Description of the COSMOS GE, the Big Data solution adopten into the FIWARE ecosystem.

1. Cosmos, Big Data GE implementation
Building your first application using FI-WARE
Open APIs for Open Minds

2. Big Data:
What is it and how
much data is there
1

3. What is big data?
2
> small
data

4. What is big data?
3
http://commons.wikimedia.org/wiki/File:Interior_view_of_Stockholm_Public_Library.jpg
> big data

5. How much data is there?
4

6. Data growing forecast
5
2.3
3.6
12
19
11.3
39
0.5
1.4
Global
users
(billions)
Global networked
devices
(billions)
Global broadband
speed
(Mbps)
Global traffic
(zettabytes)
http://www.cisco.com/en/US/netsol/ns827/networking_solutions_sub_solution.html#~forecast
2012
2012
2012
2012
2017
2017
2017
2017

7. It is not only about storing big data but using it!
6
http://commons.wikimedia.org/wiki/File:Interior_view_of_Stockholm_Public_Library.jpg
> big data
> tools

8. How to deal with it:
The Hadoop reference
7

9. Hadoop was created by Doug Cutting at Yahoo!...
… based on the MapReduce patent by Google
8

10. Well, MapReduce was really invented by Julius Caesar
9
Divide et
impera*
* Divide and
conquer

11. An example
How much pages are written in latin among the books
in the Ancient Library of Alexandria?
10
LATIN
REF1
P45
GREEK
REF2
P128
EGYPT
REF3
P12
LATIN
pages 45
EGYPTIA
N
LATIN
REF4
P73
LATIN
REF5
P34
EGYPT
REF6
P10
GREEK
REF7
P20
GREEK
REF8
P230
45 (ref 1)
still
reading…
Mappers
Reducer

12. An example
How much pages are written in latin among the books
in the Ancient Library of Alexandria?
11
GREEK
REF2
P128
still
reading…
EGYPTIA
N
LATIN
REF4
P73
LATIN
REF5
P34
EGYPT
REF6
P10
GREEK
REF7
P20
GREEK
REF8
P230
GREEK
45 (ref 1)
Mappers
Reducer

13. An example
How much pages are written in latin among the books
in the Ancient Library of Alexandria?
LATIN
pages 73
EGYPTIA
N
12
LATIN
REF4
P73
LATIN
REF5
P34
GREEK
REF7
P20
GREEK
REF8
P230
LATIN
pages 34
45 (ref 1)
+73 (ref 4)
+34 (ref 5)
Mappers
Reducer

14. An example
How much pages are written in latin among the books
in the Ancient Library of Alexandria?
GREEK
GREEK
13
GREEK
REF7
P20
GREEK
REF8
P230
idle…
45 (ref 1)
+73 (ref 4)
+34 (ref 5)
Mappers
Reducer

15. An example
How much pages are written in latin among the books
in the Ancient Library of Alexandria?
idle…
idle…
idle…
14
45 (ref 1)
+73 (ref 4)
+34 (ref 5)
152 TOTAL
Mappers
Reducer

16. Hadoop architecture
15
head node

17. FI-WARE proposal:
Cosmos Big Data
16

18. What is Cosmos?
• Cosmos is Telefónica's Big Data platform
• Dynamic creation of private computing clusters as a
17
service
• Infinity, a cluster for persistent storage
• Cosmos is Hadoop ecosystem-based
• HDFS as its distributed file system
• Hadoop core as its MapReduce engine
• HiveQL and Pig for querying the data
• Oozie as remote MapReduce jobs and Hive launcher
• Plus other proprietary features
• Infinity protocol (secure WebHDFS)
• Cygnus, an injector for context data coming from Orion
CB

19. Cosmos architecture
18

20. What can be done with Cosmos?
19
What
Locally
(ssh’ing into the Head
Node)
Remotely
(connecting your app)
Clusters operation Cosmos CLI REST API
I/O operation ‘hadoop fs’ command
REST API
(WebHDFS, HttpFS,
Infinity protocol)
Querying tools
(basic analysis)
Hive CLI JDBC, Thrift*
MapReduce
(advanced analysis)
‘hadoop jar’
command
Oozie REST API

