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[DOCS] fix documentation for selecting algorithm for percentiles agg

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Colin Goodheart-Smithe 2016-07-27 08:48:35 +01:00
parent 24d7fa6d54
commit 3f344d3154
2 changed files with 23 additions and 20 deletions
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23
docs/reference/aggregations/metrics/percentile-aggregation.asciidoc
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@ -190,7 +190,9 @@ This balance can be controlled using a `compression` parameter:
"load_time_outlier" : {
"percentiles" : {
"field" : "load_time",
"compression" : 200 <1>
"tdigest": {
"compression" : 200 <1>
}
}
}
}
@ -218,11 +220,11 @@ the TDigest will use less memory.
experimental[]
https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
that can be useful when calculating percentiles for latency measurements as it can be faster than the t-digest implementation
with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified
as a number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour
(3,600,000,000 microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond
https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
that can be useful when calculating percentiles for latency measurements as it can be faster than the t-digest implementation
with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified
as a number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour
(3,600,000,000 microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond
for values up to 1 millisecond and 3.6 seconds (or better) for the maximum tracked value (1 hour).
The HDR Histogram can be used by specifying the `method` parameter in the request:
@ -235,17 +237,18 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
"percentiles" : {
"field" : "load_time",
"percents" : [95, 99, 99.9],
"method" : "hdr", <1>
"number_of_significant_value_digits" : 3 <2>
"hdr": { <1>
"number_of_significant_value_digits" : 3 <2>
}
}
}
}
}
--------------------------------------------------
<1> The `method` parameter is set to `hdr` to indicate that HDR Histogram should be used to calculate the percentiles
<1> `hdr` object indicates that HDR Histogram should be used to calculate the percentiles and specific settings for this algorithm can be specified inside the object
<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits
The HDRHistogram only supports positive values and will error if it is passed a negative value. It is also not a good idea to use
The HDRHistogram only supports positive values and will error if it is passed a negative value. It is also not a good idea to use
the HDRHistogram if the range of values is unknown as this could lead to high memory usage.
==== Missing value

20
docs/reference/aggregations/metrics/percentile-rank-aggregation.asciidoc
View File

@ -115,11 +115,11 @@ TIP: for indexed scripts replace the `file` parameter with an `id` parameter.
experimental[]
https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
that can be useful when calculating percentile ranks for latency measurements as it can be faster than the t-digest implementation
with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified as a
number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour (3,600,000,000
microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond for values up to
https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
that can be useful when calculating percentile ranks for latency measurements as it can be faster than the t-digest implementation
with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified as a
number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour (3,600,000,000
microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond for values up to
1 millisecond and 3.6 seconds (or better) for the maximum tracked value (1 hour).
The HDR Histogram can be used by specifying the `method` parameter in the request:
@ -132,17 +132,18 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
"percentile_ranks" : {
"field" : "load_time",
"values" : [15, 30],
"method" : "hdr", <1>
"number_of_significant_value_digits" : 3 <2>
"hdr": { <1>
"number_of_significant_value_digits" : 3 <2>
}
}
}
}
}
--------------------------------------------------
<1> The `method` parameter is set to `hdr` to indicate that HDR Histogram should be used to calculate the percentile_ranks
<1> `hdr` object indicates that HDR Histogram should be used to calculate the percentiles and specific settings for this algorithm can be specified inside the object
<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits
The HDRHistogram only supports positive values and will error if it is passed a negative value. It is also not a good idea to use
The HDRHistogram only supports positive values and will error if it is passed a negative value. It is also not a good idea to use
the HDRHistogram if the range of values is unknown as this could lead to high memory usage.
==== Missing value
@ -166,4 +167,3 @@ had a value.
--------------------------------------------------
<1> Documents without a value in the `grade` field will fall into the same bucket as documents that have the value `10`.