Elastic-search missing bucket aggregation - elasticsearch

Updated
I have the following elastic-search query. Which gives me the following results, with aggregation.
Tried following what Andrey Borisko example but for the life of me i can not get it working.
The main query with filter of companyId finds all the fullnames with the name 'Brenda'
The companyId agg returns best match companyId for fullnames brenda, based of the main filter.
My exact query
GET employee-index/_search
{
"aggs": {
"companyId": {
"terms": {
"field": "companyId"
},
"aggs": {
"filtered": {
"filter": {
"multi_match": {
"fields": [
"fullName.edgengram",
"number"
],
"query": "brenda"
}
}
}
}
}
},
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": [
"fullName.edgengram",
"number"
],
"query": "brenda"
}
}
],
"filter": [
{
"terms": {
"companyId": [
3849,
3867,
3884,
3944,
3260,
4187,
3844,
2367,
158,
3176,
3165,
3836,
4050,
3280,
2298,
3755,
3854,
7161,
3375,
7596,
836,
4616
]
}
}
]
}
}
}
My exact result
{
"took" : 14,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 8.262566,
"hits" : [
{
"_index" : "employee-index",
"_type" : "_doc",
"_id" : "67207",
"_score" : 8.262566,
"_source" : {
"companyGroupId" : 1595,
"companyId" : 158,
"fullName" : "Brenda Grey",
"companyTradingName" : "Sky Blue",
}
},
{
"_index" : "employee-index",
"_type" : "_doc",
"_id" : "7061",
"_score" : 7.868355,
"_source" : {
"companyGroupId" : 1595,
"companyId" : 158,
"fullName" : "Brenda Eaton",
"companyTradingName" : "Sky Blue",
}
},
{
"_index" : "employee-index",
"_type" : "_doc",
"_id" : "107223",
"_score" : 7.5100465,
"_source" : {
"companyGroupId" : 1595,
"companyId" : 3260,
"fullName" : "Brenda Bently",
"companyTradingName" : "Green Ice",
}
}
]
},
"aggregations" : {
"companyId" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "158",
"doc_count" : 2,
"filtered" : {
"doc_count" : 2
}
},
{
"key" : "3260",
"doc_count" : 1,
"filtered" : {
"doc_count" : 1
}
}
]
}
}
}
**This is how i want the filtered-companies results to look**
"aggregations": {
"companyId": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "158",
"doc_count": 2,
"filtered": {
"doc_count": 2 (<- 2 records found of brenda)
}
},
{
"key": "3260",
"doc_count": 1,
"filtered": {
"doc_count": 1 (<- 1 records found of brenda)
}
},
{
"key": "4616",
"doc_count": 0,
"filtered": {
"doc_count": 0 (<- 0 records found of brenda)
}
},
... and so on. Basically all the other companies that are in the filtered list i want to display with a doc_count of 0.
]
}

As I understood you correctly, you want to run aggregation or a part of aggregation independently from the query. In this case you should use Global Aggregation
UPDATE after your comment
In this case you need to use filter aggregation. So for example this type query (simplified your example) you have currently:
GET indexName/_search
{
"size": 0,
"query": {
"match": {
"firstName": "John"
}
},
"aggs": {
"by_phone": {
"terms": {
"field": "cellPhoneNumber"
}
}
}
}
becomes this:
GET indexName/_search
{
"size": 0,
"aggs": {
"by_phone": {
"terms": {
"field": "cellPhoneNumber"
},
"aggs": {
"filtered": {
"filter": {
"match": {
"firstName": "John"
}
}
}
}
}
}
}
the output will look slightly different though:
...
"aggregations" : {
"by_phone" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 260072,
"buckets" : [
{
"key" : "+9649400",
"doc_count" : 270,
"filtered" : {
"doc_count" : 0 <-- not John
}
},
{
"key" : "+8003000",
"doc_count" : 184,
"filtered" : {
"doc_count" : 3 <-- this is John
}
},
{
"key" : "+41025026",
"doc_count" : 168,
"filtered" : {
"doc_count" : 0 <-- not John
}
}
...
And now if you need the results of query as well then you have to wrap it in global aggregation like so:
GET indexName/_search
{
"size": 20,
"from": 0,
"query": {
"match": {
"firstName": "John"
}
},
"aggs": {
"all": {
"global": {},
"aggs": {
"by_phone": {
"terms": {
"field": "cellPhoneNumber"
},
"aggs": {
"filtered": {
"filter": {
"match": {
"firstName": "John"
}
}
}
}
}
}
}
}
}
Reviewed version based on your query:
GET employee-index/_search
{
"size": 0,
"aggs": {
"filtered": {
"filter": {
"bool": {
"filter": [
{
"terms": {
"companyId": [
3849,
3867,
3884,
3944,
3260,
4187,
3844,
2367,
158,
3176,
3165,
3836,
4050,
3280,
2298,
3755,
3854,
7161,
3375,
7596,
836,
4616
]
}
}
]
}
},
"aggs": {
"by_companyId": {
"terms": {
"field": "companyId",
"size": 1000
},
"aggs": {
"testing": {
"filter": {
"multi_match": {
"fields": [
"fullName"
],
"query": "brenda"
}
}
}
}
}
}
}
}
}

