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Posted to commits@ambari.apache.org by av...@apache.org on 2016/03/28 22:16:29 UTC
ambari git commit: AMBARI-15607 : HDFS Alerts for AMS Throw
Unsupported operand type error (avijayan)
Repository: ambari
Updated Branches:
refs/heads/branch-2.2 30b7fac65 -> 55fe5c494
AMBARI-15607 : HDFS Alerts for AMS Throw Unsupported operand type error (avijayan)
Project: http://git-wip-us.apache.org/repos/asf/ambari/repo
Commit: http://git-wip-us.apache.org/repos/asf/ambari/commit/55fe5c49
Tree: http://git-wip-us.apache.org/repos/asf/ambari/tree/55fe5c49
Diff: http://git-wip-us.apache.org/repos/asf/ambari/diff/55fe5c49
Branch: refs/heads/branch-2.2
Commit: 55fe5c494ac50f28639eebd61b1dd94e022915f7
Parents: 30b7fac
Author: Aravindan Vijayan <av...@hortonworks.com>
Authored: Mon Mar 28 13:16:16 2016 -0700
Committer: Aravindan Vijayan <av...@hortonworks.com>
Committed: Mon Mar 28 13:16:24 2016 -0700
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.../2.1.0.2.0/package/alerts/alert_metrics_deviation.py | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
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http://git-wip-us.apache.org/repos/asf/ambari/blob/55fe5c49/ambari-server/src/main/resources/common-services/HDFS/2.1.0.2.0/package/alerts/alert_metrics_deviation.py
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diff --git a/ambari-server/src/main/resources/common-services/HDFS/2.1.0.2.0/package/alerts/alert_metrics_deviation.py b/ambari-server/src/main/resources/common-services/HDFS/2.1.0.2.0/package/alerts/alert_metrics_deviation.py
index 741c201..9a122fa 100644
--- a/ambari-server/src/main/resources/common-services/HDFS/2.1.0.2.0/package/alerts/alert_metrics_deviation.py
+++ b/ambari-server/src/main/resources/common-services/HDFS/2.1.0.2.0/package/alerts/alert_metrics_deviation.py
@@ -312,11 +312,11 @@ def execute(configurations={}, parameters={}, host_name=None):
if not metrics or len(metrics) < 2:
return (RESULT_STATE_UNKNOWN, ["Unable to calculate the standard deviation for {0} datapoints".format(len(metrics))])
- # Filter out points below min threshold
- metrics = [metric for metric in metrics if metric > (minimum_value_threshold * 1000)]
-
- if len(metrics) < 2:
- return (RESULT_STATE_OK, ['No datapoints found above the minimum threshold of {0} seconds'.format(minimum_value_threshold)])
+ if minimum_value_threshold:
+ # Filter out points below min threshold
+ metrics = [metric for metric in metrics if metric > (minimum_value_threshold * 1000)]
+ if len(metrics) < 2:
+ return (RESULT_STATE_OK, ['No datapoints found above the minimum threshold of {0} seconds'.format(minimum_value_threshold)])
mean = calculate_mean(metrics)
stddev = calulate_sample_std_deviation(metrics)