Elasticsearch vs Mapbox: What are the differences?
Elasticsearch: Open Source, Distributed, RESTful Search Engine. Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack); Mapbox: Design and publish beautiful maps. We make it possible to pin travel spots on Pinterest, find restaurants on Foursquare, and visualize data on GitHub.
Elasticsearch can be classified as a tool in the "Search as a Service" category, while Mapbox is grouped under "Mapping APIs".
Some of the features offered by Elasticsearch are:
- Distributed and Highly Available Search Engine.
- Multi Tenant with Multi Types.
- Various set of APIs including RESTful
On the other hand, Mapbox provides the following key features:
- Build native applications on iOS with the Mapbox iOS SDK or on iOS and OS X with MBXMapKit.
- Build native applications for Android. Use Mapbox, OpenStreetMap, and other tile sources in your app, as well as overlays like GeoJSON data and interactive tooltips.
"Powerful api" is the top reason why over 310 developers like Elasticsearch, while over 19 developers mention "Best mapping service outside of Google Maps" as the leading cause for choosing Mapbox.
Elasticsearch is an open source tool with 41.9K GitHub stars and 14K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.
Uber Technologies, Udemy, and DigitalOcean are some of the popular companies that use Elasticsearch, whereas Mapbox is used by Foursquare, Instacart, and Key Location. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 937 developers stacks; compared to Mapbox, which is listed in 82 company stacks and 25 developer stacks.
What is Elasticsearch?
What is Mapbox?
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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.
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.
We use ElasticSearch for
- Session Logs
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.
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.
Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.