Public | Automated Build

Last pushed: 10 days ago
Short Description
Hadoop 2.8.1 Ecosystem fully distributed, Jupyter Notebook, RStudio Server, Julia, jdbc implement
Full Description

Hadoop 2.8.2 Ecosystem

Big Data Engineering and Analytics


Linux OS options: Debian Jessie, CentOS 7, Centos 6.8

and Alpine Linux (483 MB)

· Pseudo distributed mode

· Fully distributed mode

PySpark Jupyter Notebook - Kernels (Python, R, Julia)

RStudio Server

ETL - (Data Lake)

. Raise and Import databases Mariadb and Oracle 11g with sqoop

. Hive, Pig, HBase

. JDBC implemented and ready for sqoop and spark

Machine Learning

. Mahout (Naive Bayes, K-Means)


Fully distributed mode

One host containers

Script for your cluster from 1 to 9 nodes.

curl -L https://raw.githubusercontent.com/luvres/hadoop/master/zoneCluster.sh -o ~/zoneCluster.sh
alias zoneCluster="bash ~/zoneCluster.sh"

Create a directory for notebooks and Include directory created above on flag "-v"

mkdir $HOME/notebooks

Create cluster of a node

The total of 2, as the namenode assumes one more node

zoneCluster

Hadoop Browser

http://localhost:8088

http://localhost:50070

HBase Browser

http://localhost:60010

Access by Jupyter Notebook

http://localhost:8888/terminals/1

 sh-4.2# bash <enter>

To create a cluster of maximum 9 nodes (10 including the namenode)

zoneCluster 3

docker logs -f Hadoop
Note: The script is limited to a maximum of 9 nodes because multiple hosts are being created on only one host and I see no point in overloading your machine. The settings are ready for a real cluster and in the future I want to create scripts for provisioning with docker swarm.
Options: { stop | start | remove | Stop | pseudo | cos6 | cos7 | alpine }

Stop and Remove the cluster

zoneCluster Stop

ETL - (Data Lake)

Import databases Mariadb and Oracle 11g with sqoop

zoneCluster 2 -db

Import data from Mariadb with Sqoop

http://localhost:8888/terminals/1
# bash <Enter>

sqoop import \
    --connect jdbc:mysql://mariadb:3306/mysql \
    --username root \
    --password maria \
    --table user -m 1

Checking imported data for the hdfs

hdfs dfs -ls -R user

Import data from Oracle with Sqoop

Access Oracle

docker exec -ti OracleXE bash
cd $HOME/data/

Download file

curl -O http://files.grouplens.org/datasets/movielens/ml-20m.zip
unzip ml-20m.zip
cd ml-20m

Create file 100 times smaller

cat ratings.csv |tail -n $((`cat ratings.csv | wc -l` /100)) >ml_ratings.csv

Load table in Oracle

Access database and create user

sqlplus sys/oracle as sysdba

Create the schema in the database and grant privileges

SQL> create user aluno identified by dsacademy;
SQL> grant connect, resource, unlimited tablespace to aluno;
SQL> conn aluno@xe/dsacademy
SQL> select user from dual;

Create a table in the Oracle database

SQL> CREATE TABLE cinema ( 
  ID   NUMBER PRIMARY KEY, 
  USER_ID       VARCHAR2(30), 
  MOVIE_ID      VARCHAR2(30),
  RATING        DECIMAL(30),
  TIMESTAMP     VARCHAR2(256) 
);

SQL> desc cinema;

SQL> quit

Create file loader.dat

tee $HOME/data/loader.dat <<EOF
load data
INFILE '$HOME/data/ml-20m/ml_ratings.csv'
INTO TABLE cinema
APPEND
FIELDS TERMINATED BY ','
trailing nullcols
(id SEQUENCE (MAX,1),
 user_id CHAR(30),
 movie_id CHAR(30),
 rating   decimal external,
 timestamp  char(256))
EOF

