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I am trying to make a custom word cloud visualization from the existing index using vega in kibana dashboard.
It is showing error as "Cannot read properties of undefined (reading 'datum')".
I have used type-formula in "transform property" to create custom visualization word cloud rather than these any types are there?
{
"$schema": "https://vega.github.io/schema/vega/v5.json",
"title": "A Wordcloud",
"width": 900,
"height": 500,
"padding": 100,
"autosize": "none",
"background": "pink",
"data": [
{
"name": "table",
"url": {
"index": "nupur2",
"body": {
"aggs": {
"2": {
"terms": {"field": "hashtags","order":{"_count": "asc"}, "size": 100},
"aggs": {
"_count": {
"avg": {"field": "vaderSentiment"}
}
}
}
}
}
},
"format": {"property": "aggregations.2.buckets"},
"transform": [
{
"type": "formula",
"as": "angle",
"expr": "datum.size >= 4 ? 0 : [-45,-30, -15, 0, 15, 30, 45][floor(random() * 7)]"
}
]
}
],
"scales": [
{
"name": "color",
"type": "ordinal",
"domain": {"data": "table", "field": "hashtags"},
"range": ["green", "orange", "red"]
}
],
"marks": [
{
"type": "group",
"from": {"data": "table"},
"encode": {
"enter": {
"text": {"field": "hashtags"},
"align": {"value": "center"},
"baseline": {"value": "alphabetic"},
"fill": {"scale": "color", "field": "hashtags"}
},
"update": {
"fillOpacity": {"value": 1}
},
"hover": {
"fillOpacity": {"value": 0.5}
}
},
"transform": [
{
"type": "wordcloud",
"size": [800, 400],
"text": {"field": "hashtags"},
"rotate": {"field": "datum.angle"},
"font": "Helvetica Neue, Arial",
"fontSize": {"field": "datum.size"},
"fontWeight": {"field": "datum.weight"},
"fontSizeRange": [12, 56],
"padding": 2
}
]
}
]
}
This is the image which I got output : https://i.stack.imgur.com/nbb8f.png
I want to have a tree chart of my data using vega in kibana 7.9.0, but I don't know how to write the query for that.
the below code is an example of tree chart from github website. I want such layout for my own index which I have it in kibanan.
Help me how to do that.
tree chart example
Sample tree chart code:
{
"$schema": "https://vega.github.io/schema/vega/v5.json",
"description": "An example of Cartesian layouts for a node-link diagram of hierarchical data.",
"width": 1000,
"height": 1600,
"padding": 5,
"signals": [
{
"name": "labels", "value": true,
"bind": {"input": "checkbox"}
},
{
"name": "layout", "value": "tidy",
"bind": {"input": "radio", "options": ["tidy", "cluster"]}
},
{
"name": "links", "value": "diagonal",
"bind": {
"input": "select",
"options": ["line", "curve", "diagonal", "orthogonal"]
}
},
{
"name": "separation", "value": false,
"bind": {"input": "checkbox"}
}
],
"data": [
{
"name": "tree",
"url": "data/flare.json",
"transform": [
{
"type": "stratify",
"key": "id",
"parentKey": "parent"
},
{
"type": "tree",
"method": {"signal": "layout"},
"size": [{"signal": "height"}, {"signal": "width - 100"}],
"separation": {"signal": "separation"},
"as": ["y", "x", "depth", "children"]
}
]
},
{
"name": "links",
"source": "tree",
"transform": [
{ "type": "treelinks" },
{
"type": "linkpath",
"orient": "horizontal",
"shape": {"signal": "links"}
}
]
}
],
"scales": [
{
"name": "color",
"type": "linear",
"range": {"scheme": "magma"},
"domain": {"data": "tree", "field": "depth"},
"zero": true
}
],
"marks": [
{
"type": "path",
"from": {"data": "links"},
"encode": {
"update": {
"path": {"field": "path"},
"stroke": {"value": "#ccc"}
}
}
},
{
"type": "symbol",
"from": {"data": "tree"},
"encode": {
"enter": {
"size": {"value": 100},
"stroke": {"value": "#fff"}
},
"update": {
"x": {"field": "x"},
"y": {"field": "y"},
"fill": {"scale": "color", "field": "depth"}
}
}
},
{
"type": "text",
"from": {"data": "tree"},
"encode": {
"enter": {
"text": {"field": "name"},
"fontSize": {"value": 9},
"baseline": {"value": "middle"}
},
"update": {
"x": {"field": "x"},
"y": {"field": "y"},
"dx": {"signal": "datum.children ? -7 : 7"},
"align": {"signal": "datum.children ? 'right' : 'left'"},
"opacity": {"signal": "labels ? 1 : 0"}
}
}
}
]
}
I recommend going and learning the Kibana Vega interaction, Vega spec and elasticsearch search api. This is too broad of a question, you are basically asking someone to do the work for you which is easily deducible from the the documentation.