Related

Query filter for searching rollup index works with epoch time fails with date math

`How do we query (filter) a rollup index?
For example, based on the query here
Request:
{
"size": 0,
"aggregations": {
"timeline": {
"date_histogram": {
"field": "timestamp",
"fixed_interval": "7d"
},
"aggs": {
"nodes": {
"terms": {
"field": "node"
},
"aggs": {
"max_temperature": {
"max": {
"field": "temperature"
}
},
"avg_voltage": {
"avg": {
"field": "voltage"
}
}
}
}
}
}
}
}
Response:
{
"took" : 93,
"timed_out" : false,
"terminated_early" : false,
"_shards" : ... ,
"hits" : {
"total" : {
"value": 0,
"relation": "eq"
},
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"timeline" : {
"buckets" : [
{
"key_as_string" : "2018-01-18T00:00:00.000Z",
"key" : 1516233600000,
"doc_count" : 6,
"nodes" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "a",
"doc_count" : 2,
"max_temperature" : {
"value" : 202.0
},
"avg_voltage" : {
"value" : 5.1499998569488525
}
},
{
"key" : "b",
"doc_count" : 2,
"max_temperature" : {
"value" : 201.0
},
"avg_voltage" : {
"value" : 5.700000047683716
}
},
{
"key" : "c",
"doc_count" : 2,
"max_temperature" : {
"value" : 202.0
},
"avg_voltage" : {
"value" : 4.099999904632568
}
}
]
}
}
]
}
}
}
How to filter say last 3 days, is it possible?
For a test case, I used fixed_interval rate of 1m (one minute, and also 60 minutes) and I tried the following and the error was all query shards failed. Is it possible to query filter rollup agggregations?
Test Query for searching rollup index
{
"size": 0,
"query": {
"range": {
"timestamp": {
"gte": "now-3d/d",
"lt": "now/d"
}
}
}
"aggregations": {
"timeline": {
"date_histogram": {
"field": "timestamp",
"fixed_interval": "7d"
},
"aggs": {
"nodes": {
"terms": {
"field": "node"
},
"aggs": {
"max_temperature": {
"max": {
"field": "temperature"
}
},
"avg_voltage": {
"avg": {
"field": "voltage"
}
}
}
}
}
}
}
}

How do I compare two source IP from two different specific log in elastic search

In Elasticsearch I want to compare two logs (natlog and Gateway log) with DSL Query.
In nat log there is srcip1 and In gateway log there is srcip2
I want to if this condition srcip1 === srcip2 satisfied, "agent.id" display in result.
On top of it I will put my already corelated query which I have made
{
"query": {
"bool": {
"should": [
{
"match": {
"location": "\\Users\\Saad\\Desktop\\nat.log"
}
},
{
"match": {
"location": "\\Users\\Saad\\Desktop\\attendance-logs-with-ports.log"
}
}
],
"must": [
{
"term": {
"data.srcip": "1.1.1.1"
}
}
]
}
},
"fields": [
"data.srcip1"
],
"_source": false
}
I tried multiple things but not succeeded.
To display summaries of data you use aggregations. In case you want to compare the different agents depending on the log type for a certain ip the query will be this one:
Ingest data
POST test_saad/_doc
{
"location": "\\Users\\Saad\\Desktop\\nat.log",
"data": {
"srcip1": "1.1.1.1"
},
"agent": {
"id": "agent_1"
}
}
POST test_saad/_doc
{
"location": "\\Users\\Saad\\Desktop\\attendance-logs-with-ports.log",
"data": {
"srcip2": "1.1.1.1"
},
"agent": {
"id": "agent_1"
}
}
POST test_saad/_doc
{
"location": "\\Users\\Saad\\Desktop\\nat.log",
"data": {
"srcip1": "1.1.1.1"
},
"agent": {
"id": "agent_2"
}
}
Request
POST test_saad/_search
{
"size": 0,
"query": {
"bool": {
"must": [
{
"bool": {
"should": [
{
"term": {
"data.srcip1.keyword": "1.1.1.2"
}
},
{
"term": {
"data.srcip2.keyword": "1.1.1.2"
}
}
],
"minimum_should_match": 1
}
},
{
"bool": {
"should": [
{
"term": {
"location.keyword": """\Users\Saad\Desktop\nat.log"""
}
},
{
"term": {
"location.keyword": """\Users\Saad\Desktop\attendance-logs-with-ports.log"""
}
}
],
"minimum_should_match": 1
}
}
]
}
},
"aggs": {
"log_types": {
"terms": {
"field": "location.keyword",
"size": 10
},
"aggs": {
"agent_types": {
"terms": {
"field": "agent.id.keyword",
"size": 10
}
}
}
}
}
}
Response
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"log_types" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : """\Users\Saad\Desktop\nat.log""",
"doc_count" : 2,
"agent_types" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "agent_1",
"doc_count" : 1
},
{
"key" : "agent_2",
"doc_count" : 1
}
]
}
},
{
"key" : """\Users\Saad\Desktop\attendance-logs-with-ports.log""",
"doc_count" : 1,
"agent_types" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "agent_1",
"doc_count" : 1
}
]
}
}
]
}
}
}

