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jpountz
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LGTM |
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Thanks @rjernst! |
They have been implemented in https://issues.apache.org/jira/browse/LUCENE-7289. Ranges are implemented so that the accuracy loss only occurs at index time, which means that if you are searching for values between A and B, the query will match exactly all documents whose value rounded to the closest half-float point is between A and B.
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I did some unscientific benchmarking in order to make sure that half floats do not make things too slow. Query times are the same with floats and half floats, which makes sense since queries operate directly on the encoded representation anyway. However, a sum aggregation on the whole dataset runs about 8% slower with half floats due to the fact that half float bits are converted to doubles using software ( |
Bargs
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Jul 22, 2016
Elasticsearch added a couple of new numeric datatypes, which means we need to update our type casting list to include them. Kibana should see them as "numbers" so they work properly in searches and aggs. Fixes elastic#7782 Related elastic/elasticsearch#18887 Related elastic/elasticsearch#19264
airow
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Feb 16, 2017
Elasticsearch added a couple of new numeric datatypes, which means we need to update our type casting list to include them. Kibana should see them as "numbers" so they work properly in searches and aggs. Fixes elastic#7782 Related elastic/elasticsearch#18887 Related elastic/elasticsearch#19264 Former-commit-id: 298ee35
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They have been implemented in https://issues.apache.org/jira/browse/LUCENE-7289.
Ranges are implemented so that the accuracy loss only occurs at index time,
which means that if you are searching for values between A and B, the query will
match exactly all documents whose value rounded to the closest half-float point
is between A and B.