21. Clusters operation:
Getting your own roman
legion
20

22. Using the RESTful API (1)
21

23. Using the RESTful API (2)
22

24. Using the RESTful API (3)
23

25. Using the Python CLI
24
• Creating a cluster
$ cosmos create --name <STRING> --size <INT>
• Listing all the clusters
$ cosmos list
• Showing a cluster details
$ cosmos show <CLUSTER_ID>
• Connecting to the Head Node of a cluster
$ cosmos ssh <CLUSTER_ID>
• Terminating a cluster
$ cosmos terminate <CLUSTER_ID>
• Listing available services
$ cosmos list-services
• Creating a cluster with specific services
$ cosmos create --name <STRING> --size <INT>
--services <SERVICES_LIST>

26. How to exploit the data:
Commanding your
roman legion
25

27. 1. Hadoop filesystem commands
26
• Hadoop general command
$ hadoop
• Hadoop file system subcommand
$ hadoop fs
• Hadoop file system options
$ hadoop fs –ls
$ hadoop fs –mkdir <hdfs-dir>
$ hadoop fs –rmr <hfds-file>
$ hadoop fs –cat <hdfs-file>
$ hadoop fs –put <local-file> <hdfs-dir>
$ hadoop fs –get <hdfs-file> <local-dir>
• http://hadoop.apache.org/docs/current/hadoop-project-dist/
hadoop-common/CommandsManual.html

28. 2. WebHDFS/HttpFS REST API
27
• List a directory
GET http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=LISTSTATUS
• Create a new directory
PUT http://<HOST>:<PORT>/<PATH>?op=MKDIRS[&permission=<OCTAL>]
• Delete a file or directory
DELETE http://<host>:<port>/webhdfs/v1/<path>?op=DELETE
[&recursive=<true|false>]
• Rename a file or directory
PUT
http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=RENAME&destination=<PATH>
• Concat files
POST
http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=CONCAT&sources=<PATHS>
• Set permission
PUT http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=SETPERMISSION
[&permission=<OCTAL>]
• Set owner
PUT http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=SETOWNER
[&owner=<USER>][&group=<GROUP>]

29. 2. WebHDFS/HttpFS REST API (cont.)
• Create a new file with initial content (2 steps operation)
PUT http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=CREATE
[&overwrite=<true|false>][&blocksize=<LONG>][&replication=<SHORT>]
[&permission=<OCTAL>][&buffersize=<INT>]
HTTP/1.1 307 TEMPORARY_REDIRECT
Location: http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=CREATE...
Content-Length: 0
PUT -T <LOCAL_FILE>
http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=CREATE...
28
• Append to a file (2 steps operation)
POST
http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=APPEND[&buffersize=<INT>]
HTTP/1.1 307 TEMPORARY_REDIRECT
Location: http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=APPEND...
Content-Length: 0
POST -T <LOCAL_FILE>
http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=APPEND...

30. 2. WebHDFS/HttpFS REST API (cont.)
• Open and read a file (2 steps operation)
GET http://<HOST>:<PORT>/webhdfs/v1/<PATH>?op=OPEN
[&offset=<LONG>][&length=<LONG>][&buffersize=<INT>]
HTTP/1.1 307 TEMPORARY_REDIRECT
Location: http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=OPEN...
Content-Length: 0
GET http://<DATANODE>:<PORT>/webhdfs/v1/<PATH>?op=OPEN...
• http://hadoop.apache.org/docs/current/hadoop-project-dist/
hadoop-hdfs/WebHDFS.html
• HttpFS does not redirect to the Datanode but to the HttpFS
server, hidding the Datanodes (and saving tens of public IP
addresses)
• The API is the same
• http://hadoop.apache.org/docs/current/hadoop-hdfs-httpfs/
29
index.html

31. 3. Local Hive CLI
• Hive is a querying tool
• Queries are expresed in HiveQL, a SQL-like
language
• https://cwiki.apache.org/confluence/display/Hive/Language
30
Manual
• Hive uses pre-defined MapReduce jobs for
• Column selection
• Fields grouping
• Table joining
• …
• All the data is loaded into Hive tables

32. 3. Local Hive CLI (cont.)
• Log on to the Master node
• Run the hive command
• Type your SQL-like sentence!
$ hive
$ Hive history file=/tmp/myuser/hive_job_log_opendata_XXX_XXX.txt
hive>select column1,column2,otherColumns from mytable where
column1='whatever' and columns2 like '%whatever%';
Total MapReduce jobs = 1
Launching Job 1 out of 1
Starting Job = job_201308280930_0953, Tracking URL =
http://cosmosmaster-gi:50030/jobdetails.jsp?jobid=job_201308280930_0953
Kill Command = /usr/lib/hadoop/bin/hadoop job -
Dmapred.job.tracker=cosmosmaster-gi:8021 -kill job_201308280930_0953
2013-10-03 09:15:34,519 Stage-1 map = 0%, reduce = 0%
2013-10-03 09:15:36,545 Stage-1 map = 67%, reduce = 0%
2013-10-03 09:15:37,554 Stage-1 map = 100%, reduce = 0%
2013-10-03 09:15:44,609 Stage-1 map = 100%, reduce = 33%
…
31

