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StackHive VS Apache Solr

Compare StackHive VS Apache Solr and see what are their differences

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Contents:
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  3. » Reviews
  4. » Alternatives

StackHive logo StackHive

Design, develop or publish websites right from your browser

Apache Solr logo Apache Solr

Solr is an open source enterprise search server based on Lucene search library, with XML/HTTP and...
  • StackHive Landing page
    Landing page //
    2023-02-09
  • Apache Solr Landing page
    Landing page //
    2023-04-28

StackHive features and specs

  • User-Friendly Interface
    StackHive offers a drag-and-drop interface that makes it easy for users, including those with little coding experience, to design websites quickly.
  • Responsive Design
    The platform allows users to create responsive websites that work well on various devices, which is crucial for modern web development.
  • Time-Saving Features
    With pre-built components and templates, StackHive helps users speed up the web design process, reducing time spent on repetitive tasks.
  • Integration with Popular Tools
    StackHive integrates with popular web development tools and platforms, enhancing its usability and flexibility for developers.
  • Real-time Preview
    The platform enables users to see changes in real-time, providing instant feedback and reducing the cycle of design and testing.

Possible disadvantages of StackHive

  • Limited Customization
    For advanced users who need full control over their code, StackHive may offer limited customization options compared to coding manually.
  • Learning Curve
    While designed to be user-friendly, there may still be a learning curve for complete beginners unfamiliar with web design concepts.
  • Dependency on Platform
    Using StackHive may create dependency on the platform for future website updates, which could be a concern if the service changes or discontinues.
  • Potential for Overhead
    Generated code might include unnecessary elements leading to bloated files, which can affect website performance and load times.
  • Cost Implications
    While it offers powerful tools, users need to consider any associated costs with using the platform, as it might not be attainable for all budgets.

Apache Solr features and specs

  • Scalability
    Apache Solr is highly scalable, capable of handling large amounts of data and numerous queries per second. It supports distributed search and indexing, which allows for horizontal scaling by adding more nodes.
  • Flexibility
    Solr provides flexible schema management, allowing for dynamic field definitions and easy handling of various data types. It supports a variety of search query types and can be customized to meet specific search requirements.
  • Rich Feature Set
    Solr comes with a wealth of features out-of-the-box, including faceted search, result highlighting, multi-index search, and advanced filtering capabilities. It also offers robust analytics and joins support.
  • Community and Documentation
    Being an open-source project, Apache Solr has a strong community and comprehensive documentation, which ensures continuous improvements, updates, and extensive support resources for developers.
  • Integrations
    Solr integrates well with a variety of databases and data sources, and it provides REST-like APIs for ease of integration with other applications. It also has strong support for popular programming languages like Java, Python, and Ruby.
  • Performance
    Solr is built on top of Apache Lucene, which provides high performance for searching and indexing. It is optimized for speed and can handle rapid data ingestion and real-time indexing.

Possible disadvantages of Apache Solr

  • Complexity
    The initial setup and configuration of Apache Solr can be complex, particularly for those not already familiar with search engines and indexing concepts. Managing a distributed Solr installation also requires considerable expertise.
  • Resource Intensive
    Running Solr, especially for large datasets, can be resource-intensive in terms of both memory and CPU. It requires careful tuning and adequate hardware to maintain performance.
  • Learning Curve
    The learning curve for Apache Solr can be steep due to its extensive feature set and the complexity of its configuration options. New users may find it challenging to get up to speed quickly.
  • Consistency Issues
    In distributed setups, ensuring data consistency can be challenging, particularly for users unfamiliar with managing clustered environments. There may be delays or issues with synchronizing indexes across multiple nodes.
  • Maintenance
    Ongoing maintenance of a Solr instance, including monitoring, tuning, and scaling, can be labor-intensive. This requires dedicated effort to keep the system running efficiently over time.
  • Limited Real-time Capabilities
    Although Solr provides near real-time indexing, it may not be as effective as some specialized real-time search engines. For applications requiring truly real-time capabilities, additional solutions might be necessary.

Analysis of Apache Solr

Overall verdict

  • Yes, Apache Solr is generally considered a good option for organizations seeking a reliable, scalable, and flexible search platform. It offers extensive features and is supported by a strong community, making it a solid choice for many use cases.

Why this product is good

  • Apache Solr is highly regarded for its robust full-text search capabilities, scalability, and ease of integration. As an open-source search platform, it is built on Apache Lucene and provides powerful distributed search and indexing, replication, load-balanced querying, and automated failover and recovery. Solr is designed to handle large volumes of data efficiently and supports various data formats with powerful data management features.