https://www.elastic.co/guide/en/kibana/current/vega.html
https://vega.github.io/vega/docs/
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-search.html
I'm filtering prices dynamically with the given currency rates and sorting them with the score which is generated by script. But there is one thing I could not figure out how to do is range filter.
For example I only want to get product_platforms only match score between 10 and 100.
Index request.
PUT /test_products
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 0,
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": "2",
"max_gram": "15"
}
},
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
},
"mappings": {
"properties": {
"id": {
"type": "keyword",
"doc_values": true
},
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
},
"raw": {
"type": "keyword"
}
},
"analyzer": "autocomplete",
"search_analyzer": "standard"
},
"product_platforms": {
"type": "nested",
"properties": {
"id": {
"type": "long"
},
"platform_id": {
"type": "long"
},
"price": {
"type": "float"
},
"currency_id": {
"type": "long"
},
"currency_code": {
"enabled": false
},
"sku": {
"type": "keyword"
},
"quantity": {
"type": "long"
}
}
}
}
}
}
Insert test documents:
POST /test_products/_bulk?pretty&refresh
{"index":{"_id": 1}}
{"id": 1, "name": "1. Product", "product_platforms": [{"id": 11, "platform_id": 3, "price": 100, "currency_id": 1, "currency_code": "TRY", "sku": "URN_1_1", "quantity": 1},{"id": 12, "platform_id": 3, "price": 75, "currency_id": 2, "currency_code": "USD", "sku": "URN_1_2", "quantity": 1},{"id": 13, "platform_id": 2, "price": 15, "currency_id": 2, "currency_code": "USD", "sku": "URN_1_3", "quantity": 1}]}
{"index":{"_id": 2}}
{"id": 2, "name": "2. Product", "product_platforms": [{"id": 21, "platform_id": 3, "price": 50, "currency_id": 1, "currency_code": "TRY", "sku": "URN_2_1", "quantity": 1},{"id": 22, "platform_id": 3, "price": 25, "currency_id": 2, "currency_code": "USD", "sku": "URN_2_2", "quantity": 1},{"id": 23, "platform_id": 3, "price": 75, "currency_id": 1, "currency_code": "TRY", "sku": "URN_2_3", "quantity": 1}, {"id": 24, "platform_id": 3, "price": 20, "currency_id": 2, "currency_code": "USD", "sku": "URN_2_4", "quantity": 1}]}
And here is the my search query:
GET /test_products/_search
{
"query": {
"nested": {
"path": "product_platforms",
"score_mode": "max",
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{
"term": {
"product_platforms.platform_id": {
"value": "3"
}
}
}
]
}
},
"boost_mode": "replace",
"script_score": {
"script": {
"source": """
doc['product_platforms.price'].value * (doc['product_platforms.currency_id'].value == 2 ? params.rate_usd : (doc['product_platforms.currency_id'].value == 3 ? params.rate_eur : params.rate_try)) """,
"params": {
"rate_try": 1,
"rate_usd": 7,
"rate_eur": 8
}
}
}
}
},
"inner_hits": {
"name": "product_platforms",
"_source": true,
"size": 5,
"sort": {
"_script": {
"type": "number",
"script": {
"lang": "painless",
"source": """ doc['product_platforms.price'].value * (doc['product_platforms.currency_id'].value == 2 ? params.rate_usd : (doc['product_platforms.currency_id'].value == 3 ? params.rate_eur : params.rate_try)) """,
"params": {
"rate_try": 1,
"rate_usd": 7,
"rate_eur": 8
}
},
"order": "desc"
}
}
}
}
},
"sort": [
{
"_score": {
"order": "desc"
}
}
]
}
I'm using version 7.10 btw.