ELASTICSEARCH - Get a count of values from the most recent document

I can't get a count of fields with a filtered document value.
I have this json
``
{
"took" : 6,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "net",
"_type" : "_doc",
"_id" : "RTHRTH",
"_score" : 1.0,
"_source" : {
"created_at" : "2020-05-31 19:01:01",
"data" : [...]
{
"_index" : "net",
"_type" : "_doc",
"_id" : "LLLoIJBHHM",
"_score" : 1.0,
"_source" : {
"created_at" : "2020-06-23 15:11:59",
"data" : [...]
}
}
]
}
}
``
In the "data" field, there are more fields within other fields respectively.
I want to filter the most recent document, and then count a certain value in the most recent document.
This is my query:
`{
"query": {
"match": {
"name.keyword": "net"
}
},
"sort": [
{
"created_at.keyword": {
"order": "desc"
}
}
],
"size": 1,
"aggs": {
"CountValue": {
"terms": {
"field": "data.add.serv.desc.keyword",
"include": "nginx"
}
}
}
}`
And the output is:
`{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"CountValue" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "nginx",
"doc_count" : 2
}
]
}
}`
I suspect that doc_count is the number of documents the value appears in, not the number of times the value is repeated within the filtered document.
Any advice I will be very grateful!
Unless any of the fields under the path data.add.serv are of the nested type, the terms agg will produce per-whole-doc results, not per-field.
Exempli gratia:
POST example/_doc
{
"serv": [
{
"desc": "nginx"
},
{
"desc": "nginx"
},
{
"desc": "nginx"
}
]
}
then
GET example/_search
{
"size": 0,
"aggs": {
"NAME": {
"terms": {
"field": "serv.desc.keyword"
}
}
}
}
produces doc_count==1.
When, however, specified as nested:
DELETE example
PUT example
{
"mappings": {
"properties": {
"serv": {
"type": "nested"
}
}
}
}
POST example/_doc
{"serv":[{"desc":"nginx"},{"desc":"nginx"},{"desc":"nginx"}]}
then
GET example/_search
{
"size": 0,
"aggs": {
"NAME": {
"nested": {
"path": "serv"
},
"aggs": {
"NAME": {
"terms": {
"field": "serv.desc.keyword"
}
}
}
}
}
}
we end up with doc_count==3.
This has to do with the way non-nested array types are flattened and de-duplicated. At the end, you may need to reindex your collections after having applied the nested mapping.
EDIT
In order to only take the latest doc, you could do the following:
PUT example
{
"mappings": {
"properties": {
"serv": {
"type": "nested"
},
"created_at": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss"
}
}
}
}
then
POST example/_doc
{
"created_at" : "2020-05-31 19:01:01",
"serv": [
{
"desc": "nginx"
},
{
"desc": "nginx"
},
{
"desc": "nginx"
}
]
}
POST example/_doc
{
"created_at" : "2020-06-23 15:11:59",
"serv": [
{
"desc": "nginx"
},
{
"desc": "nginx"
}
]
}
then use a terms agg of size 1, sorted by timestamp desc:
GET example/_search
{
"size": 0,
"aggs": {
"NAME": {
"terms": {
"field": "created_at",
"order": {
"_term": "desc"
},
"size": 1
},
"aggs": {
"NAME2": {
"nested": {
"path": "serv"
},
"aggs": {
"NAME": {
"terms": {
"field": "serv.desc.keyword"
}
}
}
}
}
}
}
}