33. 4. Remote Hive client
• Hive CLI is OK for human-driven testing purposes
• But it is not usable by remote applications
• Hive has no REST API
• Hive has several drivers and libraries
• JDBC for Java
• Python
• PHP
• ODBC for C/C++
• Thrift for Java and C++
• https://cwiki.apache.org/confluence/display/Hive/HiveClie
32
nt
• A remote Hive client usually performs:
• A connection to the Hive server (TCP/10000)
• The query execution

34. 4. Remote Hive client – Get a connection
33
private Connection getConnection(
String ip, String port, String user, String password) {
try {
// dynamically load the Hive JDBC driver
Class.forName("org.apache.hadoop.hive.jdbc.HiveDriver");
} catch (ClassNotFoundException e) {
System.out.println(e.getMessage());
return null;
} // try catch
try {
// return a connection based on the Hive JDBC driver, default DB
return DriverManager.getConnection("jdbc:hive://" + ip + ":" +
port + "/default?user=" + user + "&password=" + password);
} catch (SQLException e) {
System.out.println(e.getMessage());
return null;
} // try catch
} // getConnection
https://github.com/telefonicaid/fiware-connectors/tree/develop/resources/hive-basic-client

35. 4. Remote Hive client – Do the query
34
private void doQuery() {
try {
// from here on, everything is SQL!
Statement stmt = con.createStatement();
ResultSet res = stmt.executeQuery("select column1,column2," +
"otherColumns from mytable where column1='whatever' and " +
"columns2 like '%whatever%'");
// iterate on the result
while (res.next()) {
String column1 = res.getString(1);
Integer column2 = res.getInteger(2);
// whatever you want to do with this row, here
} // while
// close everything
res.close(); stmt.close(); con.close();
} catch (SQLException ex) {
System.exit(0);
} // try catch
} // doQuery
https://github.com/telefonicaid/fiware-connectors/
tree/develop/resources/hive-basic-client

36. 4. Remote Hive client – Plague Tracker demo
https://github.com/telefonicaid/fiware-connectors/tree/develop/resources/plague-tracker
35

37. 5. MapReduce applications
• MapReduce applications are commonly written in Java
• Can be written in other languages through Hadoop Streaming
• They are executed in the command line
$ hadoop jar <jar-file> <main-class> <input-dir> <output-dir>
• A MapReduce job consists of:
• A driver, a piece of software where to define inputs, outputs, formats,
etc. and the entry point for launching the job
• A set of Mappers, given by a piece of software defining its behaviour
• A set of Reducers, given by a piece of software defining its behaviour
36
• There are 2 APIS
• org.apache.mapred  old one
• org.apache.mapreduce  new one
• Hadoop is distributed with MapReduce examples
• [HADOOP_HOME]/hadoop-examples.jar

38. 5. MapReduce applications – Map
/* org.apache.mapred example */
public static class MapClass 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 {
/* use the input value, the input key is the offset within the
file and it is not necessary in this example */
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
/* iterate on the string, getting each word */
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
/* emit an output (key,value) pair based on the word and 1 */
output.collect(word, one);
37
} // while
} // map
} // MapClass

39. 5. MapReduce applications – Reduce
/* org.apache.mapred example */
public static class ReduceClass extends MapReduceBase
implements Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
38
int sum = 0;
/* iterate on all the values and add them */
while (values.hasNext()) {
sum += values.next().get();
} // while
/* emit an output (key,value) pair based on the word and its count */
output.collect(key, new IntWritable(sum));
} // reduce
} // ReduceClass

40. 5. MapReduce applications – Driver
39
/* org.apache.mapred example */
package my.org
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class WordCount {
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MapClass.class);
conf.setCombinerClass(ReduceClass.class);
conf.setReducerClass(ReduceClass.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
} // main
} // WordCount

