Code cleanup

Author: gianlucaborello

examples/add_notification_email.py

@@ -32,6 +32,6 @@
 # Return the result
 #
 if res[0]:
-	print 'Recipient added successfully'
+    print 'Recipient added successfully'
 else:
-	print res[1]
+    print res[1]

examples/create_alert.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 #
 # This script shows how to use the create_alert() call to create the following
-# Sysdig Cloud alert: 'send an email notification when the CPU of any tomcat 
+# Sysdig Cloud alert: 'send an email notification when the CPU of any tomcat
 # process running on any of the instrumented machines goes over 80%'
 #
 #
@@ -29,24 +29,23 @@
 # Create the alert.
 #
 res = sdclient.create_alert('tomcat cpu > 80% on any host',  # Alert name.
-	'this alert was automatically created using the python Sysdig Cloud library', # Alert description.
-	6, # Syslog-encoded severity. 6 means 'info'.
-	60, # The alert will fire if the condition is met for at least 60 seconds.
-	'avg(cpu.used.percent) > 80', # The condition.
-	['host.mac', 'proc.name'], # Segmentation. We want to check this metric for every process on every machine.
-	'ANY', # in case there is more than one tomcat process, this alert will fire when a single one of them crosses the 80% threshold.
-	'proc.name = "tomcat"', # Filter. We want to receive a notification only if the name of the process meeting the condition is 'tomcat'.
-	['EMAIL'], # Notification target. We want an email to be sent. Alerts email recipients can be defined here: https://app.sysdigcloud.com/#/settings/notifications
-	False) # This alert will be disabled when it's created.
-
+                            'this alert was automatically created using the python Sysdig Cloud library', # Alert description.
+                            6, # Syslog-encoded severity. 6 means 'info'.
+                            60, # The alert will fire if the condition is met for at least 60 seconds.
+                            'avg(cpu.used.percent) > 80', # The condition.
+                            ['host.mac', 'proc.name'], # Segmentation. We want to check this metric for every process on every machine.
+                            'ANY', # in case there is more than one tomcat process, this alert will fire when a single one of them crosses the 80% threshold.
+                            'proc.name = "tomcat"', # Filter. We want to receive a notification only if the name of the process meeting the condition is 'tomcat'.
+                            ['EMAIL'], # Notification target. We want an email to be sent. Alerts email recipients can be defined here: https://app.sysdigcloud.com/#/settings/notifications
+                            False) # This alert will be disabled when it's created.
 
 #
 # Validate a print the results.
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
-print res
+print res

examples/create_dashboard.py

@@ -29,32 +29,32 @@
 # Create the new dashboard, apllying to cassandra in production
 #
 res = sdclient.create_dashboard_from_view("API test - cassandra in prod", # The name we're giving to the new dashboard.
-	"Overview by Process", # The view we're copying.
-	'kubernetes.namespace.name=prod and proc.name = cassandra') # the filter specifying what this dasboard appies to.
-																# Remember that here you can use combinations of any
-																# segmentation criteria that you find in the Sysdig 
-																# Cloud explore page.
+                                          "Overview by Process", # The view we're copying.
+                                          "kubernetes.namespace.name=prod and proc.name = cassandra") # the filter specifying what this dasboard appies to.
+                                                                                                      # Remember that here you can use combinations of any
+                                                                                                      # segmentation criteria that you find in the Sysdig
+                                                                                                      # Cloud explore page.
 #
 # Check the result
 #
 if res[0]:
-	print 'Dashboard created successfully'
+    print 'Dashboard created successfully'
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 #
 # Make a Copy the just created dasboard, this time applying it to cassandra in the dev namespace
 #
 res = sdclient.create_dashboard_from_dashboard("API test - cassandra in dev", # The name we're giving to the new dashboard.
-	"API test - cassandra in prod", # The view we're copying.
-	'kubernetes.namespace.name=dev and proc.name = cassandra') # the filter specifying what this dasboard appies to.
+                                               "API test - cassandra in prod", # The view we're copying.
+                                               "kubernetes.namespace.name=dev and proc.name = cassandra") # the filter specifying what this dasboard appies to.
 
 #
 # Check the result
 #
 if res[0]:
-	print 'Dashboard copied successfully'
+    print 'Dashboard copied successfully'
 else:
-	print res[1]
+    print res[1]
 

examples/get_data_advanced.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 #
-# This script shows an advanced Sysdig Cloud data request that leverages 
+# This script shows an advanced Sysdig Cloud data request that leverages
 # filtering and segmentation.
 #
 # The request returns the last 10 minutes of CPU utilization for all of the
@@ -28,20 +28,20 @@
 sdclient = SdcClient(sdc_token)
 