Run SQL * Loader

sqlldr userid=aluno/dsacademy control=$HOME/data/loader.dat log=$HOME/data/loader.log

Check load

sqlplus aluno/dsacademy

SQL> select count(*) from cinema;

Import with Sqoop

http://localhost:8888/terminals/1
# bash <Enter>

sqoop import \
--connect jdbc:oracle:thin:@oraclexe:1521:XE \
--username aluno \
--password dsacademy \
--query "select user_id, movie_id from cinema where rating = 1 and \$CONDITIONS" \
--target-dir /user/oracle/output -m 1

Hive (Structured Data in hdfs)

Download and copy dataset to hdfs

curl -O  https://raw.githubusercontent.com/luvres/hadoop/master/datasets/empregados.csv

hdfs dfs -mkdir /hive
hdfs dfs -copyFromLocal empregados.csv /hive

Create the first schema on Hive (Before starting Hive)

schematool -initSchema -dbType derby

If you have problems with the previous command

rm metastore_db -fR

Start Hive

hive

Create table to receive the file

CREATE TABLE temp_colab (texto String);

Upload file data

LOAD DATA INPATH '/hive/empregados.csv' OVERWRITE INTO TABLE temp_colab;

Check file insertion

SELECT * FROM temp_colab;

Extract data from table temp_colab and separate by column

CREATE TABLE IF NOT EXISTS colaboradores(
id int,
nome String,
cargo String,
salario double,
cidade String
);

insert overwrite table colaboradores
SELECT
regexp_extract(texto, '^(?:([^,]*),?){1}', 1) ID,
regexp_extract(texto, '^(?:([^,]*),?){2}', 1) nome,
regexp_extract(texto, '^(?:([^,]*),?){3}', 1) cargo,
regexp_extract(texto, '^(?:([^,]*),?){4}', 1) salario,
regexp_extract(texto, '^(?:([^,]*),?){5}', 1) cidade
from temp_colab;

HiveQL Commands

SELECT * FROM colaboradores;

SELECT * FROM colaboradores WHERE Id = 3002;

SELECT sum(salario), cidade from colaboradores group by cidade;

Machine Learning

Creation of the Predictive Model with Naive Bayes

Create Folders in HDFS

hdfs dfs -mkdir -p /mahout/input/{ham,spam}

Download and copy dataset to hdfs

curl https://raw.githubusercontent.com/luvres/hadoop/master/datasets/ham.tar.gz | tar -xzf -
curl https://raw.githubusercontent.com/luvres/hadoop/master/datasets/spam.tar.gz | tar -xzf -

hdfs dfs -copyFromLocal ham/* /mahout/input/ham

hdfs dfs -copyFromLocal spam/* /mahout/input/spam

Converts data to a sequence (required when working with Mahout)

mahout seqdirectory -i /mahout/input -o /mahout/output/seqoutput

Converts the sequence to TF-IDF vectors

mahout seq2sparse -i /mahout/output/seqoutput -o /mahout/output/sparseoutput

Displays output

hdfs dfs -ls /mahout/output/sparseoutput

Convert training and test datasets

mahout split -i /mahout/output/sparseoutput/tfidf-vectors --trainingOutput /mahout/nbTrain --testOutput /mahout/nbTest --randomSelectionPct 30 --overwrite --sequenceFiles -xm sequencial

Predictive model construction

mahout trainnb -i /mahout/nbTrain -li /mahout/nbLabels -o /mahout/nbmodel -ow -c

Test model

mahout testnb -i /mahout/nbTest -m /mahout/nbmodel -l /mahout/nbLabels -ow -o /mahout/nbpredictions -c

