139 lines
4.0 KiB
Plaintext
139 lines
4.0 KiB
Plaintext
[[search-facets-histogram-facet]]
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=== Histogram Facets
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The histogram facet works with numeric data by building a histogram
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across intervals of the field values. Each value is "rounded" into an
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interval (or placed in a bucket), and statistics are provided per
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interval/bucket (count and total). Here is a simple example:
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[source,js]
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--------------------------------------------------
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{
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"query" : {
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"match_all" : {}
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},
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"facets" : {
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"histo1" : {
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"histogram" : {
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"field" : "field_name",
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"interval" : 100
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}
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}
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}
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}
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--------------------------------------------------
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The above example will run a histogram facet on the `field_name` filed,
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with an `interval` of `100` (so, for example, a value of `1055` will be
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placed within the `1000` bucket).
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The interval can also be provided as a time based interval (using the
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time format). This mainly make sense when working on date fields or
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field that represent absolute milliseconds, here is an example:
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[source,js]
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--------------------------------------------------
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{
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"query" : {
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"match_all" : {}
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},
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"facets" : {
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"histo1" : {
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"histogram" : {
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"field" : "field_name",
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"time_interval" : "1.5h"
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}
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}
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}
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}
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--------------------------------------------------
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==== Key and Value
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The histogram facet allows to use a different key and value. The key is
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used to place the hit/document within the appropriate bucket, and the
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value is used to compute statistical data (for example, total). Here is
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an example:
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[source,js]
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--------------------------------------------------
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{
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"query" : {
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"match_all" : {}
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},
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"facets" : {
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"histo1" : {
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"histogram" : {
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"key_field" : "key_field_name",
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"value_field" : "value_field_name",
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"interval" : 100
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}
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}
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}
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}
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--------------------------------------------------
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==== Script Key and Value
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Sometimes, some munging of both the key and the value are needed. In the
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key case, before it is rounded into a bucket, and for the value, when
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the statistical data is computed per bucket
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<<modules-scripting,scripts>> can be used. Here
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is an example:
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[source,js]
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--------------------------------------------------
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{
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"query" : {
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"match_all" : {}
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},
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"facets" : {
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"histo1" : {
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"histogram" : {
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"key_script" : "doc['date'].date.minuteOfHour",
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"value_script" : "doc['num1'].value"
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}
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}
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}
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}
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--------------------------------------------------
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In the above sample, we can use a date type field called `date` to get
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the minute of hour from it, and the total will be computed based on
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another field `num1`. Note, in this case, no `interval` was provided, so
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the bucket will be based directly on the `key_script` (no rounding).
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Parameters can also be provided to the different scripts (preferable if
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the script is the same, with different values for a specific parameter,
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like "factor"):
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[source,js]
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--------------------------------------------------
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{
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"query" : {
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"match_all" : {}
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},
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"facets" : {
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"histo1" : {
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"histogram" : {
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"key_script" : "doc['date'].date.minuteOfHour * factor1",
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"value_script" : "doc['num1'].value + factor2",
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"params" : {
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"factor1" : 2,
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"factor2" : 3
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}
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}
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}
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}
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}
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--------------------------------------------------
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==== Memory Considerations
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In order to implement the histogram facet, the relevant field values are
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loaded into memory from the index. This means that per shard, there
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should be enough memory to contain them. Since by default, dynamic
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introduced types are `long` and `double`, one option to reduce the
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memory footprint is to explicitly set the types for the relevant fields
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to either `short`, `integer`, or `float` when possible.
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