Elastic Enterprise Search Reviews

It is just the best back end for search engines in the market. A NoSQL database that is trustworthy. Also, it is open source. Incredibly easy to use.

Mostly for developers: other people would have a hard time with it.

The fact that Elasticsearch offers really efficient & quick querying of data without a compromise on the different range of queries it can support is really awesome, also Elasticsearch can rank matching documents based on matching criteria which is also very useful. Elasticsearch also handles distributed queries very efficiently.

Reindexing documents can be a real pain if there is a major change in the schema, otherwise no complaints.

Federated search across multiple platforms

The implementation was bad. The cost to implement to get anything close to what I would get with Algolia or Coveo is approximately 10 times the cost of the software.

Elastic search has completely changed how we can fetch and visualize our customer's data. With the advanced text indexing, we can easily build a text search engine which gives accurate results. The powerful query format allows us to construct complex queries from deep filter trees and aggregate data in any way we want. Most importantly, all of this is extemely fast - giving results within milliseconds when working with complex queries on milions of documents. If performance ever becomes a problem, Elastic search is easily horizontally scalable and therefore future-proof.

One critical thing I'm missing is a proper way of handling migrations. If you for example need to change the format of indexed documents - and therefore the mapping, you need to develop your own way of programatically handling the reindexing - or do it manually.

As a backend developer, I know for a fact that I can rely upon elasticsearch for all the times when I have to support search based on string across the database.
You throw some set of documents to elasticseatch, and you are good to go to search anything amongst the provided documents. They have some supercool features to support string based search for say support for distributed systems, support for aggregations and analysers & folding, etc.
The documentation out there makes it lot easier to get going with it :)

The memory optimisation related issues could be alarming sometimes if not taken care of properly. You may end up having some weird system crashes.

* Easy to use, especially when compared with alternatives s.a. Solr.
* Free software.
* Elastic has a great customer support.
* It is very powerful, supporting heavy loads.
* Well-documented.

* Demands a specific knowledge of the theory of search.
* While way higher level than most services, it is not as high level as, let us say, a Google search.
* Often used as a NoSQL in inappropriate settings.

Easy to get started, fast with basically no optimization. It can handle massive amounts of data without breaking a sweat.

Getting data INTO Elasticsearch can be tricky, depending on the source. Version 7 fixes a lot of these issues, however. Licensing is less than ideal, too: many useful features are locked behind a paid support license, and the open source version is receiving fewer and fewer new features.

If you worked in a big online store on the analytics side you can really understand the hard work that this software is doing, and the value that it adds to your conversion rate.
It has great potential and used in combination with Kibana it proves a very good choice for fast and great search results.

Depending on your users conversion rate and volume it can become a high cost operation, if you have low margins.

It was easy to setup installing it with AWS or as a server. also has a lot of implementations options with Rails and other platforms.

It requires a lot of hardware >1GB of memory since is based on Java.

ElasticSearch provides some of the mos accurate & fast search results of the market. We moved from a Sphinx installation into ES and have never looked back. Multiple indexes, lots of querying options & great support from tooling.

Can be a bit hard to get into at first. However, the product is very well documented, so you just need to invest some time in their docs.

easy to set up and configure .

Unfortunately, and this is VERY SERIOUS, we have noticed that the search results returned by Switftype are incorrect. Some of the results that it SHOULD BE including for a given user keyword request are NOT included and some of the results that it IS including DO NOT match the keyword request.

We have worked with their technical support to no avail.

Unless you test your search results carefully, you wont notice this very serious flaw in the product.

Given that creating the correct set of search results is the basic function of this kind of product, this flaw means that Swiftype is unusable.

We are now in the process of converting to another product.

We used Google Custom Search and were really disappointed when Google dropped paid support. We changed to Swiftype and are very happy. It has given us the similar functionality with improved UI. The UI for optimizing the search is really easy to use yet powerful.

Unlike Google, Swiftype does not have indexes ready to go. As a result, when you add a new site to the search set, it takes some time to index the site. Its understandable but using Google was really simple.

ElasticSearch, as of Solr, is the right tool if you want to create a powerful search engine inside an application or a website. Indexing documents can be done through a REST API, as for querying data. Most of the modern frameworks and content management systems can ship with modules that interact with ElasticSearch to index and retrieve documents. For most complex systems, you can implement synonyms, auto-completion, auto-correction (using fuzzy search), and also configure how results should be sorted using a full text search. Searching with complicated parameters is really fast using ElasticSearch, and you can also retrieve facets to display to restrict results when needed.

Learning ElasticSearch seems to be a bit more difficult than learning it's competitor, Solr. In my experience, Solr has a better support for Drupal CMS which we use a lot in my company. But our last project using ElasticSearch was a success, thanks to all it's powerful features.

Easier to horizontally scale
Distributed by design
Excellent full-text search
Elastic Search is schema free'instead, it accepts JSON documents, as well as tries to detect the data structure, index the data, and make it searchable.
Elastic Search is document-oriented. It stores real world complex entities as structured JSON documents and indexes all fields by default, with a higher performance result.
Elastic Search is API driven; actions can be performed using a simple Restful API.
Elastic Search records any changes made in transactions logs on multiple nodes in the cluster to minimize the chance of data loss.

Elastic-search query DSL is less common and less flexible than Postgres SQL .
Everything is indexed by default, which creates an index overhead.
in order to understand how to properly query your data and how it is stored, you have less control over consistency.

As a web developer, I have been looking for opportunities to make our apps better, smarter and more developed. Elasticsearch made a revolution in our business by providing complex filtration systems and search, which weren't easy to implement previously: too much code and server consumption. Our products got better, we make good money and save our time.

I still haven't figure the implementation of replication and clusterization of Elasticsearch, but maybe it is a lack of information.

The ease of search makes it possible for clients to get the answers they need quickly.

I didn't find any downsides to the software.

It is very easy to integrate with javascript projects because it stores data in json format. Querying is easy with lot of selectors and modifiers available.

Reindexing is very time taking due to which there is data mismatch sometimes.

It's simply the industry standard for search. The extensive functionalities make complex problems look easy.

Documentation could be a bit more helpful, especially in dealing with breaking changes.

We loved this product. It made a huge improvement to our site searches.

We wished we had a bit more control over how search results were generated.

Excellent to use smart text, to management number of impression on search, distribution of data, autosuggest, soundex search. So more efficiently and quickly than search on a data base.

It has not a table of error, errors are so much generic. Documentation is very ambiguous and so short.

Its decent search to NOSQL DB and I find it good for content management

it has scalable and reliability issues. I would not recommend this for your SOR

well thought out restful API and they did CORS headers right

recent emphasis on scraping sites doesn't apply for large customers