Nested filter in Elasticsearch aggregation query

I am running following aggregation query with nested filter
GET <indexname>/_search
{
"aggs": {
"NAME": {
"nested": {
"path": "crm.LeadStatusHistory"
},
"aggs": {
"agg_filter": {
"filter": {
"bool": {
"must": [
{
"nested": {
"path": "crm",
"query": {
"terms": {
"crm.City.keyword": [
"Rewa"
]
}
}
}
},
{
"nested": {
"path": "crm",
"query": {
"terms": {
"crm.LeadID": [
27961
]
}
}
}
}
]
}
},
"aggs": {
"agg_terms":{
"terms": {
"field": "crm.LeadStatusHistory.StatusID",
"size": 1000
}
}
}
}
}
}
}
}
-----> i have following document
{
"_index" : "crm",
"_type" : "_doc",
"_id" : "4478",
"_score" : 1.0,
"_source" : {
"crm" : [
{
"LeadStatusHistory" : [
{
"StatusID" : 3
},
{
"StatusID" : 2
},
{
"StatusID" : 1
}
],
"LeadID" : 27961,
"City" : "Rewa"
},
{
"LeadStatusHistory" : [
{
"StatusID" : 1
},
{
"StatusID" : 3
},
{
"StatusID" : 2
}
],
"LeadID" : 27959,
"City" : "Rewa"
}
]
}
}]
However in response i am getting following result
"aggregations" : {
"NAME" : {
"doc_count" : 4332,
"agg_filter" : {
"doc_count" : 1,
"agg_terms" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 1,
"doc_count" : 1
}
]
}
}
}
}
Query===> As per source document, i have 3 nested 'crm.LeadStatusHistory' documents for crm.LeadID = 27961. However, results shows for agg_filter equals to 1 instead of 3. Can you please let me know the reason for this case.
Your agg_filter is on the crm.LeadStatusHistory => will target only 1 doc (LeadStatusHistory is one doc, contaning in your case link to others doc).
i build a query who show that, and i thinck will answer to your problem. You will see the different doc_count for each aggregation.
{
"size": 0,
"aggs": {
"NAME": {
"nested": {
"path": "crm"
},
"aggs": {
"agg_LeadID": {
"terms": {
"field": "crm.LeadID"
},
"aggs": {
"agg_LeadStatusHistory": {
"nested": {
"path": "crm.LeadStatusHistory"
},
"aggs": {
"home_type_name": {
"terms": {
"field": "crm.LeadStatusHistory.StatusID"
}
}
}
}
}
}
}
}
}
}
with this one you can count them, with a script (and filter if needed so):
{
"size": 0,
"aggs": {
"NAME": {
"nested": {
"path": "crm"
},
"aggs": {
"agg_LeadID": {
"terms": {
"field": "crm.LeadID"
},
"aggs": {
"agg_LeadStatusHistory": {
"nested": {
"path": "crm.LeadStatusHistory"
},
"aggs": {
"agg_LeadStatusHistory_sum": {
"sum": {
"script": "doc['crm.LeadStatusHistory.StatusID'].values.length"
}
}
}
}
}
}
}
}
}
}
note: if want to get the number of nested documents, take a look to inner_hits:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-body.html#request-body-search-inner-hits
I differ with the response that in 'crm.LeadStatusHistory' is one doc. I have run aggregation query on crm.LeadstatusHistory without filters.
GET crm/_search
{
"_source": ["crm.LeadID","crm.LeadStatusHistory.StatusID","crm.City"],
"size": 10000,
"query": {
"nested": {
"path": "crm",
"query": {
"match": {
"crm.LeadID": "27961"
}
}
}
},
"aggs": {
"agg_statuscount": {
"nested": {
"path": "crm.LeadStatusHistory"
},
"aggs": {
"agg_terms":{
"terms": {
"field": "crm.LeadStatusHistory.StatusID",
"size": 1000
}
}
}
}
}
}
I get following response from above query which shows 'agg_statuscount' as 6 docs without filters
{
"took" : 6,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "crm",
"_type" : "_doc",
"_id" : "4478",
"_score" : 1.0,
"_source" : {
"crm" : [
{
"LeadStatusHistory" : [
{
"StatusID" : 3
},
{
"StatusID" : 2
},
{
"StatusID" : 1
}
],
"LeadID" : 27961,
"City" : "Rewa"
},
{
"LeadStatusHistory" : [
{
"StatusID" : 1
},
{
"StatusID" : 3
},
{
"StatusID" : 2
}
],
"LeadID" : 27959,
"City" : "Rewa"
}
]
}
}
]
},
"aggregations" : {
"agg_statuscount" : {
"doc_count" : 6,
"agg_terms" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 1,
"doc_count" : 2
},
{
"key" : 2,
"doc_count" : 2
},
{
"key" : 3,
"doc_count" : 2
}
]
}
}
}
}
Hence with crm.LeadID = 27961 in aggregation filter, i expected 3 'crm.LeadStatusHistory' docs. Currently the response is 1 as in my original question.

