Update Now that the question scope has been corrected, I might add something in this regard as well: There are many comparisons between Apache Solr and ElasticSearch available, so I’ll reference those I found most useful myself, i.e. covering the most important aspects: Bob Yoplait already linked kimchy’s answer to ElasticSearch, Sphinx, Lucene, Solr, Xapian. … Read more
The filtered query has been deprecated and removed in ES 5.0. You should now use the bool/must/filter query instead. { “query”: { “bool”: { “must”: { “multi_match”: { “operator”: “and”, “fields”: [ “author”, “title”, “publisher”, “year” ], “query”: “George Orwell” } }, “filter”: { “terms”: { “year”: [ 1980, 1981 ] } } } } … Read more
I’ll try to explain with a real example since the answer and replies you got don’t seem to help you. When you download elasticsearch and start it up, you create an elasticsearch node which tries to join an existing cluster if available or creates a new one. Let’s say you created your own new cluster … Read more
As the creator of ElasticSearch, maybe I can give you some reasoning on why I went ahead and created it in the first place :). Using pure Lucene is challenging. There are many things that you need to take care for if you want it to really perform well, and also, its a library, so … Read more
It’s a critical search use case, and MongoDB and Redis are perfect for key-based lookups and not use for Search purposes, while Elasticsearch is a distributed search engine, built specifically for such use-case. Before choosing the system, you should know how your feature works internally And below the consideration for selecting it. Non-functional requirements for … Read more
This is an interesting use case. Here’s my take: { “settings”: { “analysis”: { “analyzer”: { “my_ngram_analyzer”: { “tokenizer”: “my_ngram_tokenizer”, “filter”: [“lowercase”] }, “my_edge_ngram_analyzer”: { “tokenizer”: “my_edge_ngram_tokenizer”, “filter”: [“lowercase”] }, “my_reverse_edge_ngram_analyzer”: { “tokenizer”: “keyword”, “filter” : [“lowercase”,”reverse”,”substring”,”reverse”] }, “lowercase_keyword”: { “type”: “custom”, “filter”: [“lowercase”], “tokenizer”: “keyword” } }, “tokenizer”: { “my_ngram_tokenizer”: { “type”: “nGram”, “min_gram”: … Read more
Using requests: import requests url=”http://ES_search_demo.com/document/record/_search?pretty=true” data=””‘{ “query”: { “bool”: { “must”: [ { “text”: { “record.document”: “SOME_JOURNAL” } }, { “text”: { “record.articleTitle”: “farmers” } } ], “must_not”: [], “should”: [] } }, “from”: 0, “size”: 50, “sort”: [], “facets”: {} }”’ response = requests.post(url, data=data) Depending on what kind of response your API returns, … Read more
I’m using nGram, too. I use standard tokenizer and nGram just as a filter. Here is my setup: { “index”: { “index”: “my_idx”, “type”: “my_type”, “analysis”: { “index_analyzer”: { “my_index_analyzer”: { “type”: “custom”, “tokenizer”: “standard”, “filter”: [ “lowercase”, “mynGram” ] } }, “search_analyzer”: { “my_search_analyzer”: { “type”: “custom”, “tokenizer”: “standard”, “filter”: [ “standard”, “lowercase”, “mynGram” … Read more
You’ve pretty much listed the two main options there are when it comes to search across multiple data stores, i.e. search in one central data store (option #1) or search in all data stores and aggregate the results (option #2). Both options would work, although option #2 has two main drawbacks: It will require a … Read more