Solr vs ElasticSearch

A key differentiator of Solr is the level of customizability the SearchComponent feature provides.

SearchComponent provides the developer astonishing flexibility in the way search queries are assembled and executed. At the time of writing, there does not appear to be a ElasticSearch equivalent of SearchComponent. source

Whilst ElasticSearch has a number of plugin-points there doesn't appear to be an equivalent of Solr's SearchComponent that enables you to modify the workflow of existing API endpoints.

Solr allows to view average, standard deviation, maximum, minimum, sum of squares of a particular numeric field. It also allows faceting of that numeric field based on the value(s) of other fields.

Solr allows you to group search results. Results can be grouped by:

• Field Value
• Query
• Function Query

You can also collapse multiple results with the same field value down to a single result.

Solr has a faceting feature called pivot facets or 'decision tree facets'. Pivot facets enable you to calculate facets inside a parents facet, for example pivoting on 'size' than 'color' returns 'color' facet counts for each 'size' facet

Solr has a great feature that enables you to use LocalParams to perform more advanced faceting. They provide a way to "localize" information about a specific argument that is being sent to Solr. In other words, LocalParams provide a way to add meta-data to certain argument types such as query strings. From the Solr Wiki:

LocalParams are expressed as prefixes to arguments to be sent to Solr. For example:

Assume we have the existing query parameter

q=solr rocks

We can prefix this query string with LocalParams to provide more information to the query parser, for example changing the default operator type to "AND" and the default field to "title" for the lucene query parser:

q={!q.op=AND df=title}solr rocks

The following services will host Solr for you. The great thing about these services is that they abstract away some of the difficulty of scaling Solr:

• WebSolr
• SolrHQ

Solr allows has the functionality to check and correct spelling mistakes in search queries. The three main implementations are:

• IndexBasedSpellChecker
• WordBreadkSolrSpellChecker
• DirectSolrSpellChecker

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.