I am trying to build an autocomplete feature for our database running on MongoDB. We need to provide autocomplete which lets users complete their queries by offering suggestions while they are typing in the search box.
I have a collection of articles from various sources, which is having the following fields :
{
"title" : "Its the title of a random article",
"cont" : { "paragraphs" : [ .... ] },
and so on..
}
I went through a video by Clinton Gormley. From 37:00 through 42:00 minute, Gormley describes an autocomplete using edgeNGram. Also, I referred to this question to recognize that both are almost the same things, just the mappings differ.
So based on these experiences, I built almost identical settings and mapping and then restored articles collection to ensure that it is indexed by ElasticSearch
The indexing scheme is as follows:
POST /title_autocomplete/title
{
"settings": {
"analysis": {
"filter": {
"autocomplete": {
"type": "edgeNGram",
"min_gram": 2,
"max_gram": 50
}
},
"analyzer": {
"title" : {
"type" : "standard",
"stopwords":[]
},
"autocomplete": {
"type" : "autocomplete",
"tokenizer": "standard",
"filter": ["lowercase", "autocomplete"]
}
}
}
},
"mappings": {
"title": {
"type": "multi_field",
"fields" : {
"title" : {
"type": "string",
"analyzer": "title"
},
"autocomplete" : {
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer" : "title"
}
}
}
}
}
But when I run the search query, I am unable to get any hits!
GET /title_autocomplete/title/_search
{
"query": {
"bool" : {
"must" : {
"match" : {
"title.autocomplete" : "Its the titl"
}
},
"should" : {
"match" : {
"title" : "Its the titl"
}
}
}
}
}
Can anybody please explain what's wrong with the mapping query or settings? I have been reading ElasticSearch docs for over 7 days now but seem to get nowhere more than full text searches!
ElastiSearch version : 0.90.10
MongoDB version : v2.4.9
using _river
Ubuntu 12.04 64bit
UPDATE
I realised that mapping is screwed after applying previous settings:
GET /title_autocomplete/_mapping
{
"title_autocomplete": {
"title": {
"properties": {
"analysis": {
"properties": {
"analyzer": {
"properties": {
"autocomplete": {
"properties": {
"filter": {
"type": "string"
},
"tokenizer": {
"type": "string"
},
"type": {
"type": "string"
}
}
},
"title": {
"properties": {
"type": {
"type": "string"
}
}
}
}
},
"filter": {
"properties": {
"autocomplete": {
"properties": {
"max_gram": {
"type": "long"
},
"min_gram": {
"type": "long"
},
"type": {
"type": "string"
}
}
}
}
}
}
},
"content": {
... paras and all ...
}
"title": {
"type": "string"
},
"url": {
"type": "string"
}
}
}
}
}
Analyzers and filters are actually mapped into the document after the settings are applied whereas original title field is not affected at all! Is this normal??
I guess this explains why the query is not matching. There is no title.autocomplete field or title.title field at all.
So how should I proceed now?
For those facing this problem, its better to delete the index and start again instead of wasting time with the _river just as DrTech pointed out in the comment.
This saves time but is not a solution. (Therefore not marking it as answer.)
The key is to set up the mappings and index before you initiate the river.
We had an existing setup with a mongodb river and an index called coresearch that we wanted to add autocomplete capacity to, this is the set of commands we used to delete the existing index and river and start again.
Stack is:
ElasticSearch 1.1.1
MongoDB 2.4.9
ElasticSearchMapperAttachments v2.0.0
ElasticSearchRiverMongoDb/2.0.0
Ubuntu 12.04.2 LTS
curl -XDELETE "localhost:9200/_river/node"
curl -XDELETE "localhost:9200/coresearch"
curl -XPUT "localhost:9200/coresearch" -d '
{
"settings": {
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
},
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
}
}'
curl -XPUT "localhost:9200/coresearch/_mapping/users" -d '{
"users": {
"properties": {
"firstname": {
"type": "string",
"search_analyzer": "standard",
"index_analyzer": "autocomplete"
},
"lastname": {
"type": "string",
"search_analyzer": "standard",
"index_analyzer": "autocomplete"
},
"username": {
"type": "string",
"search_analyzer": "standard",
"index_analyzer": "autocomplete"
},
"email": {
"type": "string",
"search_analyzer": "standard",
"index_analyzer": "autocomplete"
}
}
}
}'
curl -XPUT "localhost:9200/_river/node/_meta" -d '
{
"type": "mongodb",
"mongodb": {
"servers": [
{ "host": "127.0.0.1", "port": 27017 }
],
"options":{
"exclude_fields": ["time"]
},
"db": "users",
"gridfs": false,
"options": {
"import_all_collections": true
}
},
"index": {
"name": "coresearch",
"type": "documents"
}
}'
Related
I have to create an ElasticSearch mapping like this using elastic4s:
"mappings": {
"properties": {
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "ngram_analyzer",
"fielddata": true
},
"lang": {
"type": "keyword"
},
"order": {
"type": "long"
},
"active": {
"type": "boolean"
}
"description": {
"type": "text"
}
}
}
I can do
def mapping: Option[MappingDefinition] =
Some(
properties(
KeywordField("id"),
KeywordField("lang"),
BasicField("order", "long"),
BasicField("active", "boolean"),
TextField("description")
)
)
for id, lang, order, active and description.
