2013-11-24 06:13:08 -05:00
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[[search-aggregations-bucket-histogram-aggregation]]
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2014-05-12 19:35:58 -04:00
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=== Histogram Aggregation
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2013-11-24 06:13:08 -05:00
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2014-01-29 14:55:19 -05:00
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A multi-bucket values source based aggregation that can be applied on numeric values extracted from the documents.
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It dynamically builds fixed size (a.k.a. interval) buckets over the values. For example, if the documents have a field
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that holds a price (numeric), we can configure this aggregation to dynamically build buckets with interval `5`
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(in case of price it may represent $5). When the aggregation executes, the price field of every document will be
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evaluated and will be rounded down to its closest bucket - for example, if the price is `32` and the bucket size is `5`
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2015-02-02 11:46:08 -05:00
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then the rounding will yield `30` and thus the document will "fall" into the bucket that is associated with the key `30`.
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2014-01-29 14:55:19 -05:00
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To make this more formal, here is the rounding function that is used:
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2013-11-24 06:13:08 -05:00
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[source,java]
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--------------------------------------------------
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2016-07-22 06:16:45 -04:00
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bucket_key = Math.floor((value - offset) / interval) * interval + offset
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2013-11-24 06:13:08 -05:00
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--------------------------------------------------
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2016-07-22 06:16:45 -04:00
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The `interval` must be a positive decimal, while the `offset` must be a decimal in `[0, interval[`.
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2015-08-06 18:00:08 -04:00
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2013-11-24 06:13:08 -05:00
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The following snippet "buckets" the products based on their `price` by interval of `50`:
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[source,js]
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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POST /sales/_search?size=0
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2013-11-24 06:13:08 -05:00
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{
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"aggs" : {
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"prices" : {
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2014-05-12 19:35:58 -04:00
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"histogram" : {
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2013-11-24 06:13:08 -05:00
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"field" : "price",
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"interval" : 50
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}
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}
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}
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}
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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// CONSOLE
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// TEST[setup:sales]
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2013-11-24 06:13:08 -05:00
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And the following may be the response:
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[source,js]
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--------------------------------------------------
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{
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2017-02-07 15:59:40 -05:00
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...
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2013-11-24 06:13:08 -05:00
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"aggregations": {
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2014-01-28 11:46:26 -05:00
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"prices" : {
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"buckets": [
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{
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2017-02-07 15:59:40 -05:00
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"key": 0.0,
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"doc_count": 1
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2014-01-28 11:46:26 -05:00
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},
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{
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2017-02-07 15:59:40 -05:00
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"key": 50.0,
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"doc_count": 1
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2014-01-28 11:46:26 -05:00
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},
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2015-04-30 08:55:34 -04:00
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{
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2017-02-07 15:59:40 -05:00
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"key": 100.0,
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2015-04-30 08:55:34 -04:00
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"doc_count": 0
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},
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2014-01-28 11:46:26 -05:00
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{
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2017-02-07 15:59:40 -05:00
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"key": 150.0,
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"doc_count": 2
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},
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{
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"key": 200.0,
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2014-01-28 11:46:26 -05:00
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"doc_count": 3
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}
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]
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}
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2013-11-24 06:13:08 -05:00
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}
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}
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
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2013-11-24 06:13:08 -05:00
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2015-04-30 08:55:34 -04:00
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==== Minimum document count
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The response above show that no documents has a price that falls within the range of `[100 - 150)`. By default the
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response will fill gaps in the histogram with empty buckets. It is possible change that and request buckets with
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a higher minimum count thanks to the `min_doc_count` setting:
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2013-11-24 06:13:08 -05:00
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[source,js]
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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POST /sales/_search?size=0
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2013-11-24 06:13:08 -05:00
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{
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"aggs" : {
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"prices" : {
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2014-05-12 19:35:58 -04:00
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"histogram" : {
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2013-11-24 06:13:08 -05:00
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"field" : "price",
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"interval" : 50,
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2015-04-30 08:55:34 -04:00
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"min_doc_count" : 1
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2013-11-24 06:13:08 -05:00
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}
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}
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}
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}
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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// CONSOLE
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// TEST[setup:sales]
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2013-11-24 06:13:08 -05:00
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Response:
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[source,js]
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--------------------------------------------------
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{
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2017-02-07 15:59:40 -05:00
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...
