Elasticsearch vs Ionic: What are the differences?
Elasticsearch belongs to "Search as a Service" category of the tech stack, while Ionic can be primarily classified under "Cross-Platform Mobile Development".
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, Ionic provides the following key features:
- Performance obsessed
- Utilizes Angular and React
- Native focused
"Powerful api", "Great search engine" and "Open source" are the key factors why developers consider Elasticsearch; whereas "Allows for rapid prototyping", "Hybrid mobile" and "It's angularjs" are the primary reasons why Ionic is favored.
Elasticsearch and Ionic are both open source tools. It seems that Elasticsearch with 41.9K GitHub stars and 14K forks on GitHub has more adoption than Ionic with 38.4K GitHub stars and 13.1K GitHub forks.
Instacart, Slack, and Stack Exchange are some of the popular companies that use Elasticsearch, whereas Ionic is used by Sellsuki, Edify, and eTobb. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to Ionic, which is listed in 392 company stacks and 350 developer stacks.
What is Elasticsearch?
What is Ionic?
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great framework,lots of resources,great community,easy to create UI
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.
We use Ionic as it is an awesome framework to build mobile hybrid apps with nativ access. Also Ionic has a nice community!
Cross-Platform goodness. I am a noob here...learning how to implement Ionic is on the top of my ToDo's
used on a recent project, an internal custom app developed for both ios and android.