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|Description||Open Source, Distributed, RESTful Search Engine||Developer-friendly API for building your app's search feature||Site Search that works.|
|Why people like using this service||
|Companies using this service|
Handling 130M+ users for Socialcam with ease
March 19, 2014 21:04
We tried a lot of services at Socialcam to handle our massive user base. All of them couldn't handle that number of users.
Algolia handles it without any problem but on top of that, it does it at full speed: we get results back in under a few milliseconds. Last but not least, it does it with error handling, which is great as typos are very frequent on mobile...
Cool for startups too
March 20, 2014 02:35
I'm Antonio, TVShow Time's CEO, a startup that has more than 100k+ active users.
Before Algolia, we were using Elastic Search that was costing a lot (hosted on 2 big EC2 instances) and with results that weren't that relevant.
Then we switched to Algolia, in 1 hour. We were blown away by how easy the integration was for such a good relevance in results and high performance.
Great product & awesome support
March 21, 2014 01:37
We were looking for a better search solution for GrowthHackers.com for a couple months. All the options we looked at were either too complicated to setup, didn't have the features we needed, or were too expensive. Algolia hit the right balance for us. It's super fast and easy to customize, and the documentation and examples for getting started are great. Most importantly though, their support rocks. It's always a pleasure talking with their team.
I thought good search would be hard
March 23, 2014 17:46
Maxime is a big supporter of Product Hunt, recognizing the continual request that I add search to the product from others in the community. Having seen many frustrating search implementations on other sites, I assumed it would be hard to integrate and provide something useful. Algolia proved me wrong (see the results here: http://producthunt.co/search).
I'm impressed with the speed and amazing support from the Algolia team. The dashboard analytics and management are incredibly useful, providing insight into how people are using the product and ability to act on those learning without changing a line of code. I would highly recommend it.
Easiest and simplest way to implement search
April 03, 2014 12:04
Implementing Algolia into our application took about 2 hours and the search results come back faster then our own internal APIs. We couldn't be happier!
Using Algolia for the Pebble search engine
April 09, 2014 21:01
Having a great search engine is extremely important for our app store. We find that users love to search, not only when they know what they are looking for but also to discover content around different themes.
In a very rushed period with lots of things to do in parallel, we found that Algolia offered a high quality solution that perfectly solved our problem and we had a first version working great in less than a day.
We also enjoyed getting their feedbacks and ideas to help us improve our search and we are now using Algolia in our internal tools as well. We strongly recommend them!
Seriously, it's awesome.
August 29, 2016 11:27
Fast, reliable (never saw a uniq downtime), fast again and really, really easy to use it! They're crushing it.
Swiftype is an amazing search platform! A+
September 10, 2014 11:23
We integrated the swiftype technology into our website. Initial setup was super easy and the service has been amazing so far. Swiftype dashboard offers many services to help you understand and optimize the search experience for your users. I highly recommend Swiftype to any website or app that needs an easy to setup, powerful and reliable search solution. The sales and the support team are amazing! Tip, if you want to try out the system, ask for Lucy Yu. She is amazing and walking you through the process!
Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.
Most of what people want to do with Wirkn is related to searching or filtering in some form or another. We normalize our data into ElasticSearch for fast, robust, search capabilities. Why not use PostgreSQL's great full-text search? Because ElasticSearch allows us to use Kibana as well.
We use ElasticSearch for
We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.
The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.
All social data we track is stored in ElasticSearch to make it easily searchable and for advanced statistics. Our several ElasticSearch clusters hold several billion social messages.
Enables fast and efficient indexing and searching of complex and big amounts of data.
Ferramenta que permite oferecer um serviço de busca inteligente para pesquisas incompleta ou aproximadas ( em analise )
Indexes our data to provide advanced rule-based promotions and categories and also full-text search and faceted filtering on client front-end websites.
Mainly for logs. Some apps also rely on elasticsearch for storage.
내부 검색 엔진을 Solr 기반의 AWS CloudSearch 에서 Elasticsearch 로 옮기고 있는 중입니다. 관리의 편의성 등에서 좋은 점수를 줄 수 있습니다.
Internal Jezebel functionality, not directly exposed to the client. Part of the internal search scoring, main communication happens via binary port 9300.
We use ElasticSearch to index and query items that need to be searched full-text (such as jobs and candidates). We mirror changes to our main datastore (PostgreSQL) onto ElasticSearch using SQLAlchemy's events, denormalizing as needed.
The very first version of the search was just a Postgres database query. It wasn’t terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn’t come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.
Cloudify uses Elasticsearch both to store runtime information (it’s actually a little known fact that Elasticsearch can serve as a NoSQL database), as well as, in its more classic capacity to index system logs and events, making them available over API calls.
Elasticsearch is the engine that powers search on the site. From a high level perspective, it’s a Lucene wrapper that exposes Lucene’s features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.
Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.