====Making an AQL Query====

ARDI uses its own query language named AQL to ask for information.

It returns JSON information that you should be able to use in a variety of applications.

When you're asking for historical information (using the AQL **GETHISTORY** function for example), there are also special functions to return a [[complete dataframe]] of that information in Pandas. Read up on [[reading_historical_data|reading historical data]] for more information.

===Creating a Query===

Once you're connected to the ARDI server, you'll need to create an AQLQuery

<code>
query = ardi.AQLQuery(server)
</code>

===Executing the Query===

You execute the query using the [[AQLQuery_Execute|Execute]] function, passing the actual AQL query you'd like to run.

The response comes back as a parsed JSON object.

<code>
query = ardi.AQLQuery(server)
</code>

===Process the Response===

Now you can process the response however you like.

In the example below, send a query asking for //every point that uses the property 'Temperature'//.

We then go through that list and display each of those temperatures. 

===Complete Code===
<code>
import ardi
import sys

#Setup ARDI Connection
svr = ardi.Server("localhost","cr")

#Connect to ARDI Server
connected = False
try:
    connected = svr.Connect()
except:
    pass

if connected == False:
    print("Unable to Connect to ARDI Server")
    sys.exit(-1)

query = ardi.AQLQuery(svr)
data = query.Execute("'Temperature' PROPERTY ALLPOINTS")

for r in data['results'][0]['value']:
    print(r['name'] + " " + r['propname'] + ": " + str(r['value']))
</code>