According to Official 7.x document Link
While a field is deemed non-existent if the JSON value is null or [],
these values will indicate the field does exist:
Empty strings, such as "" or "-" Arrays containing null and another
value, such as [null, "foo"] A custom null-value, defined in field
mapping
However, My es not consider "" as not existed.
Here is my Data:
"_source" : {
"chat_msg" : {
"action" : "send",
"from" : "t",
"msgid" : "6505946507184390735_161_external",
"msgtime" : 1623396135015,
"msgtype" : "text",
"roomid" : "",
Now, When I do Query As :
GET enterprise_chat_data/_search
{
"query": {
"bool": {
"must_not": [
{
"exists": {
"field": "chat_msg.roomid"
}
}
]
}
}
}
Result:
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 2,
"successful" : 2,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
It Hit Nothing. Am I Wrong About Something?
I think you have misunderstood the documentation. In the documentation, it is written that if a field have value as empty strings, such as "" or "-", then that field will be considered to be existing.
Due to this when you are querying for must_not exists query for "chat_msg.roomid" field, you are getting empty results, as in the data you have indexed the value of "chat_msg.roomid" field as ""
Update 1:
You can use term query to search for documents having field value of chat_msg.roomid as ""
{
"query": {
"term": {
"chat_msg.roomid.keyword": ""
}
}
}
Related
I have an error in kibana
"The length [2658823] of field [message] in doc[235892]/index[mylog-2023.02.10] exceeds the [index.highlight.max_analyzed_offset] limit [1000000]. To avoid this error, set the query parameter [max_analyzed_offset] to a value less than index setting [1000000] and this will tolerate long field values by truncating them."
I know how to deal with it (change "index.highlight.max_analyzed_offset" for an index, or set the query parameter), but I want to find the document with long field and examine it.
If i try to find it by id, i get this:
q:
GET mylog-2023.02.10/_search
{
"query": {
"terms": {
"_id": [ "235892" ]
}
}
}
a:
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
q:
GET mylog-2023.02.10/_doc/235892
a:
{ "_index" : "mylog-2023.02.10", "_type" : "_doc", "_id" :
"235892", "found" : false }
Maybe this number (doc[235892]) is not id? How can i find this document?
try use Query IDs:
GET /_search
{
"query": {
"ids" : {
"values" : ["1", "4", "100"]
}
}
}
I am looking for elastic search aggregation + mapping
that will return the most common list for a certain field.
For example for docs:
{"ToneCurvePV2012": [1,2,3]}
{"ToneCurvePV2012": [1,5,6]}
{"ToneCurvePV2012": [1,7,8]}
{"ToneCurvePV2012": [1,2,3]}
I wish for the aggregation result:
[1,2,3] (since it appears twice).
so far any aggregation that i made would return: 1
This is not possible with default terms aggregation. You need to use terms aggregation with script. Please note that this might impact your cluster performance.
Here, i have used script which will create string from array and used it for aggregation. so if you have array value like [1,2,3] then it will create string representation of it like '[1,2,3]' and that key will be used for aggregation.
Below is sample query you can use to generate aggregation as you expected:
POST index1/_search
{
"size": 0,
"aggs": {
"tone_s": {
"terms": {
"script": {
"source": "def value='['; for(int i=0;i<doc['ToneCurvePV2012'].length;i++){value= value + doc['ToneCurvePV2012'][i] + ',';} value+= ']'; value = value.replace(',]', ']'); return value;"
}
}
}
}
}
Output:
{
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"tone_s" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "[1,2,3]",
"doc_count" : 2
},
{
"key" : "[1,5,6]",
"doc_count" : 1
},
{
"key" : "[1,7,8]",
"doc_count" : 1
}
]
}
}
}
PS: key will be come as string and not as array in aggregation response.
The question is based on the previous post where the Exact Search did not work either based on Match or MatchPhrasePrefix.
Then I found a similar kind of post here where the search field is set to be not_analyzed in the mapping definition (by #Russ Cam).
But I am using
package id="Elasticsearch.Net" version="7.6.0" targetFramework="net461"
package id="NEST" version="7.6.0" targetFramework="net461"
and might be for that reason the solution did not work.
Because If I pass "SOME", it matches with "SOME" and "SOME OTHER LOAN" which should not be the case (in my earlier post for "product value").
How can I do the same using NEST 7.6.0?
Well I'm not aware of how your current mapping looks. Also I don't know about NEST as well but I will explain
How to make Elastic Engine understand a field is not to be analyzed for an exact match?
by an example using elastic dsl.
For exact match (case sensitive) all you need to do is to define the field type as keyword. For a field of type keyword the data is indexed as it is without applying any analyzer and hence it is perfect for exact matching.
PUT test
{
"mappings": {
"properties": {
"field1": {
"type": "keyword"
}
}
}
}
Now lets index some docs
POST test/_doc/1
{
"field1":"SOME"
}
POST test/_doc/2
{
"field1": "SOME OTHER LOAN"
}
For exact matching we can use term query. Lets search for "SOME" and we should get document 1.
GET test/_search
{
"query": {
"term": {
"field1": "SOME"
}
}
}
O/P that we get:
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.6931472,
"hits" : [
{
"_index" : "test",
"_type" : "_doc",
"_id" : "1",
"_score" : 0.6931472,
"_source" : {
"field1" : "SOME"
}
}
]
}
}
So the crux is make the field type as keyword and use term query.
Suppose I have the following data:
{"field": [{"type": "A"}, {"type": "B"}]},
{"field": [{"type": "B"}]}
How do you construct a query in Elasticsearch to get the count of all records with a specific field type value, given field is an array?
You can use the Count API, with the following query
Query:
GET /index/index_type/_count
{
"query" : {
"term" : { "field.type" : "A" }
}
}
Response:
{
"count" : <number of docs>,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
}
}
I currently have a fairly simple document stored in ElasticSearch that I generated with an integration test:
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [ {
"_index" : "unit-test_project600",
"_type" : "recordDefinition505",
"_id" : "400",
"_score" : 1.0, "_source" : {
"field900": "test string",
"field901": "500",
"field902": "2050-01-01T00:00:00",
"field903": [
"Open"
]
}
} ]
}
}
I would like to filter for specifically field903 and a value of "Open", so I perform the following query:
{
query: {
filtered: {
filter: {
term: {
field903: "Open",
}
}
}
}
}
This returns no results. However, I can use this with other fields and it will return the record:
{
query: {
filtered: {
filter: {
term: {
field901: "500",
}
}
}
}
}
It would appear that I'm unable to search in arrays with ElasticSearch. I have read a few instances of people with a similar problem, but none of them appear to have solved it. Surely this isn't a limitation of ElasticSearch?
I thought that it might be a mapping problem. Here's my mapping:
{
"unit-test_project600" : {
"recordDefinition505" : {
"properties" : {
"field900" : {
"type" : "string"
},
"field901" : {
"type" : "string"
},
"field902" : {
"type" : "date",
"format" : "dateOptionalTime"
},
"field903" : {
"type" : "string"
}
}
}
}
}
However, the ElasticSearch docs indicate that there is no difference between a string or an array mapping, so I don't think I need to make any changes here.
Try searching for "open" rather than "Open." By default, Elasticsearch uses a standard analyzer when indexing fields. The standard analyzer uses a lowercase filter, as described in the example here. From my experience, Elasticsearch does search arrays.