elasticsearch-5

I've already created an index, and it contains data from my MySQL database. I've got few fields which are string in my table, where I need them as different types ( integer & double ) in Elasticsearch . So I'm aware that I could do it through mapping as follows: { "mappings": { "my_type": { "properties": { "userid": { "type": "text", "fielddata": true }, "responsecode": { "type": "integer" }, "chargeamount": { "type": "double" } } } } } But I've tried this when I'm creating the index as a new one. What I wanted to know is how can I update an existing field (ie: chargeamount in this scenario)

Viewing documents on per weekday classification? My data is in a format like this: {"text": "hi","created_at": "2016-02-21T18:30:36.000Z"} For this I am using a dateConversion.groovy script and kept in the scripts folder in ES 5.1.1. Date date = new Date(doc[date_field].value); java.text.SimpleDateFormat format = new java.text.SimpleDateFormat(format); format.format(date) When I executed the following code in ES PLUGIN: "aggs": { "byDays": { "terms": { "script": { "lang": "groovy", "file": "dateConversion", "params": { "date_field": "created_at", "format": "EEEEEE" } } } } `` I am getting an

Am trying to use elasticsearch with my neo4j database for fast querying.I tried many sites but they are all old articles so i didn't get any clear idea. Steps I followed until now, Installed neo4j Installed elasticsearch Copy pasted elastic search plugins into neo4j plugins folder added this line into neo4j. properties file elasticsearch.host_name=http://localhost:9200 elasticsearch.index_spec=people:Person(first_name,last_name), places:Place(name) Here my question is, How elasticsearch and neo4j are integrated. Please clarify me on this. I followed this , Link You have to install Apoc

I am trying to filter empty buckets in side a filter aggregation block, and I get an error from elasticsearch. without this the response is huge, as I am querying lots of metric, and nested aggregation (this is part of bigger query for simplicity ) GET index/type/_search?ignore_unavailable { "size": 0, "aggs": { "groupby_country": { "terms": { "field": "country", "size": 2000 }, "aggs": { "exists__x__filter": { "filter": { "bool": { "filter": [ { "exists": { "field": "x" } } ] } }, "aggs": { "sum": { "sum": { "script": "def val = doc['x'].value; if(val>0) Math.min(val , 20000)" } }, "average

In a Elasticsearch index, I have a few fields which are referencing main categories' ids (e.g. sector_id, country_id, etc...). These fields are solely use for filtering (using the term/terms filters) and for creating buckets in terms aggregations (among others). Each one of them is currently using the smallest suitable numeric datatype (e.g. byte, short, etc..) Is this the best datatype to be used on these for heavy aggregations? Or should these be using the keyword datatype? Thanks in advance for any advice! If the values of those fields are numeric, you should go for a numeric type, if they

I recently upgraded to elasticsearch version 6.1.1 and now I can't bulk index documents from a json file. Wehn I do it inline, it works fine. Here are the contents of the document: {"index" : {}} {"name": "Carlson Barnes", "age": 34} {"index":{}} {"name": "Sheppard Stein","age": 39} {"index":{}} {"name": "Nixon Singleton","age": 36} {"index":{}} {"name": "Sharron Sosa","age": 33} {"index":{}} {"name": "Kendra Cabrera","age": 24} {"index":{}} {"name": "Young Robinson","age": 20} When I run this command, curl -XPUT 'localhost:9200/subscribers/ppl/_bulk?pretty' -H 'Content-Type: application/json'

I have documents in ElasticSearch with the following structure : "_source": { "last_updated": "2017-10-25T18:33:51.434706", "country": "Italia", "price": [ "€ 139", "€ 125", "€ 120", "€ 108" ], "max_occupancy": [ 2, 2, 1, 1 ], "type": [ "Type 1", "Type 1 - (Tag)", "Type 2", "Type 2 (Tag)", ], "availability": [ 10, 10, 10, 10 ], "size": [ "26 m²", "35 m²", "47 m²", "31 m²" ] } } Basically, the details records are split in 5 arrays, and fields of the same record have the same index position in the 5 arrays. As can be seen in the example data there are 5 array(price, max_occupancy, type,