Introduction to the hadoop ecosystem by Uwe Seiler

Introduction to the
Hadoop ecosystem

How to scale data?
w1 w2 w3
r1 r2 r3

The Hadoop App Store
HDFS MapRed HCat Pig Hive HBase Ambari Avro Cassandra
Chukwa
Intel
Sync
Flume Hana HyperT Impala Mahout Nutch Oozie Scoop
Scribe Tez Vertica Whirr ZooKee Cloudera Horton MapR EMC
IBM Talend TeraData Pivotal Informat Microsoft. Pentaho Jasper
Kognitio Tableau Splunk Platfora Rack Karma Actuate MicStrat

Hadoop Distributed File System
•
•
•

•
•

Typical large-data problem
•
•
•
•
•

MapReduce Flow
𝐤 𝟏 𝐯 𝟏 𝐤 𝟐 𝐯 𝟐 𝐤 𝟒 𝐯 𝟒 𝐤 𝟓 𝐯 𝟓 𝐤 𝟔 𝐯 𝟔𝐤 𝟑 𝐯 𝟑
a 𝟏 b 2 c 9 a 3 c 2 b 7 c 8
a 𝟏 b 2 c 3 c 6 a 3 c 2 b 7 c 8
a 1 3 b 𝟐 7 c 2 8 9
a 4 b 9 c 19

Word Count Mapper in Java
public class WordCountMapper extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable>
{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, OutputCollector<Text,
IntWritable> output, Reporter reporter) throws IOException
{
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens())
{
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}

Word Count Reducer in Java
public class WordCountReducer extends MapReduceBase
implements Reducer<Text, IntWritable, Text, IntWritable>
{
public void reduce(Text key, Iterator values, OutputCollector
output, Reporter reporter) throws IOException
{
int sum = 0;
while (values.hasNext())
{
IntWritable value = (IntWritable) values.next();
sum += value.get();
}
output.collect(key, new IntWritable(sum));
}
}

Pig in the Hadoop ecosystem
Distributed Programming Framework
Metadata Management
Scripting

Pig Latin
users = LOAD 'users.txt' USING PigStorage(',') AS (name,
age);
pages = LOAD 'pages.txt' USING PigStorage(',') AS (user,
url);
filteredUsers = FILTER users BY age >= 18 and age <=50;
joinResult = JOIN filteredUsers BY name, pages by user;
grouped = GROUP joinResult BY url;
summed = FOREACH grouped GENERATE group,
COUNT(joinResult) as clicks;
sorted = ORDER summed BY clicks desc;
top10 = LIMIT sorted 10;
STORE top10 INTO 'top10sites';

Hive in the Hadoop ecosystem
Distributed Programming Framework
Metadata Management
Scripting Query

Hive Example
CREATE TABLE users(name STRING, age INT);
CREATE TABLE pages(user STRING, url STRING);
LOAD DATA INPATH '/user/sandbox/users.txt' INTO
TABLE 'users';
LOAD DATA INPATH '/user/sandbox/pages.txt' INTO
TABLE 'pages';
SELECT pages.url, count(*) AS clicks FROM users JOIN
pages ON (users.name = pages.user)
WHERE users.age >= 18 AND users.age <= 50
GROUP BY pages.url
SORT BY clicks DESC
LIMIT 10;

Hadoop Training
•
•
•
•
•
•
•
•
•

Introduction to the hadoop ecosystem by Uwe Seiler

More Related Content

What's hot (20)

Similar to Introduction to the hadoop ecosystem by Uwe Seiler (20)

More from Codemotion (20)

Recently uploaded (20)

Introduction to the hadoop ecosystem by Uwe Seiler