Both Neo4j 4.0 and elasticsearch have full text seach and inverted index with apache lucene.
So how elastic search is better than neo4j full text search?
Consider that we are dealing with the knowledge graph as a data storage model developed in Neo4j.
Apart from that why should we use elasticsearch with Neo4j 4.0. what are things that elasticsearch offer but not neo4j 4.0
So how elastic search is better than neo4j full text search?
"Better" is largely dependent on your use case. But the tools (Neo4j and ElasticSearch) were built for drastically different purposes.
Neo4j is best when used as a graph-traversal engine, returning data from edge (relationship) based queries. It might have similar capabilities, but it just wasn't meant to be used as a search engine.
Want things like "fuzzy" matching and relevance ranking? Neo4j is not going to do any of that. Also, ElasticSearch is a true out-of-the-box distributed datastore. Neo4j can't distribute without an enterprise license.
Basically, it comes down to business requirements. If a datastore mainly needs to execute graph traversals, and serve some simple search-like requests, Neo4j might be enough on its own. Need a full-featured search engine to serve that same data? ElasticSearch is the better suited to handle that.
Related
I'm building a simple web application that will list/search retail items for sale.
design is like this ...
MySQL database -> Elastic Search deployment -> Spring Boot REST service -> Web UI (JSP/Bootstrap or Angular)
I am planning to write Java client code to read the database and post records to Elastic Search for indexing.
Googling, it looks like Logstash is used for this sort of thing. I'm not familiar with Logstash, I am very familiar with Java.
QUESTION: Is Java client considered a "deprecated" or "legacy" way to submit data to Elastic Search for indexing?
I'm very familiar with Java, should I use Java or Logstash?
Adding to #chris answer, logstash will add complexity and another Infrastructure to maintain in your stack, and logstash is known for getting stuck and is not as resilient as Elasticsearch is.
You are already using Java for your application code and btw elasticsearch now officially has a java client known as java high-level rest client(JHLRC) , which is very popular and provides an exhaustive list of APIs for indexing/searching and building a modern search system.
IMHO you should use the JHLRC,
which will save you to the pain points of logstash
you don't have to learn another tool
simple infrastructure
simple deployment
last but not least simple and easy to maintain codebase.
Logstash is good tool to be used to migrate the data from many sources to elastic search. It's build in java language only.
You can use Logstash. It also has options to mutate the data or filter the data. Its a ready to use to tool which will save lot of your development time and efforts.
But if you have a requirement for lot of customisation and need lot of control over your data before pushing it to elastic search then you can build your own application for the same.
Coming back to your question..java is not deprecated for indexing data to elastic search. It is still a preferred option.
I am trying to create a local index for my notes which comprises mainly of markdown files, text files, codes in python, javascript and dart.
I came across Solr and Elasticsearch.
But the main differences are focused around online use and distributedness.
Which can be a better choice if i need a good integrarion with javascript through electronjs?
Keeping in mind that the files are on local storage and there is not much focus on distributedness but on integration with javascript frontend and efficiency on local system.
Elasticsearch is more popular among newer developers due to its ease of use. But if you are already used to working with Solr, stay with it because there is no specific advantage of migrating to Elasticsearch.
I believe for your use case either of them would work.
However, If you need it to handle analytical queries in addition to searching text, Elasticsearch is the better choice
In terms of popularity, a larger community, documentations I would say elasticsearch is the winner, You can look at the below google trends
You can use the solr along with Apache Tika.
Apache Tika help in extracting the content/Text of different file system.
Using the above the you can index the metadata of the files and content of the files to the Apache solr.
You get admin tool for the analysis of the index and the fields to determine if you able to achieve the desired result.
Is there any logstash plugin or other kind of mechanism to sync ArangoDB data with elasticsearch?
I'm creating a social network platform and want my users and posts data searchable with ES.
My initial thought was using MongoDB as data store because MongoDb offer ways to sync data to ES via logstash plugin.
However ArangoDB provides graph database capabilities which are quite helpful for storing and querying social relations which is best suited to my requirements. This makes me more inclined towards ArangoDB.
I didn't find any ES/logstash plugin for syncing ArangoDB data with my ES cluster. There are few mentions of a river plugin which exists no more. Two questions:
Is there an efficient async way to sync ArangoDB data with ES?
If not, should I drop ArangoDB and go with MongoDB which does not provide graph capabilities?
Of Course, There is a way to Sync ArangoDB with ES. I don't remember exactly how to do it, but take a look at this https://www.arangodb.com/2013/11/new-plugin-elasticsearch-arangodb/
I am new to CMS search abilities and Azure search. I am looking at CMS options and was looking at Orchard Core CMS and saw it had Lucene search and GraphQL for searching via Headless CMS implementation as well. I also see Azure search services out in Azure. I am confused on if I should simply use the built-in search Orchard Core CMS already has or if Azure search is much better and what it can offer me. Clueless here....
Orchard Core as of now uses Lucene directly. The only drawback of using Lucene directly is that it doesn't scale as well as an Azure Search for example which is a distributed service.
The Lucene implementation in OC has been inspired by ElasticSearch. You can create Lucene Queries which are using quite the same syntax as ElasticSearch Queries. In fact we try to use the ElasticSearch documentation to replicate as much as we can the same Query syntax.
Right now the plan is to eventually support Reddis + ElasticSearch as distributed services but I've heard nothing about Azure Search. Remember that ElasticSearch is probably a little more polyvalent as it can also be installed on premise.
I understand that generally speaking Elastic search is better to use in most cases but i would like to know specifically for Umbraco, whether it is as good to use or not? Or if lucene is better
I've not use elastic search before, but I use Umbraco a lot and I use lucene, mainly via Examine which uses lucene.
Read about Examine here https://our.umbraco.org/documentation/reference/searching/examine/
See an example of some advanced search I set up in Umbraco using Examine here
http://www.codeshare.co.uk/blog/how-to-search-by-document-type-and-property-in-umbraco/