But, how can I do such mapping for name. the problem is analyzer and fielddata inside it.
You should use this:
TextField("name").fielddata(true).analyzer("ngram_analyzer")
You also need to make sure to properly create the ngram_analyzer in your index settings.
I am trying to install ELK for logs centralization. I am following steps described in digital ocean website: https://www.digitalocean.com/community/tutorials/how-to-install-elasticsearch-logstash-and-kibana-elk-stack-on-ubuntu-14-04
I fail to download the filebeat index template using curl from this link:
https://gist.githubusercontent.com/thisismitch/3429023e8438cc25b86c/raw/d8c479e2a1adcea8b1fe86570e42abab0f10f364/filebeat-index-template.json
Can someone give me another source to get it ?
Here is the content of that link:
{
"mappings": {
"_default_": {
"_all": {
"enabled": true,
"norms": {
"enabled": false
}
},
"dynamic_templates": [
{
"template1": {
"mapping": {
"doc_values": true,
"ignore_above": 1024,
"index": "not_analyzed",
"type": "{dynamic_type}"
},
"match": "*"
}
}
],
"properties": {
"#timestamp": {
"type": "date"
},
"message": {
"type": "string",
"index": "analyzed"
},
"offset": {
"type": "long",
"doc_values": "true"
},
"geoip" : {
"type" : "object",
"dynamic": true,
"properties" : {
"location" : { "type" : "geo_point" }
}
}
}
}
},
"settings": {
"index.refresh_interval": "5s"
},
"template": "filebeat-*"
}
Just make a file called filebeat-index-template.json with this command:
touch filebeat-index-template.json
Then open the file in an editor like this:
nano filebeat-index-template.json
Then copy and paste the contents from the link above and save the file (ctrl + x).
After that you should be able to continue through the digital ocean walkthrough at the "load the template" part.
Given a json schema like the one below, the react-jsonschema-form validator essentially requires both shipping_address and billing_address even though the billing_address is not listed as required. This is because the address type requires all three of its properties. How can I make the billing_address optional? It seems that react-jsonschema-form should simply no submit billing_address if not all of its address properties are filled in. Here is a link to the react-jsonschema-form playground.
{
"definitions": {
"address": {
"type": "object",
"properties": {
"street_address": {
"type": "string"
},
"city": {
"type": "string"
},
"state": {
"type": "string"
}
},
"required": [
"street_address",
"city",
"state"
]
}
},
"type": "object",
"properties": {
"billing_address": {
"title": "Billing address",
"$ref": "#/definitions/address"
},
"shipping_address": {
"title": "Shipping address",
"$ref": "#/definitions/address"
}
},
"required": [
"shipping_address"
]
}
You can use dynamic schema dependencies to make the billing address conditionally displayed and required. This isn't the same as having an optional object but seem to suffice if you're willing to have a slightly different user experience. Here is a link to the react-jsonschema-form playground. It is best viewed, in my opinion, with live validation disabled (there's a checkbox in the upper-right of the page).
{
"definitions": {
"address": {
"type": "object",
"properties": {
"street_address": {
"type": "string"
},
"city": {
"type": "string"
},
"state": {
"type": "string"
}
},
"required": [
"street_address",
"city",
"state"
]
}
},
"type": "object",
"properties": {
"different_addresses": {
"title": "My billing address is different than my shipping address.",
"type": "boolean",
"default": false
},
"shipping_address": {
"title": "Shipping address",
"$ref": "#/definitions/address"
}
},
"required": [
"shipping_address"
],
"dependencies": {
"different_addresses": {
"oneOf": [
{
"properties": {
"different_addresses": {
"enum": [
false
]
}
}
},
{
"properties": {
"different_addresses": {
"enum": [
true
]
},
"billing_address": {
"title": "Billing address",
"$ref": "#/definitions/address"
}
},
"required": [
"billing_address"
]
}
]
}
}
}
my question is related to your JSON schema.
