There are many possible reason why allocation won’t occur: You are running different versions of Elasticsearch on different nodes You only have one node in your cluster, but you have number of replicas set to something other than zero. You have insufficient disk space. You have shard allocation disabled. You have a firewall or SELinux … Read more
Assuming you are using the Standard Analyzer GET becomes get when stored in the index. The source document will still have the original “GET”. The match query will apply the same standard analyzer to the search term and will therefore match what is stored in the index. The term query does not apply any analyzers … Read more
The answer for this one was tricky, but thanks to samklr, I have managed to figure about what the problem was. The solution isn’t straightforward nevertheless and might consider some “unnecessary” transformations. First let’s talk about Serialization. There are two aspects of serialization to consider in Spark serialization of data and serialization of functions. In … Read more
Use a terms aggregation on the color field. And you need to pay attention to how that field you want to get distinct values on is analyzed, meaning you need to make sure you’re not tokenizing it while indexing, otherwise every entry in the aggregation will be a different term that is part of the … Read more
“my_field2”: { “properties”: { “title”: { “type”: “string”, “index”: “not_analyzed” } } } Check you here, https://www.elastic.co/guide/en/elasticsearch/reference/1.4/mapping-core-types.html, for further info.
Just create a template. run curl -XPUT localhost:9200/_template/template_1 -d ‘{ “template”: “*”, “settings”: { “index.refresh_interval”: “5s” }, “mappings”: { “_default_”: { “_all”: { “enabled”: true }, “dynamic_templates”: [ { “string_fields”: { “match”: “*”, “match_mapping_type”: “string”, “mapping”: { “index”: “not_analyzed”, “omit_norms”: true, “type”: “string” } } } ], “properties”: { “@version”: { “type”: “string”, “index”: “not_analyzed” … Read more
I thought that if “store” is set to “no” it would mean I could not retrieve the specific field, but had to get the whole _source and parse it on the client side. That’s exactly what elasticsearch does for you when a field is not stored (default) and the _source field is enabled (default too). … Read more
The difference is simple: filters are cached and don’t influence the score, therefore faster than queries. Have a look here too. Let’s say a query is usually something that the users type and pretty much unpredictable, while filters help users narrowing down the search results , for example using facets.
There are 2 steps to this — a dynamic_mapping and an ingest_pipeline. I’m assuming your field c is non-trivial so you may want to match that field in a dynamic template using a match and assign the keyword mapping to it: PUT synthetic { “mappings”: { “dynamic_templates”: [ { “c_like_field”: { “match_mapping_type”: “string”, “match”: “c*”, … Read more
You could use the update by query plugin in order to do just that. The idea is to select all document without a category and whose url matches a certain string and add the category you wish. curl -XPOST ‘localhost:9200/webproxylog/_update_by_query’ -H “Content-Type: application/json” -d ‘ { “query”: { “filtered”: { “filter”: { “bool”: { “must”: … Read more