Using named queries (matched_queries) for nested types in Elasticsearch?

Using named queries, I can get a list of the matched_queries for boolean expressions such as:
(query1) AND (query2 OR query3 OR true)
Here is an example of using named queries to match on top-level document fields:
DELETE test
PUT /test
PUT /test/_mapping/_doc
{
"properties": {
"name": {
"type": "text"
},
"type": {
"type": "text"
},
"TAGS": {
"type": "nested"
}
}
}
POST /test/_doc
{
"name" : "doc1",
"type": "msword",
"TAGS" : [
{
"ID" : "tag1",
"TYPE" : "BASIC"
},
{
"ID" : "tag2",
"TYPE" : "BASIC"
},
{
"ID" : "tag3",
"TYPE" : "BASIC"
}
]
}
# (query1) AND (query2 or query3 or true)
GET /test/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": {
"query": "doc1",
"_name": "query1"
}
}
}
],
"should": [
{
"match": {
"type": {
"query": "msword",
"_name": "query2"
}
}
},
{
"exists": {
"field": "type",
"_name": "query3"
}
}
]
}
}
}
The above query correctly returns all three matched_queries in the response:
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.5753641,
"hits" : [
{
"_index" : "test",
"_type" : "_doc",
"_id" : "TKNJ9G4BbvPS27u-ZYux",
"_score" : 1.5753641,
"_source" : {
"name" : "doc1",
"type" : "msword",
"TAGS" : [
{
"ID" : "ds1",
"TYPE" : "BASIC"
},
{
"ID" : "wb1",
"TYPE" : "BASIC"
}
]
},
"matched_queries" : [
"query1",
"query2",
"query3"
]
}
]
}
}
However, I'm trying to run a similar search:
(query1) AND (query2 OR query3 OR true)
only this time on the nested TAGS object rather than top-level document fields.
I've tried the following query, but the problem is I need to supply the inner_hits object for nested objects in order to get the matched_queries in the response, and I can only add it to one of the three queries.
GET /test/_search
{
"query": {
"bool": {
"must": {
"nested": {
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag1",
"_name": "tag1-query"
}
}
},
// "inner_hits" : {}
}
},
"should": [
{
"nested": {
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag2",
"_name": "tag2-query"
}
}
},
// "inner_hits" : {}
}
},
{
"nested": {
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag3",
"_name": "tag3-query"
}
}
},
// "inner_hits" : {}
}
}
]
}
}
}
Elasticsearch will complain if I add more than one 'inner_hits'. I've commented out the places above where I can add it, but each of these will only return the single matched query.
I want my response to this query to return:
"matched_queries" : [
"tag1-query",
"tag2-query",
"tag3-query"
]
Any help is much appreciated, thanks!
A colleague helpfully provided a solution to this; move the _named parameter to directly under each nested section:
GET /test/_search
{
"query": {
"bool": {
"must": {
"nested": {
"_name": "tag1-query",
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag1"
}
}
}
}
},
"should": [
{
"nested": {
"_name": "tag2-query",
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag2"
}
}
}
}
},
{
"nested": {
"_name": "tag3-query",
"path": "TAGS",
"query": {
"match": {
"TAGS.ID": {
"query": "tag3"
}
}
}
}
}
]
}
}
}
This correctly returns all three tags now in the matched_queries response:
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 2.9424875,
"hits" : [
{
"_index" : "test",
"_type" : "_doc",
"_id" : "TaNy9G4BbvPS27u--oto",
"_score" : 2.9424875,
"_source" : {
"name" : "doc1",
"type" : "msword",
"TAGS" : [
{
"ID" : "ds1",
"TYPE" : "DATASOURCE"
},
{
"ID" : "wb1",
"TYPE" : "WORKBOOK"
},
{
"ID" : "wb2",
"TYPE" : "WORKBOOK"
}
]
},
"matched_queries" : [
"tag1-query",
"tag2-query",
"tag3-query"
]
}
]
}
}

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