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2013-11-24 06:13:08 -05:00
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"aggregations": {
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2014-01-28 11:46:26 -05:00
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"prices" : {
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"buckets": [
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{
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2017-02-07 15:59:40 -05:00
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"key": 0.0,
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"doc_count": 1
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},
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{
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"key": 50.0,
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"doc_count": 1
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2014-01-28 11:46:26 -05:00
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},
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{
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2017-02-07 15:59:40 -05:00
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"key": 150.0,
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"doc_count": 2
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2014-01-28 11:46:26 -05:00
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},
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{
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2017-02-07 15:59:40 -05:00
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"key": 200.0,
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2014-01-28 11:46:26 -05:00
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"doc_count": 3
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}
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]
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}
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2013-11-24 06:13:08 -05:00
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}
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}
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
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2013-11-24 06:13:08 -05:00
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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[[search-aggregations-bucket-histogram-aggregation-extended-bounds]]
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2016-04-13 08:18:30 -04:00
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By default the `histogram` returns all the buckets within the range of the data itself, that is, the documents with
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the
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2015-10-13 15:13:44 -04:00
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documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when
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2015-04-30 08:55:34 -04:00
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requesting empty buckets, this causes a confusion, specifically, when the data is also filtered.
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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To understand why, let's look at an example:
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Lets say the you're filtering your request to get all docs with values between `0` and `500`, in addition you'd like
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to slice the data per price using a histogram with an interval of `50`. You also specify `"min_doc_count" : 0` as you'd
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like to get all buckets even the empty ones. If it happens that all products (documents) have prices higher than `100`,
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the first bucket you'll get will be the one with `100` as its key. This is confusing, as many times, you'd also like
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to get those buckets between `0 - 100`.
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With `extended_bounds` setting, you now can "force" the histogram aggregation to start building buckets on a specific
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`min` values and also keep on building buckets up to a `max` value (even if there are no documents anymore). Using
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`extended_bounds` only makes sense when `min_doc_count` is 0 (the empty buckets will never be returned if `min_doc_count`
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is greater than 0).
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Note that (as the name suggest) `extended_bounds` is **not** filtering buckets. Meaning, if the `extended_bounds.min` is higher
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than the values extracted from the documents, the documents will still dictate what the first bucket will be (and the
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same goes for the `extended_bounds.max` and the last bucket). For filtering buckets, one should nest the histogram aggregation
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under a range `filter` aggregation with the appropriate `from`/`to` settings.
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Example:
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[source,js]
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--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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POST /sales/_search?size=0
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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{
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"query" : {
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2015-09-11 04:35:56 -04:00
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"constant_score" : { "filter": { "range" : { "price" : { "to" : "500" } } } }
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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},
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"aggs" : {
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"prices" : {
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"histogram" : {
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"field" : "price",
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"interval" : 50,
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"extended_bounds" : {
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"min" : 0,
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"max" : 500
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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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2017-02-07 15:59:40 -05:00
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// CONSOLE
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// TEST[setup:sales]
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Added extended_bounds support for date_/histogram aggs
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes #5224
2014-03-16 20:06:07 -04:00
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2013-11-24 06:13:08 -05:00
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==== Order
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2017-05-11 13:06:26 -04:00
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By default the returned buckets are sorted by their `key` ascending, though the order behaviour can be controlled using
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the `order` setting. Supports the same `order` functionality as the <<search-aggregations-bucket-terms-aggregation-order,`Terms Aggregation`>>.
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2014-02-27 10:58:28 -05:00
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2015-02-02 11:46:08 -05:00
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==== Offset
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By default the bucket keys start with 0 and then continue in even spaced steps of `interval`, e.g. if the interval is 10 the first buckets
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(assuming there is data inside them) will be [0 - 9], [10-19], [20-29]. The bucket boundaries can be shifted by using the `offset` option.
