What is the use to use both ElasticSearch and a separated Nosql database ? Can't Elasticsearch be used both as a database and for search indexing ?
Completely open source and built with Java, Elasticsearch is a NoSQL database. That means it stores data in an unstructured way and that you cannot use SQL to query it. This Elasticsearch tutorial could also be considered a NoSQL tutorial.
As you have seen in this blog post, it is possible to prevent duplicates in Elasticsearch by specifying a document identifier externally prior to indexing data into Elasticsearch. The type and structure of the identifier can have a significant impact on indexing performance.
Both Elasticsearch and MongoDB support document-based data models but can also support traditional relational data represented by rows and columns. Elasticsearch has two ways of dealing with relational data: a nested document model and a parent-child document model.
You can prevent data duplicates in a database by creating a unique index. The unique index will demand that each data in the indexed field is unique in value and properties. You can create a new index by setting up a data-definition query that creates a new index. This can be done using SQL view.
Yes, you can use ElasticSearch as a data source as well as an index.
By default each document you send to the ElasticSearch system is index, and, the original document is stored as well. This means whenever you query ElasticSearch you can also retrieve the original JSON document that you indexed.
If you have large documents and you want to be able to retrieve a smaller amount of data then when you can use the mapping API to set "store" to "yes" for specific fields, and then use the "fields" key to pull out specific fields you might want.
In my system I have address autocompletion and I only fetch the address field of a property. Here is an example from my system:
_search?q=FullAddress:main&fields:FullAddress
Then when a user selects the address I pull up the entire JSON document (along with others).
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