You could repeat that score calculator once again, this time in a boolean script query of its own.
Now, since your currency conversion script repeats itself one too many times, you could store it and reference it by its ID every time you need it. You'll of course keep the rates parametrized but the whole thing will be a bit more readable and maintainable.
So, let's save the script first:
POST _scripts/product-platforms-converter
{
"script": {
"source": """
def price = doc['product_platforms.price'].value;
def currency_id = doc['product_platforms.currency_id'].value;
def converted_price = price * (currency_id == 2
? params.rate_usd : (currency_id == 3
? params.rate_eur : params.rate_try));
if (params.final_range != null) {
def is_in_range = converted_price >= params.final_range.gte
&& converted_price <= params.final_range.lte;
return is_in_range;
}
return converted_price;
""",
"lang": "painless"
}
}
Notice that if final_range is provided in the params, the script returns a boolean; if not, it'll simply return the converted_price.
After that, the original query can be rewritten as:
GET /test_products/_search
{
"query": {
"nested": {
"path": "product_platforms",
"score_mode": "max",
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{
"term": {
"product_platforms.platform_id": {
"value": "3"
}
}
},
{
"script": {
"script": {
"id": "product-platforms-converter",
"params": {
"rate_try": 1,
"rate_usd": 7,
"rate_eur": 8,
"final_range": { <--- the desired "range" query
"gte": 10,
"lte": 100
}
}
}
}
}
]
}
},
"boost_mode": "replace",
"script_score": {
"script": {
"id": "product-platforms-converter",
"params": {
"rate_try": 1,
"rate_usd": 7,
"rate_eur": 8
}
}
}
}
},
"inner_hits": {
"name": "product_platforms",
"_source": true,
"size": 5,
"sort": {
"_script": {
"type": "number",
"script": {
"id": "product-platforms-converter",
"params": {
"rate_try": 1,
"rate_usd": 7,
"rate_eur": 8
}
},
"order": "desc"
}
}
}
}
},
"sort": [
{
"_score": {
"order": "desc"
}
}
]
}
I'm very new with elastic search and kibana . I'm using vega plugin in kibana visualization.
But not able to create Bar Chart using elastic search aggs.
I'm getting proper result when I'm using kibana dev tools.
I'am attaching the following details with the sample code after run this I'm getting a blank page
Visualization Section:
{
"$schema": "https://vega.github.io/schema/vega/v3.0.json",
"autosize": "fit",
"padding": 6,
"data": [
{
"name": "traffic-revenue",
"url": {
"index": "brnl_tms_plaza",
"body": {
"size": "0",
"aggs": {
"group_by_vehicle_subcat": {
"terms": {
"field": "VehicleSubCatCode.keyword"
}
}
}
},
"format": {
"property": "aggregations.group_by_vehicle_subcat.buckets"
}
}
}
],
"scales": [
{
"name": "xscale",
"type": "band",
"domain": {
"data": "traffic-revenue",
"field": "key"
},
"range": "width",
"padding": 0.05,
"round": true
},
{
"name": "yscale",
"domain": {
"data": "traffic-revenue",
"field": "doc_count"
},
"nice": true,
"range": "height"
}
],
"axes": [
{
"orient": "bottom",
"scale": "xscale"
},
{"orient": "left", "scale": "yscale"}
],
"marks": [
{
"type": "rect",
"from": {
"data": "traffic-revenue"
},
"encode": {
"enter": {
"x": {
"scale": "xscale",
"field": "key",
"axis": {"title": "Vehicle category"}
},
"width": {
"scale": "xscale",
"band": 1
},
"y": {
"scale": "yscale",
"field": "doc_count",
"axis": {"title": "Vehicle Rate Count"}
},
"y2": {
"scale": "yscale",
"value": 0
}
},
"update": {
"fill": {"value": "steelblue"}
},
"hover": {"fill": {"value": "red"}}
}
}
]
}
Data Set
{
"took": 7,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 48,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_vehicle_subcat": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "LMV",
"doc_count": 35
},
{
"key": "BUS",
"doc_count": 3
},
{
"key": "LCV",
"doc_count": 3
},
{
"key": "MAV-5",
"doc_count": 3
},
{
"key": "MAV-4 with trailer",
"doc_count": 2
},
{
"key": "MAV-3 without trailer",
"doc_count": 1
},
{
"key": "MINI-BUS",
"doc_count": 1
}
]
}
}
}
I would recommend debugging your vega code using static data to make sure it is defined properly.
