Location of custom Kibana dashboards in ElasticSearch

Yes, the Kibana dashboards are being saved in Elasticsearch under kibana-int index (by default, you can override that in the config.js file). If you want to move your Kibana dashboards to another ES cluster you have two options:

1. Export manually the dashboards. Click on Save -> Advanced -> Export Schema. You have to save the file and then in the new Kibana you have to import click over Load -> Advanced -> Choose File and choosing the file that have selected. This is a pain, because you have to do this operation per dashboard that you want to migrate.
2. You can use an utility to migrate an ES index from one ES cluster to another. There are some utilities already that can perform this operation. Searching in SO, I found this answer that suggest you to use the Elasticsearch.pm library (Perl :S) to do this. Probably there are more utilities like this but I do not think that doing a script that migrate an index to another cluster is such a difficult task.

EDIT: For the second option, you can use the python elasticsearch library and its helper reindex, if you feel more confortable with Python: https://elasticsearch-py.readthedocs.org/en/latest/helpers.html#elasticsearch.helpers.reindex

In fact, very easy,Copy two folders:

1) .\elasticsearch\data\nodes\0\indices\.kibana 2) .\elasticsearch\data\nodes\0\indices\kibana-int

paste in new elasticsearch.

Here's a standalone Python script that can copy Kibana dashboards from elasticsearch host to another.

#!/bin/env python"""Migrate all the kibana dashboard from SOURCE_HOST to DEST_HOST.This script may be run repeatedly, but any dashboard changes onDEST_HOST will be overwritten if so."""import urllib2, urllib, jsonSOURCE_HOST = "your-old-es-host"DEST_HOST = "your-new-es-host"def http_post(url, data):    request = urllib2.Request(url, data)    return urllib2.urlopen(request).read()def http_put(url, data):    opener = urllib2.build_opener(urllib2.HTTPHandler)    request = urllib2.Request(url, data)    request.get_method = lambda: 'PUT'    return opener.open(request).read()if __name__ == '__main__':    old_dashboards_url = "http://%s:9200/kibana-int/_search" % SOURCE_HOST    # All the dashboards (assuming we have less than 9999) from    # kibana, ignoring those with _type: temp.    old_dashboards_query = """{       size: 9999,       query: { filtered: { filter: { type: { value: "dashboard" } } } } }    }"""    old_dashboards_results = json.loads(http_post(old_dashboards_url, old_dashboards_query))    old_dashboards_raw = old_dashboards_results['hits']['hits']    old_dashboards = {}    for doc in old_dashboards_raw:        old_dashboards[doc['_id']] = doc['_source']    for id, dashboard in old_dashboards.iteritems():        put_url = "http://%s:9200/kibana-int/dashboard/%s" % (DEST_HOST, urllib.quote(id))        print http_put(put_url, json.dumps(dashboard))