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Swiftype integrates with all major third party platforms, offering a Shopify App, Magento Extension, and WordPress Plugin, with more to come. Swiftype also provides tutorials for adding Swiftype to Tumblr, Jimdo, Heroku, Weebly, Tumblr, CloudFlare, WebStarts and Desk.com. They also have questions dealing with fixes for WooCommerce, how to add Swiftype to any CMS (such as Drupal or Jekyll) and searching across content types (like WordPress using GoDaddy Shopping Cart) See More
Algolia's engine has been built in such a way that you can index and search any language, or even several languages at the same time. The engine is also typo tolerant, and will allow for up to two typos in each words of the search query. This typo tolerance feature is also language agnostic, as it relies on optimized data structures and "fuzzy" tree traversals (implementing a Damarau-Levenshtein distance algorithm) instead of using dictionaries. See More
As a relatively new project, the documentation for ElasticSearch still leaves much to be desired. Documentation assumes that the user at least has familiarity with similar document stores, and is largely oriented toward those already familiar with other search solutions, such as Solr. Errors, while often quite simple to resolve, can be difficult to troubleshoot, as they are often insufficiently descriptive and missing from documentation. New users should be sure to check the tutorials section on elasticsearch.org for supplementary information lacking from the guide, such as more detailed installation instructions. See More
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. See More
The Sematex blog explains a problem with the way Elasticsearch handles its clusters, called the 'Split Brain Situation': Imagine a situation, where you cluster is divided into half, so half of your nodes don’t see the other half, for example because of the network failure. In such cases Elasticsearch will try to elect a new master in the cluster part that doesn’t have one and this will lead to creation of two independent clusters running at the same time. This can be limited with a small degree of configuration, but it can still happen. Users have already run into this problem in production and ElasticSearch host Bonsai also have had issues with this problem as recently as March 2012. See More
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" }
}
}
}' See More
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>" : { ... } ]*
} See More
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. See More
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. See More
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. See More
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. See More
Although this is provided by the creators of elastic search, the support is not as effective. It seems they only want you to buy the Gold / Platinum package for support. Even though you clearly give proof to the support personnel that there is something wrong with the provisioning of the cluster do not be surprised if you get a mail back saying "Everything looks fine...blah blah ...consider taking gold/platinum support...blah blah." See More
Rather than pricing by number of documents or API calls, they price by instance size. So if you have a lot of documents (>100k) that are very small (50MB), or a large number of shards which are small, you won't be arbitrarily forced into a higher pricing tier. Same deal with number of requests - if you need more memory on your instance, then upgrade. If you have a lot of very fast requests, you can stay on a lower tier. See More
Clusters aren't shared, customers get their own dedicated cluster. Memory and storage is reserved - there's no upper limit in terms of indexes or documents to be stored - and Linux containers are used (LXC) for and process and resource isolation, which means you get the memory you are paying for without having to compete for it with other customers. See More
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. See More
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. See More
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 See More