Is there a way to find out via the elasticsearch API how a query string query is actually parsed? You can do that manually by looking at the lucene query syntax, but it would be really nice if you could look at some representation of the actual results the parser has.
As javanna mentioned in comments there's _validate api. Here's what works on my local elastic (version 1.6):
curl -XGET 'http://localhost:9201/pl/_validate/query?explain&pretty' -d'
{
"query": {
"query_string": {
"query": "a OR (b AND c) OR (d AND NOT(e or f))",
"default_field": "t"
}
}
}
'
pl is name of index on my cluster. Different index could have different analyzers, that's why query validation is executed in a scope of an index.
The result of the above curl is following:
{
"valid" : true,
"_shards" : {
"total" : 1,
"successful" : 1,
"failed" : 0
},
"explanations" : [ {
"index" : "pl",
"valid" : true,
"explanation" : "filtered(t:a (+t:b +t:c) (+t:d -(t:e t:or t:f)))->cache(org.elasticsearch.index.search.nested.NonNestedDocsFilter#ce2d82f1)"
} ]
}
I made one OR lowercase on purpose and as you can see in explanation, it is interpreted as a token and not as a operator.
As for interpretation of the explanation. Format is similar to +- operators of query string query:
( and ) characters start and end bool query
+ prefix means clause that will be in must
- prefix means clause that will be in must_not
no prefix means that it will be in should (with default_operator equal to OR)
So above will be equivalent to following:
{
"bool" : {
"should" : [
{
"term" : { "t" : "a" }
},
{
"bool": {
"must": [
{
"term" : { "t" : "b" }
},
{
"term" : { "t" : "c" }
}
]
}
},
{
"bool": {
"must": {
"term" : { "t" : "d" }
},
"must_not": {
"bool": {
"should": [
{
"term" : { "t" : "e" }
},
{
"term" : { "t" : "or" }
},
{
"term" : { "t" : "f" }
}
]
}
}
}
}
]
}
}
I used _validate api quite heavily to debug complex filtered queries with many conditions. It is especially useful if you want to check how analyzer tokenized input like an url or if some filter is cached.
There's also an awesome parameter rewrite that I was not aware of until now, which causes the explanation to be even more detailed showing the actual Lucene query that will be executed.
Related
I recently run an Elasticsearch filter request that is
{
"from" : 0,
"size" : 10,
"query" : {
"filtered" : {
"filter" : {
"bool" : {
"must" : {
"terms" : {
"a_id" : [ 257793, 257798, 257844 ]
}
}
}
}
}
},
"explain" : false,
"fields" : "a_id"
}
So that I can find all docs with a_id in 257793, 257798, 257844 and the results are 257844, 257798, 257793. So far so good.
Then I find that whatever the sequence of the term numbers are, the return docs are always in the same a_id order. That is, even I run
"terms" : {
"a_id" : [257798, 257844, 257793 ]
}
The result docs are in the order of 257844, 257798, 257793 as well.
So I am so curious about the mechanism behind the Elasticsearch filtering. Can anyone help me and give me a hint?
By default, ES returns in descending order of _score. You can provide the sort option, to say in which order and based on what you want the results to be returned. For e.g., for based on date field
{
"sort": { "date": { "order": "desc" }}
"query" : {
"term" : { "user" : "kimchy" }
}
}
You can get more information:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-sort.html
https://www.elastic.co/guide/en/elasticsearch/guide/current/_sorting.html
I am using elastic search 1.4.1 - 1.4.4. I'm trying to index a geo polygon shape (document) into my index and now when the shape is indexed i want to know if a geo coordinate lies within the boundaries of that particular indexed geo-polygon shape.
GET /city/_search
{
"query":{
"filtered" : {
"query" : {
"match_all" : {}
},
"filter" : {
"geo_polygon" : {
"location" : {
"points" : [
[72.776491, 19.259634],
[72.955705, 19.268060],
[72.945406, 19.189611],
[72.987291, 19.169507],
[72.963945, 19.069596],
[72.914506, 18.994300],
[72.873994, 19.007933],
[72.817689, 18.896882],
[72.816316, 18.941052],
[72.816316, 19.113720],
[72.816316, 19.113720],
[72.790224, 19.192205],
[72.776491, 19.259634]
]
}
}
}
}
}
}
With above geo polygon filter i'm able get all indexed geo-coordinates lies within described polygon but i also need to know if a non-indexed geo-coordinate lies with in this geo polygon or not. My doubt is that if that is possible in the elastic search 1.4.1.
Yes, Percolator can be used to solve this problem.
As in normal use case of Elasticsearch, we index our docs into elasticsearch and then we run queries on indexed data to retrieve matched/ required documents.
But percolators works in a different way of it.
In percolators you register your queries and then you percolate your documents through registered queries and gets back the queries which matches your documents.
After going through infinite number of google results and many of blogs i wasn't able to find any thing which could explain how i can use percolators to solve this problem.