I'm not sure why, but I was able to get your visualization to draw when I set the autosize property to none and set the height and width explicitly.
Here is a vega specification based off of the one you provided which should run in the online vega editor.
{
"$schema": "https://vega.github.io/schema/vega/v3.0.json",
"autosize": "none",
"width": 400,
"height": 500,
"padding": 20,
"data": [
{
"name": "traffic-revenue",
"values": [
{"key": "a", "doc_count": 5},
{"key": "b", "doc_count": 22},
{"key": "c", "doc_count": 1},
{"key": "d", "doc_count": 7},
{"key": "e", "doc_count": 12},
{"key": "f", "doc_count": 2}
]
}
],
"scales": [
{
"name": "xscale",
"type": "band",
"domain": {
"data": "traffic-revenue",
"field": "key"
},
"range": "width",
"padding": 0.05,
"round": true
},
{
"name": "yscale",
"domain": {
"data": "traffic-revenue",
"field": "doc_count"
},
"nice": true,
"range": "height"
}
],
"axes": [
{
"orient": "bottom",
"scale": "xscale"
},
{"orient": "left", "scale": "yscale"}
],
"marks": [
{
"type": "rect",
"from": {
"data": "traffic-revenue"
},
"encode": {
"enter": {
"x": {
"scale": "xscale",
"field": "key",
"axis": {"title": "Vehicle category"}
},
"width": {
"scale": "xscale",
"band": 1
},
"y": {
"scale": "yscale",
"field": "doc_count",
"axis": {"title": "Vehicle Rate Count"}
},
"y2": {
"scale": "yscale",
"value": 0
}
},
"update": {
"fill": {"value": "steelblue"}
},
"hover": {"fill": {"value": "red"}}
}
}
]
}
You may already know this since you have the format tag on your elasticsearch data, but if your visualization is working with statically defined data, and not when you pull data from an elasticsearch query, try looking at the data source directly using the vega debuggging functions described here https://vega.github.io/vega/docs/api/debugging/.
Running the following in the browser console should let you look at the data in the format vega is receiving it. VEGA_DEBUG.view.data("")
Elasticsearch version: 2.3.1
JVM version: 1.8.0_66 / 25.66-b17
OS version: Mac OS X 10.11.4
I am having trouble getting the correct values to show up in a 4 level deep aggregation scenario where the first two levels are nested, the third is reverse_nested, and the fourth is nested again.
