135 lines
4.6 KiB
Plaintext
135 lines
4.6 KiB
Plaintext
[[search-facets-date-histogram-facet]]
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=== Date Histogram Facet
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A specific histogram facet that can work with `date` field types
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enhancing it over the regular
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<<search-facets-histogram-facet,histogram
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facet>>. Here is a quick 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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"date_histogram" : {
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"field" : "field_name",
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"interval" : "day"
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}
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}
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}
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}
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--------------------------------------------------
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==== Interval
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The `interval` allows to set the interval at which buckets will be
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created for each hit. It allows for the constant values of `year`,
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`quarter`, `month`, `week`, `day`, `hour`, `minute` ,`second`.
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It also support time setting like `1.5h` (up to `w` for weeks).
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==== Time Zone
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By default, times are stored as UTC milliseconds since the epoch. Thus,
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all computation and "bucketing" / "rounding" is done on UTC. It is
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possible to provide a time zone (both pre rounding, and post rounding)
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value, which will cause all computations to take the relevant zone into
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account. The time returned for each bucket/entry is milliseconds since
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the epoch of the provided time zone.
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The parameters are `pre_zone` (pre rounding based on interval) and
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`post_zone` (post rounding based on interval). The `time_zone` parameter
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simply sets the `pre_zone` parameter. By default, those are set to
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`UTC`.
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The zone value accepts either a numeric value for the hours offset, for
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example: `"time_zone" : -2`. It also accepts a format of hours and
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minutes, like `"time_zone" : "-02:30"`. Another option is to provide a
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time zone accepted as one of the values listed
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http://joda-time.sourceforge.net/timezones.html[here].
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Lets take an example. For `2012-04-01T04:15:30Z`, with a `pre_zone` of
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`-08:00`. For `day` interval, the actual time by applying the time zone
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and rounding falls under `2012-03-31`, so the returned value will be (in
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millis) of `2012-03-31T00:00:00Z` (UTC). For `hour` interval, applying
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the time zone results in `2012-03-31T20:15:30`, rounding it results in
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`2012-03-31T20:00:00`, but, we want to return it in UTC (`post_zone` is
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not set), so we convert it back to UTC: `2012-04-01T04:00:00Z`. Note, we
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are consistent in the results, returning the rounded value in UTC.
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`post_zone` simply takes the result, and adds the relevant offset.
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Sometimes, we want to apply the same conversion to UTC we did above for
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`hour` also for `day` (and up) intervals. We can set
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`pre_zone_adjust_large_interval` to `true`, which will apply the same
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conversion done for `hour` interval in the example, to `day` and above
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intervals (it can be set regardless of the interval, but only kick in
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when using `day` and higher intervals).
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==== Factor
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The date histogram works on numeric values (since time is stored in
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milliseconds since the epoch in UTC). But, sometimes, systems will store
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a different resolution (like seconds since UTC) in a numeric field. The
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`factor` parameter can be used to change the value in the field to
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milliseconds to actual do the relevant rounding, and then be applied
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again to get to the original unit. For example, when storing in a
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numeric field seconds resolution, the `factor` can be set to `1000`.
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==== Pre / Post Offset
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Specific offsets can be provided for pre rounding and post rounding. The
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`pre_offset` for pre rounding, and `post_offset` for post rounding. The
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format is the date time format (`1h`, `1d`, ...).
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==== Value Field
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The date_histogram facet allows to use a different key (of type date)
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which controls the bucketing, with a different value field which will
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then return the total and mean for that field values of the hits within
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the relevant bucket. For 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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"date_histogram" : {
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"key_field" : "timestamp",
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"value_field" : "price",
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"interval" : "day"
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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 Value Field
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A script can be used to compute the value that will then be used to
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compute the total and mean for a bucket. For 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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"date_histogram" : {
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"key_field" : "timestamp",
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"value_script" : "doc['price'].value * 2",
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"interval" : "day"
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}
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}
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}
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}
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--------------------------------------------------
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