Difference between revisions of "Ceilometer/AlarmEvaluatorJuno"
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| − | + | ==Statistics over sliding windows== | |
| + | Two approaches to improve the alarm evaluator were discussed at the summit. | ||
| + | |||
| + | ===Map-reduce approach=== | ||
| + | [[File:EvaluatorMapReduce.png|frameless|Map reduce approach]] | ||
| + | |||
| + | ===Increment/decrement approach=== | ||
| + | [[File:EvaluatorIncDecrement.png|frameless|Map reduce approach]] | ||
| + | |||
Statistics where increments/decrements are possible: | Statistics where increments/decrements are possible: | ||
* count, | * count, | ||
| Line 5: | Line 13: | ||
* average, | * average, | ||
* stddev | * stddev | ||
| + | |||
Statistics with special implementation: | Statistics with special implementation: | ||
* min/max : needs O(log n) space (probably in DB), where n = window size | * min/max : needs O(log n) space (probably in DB), where n = window size | ||
* percentile : classical statistics query | * percentile : classical statistics query | ||
* cardinality : approximate algorithm, extremely efficient | * cardinality : approximate algorithm, extremely efficient | ||
| + | |||
| + | Map-reduce approach covers all these statistics. | ||
Revision as of 19:02, 16 May 2014
Statistics over sliding windows
Two approaches to improve the alarm evaluator were discussed at the summit.
Map-reduce approach
Increment/decrement approach
Statistics where increments/decrements are possible:
- count,
- sum,
- average,
- stddev
Statistics with special implementation:
- min/max : needs O(log n) space (probably in DB), where n = window size
- percentile : classical statistics query
- cardinality : approximate algorithm, extremely efficient
Map-reduce approach covers all these statistics.
