ElasticSearch vs Searx

Elasticsearch gained its popularity amongst developers by being enjoyable to use. A simple feature comparison against it's competition doesn't convey the significant advantages of just how easy it is to work with. This is due to multiple design choices such as the use of JSON for the API and queries.

Search can be executed either using a simple, Lucene-based query string or using an extensive JSON-based search query DSL. By structuring the query as a JSON object you can be very explicit and can dictate exactly what ElasticSeach will return. A very basic example of a JSON query is:

curl -XGET 'http://localhost:9200/blog/_search?pretty=true' -d ' { "query" : { "range" : { "postDate" : { "from" : "2011-12-10", "to" : "2011-12-12" } } } }'

Another area where ElasticSearch shines is its aggregations features. Similarly to facets (now deprecated), aggregations allow calculating and summarizing data of a query as it happens. Aggregations brings the ability to be nested and is broadly categorized as metrics aggregations and bucket aggregations.

"aggregations" : { "<aggregation_name>" : { "<aggregation_type>" : { <aggregation_body> }, ["aggregations" : { [<sub_aggregation>]* } ] } [,"<aggregation_name_2>" : { ... } ]* }

It's free and Open Source so you can host it yourself for free or even tweak it.

The one area where Elasticsearch shines is distributed search. It was built from the ground up to be suitable for high-scale 'cloud' applications.

There are many features Elasticsearch has as a result of being designed to be distributed that aren't currently available in Solr, such as:

• Shards and replicas can to moved to any node in the cluster on demand.
• With a simple API call you can increase and decrease the number of replicas without the need of shutting down nodes or creating new nodes.
• Manipulate shard placement with the cluster reroute API on a live cluster.
• Search across multiple indexes.
• Change the schema without restarting the server.
• Automatic shard rebalancing

Elasticsearch also has a module called Gateway, that in the case of the whole cluster crashing or being taken down will enable you to easily restore the latest state of the cluster when it gets back up.

Services such as Bonsai further simplify scaling Elasticsearch by hosting and scaling the search servers for you, making it nearly as easy to get started as CloudSearch or Searchify. Elasticsearch was also specifically designed to run well and be relatively easy to setup on EC2.

Elasticsearch has a REST API for management and configuration. The following are the main features of this API:

Index Management:

• Create, delete, close and open indices by running a simple HTTP command.
• Increase and decrease the number of replicas without the need of shutting down nodes or creating new nodes.
• Manipulate shard placement with the cluster reroute API. Move shards between nodes, we can cancel shard allocation process and we can also force shard allocation – everything on a live cluster.
• Check index and types existence

Configuration:

• Majority of configuration files can be modified dynamically.
• Update Mappings
• Define, retrieve and manage warning queries
• Shut down the entire cluster or a specific node
• Clear caches on the index level

This is all done over JSON, making it a lot more structured then the methods used in Solr.

Another thing to keep in mind when choosing a search solution is the development momentum. ElasticSearch has quickly caught up to the competition and most of the currently missing features are due to be released in upcoming versions.

Elasticsearch makes it easy to get started by not requiring you to define a schema before sending documents to be indexed. Elasticsearch will automatically guess field types for you, which although will not be as accurate as creating the mappings manually, is usually pretty accurate.

Elasticsearch also lets you manually define the mappings (index structure) before creating the index. One cool feature is if you miss a field or add a new field without defining the mapping, Elasticsearch will try to guess the Type for you.

Another useful and unique feature to Elasticsearch is the ability to have multiple types of documents in a single index. You can then facet, query or filter against all document types or a single type.

Essentially a reverse search. The percolator allows you to register queries against an index, and then send percolate requests which include a doc, and getting back the queries that match on that doc out of the set of registered queries. Not possible in Solr out of the box.

ElasticSearch natively handles a nested document structure.

ElasticSearch will index nested documents as a separate indexes and are stored in such a way that allow quick join operations to access them. Nested documents require a nested query to access so that don't clutter results from standard queries.

Since you have access to all the .yaml files, you can easily modify it to your needs.

Have a vast choice for choosing search engines

Using a proxy called Morty hides the IP address of the user

Since it can be hosted on your own server/ computer, it is very private, nothing leaves your computer.
No cookies or ads too

Since it is a Python program with a web interface, it's very very fast even on a poor internet connection

Uses Google and Bing, and hence has the 'quick answers' feature of both the search engines.