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Solr vs Swagger UI: What are the differences?

Introduction:
Key differences between Solr and Swagger UI are outlined below:

1. **Purpose**: Solr is a search platform built on Apache Lucene, used for indexing and searching data, whereas Swagger UI is a tool for visualizing and interacting with APIs, primarily used for API documentation and testing.
2. **Functionality**: Solr is designed for full-text search capabilities and server-side search applications, enabling powerful search functionalities like faceted search, whereas Swagger UI focuses on providing a graphical interface for exploring API endpoints and making API requests.
3. **Technology**: Solr is written in Java and widely used for creating powerful search engines, while Swagger UI is primarily a JavaScript library that generates interactive documentation for APIs based on OpenAPI Specification (formerly Swagger Specification).
4. **Usage**: Solr is commonly utilized in enterprise search, e-commerce platforms, and large-scale data search applications, providing scalable and efficient search capabilities, whereas Swagger UI is often used during the API development process to design, document, and test APIs.
5. **Deployment**: Solr is typically deployed as a standalone search server or can be integrated into existing applications as a search component, running as a separate service or module, whereas Swagger UI is integrated into API projects to provide interactive API documentation and testing capabilities within the application itself.
6. **Customization**: Solr allows for extensive customization and configuration of search functionalities, including schema design and relevance tuning, while Swagger UI offers limited customization options for the visual representation of API endpoints and input parameters.

In Summary, Solr and Swagger UI differ in their primary purpose, functionality, technology stack, usage scenarios, deployment methods, and customization options.
Advice on Solr and Swagger UI
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From a StackShare Community member: "I just started working for a start-up and we are in desperate need of better documentation for our API. Currently our API docs is in a README.md file. We are evaluating Postman and Swagger UI. Since there are many options and I was wondering what other StackSharers would recommend?"

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Replies (3)
Jagdeep Singh
Tech Lead at ucreate.it · | 8 upvotes · 400.3K views

I use Postman because of the ease of team-management, using workspaces and teams, runner, collections, environment variables, test-scripts (post execution), variable management (pre and post execution), folders (inside collections, for better management of APIs), newman, easy-ci-integration (and probably a few more things that I am not able to recall right now).

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I use Swagger UI because it's an easy tool for end-consumers to visualize and test our APIs. It focuses on that ! And it's directly embedded and delivered with the APIs. Postman's built-in tools aren't bad, but their main focus isn't the documentation and also, they are hosted outside the project.

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Sadik Ay
Recommends
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I recommend Postman because it's easy to use with history option. Also, it has very great features like runner, collections, test scripts runners, defining environment variables and simple exporting and importing data.

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Pros of Solr
Pros of Swagger UI
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Restful
  • 5
    Apache Software Foundation
  • 4
    Great Search engine
  • 2
    Security built-in
  • 1
    Easy Operating
  • 49
    Open Source
  • 34
    Can execute api calls from the documentation
  • 29
    Free to use
  • 19
    Customizable
  • 14
    Easy to implement in .Net
  • 13
    Mature, clean spec
  • 12
    API Visualization
  • 9
    Coverage
  • 6
    Scaffolding
  • 6
    Easy to use
  • 5
    Vibrant and active community
  • 4
    Elegant
  • 3
    Adopted by tm forum api
  • 2
    Clear for React
  • 1
    Api
  • 1
    Can deploy API to AWS API Gateway and AWS Lambda

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Cons of Solr
Cons of Swagger UI
    Be the first to leave a con
    • 3
      Need to learn YAML and RAML
    • 2
      Documentation doesn't look that good
    • 1
      Doesn't generate code snippets in different languages
    • 1
      You don’t actually get in-line error highlighting
    • 1
      Does not support hypermedia

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    What is 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.

    What is Swagger UI?

    Swagger UI is a dependency-free collection of HTML, Javascript, and CSS assets that dynamically generate beautiful documentation and sandbox from a Swagger-compliant API

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    What companies use Solr?
    What companies use Swagger UI?
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    What tools integrate with Solr?
    What tools integrate with Swagger UI?

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    What are some alternatives to Solr and Swagger UI?
    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.
    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.
    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).
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    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.
    See all alternatives