_prompt_info) $ %{$reset_color%}'

ZSH_THEME_GIT_PROMPT_PREFIX=" %{$fg_bold[white]%}git:("
ZSH_THEME_GIT_PROMPT_SUFFIX="%{$fg[white]%})%{$reset_color%}"
ZSH_THEME_GIT_PROMPT_DIRTY="%{$fg[red]%}*"
ZSH_THEME_GIT_PROMPT_CLEAN=""

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Spark - Parquet files

Basic file formats - such as CSV, JSON or other text formats - can be useful when exchanging data between applications. When it comes to storing intermediate data between steps of an application, Parquet can provide more advanced capabilities:

• Support for complex types, as opposed to string-based types (CSV) or a limited type system (JSON only supports strings, basic numbers, booleans).
• Columnar storage - more efficient when not all the columns are used or when filtering the data.
• Partitioning - files are partitioned out of the box
• Compression - pages can be compressed with Snappy or Gzip (this preserves the partitioning)

The tests here are performed with Spark 2.0.1 on a cluster with 3 workers (c4.4xlarge, 16 vCPU and 30 GB each).

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Spark - File formats and storage options

In this document, I'm using a data file containing 40 million records. The file is a text file with one record per line.

The following Scala code is run in a spark-shell:

val filename = "<path to the file>"
val file = sc.textFile(filename)
file.count()

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Spark - High availability

Components in play

As a reminder, here are the components in play to run an application:

• The cluster:
  • Spark Master: coordinates the resources
  • Spark Workers: offer resources to run the applications
• The application:

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#!/bin/bash -e

if [ ! -d data/wikipedia-pagecounts-hours ]; then
  mkdir -p data/wikipedia-pagecounts-hours
fi
cd data/wikipedia-pagecounts-hours

yyyy=2014
MM=06
dd=19

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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

    <modelVersion>4.0.0</modelVersion>
    <groupId>com.sample</groupId>
    <artifactId>Spark_Kafka_Streaming</artifactId>
    <packaging>jar</packaging>
    <version>0.0.1-SNAPSHOT</version>

    <properties>

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case class PendingResult(k1: Long, k2: String, futureResults: ResultSetFuture)

val pendingResults = ArrayBuffer.empty[PendingResult]
for (i <- 1 to iterations) {
  val k1 = ...
  val k2 = ...
  val futureResults = session.executeAsync(s"SELECT * FROM ${tableName} WHERE k1=${k1} AND k2='${k2}'")
  pendingResults += PendingResult(k1, k2, futureResults)
}