Big Data Analysis

Similarly to what has been described in section Publication of context information as Open Data, Cygnus software allows to store all the selected data published in the Context Broker in an HDFS based storage. This allows having a long term historic database of context information that can be used for later analysis, for instance implementing map & reducing algorithm or performing queries over big data through Hive.

Similarly to what has been described in the section Publication of context information as Open Data, the Cygnus component can be configured to gather data from the Context Broker and store it in HDFS. The configuration in this case should include the Cosmos Namenode endpoints, the service port, user’s credential, the Cosmos API used (webhdfs, infinity, httpfs), the type of attribute  and endpoint of Hive server.

Once the Context Data has been stored, it is possible to use the Big Data GE to process it either with a map & reduce application or with Hive. It is of course also possible to process in the Big Data GEs other big datasets, either by themselves or in combination with context information.

A typical example would be to analyse massive information gathered from sensors in a city over a long period of type. All the data would have been gathered through Context Broker and Cygnus and stored in Big Data for a long period of time. In order to analyse the data, a few steps should be followed (the examples in this whitepaper are based on a global and shared instance of Cosmos Big Data GE):

Browse the Cosmos portal ( Use an already registered user in FI-LAB to create a Cosmos account. The details of your account will be given once registered, typically:

  • Cosmos username: if your FI-LAB username is<my_user>, your cosmos username will be <my_user>. This will give you a Unix-like account in the head node of the global instance, being your user space /home/<my_user>/.
  • Cosmos HDFS space: Apart from your Unix-like user space in the Head Node, you will have a HDFS space located at the entire cluster, it will be /user/<my_user>/

Now you should be ready to login into the head node of the global instance of Cosmos in FI-LAB, simply using your FI-LAB credentials:

[remote-vm]$ export COSMOS_USER= // this is not strictly necessary, junt in order the example commands can be copied&pasted
 [remote-vm]$ ssh $

Once logged, you can have access to your HDFS space by using the Hadoop file system commands:

[head-node]$ export COSMOS_USER= // this is not strictly necessary, junt in order the example commands can be copied&pasted
 [head-node]$ hadoop fs -ls /user/$COSMOS_USER // lists your HDFS space
 [head-node]$ hadoop fs -mkdir /user/$COSMOS_USER/new_folder // creates a new directory called "new_folder" under your HDFS space


Apart from using the context data stored, you can upload your own data to your HDFS space using the Hadoop file system commands. This can be only done after logging into the Head Node, and allows uploading Unix-like local files placed in the Head node:

[head-node]$ echo "long time ago, in a galaxy far far away…" > unstructured_data.txt
 [head-node]$ hadoop fs -mkdir /user/$COSMOS_USER/input/unstructured/
 [head-node]$ hadoop fs -put unstructured_data.txt /user/$COSMOS_USER/input/unstructured/

However, using the WebHDFS/HttpFS RESTful API will allow you to upload files existing outside the global instance of Cosmos in FI-LAB. The following example uses HttpFS instead of WebHDFS (uses the TCP/14000 port instead of TCP/50070), and curl is used as HTTP client (but your applications should implement your own HTTP client):

[remote-vm]$ curl -i -X PUT "$COSMOS_USER/input_data?op=MKDIRS&$COSMOS_USER"
 [remote-vm]$ curl -i -X PUT "$COSMOS_USER/input_data/unstructured_data.txt?op=CREATE&$COSMOS_USER"
 [remote-vm]$ curl -i -X PUT -T unstructured_data.txt –header "content-type: application/octet-stream"$COSMOS_USER/input_data/unstructured_data.txt?op=CREATE&$COSMOS_USER&data=true

As you can see, the data uploading is a two-step operation, as stated in the WebHDFS specification: the first invocation of the API talks directly with the Head Node, specifying the new file creation and its name; then the Head Node sends a temporary redirection response, specifying the data node among all the existing ones in the cluster where the data has to be stored, which is the endpoint of the second step. Nevertheless, the HttpFS gateway implements the same API but its internal behaviour changes, making the redirection to point to the head node itself.

If the data you have uploaded to your HDFS space is a CSV-like file, i.e. a structured file containing lines of data fields separated by a common character, then you can use Hive to query the data:

[head-node]$ echo "luke,tatooine,jedi,25" >> structured_data.txt
 [head-node]$ echo "leia,alderaan,politician,25" >> structured_data.txt
 [head-node]$ echo "solo,corellia,pilot,32" >> structured_data.txt
 [head-node]$ echo "yoda,dagobah,jedi,275" >> structured_data.txt
 [head-node]$ echo "vader,tatooine,sith,50" >> structured_data.txt
 [head-node]$ hadoop fs -mkdir /user/$COSMOS_USER/input/structured/
 [head-node]$ hadoop fs -put structured_data.txt /user/$COSMOS_USER/input/structured/

A Hive table can be created, which is like a SQL table. Log into the Head Node, invoke the Hive CLI and type the following in order to create a Hive table:

[head-node]$ hive
 hive> create external table <my_user>_star_wars (name string, planet string, profession string, age int) row format delimited fields terminated by ',' location '/user/<my_user>/input/structured/';

These Hive tables can be queried locally, by using the Hive CLI as well:

[head-node]$ hive
 hive> select * from <my_user>_star_wars; // or any other SQL-like sentence, properly called HiveQL

Or remotely, by developing a Hive client (typically, using JDBC, but there are some other options for other non-Java programming languages) connecting Several pre-loaded MapReduce examples can be found in every Hadoop distribution. You can list them by ssh'ing the head node and commanding Hadoop:

[head-node]$ hadoop jar /usr/lib/hadoop-0.20/hadoop-examples.jar

For instance, you can run the word count example (this is also known as the "hello world" of Hadoop) by typing:

[head-node]$ hadoop jar /usr/lib/hadoop-0.20/hadoop-examples.jar wordcount /user/$COSMOS_USER/input/unstructured/unstructured_data.txt /user/$COSMOS_USER/output/

Please observe the output HDFS folder is automatically created. The MapReduce results are stored in HDFS. You can download them to your Unix user space within the head node by doing:

[head-node]$ hadoop fs -getmerge /user/$COSMOS_USER/output /home/$COSMOS_USER/count_result.txt

You can also download any HDFS file to you home user in the head node by doing:

[head-node]$ hadoop fs -get /user/$COSMOS_USER/structured/structured_data.txt /home/$COSMOS_USER/

If you want to download the HDFS file directly to a remote machine, you must use the WebHDFS/HttpFS RESTful API:

[remote-vm]$ curl -i -L "$COSMOS_USER/structured/structured_data.txt?op=OPEN&$COSMOS_USER"

If you want to start experimenting and doing hands-on work, have a look at: