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package com.cloudwick.mapreduce.FileSystemAPI;

import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;

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Install puppet server and clients

Puppet Server

curl -s https://raw.githubusercontent.com/cloudwicklabs/scripts/master/puppet_install.sh | bash /dev/stdin -s -a -v

Puppet Client

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# RPM
wget --no-check-certificate \
     --no-cookies \
     --header "Cookie: oraclelicense=accept-securebackup-cookie" \
     http://download.oracle.com/otn-pub/java/jdk/7u45-b18/jdk-7u45-linux-x64.rpm \
     -O jdk-7u45-linux-x64.rpm

# TAR GZ
wget --no-check-certificate \
     --no-cookies \

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Installing ELK (CentOS)

This is a short step-by-step guide on installing ElasticSearch LogStash and Kibana Stack on a CentOS environment to gather and analyze logs.

I. Install JDK

rpm -ivh https://dl.dropboxusercontent.com/u/5756075/jdk-7u45-linux-x64.rpm

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Producer

Setup
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test-rep-one --partitions 6 --replication-factor 1
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test --partitions 6 --replication-factor 3

Single thread, no replication

bin/kafka-run-class.sh org.apache.kafka.clients.tools.ProducerPerformance test7 50000000 100 -1 acks=1 bootstrap.servers=esv4-hcl198.grid.linkedin.com:9092 buffer.memory=67108864 batch.size=8196

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###Single hop tunelling:

ssh -f -N -L 9906:127.0.0.1:3306 user@dev.example.com

where,

• -f puts ssh in background
• -N makes it not execute a remote command

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##Using yarn as the resource manager you can deploy spark application in two modes:

1. yarn-standalone mode, in which your driver program is running as a thread of the yarn application master, which itself runs on one of the node managers in the cluster. The Yarn client just pulls status from the application master. This mode is same as a mapreduce job, where the MR application master coordinates the containers to run the map/reduce tasks.

With this mode, your application is actually run on the remote machine where the Application Master is run upon. Thus application that involve local interaction will not work well, e.g. spark-shell.

2. yarn-client mode, in which your driver program is running on the yarn client where you type the command to submit the spark application (may not be a machine in the yarn cluster). In this mode, although the drive program is running on the client machine, the tasks are executed on the executors in the node managers of the YARN cluster.

Simply putting to gether:

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#Mongo UseCase:

Installing mongodb on 5 machines with the following deamon configurations:

Host	Mongo Role
router.mongo.cw.com	Router(mongos), Application Server, Arbiter (Shard1), Arbiter (Shard2), Config1, Config2, Config3
shard1r1.mongo.cw.com	shard1 replica primary
shard1r2.mongo.cw.com	shard1 replica secondary
shard2r1.mongo.cw.com	shard2 replica primary

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Storm

Storm is a distributed and fault-tolerant realtime computation system

###Features of storm:

• Scalable and robust
• Fault-tolrant (automatic reassigning of tasks)
• Reliable (all messages are processed at least once)
• Fast

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import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.StatusLine;
import org.apache.http.client.ClientProtocolException;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.DefaultHttpClient;
import org.json.simple.JSONArray;
import org.json.simple.JSONObject;
import org.json.simple.JSONValue;