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LIKE in Elasticsearch for large texts


LIKE in Elasticsearch for large texts

By : Ryan White
Date : October 20 2020, 08:10 PM
around this issue Apparently the best way to achieve this is by using Span Queries. SpanNear and multiple wildcard queries. I'll chuck in an example if it helps anyone.
code :


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How to calculate cooccurrences on a set of texts with Elasticsearch

How to calculate cooccurrences on a set of texts with Elasticsearch


By : abok
Date : March 29 2020, 07:55 AM
seems to work fine While a "terms" aggregation will indeed give you the data you described, you might want to look into the significant terms aggregation to get more insightful data.
Given your example, a search for "trump" will give you "USA" as the most common term, but that will be the case for most other queries in your "candidates" data set. A significant term aggregation would probably show "republican" as being a much more significant characteristic of the subset described by your query.
Rails 4: Clean Texts from Wysiwyg editors on ElasticSearch index

Rails 4: Clean Texts from Wysiwyg editors on ElasticSearch index


By : KmL
Date : March 29 2020, 07:55 AM
hop of those help? You absolutely should decode your text. Two options:
Save text as two different fields - one with WYSIWYG tags, and the other one clean and search against that column - problematic if you have A LOT of entries.
what to use String OR StringBuilder to keep large texts?

what to use String OR StringBuilder to keep large texts?


By : Dang Khoa Ngo
Date : March 29 2020, 07:55 AM
I wish this help you I have mulitthreaded programs that read large xmls and currently convert them to String e.g: ,
it will pool the string in JVM with multiple XMLS
Word2vec with elasticsearch for texts similarity

Word2vec with elasticsearch for texts similarity


By : geo-herrera
Date : March 29 2020, 07:55 AM
will help you This elasticsearch plugin implements a score function (dot product) for vectors stored using the delimited-payload-tokenfilter
The complexity of this search is a linear function of number of documents, and it is worse than tf-idf on a term query, since ES first searches on an inverted index then it uses tf-idf for document scores, so tf-idf is not executed on all the documents of the index. With the vector, the representation you're searching for is the vector space of the document with the lower cosine distance, without the advantages of the inverted index.
AWS Elasticsearch - Suggest how many number shard & replica create for m4.large.elasticsearch instance

AWS Elasticsearch - Suggest how many number shard & replica create for m4.large.elasticsearch instance


By : Ravikanth Nawada
Date : March 29 2020, 07:55 AM
hope this fix your issue You may have as many numbers of shards and replica depending upon your volume size and usage.
Replicas are primarily for search performance, and a user can add or remove them at any time. They give you additional capacity, higher throughput, and stronger failover. It is always recommend a production cluster to have 2 replicas for failover. Also note doubling the number of replicas will also double your disk space usage.
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