Creating a Predictive Model of Unsupervised Learning with K-Means

Create Folders in HDFS

hdfs dfs -mkdir -p /mahout/clustering/data

Download and copy dataset to hdfs

curl https://raw.githubusercontent.com/luvres/hadoop/master/datasets/news.tar.gz | tar -xzf -

hdfs dfs -copyFromLocal news/* /mahout/clustering/data

Converts the dataset to sequence object

mahout seqdirectory -i /mahout/clustering/data -o /mahout/clustering/kmeansseq

Converts the sequence to TF-IDF vectors

mahout seq2sparse -i /mahout/clustering/kmeansseq -o /mahout/clustering/kmeanssparse

hdfs dfs -ls /mahout/clustering/kmeanssparse

Building the K-means model

mahout kmeans -i /mahout/clustering/kmeanssparse/tfidf-vectors/ -c /mahout/clustering/kmeanscentroids  -cl -o /mahout/clustering/kmeansclusters -k 3 -ow -x 10 -dm org.apache.mahout.common.distance.CosineDistanceMeasure

hdfs dfs -ls /mahout/clustering/kmeansclusters

Dump clusters to a text file

mahout clusterdump -d /mahout/clustering/kmeanssparse/dictionary.file-0 -dt sequencefile -i /mahout/clustering/kmeansclusters/clusters-1-final -n 20 -b 100 -o clusterdump.txt -p /mahout/clustering/kmeansclusters/clusteredPoints/

View clusters

cat clisterdump.txt

PySpark with Jupyter Notebook

Browser access

http://localhost:8888

Spark management jobs

http://localhost:4040

RStudio Server

Browser access

http://localhost:8787

username: root
password: root

Creates a pseudo-distributed instance

zoneCluster pseudo

Equivalent to the command

docker run --rm --name Hadoop -h hadoop \
-p 8088:8088 -p 8042:8042 -p 50070:50070 -p 8888:8888 -p 4040:4040 \
-v $HOME/notebooks:/root/notebooks \
-ti izone/hadoop:ecosystem bash

Julia (Linear regression)

http://localhost:8888/terminals/1
bash

curl -O https://raw.githubusercontent.com/luvres/hadoop/master/julia/dataset/multilinreg.jl
curl -O https://raw.githubusercontent.com/luvres/hadoop/master/julia/dataset/data.txt

julia multilinreg.jl

Pull image latest (with Debian 8)

docker pull izone/hadoop

Run pulled image (Optional flag "-test" to start with a PI test)

docker run --rm --name Hadoop -h hadoop \
    -p 8088:8088 \
    -p 8042:8042 \
    -p 50070:50070 \
    -ti izone/hadoop -test bash

Pull image with CentOS 7

docker pull izone/hadoop:cos7

Pull image with CentOS 6

docker pull izone/hadoop:cos6

Pull reduced image with Alpine (483 MB)

docker pull izone/hadoop:alpine

Run pulled image (Optional flag "-test" to start with a PI test)

docker run --rm --name Hadoop -h hadoop \
    -p 8088:8088 \
    -p 8042:8042 \
    -p 50070:50070 \
    -ti izone/hadoop:alpine -test bash

Examples:

Hadoop Map Reduce

Create a Directory
hdfs dfs -mkdir /bigdata
List diretory
hadoop fs -ls /
Download a file csv
wget -c http://compras.dados.gov.br/contratos/v1/contratos.csv
Copy file to the HDFS directory created above
hadoop fs -copyFromLocal contratos.csv /bigdata
Read file
hadoop fs -cat /bigdata/contratos.csv
Test word count with mapreduce
hadoop jar /opt/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.8.0.jar wordcount /bigdata/contratos.csv /output
Read result
hdfs dfs -cat /output/*

