Remove abstraction in the percentiles aggregation. #5859
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We initially added abstraction in the percentiles aggregation in order to be able to plug in different percentiles estimators. However, only one of the 3 options that we looked into proved useful and I don't see us adding new estimators in the future. Moreover, because of this, we let the parser put unknown parameters into a hash table in case these parameters would have meaning for a specific percentiles estimator impl. But this makes parsing error-prone: for example a user reported that his percentiles aggregation reported extremely high (in the order of several millions while the maximum field value was `5`), and the reason was that he had a typo and had written `fields` instead of `field`. As a consequence, the percentiles aggregation used the parent value source which was a timestamp, hence the large values. Parsing would now barf in case of an unknown parameter.
jpountz
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Apr 24, 2014
We initially added abstraction in the percentiles aggregation in order to be able to plug in different percentiles estimators. However, only one of the 3 options that we looked into proved useful and I don't see us adding new estimators in the future. Moreover, because of this, we let the parser put unknown parameters into a hash table in case these parameters would have meaning for a specific percentiles estimator impl. But this makes parsing error-prone: for example a user reported that his percentiles aggregation reported extremely high (in the order of several millions while the maximum field value was `5`), and the reason was that he had a typo and had written `fields` instead of `field`. As a consequence, the percentiles aggregation used the parent value source which was a timestamp, hence the large values. Parsing would now barf in case of an unknown parameter. Close #5859
mikemccand
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Apr 24, 2014
We initially added abstraction in the percentiles aggregation in order to be able to plug in different percentiles estimators. However, only one of the 3 options that we looked into proved useful and I don't see us adding new estimators in the future. Moreover, because of this, we let the parser put unknown parameters into a hash table in case these parameters would have meaning for a specific percentiles estimator impl. But this makes parsing error-prone: for example a user reported that his percentiles aggregation reported extremely high (in the order of several millions while the maximum field value was `5`), and the reason was that he had a typo and had written `fields` instead of `field`. As a consequence, the percentiles aggregation used the parent value source which was a timestamp, hence the large values. Parsing would now barf in case of an unknown parameter. Close elastic#5859
clintongormley
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>enhancement
:Analytics/Aggregations
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We initially added abstraction in the percentiles aggregation in order to be
able to plug in different percentiles estimators. However, only one of the 3
options that we looked into proved useful and I don't see us adding new
estimators in the future.
Moreover, because of this, we let the parser put unknown parameters into a hash
table in case these parameters would have meaning for a specific percentiles
estimator impl. But this makes parsing error-prone: for example a user reported
that his percentiles aggregation reported extremely high (in the order of
several millions while the maximum field value was
5), and the reason was thathe had a typo and had written
fieldsinstead offield. As a consequence,the percentiles aggregation used the parent value source which was a timestamp,
hence the large values. Parsing would now barf in case of an unknown parameter.