I need to target the "grandchild" of a parent object for a dependency.
Is this possible? In case of "different_addresses" being an object.
For example:
"dependencies": {
"different_addresses": {
"properties": {
"OTHER_FIELD": {
"oneOf": [
{
"properties": {
"different_addresses": {
"properties": {
"OTHER_FIELD": {
"enum": [
false
]
}
}
}
}
}
]
}
}
}
}
I've got two CloudKit data objects that look somewhat like this:
Parent Object:
{
"records": [
{
"recordName": "14102C0A-60F2-4457-AC1C-601BC628BF47-184-000000012D225C57",
"recordType": "ParentObject",
"fields": {
"fsYear": {
"value": "2015",
"type": "STRING"
},
"displayOrder": {
"value": 2015221153856287200,
"type": "INT64"
},
"fjpFSGuidForReference": {
"value": "14102C0A-60F2-4457-AC1C-601BC628BF47-184-000000012D225C57",
"type": "STRING"
},
"fsDateSearch": {
"value": "2015221153856287158",
"type": "STRING"
},
},
"recordChangeTag": "id4w7ivn",
"created": {
"timestamp": 1439149087571,
"userRecordName": "_0d26968032e31bbc72c213037b6cb35d",
"deviceID": "A19CD995FDA3093781096AF5D818033A241D65C1BFC3D32EC6C5D6B3B4A9AA6B"
},
"modified": {
"timestamp": 1439149087571,
"userRecordName": "_0d26968032e31bbc72c213037b6cb35d",
"deviceID": "A19CD995FDA3093781096AF5D818033A241D65C1BFC3D32EC6C5D6B3B4A9AA6B"
}
}
],
"total":
}
Child Object:
{
"records": [
{
"recordName": "2015221153856287168",
"recordType": "ChildObject",
"fields": {
"District": {
"value": "002",
"type": "STRING"
},
"ZipCode": {
"value": "12345",
"type": "STRING"
},
"InspecReference": {
"value": {
"recordName": "14102C0A-60F2-4457-AC1C-601BC628BF47-184-000000012D225C57",
"action": "NONE",
"zoneID": {
"zoneName": "_defaultZone"
}
},
"type": "REFERENCE"
},
},
"recordChangeTag": "id4w7lew",
"created": {
"timestamp": 1439149090856,
"userRecordName": "_0d26968032e31bbc72c213037b6cb35d",
"deviceID": "A19CD995FDA3093781096AF5D818033A241D65C1BFC3D32EC6C5D6B3B4A9AA6B"
},
"modified": {
"timestamp": 1439149090856,
"userRecordName": "_0d26968032e31bbc72c213037b6cb35d",
"deviceID": "A19CD995FDA3093781096AF5D818033A241D65C1BFC3D32EC6C5D6B3B4A9AA6B"
}
}
],
"total": 1
}
I'm trying to write a query to directly access the CloudKit web service and return the Child Object based on the reference of the parent object.
My test JSON looks something like this:
{"query":{"recordType":"ChildObject","filterBy":{"fieldName":"InspecReference","fieldValue":{ "value" : "14102C0A-60F2-4457-AC1C-601BC628BF47-184-000000012D225C57", "type" : "string" },"comparator":"EQUALS"}},"zoneID":{"zoneName":"_defaultZone"}}
However, I'm getting the following error from CloudKit:
{"uuid":"33db91f3-b768-4a68-9056-216ecc033e9e","serverErrorCode":"BAD_REQUEST","reason":"BadRequestException:
Unexpected input"}
I'm guessing I have the Record Field Dictionary in the query wrong. However, the documentation isn't clear on what this should look like on a reference object.