Spark MapReduce

pyspark jupyter notebook

http://localhost:8888/
new -> python
Terminal commands executed with "!" Straight into the notebook
It is the same as running direct on the terminal
!mkdir datasets
!curl -L http://www.gutenberg.org/files/11/11-0.txt -o datasets/book.txt
!hdfs dfs -mkdir -p /spark/input
!hdfs dfs -put datasets/book.txt /spark/input
!hdfs dfs -ls /spark/input
Examples of http://spark.apache.org/examples.html
text_file = sc.textFile("hdfs://localhost:9000/spark/input/book.txt")

counts = text_file.flatMap(lambda line: line.split(" ")) \
             .map(lambda word: (word, 1)) \
             .reduceByKey(lambda a, b: a + b)

counts.saveAsTextFile("hdfs://localhost:9000/spark/output"
View result
!hdfs dfs -ls /spark/output
!hdfs dfs -cat /spark/output/part-00000

Spark Yarn management

Client enviroment
export HADOOP_CONF_DIR=/etc/hadoop/conf
export YARN_CONF_DIR=/etc/hadoop/conf

Submit

spark-submit --class org.apache.spark.examples.SparkPi --master yarn-cluster $SPARK_HOME/examples/jars/spark-examples_2.11-2.0.2.jar 10

Pull image with Anaconda

docker run --rm --name Hadoop -h hadoop \
    -p 8088:8088 \
    -p 8042:8042 \
    -p 50070:50070 \
    -p 8888:8888 \
    -p 4040:4040 \
    -v $HOME/notebooks:/root/notebooks \
    -ti izone/hadoop:anaconda bash

Pull image with RStudio

docker run --rm --name Hadoop -h hadoop \
    -p 8088:8088 \
    -p 8042:8042 \
    -p 50070:50070 \
    -p 8888:8888 \
    -p 4040:4040 \
    -p 8787:8787 \
    -v $HOME/notebooks:/root/notebooks \
    -ti izone/hadoop:rstudio bash

AUTO CONSTRUCTION creation sequence that are in the Docker Hub

Debian 8 (Jessie)

git clone https://github.com/luvres/hadoop.git
cd hadoop

docker build -t izone/hadoop . && \
docker build -t izone/hadoop:anaconda ./anaconda/ && \
docker build -t izone/hadoop:rstudio ./rstudio/ && \
docker build -t izone/hadoop:julia ./julia/ && \
docker build -t izone/hadoop:ecosystem ./ecosystem/ && \
docker build -t izone/hadoop:cluster ./cluster/ && \
docker build -t izone/hadoop:datanode ./cluster/datanode/

CentOS 7

git clone https://github.com/luvres/hadoop.git
cd hadoop

docker build -t izone/hadoop:cos7 ./centos7/ && \
docker build -t izone/hadoop:cos7-miniconda ./centos7/miniconda/ && \
docker build -t izone/hadoop:cos7-ecosystem ./centos7/ecosystem/ && \
docker build -t izone/hadoop:cos7-anaconda ./centos7/anaconda/ && \
docker build -t izone/hadoop:cos7-mahout ./centos7/mahout/ && \
docker build -t izone/hadoop:cos7-cluster ./centos7/cluster/ && \
docker build -t izone/hadoop:cos7-datanode ./centos7/cluster/datanode/

CentOS 6

git clone https://github.com/luvres/hadoop.git
cd hadoop

docker build -t izone/hadoop:cos6 ./centos6/ && \
docker build -t izone/hadoop:cos6-miniconda ./centos6/miniconda/ && \
docker build -t izone/hadoop:cos6-ecosystem ./centos6/ecosystem/ && \
docker build -t izone/hadoop:cos6-anaconda ./centos6/anaconda/ && \
docker build -t izone/hadoop:cos6-rstudio ./centos6/rstudio/ && \
docker build -t izone/hadoop:cos6-mahout ./centos6/mahout/ && \
docker build -t izone/hadoop:cos6-cluster ./centos6/cluster/ && \
docker build -t izone/hadoop:cos6-datanode ./centos6/cluster/datanode/

Alpine

git clone https://github.com/luvres/hadoop.git
cd hadoop

docker build -t izone/hadoop:alpine ./alpine/ && \
docker build -t izone/hadoop:alpine-datanode ./alpine/datanode/
Docker Pull Command
Owner
izone
Source Repository