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fadbe0de08
Today we require users to prepare their indices for split operations. Yet, we can do this automatically when an index is created which would make the split feature a much more appealing option since it doesn't have any 3rd party prerequisites anymore. This change automatically sets the number of routinng shards such that an index is guaranteed to be able to split once into twice as many shards. The number of routing shards is scaled towards the default shard limit per index such that indices with a smaller amount of shards can be split more often than larger ones. For instance an index with 1 or 2 shards can be split 10x (until it approaches 1024 shards) while an index created with 128 shards can only be split 3x by a factor of 2. Please note this is just a default value and users can still prepare their indices with `index.number_of_routing_shards` for custom splitting. NOTE: this change has an impact on the document distribution since we are changing the hash space. Documents are still uniformly distributed across all shards but since we are artificually changing the number of buckets in the consistent hashign space document might be hashed into different shards compared to previous versions. This is a 7.0 only change.
150 lines
4.4 KiB
Plaintext
150 lines
4.4 KiB
Plaintext
[[search-aggregations-metrics-geocentroid-aggregation]]
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=== Geo Centroid Aggregation
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A metric aggregation that computes the weighted https://en.wikipedia.org/wiki/Centroid[centroid] from all coordinate values for a <<geo-point>> field.
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Example:
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[source,js]
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--------------------------------------------------
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PUT /museums
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{
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"mappings": {
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"doc": {
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"properties": {
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"location": {
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"type": "geo_point"
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}
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}
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}
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}
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}
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POST /museums/doc/_bulk?refresh
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{"index":{"_id":1}}
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{"location": "52.374081,4.912350", "city": "Amsterdam", "name": "NEMO Science Museum"}
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{"index":{"_id":2}}
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{"location": "52.369219,4.901618", "city": "Amsterdam", "name": "Museum Het Rembrandthuis"}
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{"index":{"_id":3}}
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{"location": "52.371667,4.914722", "city": "Amsterdam", "name": "Nederlands Scheepvaartmuseum"}
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{"index":{"_id":4}}
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{"location": "51.222900,4.405200", "city": "Antwerp", "name": "Letterenhuis"}
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{"index":{"_id":5}}
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{"location": "48.861111,2.336389", "city": "Paris", "name": "Musée du Louvre"}
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{"index":{"_id":6}}
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{"location": "48.860000,2.327000", "city": "Paris", "name": "Musée d'Orsay"}
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POST /museums/_search?size=0
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{
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"aggs" : {
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"centroid" : {
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"geo_centroid" : {
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"field" : "location" <1>
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<1> The `geo_centroid` aggregation specifies the field to use for computing the centroid. (NOTE: field must be a <<geo-point>> type)
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The above aggregation demonstrates how one would compute the centroid of the location field for all documents with a crime type of burglary
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The response for the above aggregation:
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[source,js]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"centroid": {
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"location": {
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"lat": 51.00982963806018,
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"lon": 3.9662131061777472
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},
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"count": 6
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
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The `geo_centroid` aggregation is more interesting when combined as a sub-aggregation to other bucket aggregations.
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Example:
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[source,js]
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--------------------------------------------------
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POST /museums/_search?size=0
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{
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"aggs" : {
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"cities" : {
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"terms" : { "field" : "city.keyword" },
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"aggs" : {
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"centroid" : {
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"geo_centroid" : { "field" : "location" }
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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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// CONSOLE
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// TEST[continued]
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The above example uses `geo_centroid` as a sub-aggregation to a
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<<search-aggregations-bucket-terms-aggregation, terms>> bucket aggregation
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for finding the central location for museums in each city.
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The response for the above aggregation:
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[source,js]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"cities": {
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"sum_other_doc_count": 0,
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"doc_count_error_upper_bound": 0,
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"buckets": [
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{
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"key": "Amsterdam",
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"doc_count": 3,
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"centroid": {
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"location": {
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"lat": 52.371655656024814,
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"lon": 4.909563297405839
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},
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"count": 3
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}
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},
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{
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"key": "Paris",
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"doc_count": 2,
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"centroid": {
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"location": {
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"lat": 48.86055548675358,
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"lon": 2.3316944623366
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},
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"count": 2
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}
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},
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{
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"key": "Antwerp",
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"doc_count": 1,
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"centroid": {
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"location": {
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"lat": 51.22289997059852,
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"lon": 4.40519998781383
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},
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"count": 1
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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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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
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