Elasticsearch vs Qbox.io

Elasticsearch
Elasticsearch

8.3K
5.4K
1.6K
Qbox.io
Qbox.io

6
7
0
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Elasticsearch vs Qbox.io: 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); Qbox.io: Dedicated cloud hosting for Elasticsearch on Amazon EC2, Rackspace, and SoftLayer. Qbox is supported, dedicated, hosted Elasticsearch - the bleeding edge of full-text search and analytics. We provide an intuitive interface to provision, secure, and monitor ES clusters in Amazon EC2 and Rackspace datacenters everywhere.

Elasticsearch and Qbox.io can be categorized as "Search as a Service" tools.

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, Qbox.io provides the following key features:

  • Global availability
  • 24x7 support
  • Reserved cloud resources

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

- No public GitHub repository available -

What is Elasticsearch?

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).

What is Qbox.io?

Qbox is supported, dedicated, hosted Elasticsearch - the bleeding edge of full-text search and analytics. We provide an intuitive interface to provision, secure, and monitor ES clusters in Amazon EC2 and Rackspace datacenters everywhere.

Want advice about which of these to choose?Ask the StackShare community!

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        What are some alternatives to Elasticsearch and Qbox.io?
        Solr
        Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
        Lucene
        Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
        MongoDB
        MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
        Algolia
        Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
        Splunk
        Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
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        Decisions about Elasticsearch and Qbox.io
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        How developers use Elasticsearch and Qbox.io
        Avatar of imgur
        imgur uses ElasticsearchElasticsearch

        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.

        Avatar of Instacart
        Instacart uses ElasticsearchElasticsearch

        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.

        Avatar of AngeloR
        AngeloR uses ElasticsearchElasticsearch

        We use ElasticSearch for

        • Session Logs
        • Analytics
        • Leaderboards

        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.

        Avatar of Brandon Adams
        Brandon Adams uses ElasticsearchElasticsearch

        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.

        Avatar of Ana Phi Sancho
        Ana Phi Sancho uses ElasticsearchElasticsearch

        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.

        Avatar of Drillist.com
        Drillist.com uses Qbox.ioQbox.io

        Search as a Service

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