Recommended for

    Apache Solr is recommended for organizations that need to implement powerful search capabilities, especially those managing large, complex datasets. It is ideal for businesses that require full-text search features, e-commerce sites, content management systems, and big data applications that demand high query performance and scalability.

StackHive videos

+ Add

StackHive Tutorial | Creating and Manipulating Grid Structures

Apache Solr videos

+ Add

Solr Index - Learn about Inverted Indexes and Apache Solr Indexing

More videos:

  • Review - Solr Web Crawl - Crawl Websites and Search in Apache Solr

Category Popularity

0-100% (relative to StackHive and Apache Solr)

StackHive

Apache Solr

Text Editors
100 100%
Text Editors
0% 0
Custom Search Engine
0 0%
Custom Search Engine
100% 100
IDE
100 100%
IDE
0% 0
Custom Search
0 0%
Custom Search
100% 100

User comments

Share your experience with using StackHive and Apache Solr. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare StackHive and Apache Solr

StackHive Reviews

We have no reviews of StackHive yet.
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Apache Solr Reviews

Top 10 Site Search Software Tools & Plugins for 2022
Apache Solr is optimized to handle high-volume traffic and is easy to scale up or down depending on your changing needs. The near real-time indexing capabilities ensure that your content remains fresh and search results are always relevant and updated. For more advanced customization, Apache Solr boasts extensible plug-in architecture so you can easily plug in index and...
Source: influencermarketinghub.com
5 Open-Source Search Engines For your Website
Apache Solr is the popular, blazing-fast, open-source enterprise search platform built on Apache Lucene. Solr is a standalone search server with a REST-like API. You can put documents in it (called "indexing") via JSON, XML, CSV, or binary over HTTP. You query it via HTTP GET and receive JSON, XML, CSV, or binary results.
Source: vishnuch.tech
Elasticsearch vs. Solr vs. Sphinx: Best Open Source Search Platform Comparison
Solr is not as quick as Elasticsearch and works best for static data (that does not require frequent changing). The reason is due to caches. In Solr, the caches are global, which means that, when even the slightest change happens in the cache, all indexing demands a refresh. This is usually a time-consuming process. In Elastic, on the other hand, the refreshing is made by...
Source: greenice.net
Algolia Review – A Hosted Search API Reviewed
If you’re not 100% satisfied with Algolia, there are always alternative methods to accomplish similar results, such as Solr (open-source & self-hosted) or ElasticSearch (open-source or hosted). Both of these are built on Apache Lucene, and their search syntax is very similar. Amazon Elasticsearch Service provides a fully managed Elasticsearch service which makes it easy to...
Source: getstream.io

Social recommendations and mentions

Based on our record, Apache Solr seems to be more popular. It has been mentiond 19 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

StackHive mentions (0)

We have not tracked any mentions of StackHive yet. Tracking of StackHive recommendations started around Mar 2021.

Apache Solr mentions (19)

  • List of 45 databases in the world
    Solr — Open-source search platform built on Apache Lucene. - Source: dev.to / 11 months ago
  • Considerations for Unicode and Searching
    I want to spend the brunt of this article talking about how to do this in Postgres, partly because it's a little more difficult there. But let me start in Apache Solr, which is where I first worked on these issues. - Source: dev.to / 12 months ago
  • Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
    Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / almost 2 years ago
  • Looking for software
    Apache Solr can be used to index and search text-based documents. It supports a wide range of file formats including PDFs, Microsoft Office documents, and plain text files. https://solr.apache.org/. Source: about 2 years ago
  • 'google-like' search engine for files on my NAS
    If so, then https://solr.apache.org/ can be a solution, though there's a bit of setup involved. Oh yea, you get to write your own "search interface" too which would end up calling solr's api to find stuff. Source: over 2 years ago
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What are some alternatives?

When comparing StackHive and Apache Solr, you can also consider the following products

GitHub Codespaces - GItHub Codespaces is a hosted remote coding environment by GitHub based on Visual Studio Codespaces integrated directly for GitHub.

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

CloudShell - Cloud Shell is a free admin machine with browser-based command-line access for managing your infrastructure and applications on Google Cloud Platform.

Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.

CodeTasty - CodeTasty is a programming platform for developers in the cloud.

Typesense - Typo tolerant, delightfully simple, open source search 🔍

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