Here is my index mapping:
curl -XDELETE localhost:9200/orders-d
curl -XPUT localhost:9200/orders-d
curl -XPUT localhost:9200/orders-d/order-d/_mapping -d '{
"order-d": {
"properties": {
"id": {
"type": "string"
},
"orderNumber": {
"type": "string"
},
"groupId": {
"type": "string"
},
"groupOrderNumber": {
"type": "string"
},
"dateCreated": {
"type": "date"
},
"dateUpdated": {
"type": "date"
},
"location": {
"type": "object"
},
"orderSubmitter": {
"type": "object"
},
"distributor": {
"type": "object"
},
"salesRep": {
"type": "object"
},
"status": {
"type": "string"
},
"total": {
"type": "double"
},
"isTTOrder": {
"type": "boolean"
},
"lineItems": {
"type": "nested",
"include_in_parent": true,
"properties": {
"product": {
"type": "object"
},
"category": {
"type": "object"
},
"subCategory": {
"type": "object"
},
"quantity": {
"type": "double"
},
"unitPrice": {
"type": "double"
},
"totalPrice": {
"type": "double"
},
"pricedByUnitPrice": {
"type": "double"
}
}
}
}
}
}'
Here are the documents:
curl -XPUT localhost:9200/orders-d/order-d/0 -d '{
"id": "571652632a19085c008b4577",
"orderNumber": "1617590686",
"groupId": "571652632a19085c008b4578",
"groupOrderNumber": "3485944627",
"dateCreated": "2016-04-19",
"dateUpdated": null,
"location": {
"id": "54e53853505eb66b008b4569",
"name": "Andrews Diner"
},
"orderSubmitter": {
"id": "54e53853505eb66b008b4567",
"name": "Kostantino Plaitis"
},
"distributor": {
"id": "55c3879459ad0c63008b4569",
"name": "Performance Foodservice Metro NY"
},
"salesRep": null,
"status": "pending",
"total": 5410.21,
"isTTOrder": true,
"lineItems": [{
"product": {
"id": "55bfb445c440b26a008b4571",
"name": "Sabrett Sauerkraut 12 x 2 lb bags"
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b4586",
"name": "Other Sauces Dipping\/Condiments\/Savoury Toppings\/Savoury Spreads\/Marinades (Perishable)"
},
"quantity": 1,
"unitPrice": 25.24,
"totalPrice": 25.24,
"pricedByUnitPrice": 0
}, {
"product": {
"id": "55bc219238c0376e008b4570",
"name": "Franks Red Hot Cayenne Pepper Sauce 4 x 1 gallon"
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b4606",
"name": "Other Sauces Dipping\/Condiments\/Savoury Toppings\/Savoury Spreads\/Marinades (Shelf Stable)"
},
"quantity": 1,
"unitPrice": 45.06,
"totalPrice": 45.06,
"pricedByUnitPrice": 0
}, {
"product": {
"id": "56d76c41bd821fda008b459a",
"name": "Cereal, Classic Variety Pack, Kelloggs 1\/60 ct."
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b462d",
"name": "Grains\/Cereal - Ready to Eat - (Shelf Stable)"
},
"quantity": 1,
"unitPrice": 56.03,
"totalPrice": 56.03,
"pricedByUnitPrice": 0
}]
}'
curl -XPUT localhost:9200/orders-d/order-d/0 -d '{
"id": "571652632a19085c008b4576",
"orderNumber": "2041063294",
"groupId": "571652632a19085c008b4578",
"groupOrderNumber": "3485944627",
"dateCreated": "2016-04-19",
"dateUpdated": null,
"location": {
"id": "54e53853505eb66b008b4569",
"name": "Andrews Diner"
},
"orderSubmitter": {
"id": "54e53853505eb66b008b4567",
"name": "Kostantino Plaitis"
},
"distributor": {
"id": "55cdeece0a41216c008b4583",
"name": "Driscoll Foods"
},
"salesRep": null,
"status": "pending",
"total": 7575.27,
"isTTOrder": true,
"lineItems": [{
"product": {
"id": "55ad05e08d28c36b008b456c",
"name": "Pepper 3000 pcs"
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b4582",
"name": "Herbs\/Spices (Shelf Stable)"
},
"quantity": 3,
"unitPrice": 8.95,
"totalPrice": 26.85,
"pricedByUnitPrice": 0
}, {
"product": {
"id": "55b3a12f6b415c68008b4568",