So i'm explaining this with an example so that other people facing same problem can take a hint from my problem and the solution i found. I would like if someone can improve my answer or can share a better approach of doing it.
e.g:-
First of all we need to create an index.
PUT /city/
then, we need to add a mapping for user document which consist a user's
latitude-longitude for percolating against registered queries.
PUT /city/user/_mapping
{
"user" : {
"properties" : {
"location" : {
"type" : "geo_point"
}
}
}
}
Now, we can register our geo polygon queries as percolators with id as city name or any other identifier you want to.
PUT /city/.percolator/mumbai
{
"query":{
"filtered" : {
"query" : {
"match_all" : {}
},
"filter" : {
"geo_polygon" : {
"location" : {
"points" : [
[72.776491, 19.259634],
[72.955705, 19.268060],
[72.945406, 19.189611],
[72.987291, 19.169507],
[72.963945, 19.069596],
[72.914506, 18.994300],
[72.873994, 19.007933],
[72.817689, 18.896882],
[72.816316, 18.941052],
[72.816316, 19.113720],
[72.816316, 19.113720],
[72.790224, 19.192205],
[72.776491, 19.259634]
]
}
}
}
}
}
}
Let's register another geo polygon filter for another city
PUT /city/.percolator/delhi
{
"query":{
"filtered" : {
"query" : {
"match_all" : {}
},
"filter" : {
"geo_polygon" : {
"location" : {
"points" : [
[76.846998, 28.865160],
[77.274092, 28.841104],
[77.282331, 28.753252],
[77.482832, 28.596619],
[77.131269, 28.395064],
[76.846998, 28.865160]
]
}
}
}
}
}
}
Now we have registered 2 queries as percolators and we can make sure by making this API call.
GET /city/.percolator/_count
Now to know if a geo point exist with any of registered cities we can percolate a user document using below query.
GET /city/user/_percolate
{
"doc": {
"location" : {
"lat" : 19.088415,
"lon" : 72.871248
}
}
}
This will return : _id as "mumbai"
{
"took": 25,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"total": 1,
"matches": [
{
"_index": "city",
"_id": "mumbai"
}
]
}
trying another query with different lat-lon
GET /city/user/_percolate
{
"doc": {
"location" : {
"lat" : 28.539933,
"lon" : 77.331770
}
}
}
This will return : _id as "delhi"
{
"took": 25,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"total": 1,
"matches": [
{
"_index": "city",
"_id": "delhi"
}
]
}
Let's run another query with random lat-lon
GET /city/user/_percolate
{
"doc": {
"location" : {
"lat" : 18.539933,
"lon" : 45.331770
}
}
}
and this query will return no matched results.
{
"took": 5,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"total": 0,
"matches": []
}
I want to regex search an integer value in MongoDB. Is this possible?
I'm building a CRUD type interface that allows * for wildcards on the various fields. I'm trying to keep the UI consistent for a few fields that are integers.
Consider:
> db.seDemo.insert({ "example" : 1234 });
> db.seDemo.find({ "example" : 1234 });
{ "_id" : ObjectId("4bfc2bfea2004adae015220a"), "example" : 1234 }
> db.seDemo.find({ "example" : /^123.*/ });
>
As you can see, I insert an object and I'm able to find it by the value. If I try a simple regex, I can't actually find the object.
Thanks!
If you are wanting to do a pattern match on numbers, the way to do it in mongo is use the $where expression and pass in a pattern match.
> db.test.find({ $where: "/^123.*/.test(this.example)" })
{ "_id" : ObjectId("4bfc3187fec861325f34b132"), "example" : 1234 }
I am not a big fan of using the $where query operator because of the way it evaluates the query expression, it doesn't use indexes and the security risk if the query uses user input data.
Starting from MongoDB 4.2 you can use the $regexMatch|$regexFind|$regexFindAll available in MongoDB 4.1.9+ and the $expr to do this.
let regex = /123/;
$regexMatch and $regexFind
db.col.find({
"$expr": {
"$regexMatch": {
"input": {"$toString": "$name"},
"regex": /123/
}
}
})
$regexFinAll
db.col.find({
"$expr": {
"$gt": [
{
"$size": {
"$regexFindAll": {
"input": {"$toString": "$name"},
"regex": "123"
}
}
},
0
]
}
})
From MongoDB 4.0 you can use the $toString operator which is a wrapper around the $convert operator to stringify integers.
db.seDemo.aggregate([
{ "$redact": {
"$cond": [
{ "$gt": [
{ "$indexOfCP": [
{ "$toString": "$example" },
"123"
] },
-1
] },
"$$KEEP",
"$$PRUNE"
]
}}
])
If what you want is retrieve all the document which contain a particular substring, starting from release 3.4, you can use the $redact operator which allows a $conditional logic processing.$indexOfCP.
db.seDemo.aggregate([
{ "$redact": {
"$cond": [
{ "$gt": [
{ "$indexOfCP": [
{ "$toLower": "$example" },
"123"
] },
-1
] },
"$$KEEP",
"$$PRUNE"
]
}}
])
which produces:
{
"_id" : ObjectId("579c668c1c52188b56a235b7"),
"example" : 1234
}
{
"_id" : ObjectId("579c66971c52188b56a235b9"),
"example" : 12334
}
Prior to MongoDB 3.4, you need to $project your document and add another computed field which is the string value of your number.
