Why did you vote for ElasticSearch?

  • Structured Search Queries with JSON
  • Easy to Work With
  • Advanced Faceting
  • Rapid Feature Development
  • Percolator (Prospective Search)
  • Handles Nested Documents
  • Designed to be Distributed
  • RESTful JSON API for Config/Management
  • Schemaless
  • Allows Multiple types of documents per index
  • Add Another Pro

Elasticsearch is a search server based on Lucene and has an advanced distributed model. You can host the opensourced code yourself, on EC2 or use a service such as Bonsai, Found or SearchBlox.

6

Votes

Structured Search Queries with JSON

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" } } } }'

6

Votes

Easy to Work With

Forget about features for a second, elasticsearch provides a better experience in every aspect from development to operations. It's obvious that a lot of thought went into the design and the way certain features are exposed. In reality, I can probably get either one [Solr or Elasticsearch] of these products to do what I want so in the end it comes down to the level of effort needed to turn these features into an actual solution to a problem. That's what matters and it's why elasticsearch has so much momentum. There's much less friction between the developer and the platform. EgaumerElasticsearch 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.

5

Votes

Advanced Faceting

Another area where ElasticSearch shines is its faceting features, in general it just gives you more control compared to Solr. Some unique ElasticSearch features are:Regular expressions to define which terms will be included/excluded in faceting results.Combine term faceting results from different field automatically.Use scripts to modify the fields values before the calculation process steps inAwesome Range FacetingCan specify set of ranges and it will return both document counts and aggregated data. Modify the field and aggregated data with a script.

Why did you vote for Solr?

  • Customizablity
  • Hosting Support
  • Results Grouping
  • Decision tree faceting
  • Local params
  • SpellChecker
  • Stats component
  • Add Another Pro

Solr is an open-source search server based on the Lucene Java search library. You can host the opensourced code yourself, on EC2 or use a service such as WebSolr or SolrHQ.

4

Votes

Customizablity

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. sourceWhilst 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.

2

Votes

Hosting Support

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:WebSolrSolrHQ

2

Votes

Results Grouping

Unlike ElasticSearch Solr allows you to group search results. Results can be grouped by:Field ValueQueryFunction QueryYou can also collapse multiple results with the same field value down to a single result.

Why did you vote for Amazon CloudSearch?

AWS CloudSearch is a hosted search service designed to allow you to quickly integrate a search service that is simple to scale when required and provides near real-time indexing of document updates.=

5

Votes

Easy to Integrate and Manage

The primary differentiator of CloudSearch is how simple it makes the lives of the developers using it. Not only does it automatically scale, developers can change search parameters, fine tune search relevance, and apply new settings at any time without having to upload the data again and can do so from a simple dashboard.CloudSearch also automatically takes care of:Hardware provisioningData partitioning,Software patches.

3

Votes

Scalability

CloudSearch dynamically scales as the amount of searchable data increases or as the query rate changes. The search system utilizes well-understood and automated sharding and replication to scale.CloudSearch will automatically add search instances and index partitions as required as well as add and remove replicas to respond to changes in search request traffic

1

Votes

Results Ranking

CloudSearch does out-of-the-box ranking of search results with simple controls to let developers tweak the ranking. You can add stopwords, perform stemming, and add synonyms. Rank expressions are mathematical functions that you can use to change how search results are ranked. By default, documents are ranked by a text relevance score that takes into account the proximity of the search terms and the frequency of those terms within a document. You can use rank expressions to include other factors in the ranking. For example, if you have a numeric field in your domain called 'popularity,' you can define a rank expression that combines popularity with the default text relevance score to rank relevant popular documents higher in your search results.source

Why did you vote for Swiftype?

  • Real-Time Analytics
  • Easy & fast deployment
  • Custom result ranking
  • Android & iOS support
  • WordPress plugin
  • Add Another Pro

Swiftype offers a fast, modern search that is easy to set up and use. Main feature include autocomplete, query result management, and analytics. There are free and paid (starting at $49/mo) versions available.

1

Votes

Real-Time Analytics

Built-in search analytics give you live insight into what your users are looking for.

1

Votes

Easy & fast deployment

To set up, paste a Javascript snippet, the crawler will create a search engine in minutes.

1

Votes

Custom result ranking

Drag-and-drop default results to re-order them. Changes are instantly reflected on website.

Why did you vote for Searchify (Hosted IndexTank)?

  • Multiple libraries
  • Works without provided libraries
  • Geolocation search
  • Autocomplete / suggest
  • True real-time updates
  • Field weights
  • "Did you mean?" suggestions
  • Add Another Pro

Searchify is a hosted search service that utilizes the IndexTank codebase which is now open-sourced and maintained by LinkedIn.

0

Votes

Multiple libraries

Searchify has libraries in Python, Ruby, Java, PHP, Node.js and more.

0

Votes

Works without provided libraries

The API consists of REST calls that can be made through HTTP which enables Searchify to be used even without one of the provided libraries.

0

Votes

Geolocation search

Sorting and filtering by distance

Why did you vote for Denote Site Search?

A powerful & smart search that is designed for applications and websites.

1

Votes

Smart (Semantic)

Denote uses a semantic engine on websites. So for example when you search for cell phone, it also searches iphones etc.

1

Votes

Mobile

It works really nice on mobile site.

1

Votes

analytics

I love the trending option from where I can see what people are searching real time.

Why did you vote for Sphinx?

  • Searching performance
  • Better relevance ranking
  • Rich database-like querying features
  • Advanced full-text searching syntax
  • Scalability
  • Indexing performance
  • Batch and Real-Time full-text indexes
  • Non-text attributes support
  • SQL database indexing
  • Non-SQL storage indexing
  • Easy application integration
  • Flexible text processing
  • Distributed searching
  • Add Another Pro

Sphinx is an open source full text search server, designed from the ground up with performance, relevance (aka search quality), and integration simplicity in mind. It's written in C++ and works on Linux (RedHat, Ubuntu, etc), Windows, MacOS, Solaris, FreeBSD, and a few other systems.

2

Votes

Searching performance

Searching through 1,000,000-document, 1.2 GB text collection that we use for everyday development and testing runs at 500+ queries/sec on a 2-core desktop machine with 2 GB of RAM.

2

Votes

Better relevance ranking

Unlike many other engines, Sphinx does not solely rely on 30-year-old statistical ranking that only considers keyword frequencies, nor limits you to it. By default, Sphinx additionally analyzes keyword proximity, and ranks closer phrase matches higher, with perfect matches ranked on top. Also, ranking is flexible: you can choose from a number of built-in relevance functions, tweak their weights by using expressions, or develop new ones.

2

Votes

Rich database-like querying features

Sphinx does not limit you to just keyword searching. On top of full-text search result set, you can compute arbitrary arithmetic expressions, add WHERE conditions, do ORDER BY, GROUP BY, use MIN/MAX/AVG/SUM, aggregates etc. Essentially, full-blown SQL SELECT is supported.

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