 #
-# Prepare the metrics list. 
+# Prepare the metrics list.
 #
 metrics = [
     # The first metric we request is the container name. This is a segmentation
-    # metric, and you can tell by the fact that we don't specify any aggregation 
+    # metric, and you can tell by the fact that we don't specify any aggregation
     # criteria. This entry tells Sysdig Cloud that we want to see the CPU
     # utilization for each container separately.
     {"id": "container.name"},
     # The second metric we reuest is the CPU. We aggregate it as an average.
     {"id": "cpu.used.percent",
-        "aggregations": {
-            "time": "avg",
-            "group": "avg"
-        }
+     "aggregations": {
+         "time": "avg",
+         "group": "avg"
+     }
     }
 ]
 
@@ -53,20 +53,20 @@
 #
 # Fire the query.
 #
-res = sdclient.get_data(metrics, # metrics list 
-    -600,   # start_ts = 600 seconds ago
-    0,      # end_ts = now
-    60,     # 1 data point per minute
-    filter, # The filter
-    'container') # The source for our metrics is the container
+res = sdclient.get_data(metrics, # metrics list
+                        -600,   # start_ts = 600 seconds ago
+                        0,      # end_ts = now
+                        60,     # 1 data point per minute
+                        filter, # The filter
+                        'container') # The source for our metrics is the container
 
 #
 # Show the result!
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 print data

examples/get_data_simple.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python
 #
-# This script shows the basics of getting data out of Sysdig Cloud by crating a 
+# This script shows the basics of getting data out of Sysdig Cloud by crating a
 # very simple request that has no filter an no segmentation.
 #
-# The request queries for the average CPU across all of the instrumented hosts for 
+# The request queries for the average CPU across all of the instrumented hosts for
 # the last 10 minutes, with 1 minute data granularity
 #
 
@@ -27,33 +27,33 @@
 sdclient = SdcClient(sdc_token)
 
 #
-# Prepare the metrics list. In this case, we have just one metric: cpu.used.percent, 
+# Prepare the metrics list. In this case, we have just one metric: cpu.used.percent,
 # without segmentation.
 # The data will be aggregated as average both in time and across the hosts.
 #
 metrics = [
     {"id": "cpu.used.percent",
-    "aggregations": {
-        "time": "avg",
-        "group": "avg"
-    }
-}]
+     "aggregations": {
+         "time": "avg",
+         "group": "avg"
+     }
+    }]
 
 #
 # Fire the query.
 #
-res = sdclient.get_data(metrics, # metrics list 
-    -600,   # start_ts = 600 seconds ago
-    0,      # end_ts = now
-    60)     # 1 data point per minute
+res = sdclient.get_data(metrics, # metrics list
+                        -600,   # start_ts = 600 seconds ago
+                        0,      # end_ts = now
+                        60)     # 1 data point per minute
 
 #
 # Show the result
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 print data

examples/list_alerts.py

@@ -31,10 +31,10 @@
 # Show the list of alerts
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 for alert in data['alerts']:
     print 'enabled: %s, name: %s' % (str(alert['enabled']), alert['name'])

examples/list_dashboards.py

@@ -31,10 +31,10 @@
 # Show the list of alerts
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 for db in data['dashboards']:
     print "Name: %s, # Charts: %d" % (db['name'], len(db['items']))

examples/list_hosts.py

@@ -24,7 +24,7 @@
 sdclient = SdcClient(sdc_token)
 
 #
-# Prepare the query's metrics list. In this case, we have just one metric: 
+# Prepare the query's metrics list. In this case, we have just one metric:
 # host.hostName. This is a 'key' metric, and we don't include any number metric.
 # Essentially, we create an 'enumeration' of hostnames.
 #
@@ -35,18 +35,18 @@
 # Note: there's no sampling time. This means that we're requesting the result to
 #       come as a single sample.
 #
-res = sdclient.get_data(metrics, # metrics list 
-    -600,	# cover the last 300 seconds...
-    0,		# ... ending now...
-    600)	# ... with just one 300s sample
+res = sdclient.get_data(metrics, # metrics list
+                        -600,   # cover the last 300 seconds...
+                        0,      # ... ending now...
+                        600)    # ... with just one 300s sample
 
 #
 # Show the results!
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 print data

examples/post_event.py

@@ -21,7 +21,7 @@
 
 severity = 6
 if len(sys.argv) < 4:
-	severity = int(sys.argv[4])
+    severity = int(sys.argv[4])
 
 #
 # Instantiate the SDC client
@@ -37,6 +37,6 @@
 # Return the result
 #
 if res[0]:
-	print 'Event Posted Successfully'
+    print 'Event Posted Successfully'
 else:
-	print res[1]
+    print res[1]

examples/print_data_retention_info.py

@@ -31,9 +31,9 @@
 # Show the list of retention intervals
 #
 if res[0]:
-	data = res[1]
+    data = res[1]
 else:
-	print res[1]
-	sys.exit(0)
+    print res[1]
+    sys.exit(0)
 
 print data['agents']