Running the Weather Sensor demo

The Weather Sensor demo compares how long it takes to run Spark SQL queries against aggregated data for a number of weather sensors in various cities.

Using the Weather Sensor demo, you can compare how long it takes to run Spark SQL queries against aggregated data for a number of weather sensors in various cities. For example, you can view reports using different metrics, such as temperature or humidity, and get a daily roll up.

You run customize Spark SQL queries using different metrics and different dates. In addition to querying CQL tables, you time Spark SQL queries against data in DataStax Enterprise File System (DSEFS).

Note: DataStax Demos do not work with LDAP or internal authorization (username/password) enabled.

Prerequisites

Before running the demo, install the following source code and tools if you do not already have them:

Python 2.7:
- Debian and Ubuntu
```
sudo apt-get install python2.7-dev
```
- RedHat or CentOS
```
sudo yum install python27
```
- Mac OS X already has Python 2.7 installed.

pip installer tool:

Debian and Ubuntu
```
sudo apt-get install python-pip
```
RedHat or CentOS
```
sudo yum install python-pip
```
Mac OS X
```
sudo easy_install pip
```

The libsasl2-dev package:

Debian and Ubuntu
```
sudo apt-get install libsasl2-dev
```
RedHat or CentOS
```
sudo yum install cyrus-sasl-lib
```

The required Python packages:

All platforms

sudo pip install pyhs2 six flask cassandra-driver

If you installed DataStax Enterprise using a tarball, set the PATH environment variable to the DataStax Enterprise installation /bin directory.

export PATH=$PATH:installation_location/bin

Start DataStax Enterprise and import data

You start DataStax Enterprise in Spark mode, and then run a script that creates the schema for weather sensor data model. The script also imports aggregated data from CSV files into DSE tables. The script uses the hadoop fs command to put the CSV files into the DSEFS.

Start DataStax Enterprise in Spark mode.
Run the create-and-load CQL script in the demos/weather_sensors/resources directory. On Linux, for example:
```
cd installation_location/demos/weather_sensors && 
bin/create-and-load 
```
The default location of the demos directory depends on the type of installation:
- Package installations: /usr/share/dse/demos
- Tarball installations: installation_location/demos
The output confirms that the script imported the data into CQL and copied files to DSEFS.
```
. . .
10 rows imported in 0.019 seconds.
2590 rows imported in 2.211 seconds.
76790 rows imported in 33.522 seconds.
+ echo 'Copy csv files to Hadoop...'
Copy csv files to Hadoop...
+ dse hadoop fs -mkdir /datastax/demos/weather_sensors/
```

If an error occurs, set the PATH as described in Prerequisites, and retry.

Starting the Spark SQL Thrift server

You start the Spark SQL Thrift server on a specific port to avoid conflicts. Start using your local user account. Do not use sudo.

Start the Spark SQL Thrift server on port 5588. On Linux, for example:

cd installation_location && 
dse spark-sql-thriftserver start --hiveconf hive.server2.thrift.port=5588

Start the web app and query the data

Open another terminal and start the Python service that controls the web interface:
```
cd installation_location/demos/weather_sensors && 
python web/weather.py
```
Open a browser and go to the following URL: http://localhost:8983/
The weather sensors app appears. Select Near Real-Time Reports on the horizontal menu. A drop-down listing weather stations appears:
Select a weather station from the drop-down, view the graph, and select different metrics from the vertical menu on the left side of the page.
On the horizontal menu, click Sample Live Queries, then select a sample script. Click the Spark SQL button, then click Submit.
The time spent loading results using Spark appears.
Note: If you are running the demo on a SearchAnalytics datacenter, port 8983 conflicts with the Search web UI. Change the port in the demos/weather_sensors/web/weather.py to a free port.
```
app.run(host='0.0.0.0', port=8984, threaded=True, debug=True)
```
From the horizontal menu, click Custom Live Queries. Click a Week Day, and then a metric, such as Wind Direction. Click Recalculate Query. The query reflects the selections you made.
From the horizontal menu, click DSEFS Live Queries. Click Submit query. The time spent loading results from DSEFS using Spark SQL appears.

Clean up

To remove all generated data, run the following commands:

cd installation_location/demos/weather_sensors && 
bin/cleanup

To remove the keyspace from the cluster, run the following command:

cqlsh -e "DROP KEYSPACE weathercql;"