The $toLower and his sibling $toUpper operators respectively convert a string to lowercase and uppercase but they have a little unknown feature which is that they can be used to convert an integer to string.
The $match operator returns all those documents that match your pattern using the $regex operator.
db.seDemo.aggregate(
[
{ "$project": {
"stringifyExample": { "$toLower": "$example" },
"example": 1
}},
{ "$match": { "stringifyExample": /^123.*/ } }
]
)
which yields:
{
"_id" : ObjectId("579c668c1c52188b56a235b7"),
"example" : 1234,
"stringifyExample" : "1234"
}
{
"_id" : ObjectId("579c66971c52188b56a235b9"),
"example" : 12334,
"stringifyExample" : "12334"
}
Now, if what you want is retrieve all the document which contain a particular substring, the easier and better way to do this is in the upcoming release of MongoDB (as of this writing) using the $redact operator which allows a $conditional logic processing.$indexOfCP.
db.seDemo.aggregate([
{ "$redact": {
"$cond": [
{ "$gt": [
{ "$indexOfCP": [
{ "$toLower": "$example" },
"123"
] },
-1
] },
"$$KEEP",
"$$PRUNE"
]
}}
])
I am using Elasticsearch with Haystacksearch and Django and want to search the follow structure:
{
{
"title": "book1",
"category" : ["Cat_1", "Cat_2"],
"key_values" :
[
{
"key_name" : "key_1",
"value" : "sample_value_1"
},
{
"key_name" : "key_2",
"value" : "sample_value_12"
}
]
},
{
"title": "book2",
"category" : ["Cat_3", "Cat_2"],
"key_values" :
[
{
"key_name" : "key_1",
"value" : "sample_value_1"
},
{
"key_name" : "key_3",
"value" : "sample_value_6"
},
{
"key_name" : "key_4",
"value" : "sample_value_5"
}
]
}
}
Right now I have set up an index model using Haystack with a "text" that put all the data together and runs a full text search! In my opinion this is not the a well established search 'cause I am not using my data set structure and hence this is some kind odd.
As an example if for an object I have a key-value
{
"key_name": "key_1",
"value": "sample_value_1"
}
and for another object I have
{
"key_name": "key_2",
"value": "sample_value_1"
}
and we it gets a query like "Key_1 sample_value_1" comes I get a thoroughly mixed result of objects who have these words in their fields rather than using their structures.
P.S. I am totally new to ElasticSearch and better to say new to the search technologies and challenges. I have searched the web and SO button didn't find anything satisfying. Please let me know if there is something wrong with my thoughts and expectations from these search engines and if there is SO duplicate question! And also if there is a better approach to design a database for this kind of search
Read the es docs on nested mappings and do something like this:
"book_type" : {
"properties" : {
// title, cat mappings
"key_values" : {
"type" : "nested"
"properties": {
"key_name": {
"type": "string", "index": "not_analyzed"
},
"value": {
"type": "string"
}
}
}
}
}
Then query using a nested query
"nested" : {
"path" : "key_values",
"query" : {
"bool" : {
"must" : [
{
"term" : {"key_values.key_name" : "key_1"}
},
{
"match" : {"key_values.value" : "sample_value_1"}
}
]
}
}
}
I am issuing a query to elasticsearch and I am getting multiple record types. How do I limit the results to one type?
The following query will limit results to records with the type "your_type":
curl - XGET 'http://localhost:9200/_all/your_type/_search?q=your_query'
See http://www.elasticsearch.org/guide/reference/api/search/indices-types.html for more details.
You can also use query dsl to filter out results for specific type like this:
$ curl -XGET 'http://localhost:9200/_search' -d '{
"query": {
"filtered" : {
"filter" : {
"type" : { "value" : "my_type" }
}
}
}
}
'
Update for version 6.1:
Type filter is now replaced by Type Query: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-type-query.html
You can use that in both Query and Filter contexts.
{
"query" : {
"filtered" : {
"filter" : {
"bool" : {
"must" :[{"term":{"_type":"UserAudit"}}, {"term" : {"eventType": "REGISTRATION"}}]
}
}
}
},
"aggs":{
"monthly":{
"date_histogram":{
"field":"timestamp",
"interval":"1y"
},
"aggs":{
"existing_visitor":{
"terms":{
"field":"existingGuest"
}
}
}
}
}
}
"_type":"UserAudit" condition will look the records only specific to type
On version 2.3 you can query _type field like:
{
"query": {
"terms": {
"_type": [ "type_1", "type_2" ]
}
}
}
Or if you want to exclude a type:
{
"query": {
"bool" : {
"must_not" : {
"term" : {
"_type" : "Hassan"
}
}
}
}
}