QA:Testcase OpenStack ceilometer mongodb store

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Line 24: Line 24:
 
   { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.user", "name" : "_id_" }
 
   { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.user", "name" : "_id_" }
 
   { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.project", "name" : "_id_" }
 
   { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.project", "name" : "_id_" }
 +
 +
Check the number of samples currently stored:
 +
 +
  > db.meter.stats().count
 +
  588
  
 
Show the cpu_util samples retrieved earlier via the ceilometer CLI, sorted in ascending time-order:
 
Show the cpu_util samples retrieved earlier via the ceilometer CLI, sorted in ascending time-order:

Latest revision as of 16:03, 2 April 2013

Ceilometer uses mongoDB by default to store metering data (though storage drivers are also supported for sqlalchemy and more recently for HBase too).

In this test we use the mongo client to explore the metering store:

 mongo ceilometer

Show the available collections:

 > show collections
 meter
 project
 resource
 system.indexes
 user

Note the default indices:

 > db.system.indexes.find()
 { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.resource", "name" : "_id_" }
 { "v" : 1, "key" : { "user_id" : 1, "source" : 1 }, "ns" : "ceilometer.resource", "name" : "resource_idx" }
 { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.meter", "name" : "_id_" }
 { "v" : 1, "key" : { "resource_id" : 1, "user_id" : 1, "counter_name" : 1, "timestamp" : 1, "source" : 1 },
   "ns" : "ceilometer.meter", "name" : "meter_idx" }
 { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.user", "name" : "_id_" }
 { "v" : 1, "key" : { "_id" : 1 }, "ns" : "ceilometer.project", "name" : "_id_" }

Check the number of samples currently stored:

 > db.meter.stats().count
 588

Show the cpu_util samples retrieved earlier via the ceilometer CLI, sorted in ascending time-order:

 > db.meter.find({'counter_name': 'cpu_util', 'resource_id' : $INSTANCE_ID}).sort({'timestamp': 1})
 { "_id" : ObjectId("515af62a1a63121d32eaa00c"), "counter_name" : "cpu_util", "user_id" : ...

Do some simple map-reduce to determine the average CPU utilization across all instances. First define javascript functions to map the counter volumes and calculate the average respectively:

 > var mapVolumes = function() {
       emit(this.resource_id, this.counter_volume);
   }
 > var reduceAverage = function (key, values) {
       var total = 0;
       for (var i = 0; i < values.length; i++) {
           total += values[i];
       }
       return total / values.length;
   }

Then apply this to all cpu_util counters, saving the result in the 'average_util' collection:

 
  > db.meter.mapReduce(mapVolumes, reduceAverage, { out: { merge: 'average_util' }, query: {'counter_name': 'cpu_util'}});
  {
	"result" : "average_util",
	"timeMillis" : 110,
	"counts" : {
		"input" : 22,
		"emit" : 22,
		"reduce" : 1,
		"output" : 1
	},
	"ok" : 1,
  }
  

Display this temporary collection:

 > db.average_util.find()
 { "_id" : "738b4794-d5ec-48d0-b669-d31d368986ed", "value" : 5.473252670153079 }