41. 6. Launching tasks with Oozie
• Oozie is a workflow scheduler system to manage Hadoop
jobs
• Java map-reduce
• Pig and Hive
• Sqoop
• System specific jobs (such as Java programs and shell scripts)
• Oozie Workflow jobs are Directed Acyclical Graphs (DAGs)
40
of actions.
• Writting Oozie applications is about including in a package
• The MapReduce jobs, Hive/Pig scritps, etc (exeutable code)
• A Workflow
• Parameters for the Workflow
• Oozie can be use locally or remotely
• https://oozie.apache.org/docs/4.0.0/index.html#Developer_Do
cumentation

42. 6. Launching tasks with Oozie – Java client
OozieClient client = new OozieClient("http://130.206.80.46:11000/oozie/");
// create a workflow job configuration and set the workflow application path
Properties conf = client.createConfiguration();
conf.setProperty(OozieClient.APP_PATH, "hdfs://cosmosmaster-gi:
41
8020/user/frb/mrjobs");
conf.setProperty("nameNode", "hdfs://cosmosmaster-gi:8020");
conf.setProperty("jobTracker", "cosmosmaster-gi:8021");
conf.setProperty("outputDir", "output");
conf.setProperty("inputDir", "input");
conf.setProperty("examplesRoot", "mrjobs");
conf.setProperty("queueName", "default");
// submit and start the workflow job
String jobId = client.run(conf);
// wait until the workflow job finishes printing the status every 10 seconds
while (client.getJobInfo(jobId).getStatus() == WorkflowJob.Status.RUNNING) {
System.out.println("Workflow job running ...");
Thread.sleep(10 * 1000);
} // while
System.out.println("Workflow job completed");

43. Useful references
42
• Hive resources:
• HiveQL language  https://cwiki.apache.org/confluence/display/Hive/LanguageManual
• How to create Hive clients 
https://cwiki.apache.org/confluence/display/Hive/HiveClient
• Hive client example  https://github.com/telefonicaid/fiware-connectors/
tree/develop/resources/hive-basic-client
• Plague Tracker demo  https://github.com/telefonicaid/fiware-livedemoapp/
tree/master/cosmos/plague-tracker
• Plague Tracker instance  http://130.206.81.65/plague-tracker/
• Hadoop filesystem commands:
• http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/
CommandsManual.html
• WebHDFS and HttpFS REST APIs:
• http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/WebHDFS.html
• http://hadoop.apache.org/docs/current/hadoop-hdfs-httpfs/index.html
• Oozie
• https://oozie.apache.org/docs/4.0.0/index.html#Developer_Documentation

44. Cosmos place in FI-WARE:
Typical scenarios
43

45. General IoT platform
SENSOR 2 THINGS
IoT Backend
Device Management
44
CKAN
DATA
PROCESSING
COSMOS
(BIG DATA)
DATA
QUERYING
SUBS
OPEN DATA
CONTEXT BROKER
measures / commands
IoT/Sensor Open Data
T-T
Accounting & Payment & Billing
IDM & Auth
SHORT TERM
HISTORIC
REAL TIME
PRCSSING
BI
ETL
BLNK
RULES
DEFINITION
BLNK
OPERATIONAL
DASHBOARD
KPI GOVERNANCE OPEN DATA PORTALS
CEP
GIS
Service
Orchrestation
Context
Adapters
City
Services

46. Real time context data persistence (architecture)
https://github.com/telefonicaid/fiware-connectors/tree/develop/flume
https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/How_to_persist_Orion_data_in_Cosmos
45

47. Real time context data persistence (detail)
46

48. Real time context data persistence (examples)
• Information coming from city sensors
• Presence  map gradients, aglomerations…
• Services usage  distributions, top users (if
available), top POIs, unused resources…
• Information generated by smartphones
• Geolocation  routes, map gradients,
47
aglomerations…
• Issues reporting  top neighbourhooods in
incidents, crimilality, noises, garbage, plagues…
• Any other real time information
• Depending on your app, this could be product likes,
product consumption, user-2-user feedback… 
recommendations, advertisement…

49. Roadmap:
More functionalities and
integrations
48

50. Roadmap
• Integrate the clusters creation with the cloud portal
• No more REST API work
• Streaming analysis capabilities
• Not all the analysis can wait for a batch processing
• Geolocation analysis capabilities
• An important source of data nowadays
49
• Integrate with CKAN
• As a source of batch data
• Integrate with the Marketplace
• Selling datasets
• Selling analysis results
• Selling applications and algorithms

51. fiware-lab-help@lists.fi-ware.org
francisco.romerobueno@telefonica.co
m
50

52. Thanks !

http://fi-ppp.eu

http://fi-ware.eu

Follow @Fiware on Twitter!
51