Can the basics of elastic search be implemented using a nosql database by performing the following steps? For each document to index:
Generate n-grams from text
Insert n-gram into table where the n-gram is the key and a list of matching documents is the value
your question is a little confusing here, as Elasticsearch is a nosql datastore and it can create ngrams. you can also search for a word/value and then return matching documents
perhaps explaining a little more on what you are trying to achieve, ie the use case, would help provide a clearer answer
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Hi I'm trying out Elastic Enterprise Search with Elasticsearch. I have a couple of questions on data indexing.
When referring to Elasticsearch documentation, I read that there is a limit to the number of fields that an Elasticsearch index could have. Since Elasticsearch is used with Elastic Enterprise Search I believe there is no arguing that the same applies here. In that case lets say I have multiple document types with various fields. For an example Person.json and Dog.json, they both have different properties. So when indexing I use one search engine in Elastic Enterprise Search to index both Person and Dog so that when I query using the Elastic Enterprise Search API I'll get results which are both Person and Dog depending on the search term.
Is this the way to go,or should I specify a seperate search engine for each schema type?
I am assuming that your person.json and dog.json contains different fields as your heading suggest and weather to create a separate index for these entities or have them in a single index, depends on the various use-cases you have in your application and you will not find elasticsearch marking one approach better than other and mainly will explain the pros/cons based on a particular context(like relevance, performance, management etc).
Please refer to my this SO answer, where I talked about various pros/cons of both the approach and discussion in chat to get more context why OP chose an approach based on his use-case, after knowing the pros/cons.
I've been looking all over the place for a good answer to my question but I just can't find any...
I'm using ElasticSearch along with Laravel. I've used ElasticSearch on another project but never used suggestions. I'm following this tutorial as I think it provides a great starting point for using Laravel with ElasticSearch: https://blog.madewithlove.be/post/how-to-integrate-your-laravel-app-with-elasticsearch/
My question is about suggestions; I want my search to be a search-as-you-type just like the one you would find on Spotify. I want my users to type a few letters in the search box and have the results be organized into multiple categories: blogs, authors, tags.
If I index my data into one index, with authors and tags being blog's nested objects, I can easily get suggestions using the completion suggester for blog names, but not for nested objects. I could also split each model and index data separately into different indexes, but that would mean I would have to make 3 queries to get my results back.
Am I doing something wrong? Should I structure my data differently? Is making 3 queries the way to go or is there a way to have a single query output search results from different indexes?
Thanks!
Xavier
Something that I did when I built a search-as-you-type was I used a separate index for suggestions. In your situation, you'd index the name (title, author, whatever) in one field and the type in another. Then you could search on one field and display the grouped results.
The advantage here is speed. This will likely be a heck of a lot faster than trying to do a suggester on your nested data. (Which you can probably do, but I'm not sure how.) And speed is pretty important for this type of feature.
I am pretty new to elasticsearch and already love it.
Right know I am interested in understanding on how I can let elasticsearch make suggestions for similar keywords.
I have already read this article: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-mlt-query.html.
The More Like This Query (MLT Query) finds documents that are "like" a given set of documents.
This is already more than I am looking for. I dont need similar documents but only related / similar keywords.
So lets say I have an index of documents about movies and I start a query about "godfather". Then elasticsearch should suggest related keywords - e.g. "al pacino" or "Marlon Brando" because they are likely to occur in the same documents.
any ideas how this can be done?
Unfortunately, there is no built-in way to do that in Elastic. What you could possibly do, is to write a program, that will query Elastic, return matched documents, then you will get the _source data, or just retrieve it from your original datasource (like DB or file), later you will need to calculate TF-IDF for each term in the retrieved ones and somehow combine everything all together to get top K terms out of all returned terms.
I have a collection with thousands of documents each of which contains a string to be searched for. I would like to make an index for these strings like so:
index a "an apple"
index a "arbitrary value"
index s "something"
I think I will be able to improve the search performance if I create these indices so that when I search for 'something', I can only look up documents in the index 's'. I am new to database design and wonder if this is the right way to improve the performance of the queries with string values. Is there any better way to do this or does mongodb have a built in mechanism to achieve this kind of indexing? Please enlighten me.
You can create indexes based on the keys and not on the values.
Each document will have a default index created on the _id field.
You can also create compound Index, ie combining on or more fields
Creation of Index should be appropriate to your search, so that your search queries will be faster.
http://docs.mongodb.org/manual/indexes/
I want to search for multiple strings in a very large database. These strings are part of different attributes of database table. I have tried string search using LIKE in sql query. But it is taking a lot of time to get results. I have used Oracle database.
Should I use indexing of database? I found that Lucene can be used for it.
I also got some suggestions of using big data concepts. Which approach should I use?
The easiest way is:
1.) adding an index to the columns you like to search trough
2.) using oracle text as #lalitKumarB wrote
The most powerful way is:
3.) use an separate search engine (solr, elaticsearch).
But, probably you have to change you application in order to explicit use the search index for searching trough the data,...
I had the same situation some years before. Trying to search text in an big database. After a wile I found out, that database based search will never reach the performance of an dedicate search engine. And: you will have much more search features working out of the box, if you use solr (for example), like spelling correction, "More like this", ...
One option is to hold the data on orcale, searching in solr and return the ID of the document in order to only load the one row form oracle, the is referenced by the ID.
2nd option is to keep oracle as base datapool for your search engine and search in solr (or elasticsearch) in order to return the whole document/row from solr, not only the ID. So you don't need to load the data from the database any more.
The best option depends on your needs.
You have the choice between elasticsearch, solr or lucene