You have to re-create the actual object of the reference. In this particular case, the JSON looks like this:
{
"query": {
"recordType": "ChildObject",
"filterBy": {
"fieldName": "InspecReference",
"fieldValue": {
"value": {
"recordName": "14102C0A-60F2-4457-AC1C-601BC628BF47-184-000000012D225C57",
"action": "NONE"
},
"type": "REFERENCE"
},
"comparator": "EQUALS"
}
},
"zoneID": {
"zoneName": "_defaultZone"
}
}
I'm using Mongo, Elastic Search and this river plugin: https://github.com/richardwilly98/elasticsearch-river-mongodb
I have successfully set everything up in that the river keeps the ES data updated when Mongo is updated, but the river is straight up copying all the properties from the Mongo documents into ES, but I only want a small sub-set of those records. E.g. if a Mongo doc has 30 properties all of them are getting put into ES instead of only the 5 that I want. I assume the issue is with the mappings, and I've followed several docs and another Stack Overflow thread (curl -X POST -d #mapping.json + mapping not created) but it still is not working for me. Here is what I'm doing:
I'm creating my index with:
curl -XPOST "http://localhost:9200/mongoindex" -d #index.json
index.json:
{
"settings" : {
"number_of_shards" : 1
},
"analysis" : {
"analyzer" : {
"str_search_analyzer" : {
"tokenizer" : "keyword",
"filter" : ["lowercase"]
},
"str_index_analyzer" : {
"tokenizer" : "keyword",
"filter" : ["lowercase", "ngram"]
}
},
"filter" : {
"ngram" : {
"type" : "ngram",
"min_gram" : 2,
"max_gram" : 20
}
}
}
}
Then running:
curl -XPOST "http://localhost:9200/mongoindex/listing/_mapping" -d #mapping.json
With this data:
{
"listing":{
"properties":{
"_all": {
"enabled": false
},
"title": {
"type": "string",
"store": false,
"index": "not_analyzed"
},
"bathrooms": {
"type": "integer",
"store": true,
"index": "analyzed"
},
"bedrooms": {
"type": "integer",
"store": true,
"index": "analyzed"
},
"address": {
"type": "nested",
"include_in_parent": true,
"store": true,
"properties": {
"counrty": {
"type":"string"
},
"city": {
"type":"string"
},
"stateOrProvince": {
"type":"string"
},
"fullStreetAddress": {
"type":"string"
},
"postalCode": {
"type":"string"
}
}
},
"location": {
"type": "geo_point",
"full_name": "geometry.coordiantes",
"store": true
}
}
}
}
Then finally creating the river with:
curl -XPUT "http://localhost:9200/_river/mongoindex/_meta" -d #river.json
river.json:
{
"type": "mongodb",
"mongodb": {
"db": "blueprint",
"collection": "Listing",
"options": {
"secondary_read_preference": true,
"drop_collection": true
}
},
"index": {
"name": "mongoindex",
"type": "listing"
}
}
After all that the river works in that ES is populated, but its a verbatim copy of Mongo right now, and I need to modify the mappings, but it just is not taking effect. What am I missing?
This is what my mapping looks like after the river runs.... nothing like what I want it to look like.
I would set dynamic mapping to false:
The dynamic creation of mappings for unmapped types can be completely
disabled by setting index.mapper.dynamic to false.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/mapping-dynamic-mapping.html
Others have had similar issues to yours and it looks like the best solution so far has been to prevent the MongoDB River from dynamically mapping at all:
https://github.com/richardwilly98/elasticsearch-river-mongodb/issues/75
Turns out the issue was that the dynamic property was left out of the mappings config. It should be in 2 places, on the index.json as shown above, and in the mappings.json:
{
"listing":{
"_source": {
"enabled": false
},
"dynamic": false, // <--- Need to add this
"properties":{
"_all": {
"enabled": false
},
"title": {
"type": "string",
"store": false,
"index": "str_index_analyzer"
},
"bathrooms": {
"type": "integer",
"store": true,
"index": "analyzed"
},
"bedrooms": {
"type": "integer",
"store": true,
"index": "analyzed"
},
"address": {
"type": "nested",
"include_in_parent": true,
"store": true,
"properties": {
"counrty": {
"type":"string",
"index": "str_index_analyzer"
},
"city": {
"type":"string",
"index": "str_index_analyzer"
},
"stateOrProvince": {
"type":"string",
"index": "str_index_analyzer"
},
"fullStreetAddress": {
"type":"string",
"index": "str_index_analyzer"
},
"postalCode": {
"type":"string"
}
}
},
"location": {
"type": "geo_point",
"full_name": "geometry.coordiantes",
"store": true
}
}
}
}
The 902 docs vs 451, I think that is an bug in the ElasticSearch Head plugin I'm using to browse documents. It doesn't have duplicates, but a couple of spots show 902 docs as a summary of sorts.