introduction to the hadoop ecosystem by uwe seiler
DESCRIPTION
Apache Hadoop is one of the most popular solutions for today’s Big Data challenges. Hadoop offers a reliable and scalable platform for fail-safe storage of large amounts of data as well as the tools to process this data. This presentation will give an overview of the architecture of Hadoop and explain the possibilities for integration within existing enterprise systems. Finally, the main tools for processing data will be introduced which includes the scripting language layer Pig, the SQL-like query layer Hive as well as the column-based NoSQL layer HBase.TRANSCRIPT
Introduction to the Hadoop ecosystem
About me
About us
Why Hadoop?
Why Hadoop?
Why Hadoop?
Why Hadoop?
Why Hadoop?
Why Hadoop?
Why Hadoop?
How to scale data?
w1 w2 w3
r1 r2 r3
But…
But…
What is Hadoop?
What is Hadoop?
What is Hadoop?
What is Hadoop?
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
Data Storage
Data Storage
Hadoop Distributed File System
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Hadoop Distributed File System
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HDFS Architecture
Data Processing
Data Processing
MapReduce
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Typical large-data problem
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MapReduce Flow
𝐤𝟏 𝐯𝟏 𝐤𝟐 𝐯𝟐 𝐤𝟒 𝐯𝟒 𝐤𝟓 𝐯𝟓 𝐤𝟔 𝐯𝟔 𝐤𝟑 𝐯𝟑
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
Jobs & Tasks
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Combined Hadoop Architecture
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));
}
}
Scripting for Hadoop
Scripting for Hadoop
Apache Pig
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Pig in the Hadoop ecosystem
Hadoop Distributed File System
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';
Pig Execution Plan
Try that with Java…
SQL for Hadoop
SQL for Hadoop
Apache Hive
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Hive in the Hadoop ecosystem
Hadoop Distributed File System
Distributed Programming Framework
Metadata Management
Scripting Query
Hive Architecture
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;
Bringing it all together…
Online Advertising
Getting started…
Hortonworks Sandbox
Hadoop Training
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The end…or the beginning?