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This can be best illustrated with an example. If there are 10 documents with values ranging from 5 to 14, using interval `10` will result in
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two buckets with 5 documents each. If an additional offset `5` is used, there will be only one single bucket [5-14] containing all the 10
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documents.
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2013-11-24 06:13:08 -05:00
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==== Response Format
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2014-01-29 14:55:19 -05:00
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By default, the buckets are returned as an ordered array. It is also possible to request the response as a hash
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instead keyed by the buckets keys:
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2013-11-24 06:13:08 -05:00
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|
[source,js]
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|
--------------------------------------------------
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2017-02-07 15:59:40 -05:00
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POST /sales/_search?size=0
|
2013-11-24 06:13:08 -05:00
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|
{
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|
|
|
"aggs" : {
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|
"prices" : {
|
2014-05-12 19:35:58 -04:00
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"histogram" : {
|
2013-11-24 06:13:08 -05:00
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"field" : "price",
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|
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"interval" : 50,
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"keyed" : true
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}
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}
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}
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}
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|
--------------------------------------------------
|
2017-02-07 15:59:40 -05:00
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|
|
// CONSOLE
|
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|
|
// TEST[setup:sales]
|
2013-11-24 06:13:08 -05:00
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|
Response:
|
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|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
{
|
2017-02-07 15:59:40 -05:00
|
|
|
...
|
2013-11-24 06:13:08 -05:00
|
|
|
"aggregations": {
|
|
|
|
"prices": {
|
2014-01-28 11:46:26 -05:00
|
|
|
"buckets": {
|
2017-02-07 15:59:40 -05:00
|
|
|
"0.0": {
|
|
|
|
"key": 0.0,
|
|
|
|
"doc_count": 1
|
|
|
|
},
|
|
|
|
"50.0": {
|
|
|
|
"key": 50.0,
|
|
|
|
"doc_count": 1
|
2014-01-28 11:46:26 -05:00
|
|
|
},
|
2017-02-07 15:59:40 -05:00
|
|
|
"100.0": {
|
|
|
|
"key": 100.0,
|
|
|
|
"doc_count": 0
|
|
|
|
},
|
|
|
|
"150.0": {
|
|
|
|
"key": 150.0,
|
|
|
|
"doc_count": 2
|
2014-01-28 11:46:26 -05:00
|
|
|
},
|
2017-02-07 15:59:40 -05:00
|
|
|
"200.0": {
|
|
|
|
"key": 200.0,
|
2014-01-28 11:46:26 -05:00
|
|
|
"doc_count": 3
|
|
|
|
}
|
2013-11-24 06:13:08 -05:00
|
|
|
}
|
|
|
|
}
|
2014-01-28 11:46:26 -05:00
|
|
|
}
|
2013-11-24 06:13:08 -05:00
|
|
|
}
|
|
|
|
--------------------------------------------------
|
2017-02-07 15:59:40 -05:00
|
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
|
2015-05-07 10:46:40 -04:00
|
|
|
|
|
|
|
==== Missing value
|
|
|
|
|
|
|
|
The `missing` parameter defines how documents that are missing a value should be treated.
|
|
|
|
By default they will be ignored but it is also possible to treat them as if they
|
|
|
|
had a value.
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
2017-02-07 15:59:40 -05:00
|
|
|
POST /sales/_search?size=0
|
2015-05-07 10:46:40 -04:00
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"quantity" : {
|
|
|
|
"histogram" : {
|
|
|
|
"field" : "quantity",
|
|
|
|
"interval": 10,
|
|
|
|
"missing": 0 <1>
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
2017-02-07 15:59:40 -05:00
|
|
|
// CONSOLE
|
|
|
|
// TEST[setup:sales]
|
2015-05-07 10:46:40 -04:00
|
|
|
|
2015-06-15 05:29:17 -04:00
|
|
|
<1> Documents without a value in the `quantity` field will fall into the same bucket as documents that have the value `0`.
|