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  5. Elasticsearch vs Swiftype

Elasticsearch vs Swiftype

OverviewDecisionsComparisonAlternatives

Overview

Swiftype
Swiftype
Stacks192
Followers96
Votes11
Elasticsearch
Elasticsearch
Stacks35.5K
Followers27.1K
Votes1.6K

Elasticsearch vs Swiftype: What are the differences?

Introduction

Elasticsearch and Swiftype are both search platforms that are commonly used for indexing and searching large amounts of data. While they share similarities, there are several key differences between the two.

  1. Data structure: Elasticsearch uses a JSON-based structure to store and index data, while Swiftype uses a schemaless format that is optimized for text search. This means that Elasticsearch provides more flexibility in terms of data modeling and querying, whereas Swiftype is built specifically for text-based data.

  2. Scalability and performance: Elasticsearch is designed to be highly scalable and can handle large amounts of data and high query loads. It utilizes a distributed architecture that allows for horizontal scaling across multiple nodes. On the other hand, Swiftype is a hosted service that offers scalable search functionality, but it may have some limitations in terms of performance and scalability compared to Elasticsearch.

  3. Customizability: Elasticsearch provides a highly customizable search experience, allowing users to configure various settings and parameters to tailor the search functionality to their specific needs. Swiftype, on the other hand, offers a more simplified and streamlined search experience with fewer customization options.

  4. Deployment: Elasticsearch can be self-hosted, meaning users have full control over their infrastructure and can deploy Elasticsearch on their own servers or cloud environment. Swiftype, on the other hand, is a hosted service managed by Elastic, the parent company of Elasticsearch. This means that users do not have to worry about infrastructure management and can focus solely on using the search functionality provided by Swiftype.

  5. Community and ecosystem: Elasticsearch has a large and active community, with a wide range of resources, plugins, and integrations available. This makes it easier for developers to find support and extend the functionality of Elasticsearch. Swiftype, being a proprietary service, has a smaller community and fewer resources available compared to Elasticsearch.

  6. Pricing: Elasticsearch offers an open-source version that is free to use, but there are additional costs involved for enterprise features and support. Swiftype, on the other hand, has a pricing model based on usage and offers different plans depending on the scale and requirements of the user.

In summary, Elasticsearch and Swiftype are both powerful search platforms, but Elasticsearch provides more flexibility, scalability, and customization options, while Swiftype offers a simpler, hosted solution with less customization and scalability. The choice between the two depends on the specific needs and requirements of the project.

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Advice on Swiftype, Elasticsearch

Akhil Kumar
Akhil Kumar

software developer at arzooo

May 2, 2022

Needs advice

I want to design a search engine which can search with PAYMENT-ID, ORDER-ID, CUSTOMER-NAME, CUSTOMER-PHONE, STORE-NAME, STORE-NUMBER, RETAILER-NAME, RETAILER-NUMBER, RETAILER-ID, RETAILER-MARKETPLACE-ID.

All these details are stored in different tables like ORDERS, PAYMENTS, RETAILERS, STORES, CUSTOMERS, and INVOICES with relations. Right now we have only 10MBs of data with 20K records. So I need a scalable solution that can handle the search from all the tables mentioned and how can I make a dataset with so many tables with relations for search.

19.1k views19.1k
Comments
Rana Usman
Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceonFirebaseFirebaseElasticsearchElasticsearchAlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k
Comments
Phillip
Phillip

Developer at Coach Align

Mar 18, 2021

Decided

The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.

40.7k views40.7k
Comments

Detailed Comparison

Swiftype
Swiftype
Elasticsearch
Elasticsearch

Swiftype is the easiest way to add great search to your website or mobile application.

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).

Quick Start- Install by pasting a JavaScript snippet in your page.;Autocomplete- our search engines come with autocomplete built-in;Detailed Analytics- Our built-in search analytics give you real-time insight into what your users are looking for.;Custom Result Ranking- Drag-and-drop results to re-order them.;VIP-approved WordPress search plugin- Just install the plugin and your ready to go.;Great Mobile Support- Our search works great in mobile browsers, and our native iOS and Android SDKs make app integration a breeze.;Advanced Developer API- Our Developer API gives you low-level access to our search technology.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
Statistics
Stacks
192
Stacks
35.5K
Followers
96
Followers
27.1K
Votes
11
Votes
1.6K
Pros & Cons
Pros
  • 8
    Very easy setup and highly customizable for your search
  • 1
    Role devision to develop, design, manage
  • 1
    Easy setup
  • 1
    Analytics
Cons
  • 1
    Expensive
  • 1
    API Calls Monitoring/Alerts
  • 1
    Cost Prediction
  • 1
    Customer Support
Pros
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
Cons
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
Integrations
No integrations available
Kibana
Kibana
Beats
Beats
Logstash
Logstash

What are some alternatives to Swiftype, Elasticsearch?

Algolia

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.

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

MeiliSearch

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

Bonsai

Bonsai

Your customers expect fast, near-magical results from your search. Help them find what they’re looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

Azure Cognitive Search

Azure Cognitive Search

It is the only cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. Formerly known as Azure Search, it uses the same integrated Microsoft natural language stack that Bing and Office have used for more than a decade and AI services across vision, language and speech. Spend more time innovating and less time maintaining a complex cloud search solution.

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