"name": "Venice Maid Deluxe Corned Beef Hash 6 x 6 lb 10 oz"
},
"category": {
"id": "53df846c3b8e77710e7b23f7",
"name": "Meat"
},
"subCategory": {
"id": "54d8c56a279871b9078b4581",
"name": "Beef - Prepared\/Processed"
},
"quantity": 1,
"unitPrice": 59.75,
"totalPrice": 59.75,
"pricedByUnitPrice": 0
}, {
"product": {
"id": "55b145798c26dc69008b4568",
"name": "Aladdin Bakers Sesame Bread Sticks 150 x 2 packs"
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b45b0",
"name": "Dried Breads (Shelf Stable)"
},
"quantity": 8,
"unitPrice": 15.5,
"totalPrice": 124,
"pricedByUnitPrice": 0
}, {
"product": {
"id": "55ad074a8d28c36f008b456d",
"name": "Smuckers Breakfast Syrup 100 cups"
},
"category": {
"id": "53df845b3b8e77710e7b23ec",
"name": "Groceries & Dry Food"
},
"subCategory": {
"id": "53e1e8723b8e77a52b8b457d",
"name": "Syrup\/Treacle\/Molasses (Shelf Stable)"
},
"quantity": 10,
"unitPrice": 8.95,
"totalPrice": 89.5,
"pricedByUnitPrice": 0
}]
}'
Here is my query:
curl -XPOST localhost:9200/orders-d/_search -d '{
"from": 0,
"size": 0,
"aggregations": {
"totalLineItems": {
"aggs": {
"totalLineItems": {
"terms": {
"field": "lineItems.category.id",
"size": 0
},
"aggs": {
"totalLineItems": {
"terms": {
"field": "lineItems.product.id",
"size": 0
},
"aggs": {
"totalLineItems": {
"aggs": {
"totalLineItems": {
"terms": {
"field": "distributor.id",
"size": 0
},
"aggs": {
"totalLineItems": {
"aggs": {
"totalLineItems": {
"sum": {
"field": "lineItems.totalPrice"
}
}
},
"nested": {
"path": "lineItems"
}
}
}
}
},
"reverse_nested": {}
}
}
}
}
}
},
"nested": {
"path": "lineItems"
}
}
},
"query": {
"bool": {
"must": [{
"range": {
"dateCreated": {
"format": "yyyy-MM-dd",
"gte": "2016-01-01",
"lte": "2016-04-30"
}
}
}]
}
}
}'
...and here are my results:
{
"took": 8,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.0,
"hits": []
},
"aggregations": {
"totalLineItems": {
"doc_count": 4,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "53df845b3b8e77710e7b23ec",
"doc_count": 3,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55ad05e08d28c36b008b456c",
"doc_count": 1,
"totalLineItems": {
"doc_count": 1,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55cdeece0a41216c008b4583",
"doc_count": 1,
"totalLineItems": {
"doc_count": 4,
"totalLineItems": {
"value": 300.1
}
}
}]
}
}
}, {
"key": "55ad074a8d28c36f008b456d",
"doc_count": 1,
"totalLineItems": {
"doc_count": 1,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55cdeece0a41216c008b4583",
"doc_count": 1,
"totalLineItems": {
"doc_count": 4,
"totalLineItems": {
"value": 300.1
}
}
}]
}
}
}, {
"key": "55b145798c26dc69008b4568",
"doc_count": 1,
"totalLineItems": {
"doc_count": 1,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55cdeece0a41216c008b4583",
"doc_count": 1,
"totalLineItems": {
"doc_count": 4,
"totalLineItems": {
"value": 300.1
}
}
}]
}
}
}]
}
}, {
"key": "53df846c3b8e77710e7b23f7",
"doc_count": 1,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55b3a12f6b415c68008b4568",
"doc_count": 1,
"totalLineItems": {
"doc_count": 1,
"totalLineItems": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [{
"key": "55cdeece0a41216c008b4583",
"doc_count": 1,
"totalLineItems": {
"doc_count": 4,
"totalLineItems": {
"value": 300.1
}
}
}]
}
}
}]
}
}]
}
}
}
}
As you can see from the results, all the aggregated values for each drilldown of totalLineItems have the same exact value. This is obviously incorrect.
Did I do something wrong, is it a bug, or is nesting inside a reverse nesting unsupported?