A metric aggregation that aggregates a set of points as
a GeoJSON LineString ordered by some sort parameter.
#### specifics
A `geo_line` aggregation request would specify a `geo_point` field, as well
as a `sort` field. `geo_point` represents the values used in the LineString,
while the `sort` values will be used as the total ordering of the points.
the `sort` field would support any numeric field, including date.
#### sample usage
```
{
"query": {
"bool": {
"must": [
{ "term": { "person": "004" } },
{ "term": { "trajectory": "20090131002206.plt" } }
]
}
},
"aggs": {
"make_line": {
"geo_line": {
"point": {"field": "location"},
"sort": { "field": "timestamp" },
"include_sort": true,
"sort_order": "desc",
"size": 15
}
}
}
}
```
#### sample response
```
{
"took": 21,
"timed_out": false,
"_shards": {...},
"hits": {...},
"aggregations": {
"make_line": {
"type": "LineString",
"coordinates": [
[
121.52926194481552,
38.92878997139633
],
[
121.52922699227929,
38.92876998055726
],
]
}
}
}
```
#### visual response
<img width="540" alt="Screen Shot 2019-04-26 at 9 40 07 AM" src="https://user-images.githubusercontent.com/388837/56834977-cf278e00-6827-11e9-9c93-005ed48433cc.png">
#### limitations
Due to the cardinality of points, an initial max of 10k points
will be used. This should support many use-cases.
One solution to overcome this limitation is to keep a PriorityQueue of
points, and simplifying the line once it hits this max. If simplifying
makes sense, it may be a nice option, in general. The ability to use a parameter
to specify how aggressive one wants to simplify. This parameter could be
the number of points. Example algorithm one could use with a PriorityQueue:
https://bost.ocks.org/mike/simplify/. This would still require O(m) space, where m
is the number of points returned. And would also require heapifying triangles
sorted by their areas, which would be O(log(m)) operations. Since sorting is done,
anyways, simplifying would still be a O(n log(m)) operation, where n is the total number
of points to filter........... something to explore
closes#41649
- Replaces more abstract docs about object structure and values source with task-based examples.
- Relocates several sections from the current `misc.asciidoc` file.
- Alphabetically sorts agg categories in the nav.
- Removes the matrix agg family. Moves the stats matrix agg under the metric agg family
Co-authored-by: debadair <debadair@elastic.co>
Adds t_test metric aggregation that can perform paired and unpaired two-sample
t-tests. In this PR support for filters in unpaired is still missing. It will
be added in a follow-up PR.
Relates to #53692
The `top_metrics` agg is kind of like `top_hits` but it only works on
doc values so it *should* be faster.
At this point it is fairly limited in that it only supports a single,
numeric sort and a single, numeric metric. And it only fetches the "very
topest" document worth of metric. We plan to support returning a
configurable number of top metrics, requesting more than one metric and
more than one sort. And, eventually, non-numeric sorts and metrics. The
trick is doing those things fairly efficiently.
Co-Authored by: Zachary Tong <zach@elastic.co>
This PR adds a new metric aggregation called string_stats that operates on string terms of a document and returns the following:
min_length: The length of the shortest term
max_length: The length of the longest term
avg_length: The average length of all terms
distribution: The probability distribution of all characters appearing in all terms
entropy: The total Shannon entropy value calculated for all terms
This aggregation has been implemented as an analytics plugin.
This commit adds a new single value metric aggregation that calculates
the statistic called median absolute deviation, which is a measure of
variability that works on more types of data than standard deviation
Our calculation of MAD is approximated using t-digests. In the collect
phase, we collect each value visited into a t-digest. In the reduce
phase, we merge all value t-digests, then create a t-digest of
deviations using the first t-digest's median and centroids
Adds a new single-value metrics aggregation that computes the weighted
average of numeric values that are extracted from the aggregated
documents. These values can be extracted from specific numeric
fields in the documents.
When calculating a regular average, each datapoint has an equal "weight"; it
contributes equally to the final value. In contrast, weighted averages
scale each datapoint differently. The amount that each datapoint contributes
to the final value is extracted from the document, or provided by a script.
As a formula, a weighted average is the `∑(value * weight) / ∑(weight)`
A regular average can be thought of as a weighted average where every value has
an implicit weight of `1`.
Closes#15731
This commit adds a new metric aggregator for computing the geo_centroid over a set of geo_point fields. This can be combined with other aggregators (e.g., geohash_grid, significant_terms) for computing the geospatial centroid based on the document sets from other aggregation results.
