Database – Why would I use ElasticSearch if I already use a graph database

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I don't find any deep explanation on the web about a comparison between ElasticSearch and the graph databases.

Both are optimized to traverse data.
ElasticSearch seems to be optimized for analytics.
However Neo4j is also based on Lucene to manage indexes and some fulltext features.

Why would I use ElasticSearch if I already use a graph database ?

In my case, I'm using Neo4j to build a social network.
What real benefit may ElasticSearch bring?

UPDATE ———-

I've just found this paragraph:

There are myriad cases in which elasticsearch is useful. Some use
cases more clearly call for it than others. Listed below are some
tasks which for which elasticsearch is particularly well suited.

  • Searching a large number of product descriptions for the best match
    for a specific phrase (say “chef’s knife”) and returning the best
    results
  • Given the previous example, breaking down the various
    departments where “chef’s knife” appears (see Faceting later in this
    book)
  • Searching text for words that sound like “season”
  • Auto-completing a search box based on partially typed words based on
    previously issued searches while accounting for mis-spellings
  • Storing
    a large quantity of semi-structured (JSON) data in a distributed
    fashion, with a specified level of redundancy across a cluster of
    machines

It should be noted, however, that while elasticsearch is
great at solving the aforementioned problems, it’s not the best choice
for others. It’s especially bad at solving problems for which
relational databases are optimized. Problems such as those listed
below.

  • Calculating how many items are left in the inventory
  • Figuring out the
    sum of all line-items on all the invoices sent out in a given month
  • Executing two operations transactionally with rollback support
  • Creating records that are guaranteed to be unique across multiple
    given terms, for instance a phone number and extension
  • Elasticsearch
    is generally fantastic at providing approximate answers from data,
    such as scoring the results by quality. While elasticsearch can
    perform exact matching and statistical calculations, its primary task
    of search is an inherently approximate task.
  • Finding approximate
    answers is a property that separates elasticsearch from more
    traditional databases. That being said, traditional relational
    databases excel at precision and data integrity, for which
    elasticsearch and Lucene have few provisions.

Can I assert that if I don't need approximate answers, then ElasticSearch would be useless compared to an already used graph database?

Best Answer

I hesitate to call ElasticSearch a database. It is not a replacement for a database, but it makes a good addition to add functionality, specifically advanced text searching, along side your existing database.

I see where you can get them confused. They can actually fit the same need, but not always. ElasticSearch does exactly what it sounds like, searches. A graph database doesn't specify relations or indexes, where as ElasticSearch does. So fundamentally they work quite differently. ElasticSearch analyzes documents with, for example, English analyzer. What this does it will take words and analyze different variations of that word or even synonyms. For example, dig, would be anaylzed as dig,digs,dug,digging,digger .... When you run a query on elasticsearch your queries can also be analyzed, then those words are queried for and can be scored by relevance.

ElasticSearch is a great tool, because it's really flexible. You can find a wide range of relative content, or you can find a needle in the hay stack, and its relatively easy.

Graph Databases have their advantage too. Finding relevance/relations between things like hash tags for example, or things with many mutable relations. They're great and interesting pieces of technology, however I'd have to say that its not as powerful as ElasticSearch. Mostly because ElasticSearch is geared towards this sort of thing, and it handles analysis for you so you can do full-text search. However if you're looking to use a system more so like twitter's search that's based on predefined tagging/keywords, then you'd be better off using the Graph Database your already using.

The question is how robust do you want your searching to be? If you have a need to do really fine grain(full text) searches I'd use elasticsearch. Otherwise you can always implement a search relatively easily on a graph database. Once you have search implemented its not impossible to migrate to elasticsearch if you find yourself later needing a more robust search engine, just implement your search with that in mind.

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