Alternatives to Vulcanizer logo

Alternatives to Vulcanizer

Vulcan, Algolia, Solr, Elastic, and Dejavu are the most popular alternatives and competitors to Vulcanizer.
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What is Vulcanizer and what are its top alternatives?

A golang library for interacting with an Elasticsearch cluster. It's goal is to provide a high level API to help with common tasks that are associated with operating an Elasticsearch cluster such as querying health status of the cluster, migrating data off of nodes, updating cluster settings, etc.
Vulcanizer is a tool in the Search Tools category of a tech stack.
Vulcanizer is an open source tool with 641 GitHub stars and 58 GitHub forks. Here’s a link to Vulcanizer's open source repository on GitHub

Top Alternatives to Vulcanizer

  • Vulcan
    Vulcan

    Vulcan is an API-compatible alternative to Prometheus. It aims to provide a better story for long-term storage, data durability, high cardinality metrics, high availability, and scalability. Vulcan is much more complex to operate, but should integrate with ease to an existing Prometheus environment. ...

  • 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. ...

  • Solr
    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. ...

  • Elastic
    Elastic

    Elastic is an Elasticsearch client for the Go programming language.

  • Dejavu
    Dejavu

    dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana). ...

  • Mirage
    Mirage

    The Elasticsearch query DSL supports 100+ query APIs ranging from full-text search, numeric range filters, geolocation queries to nested and span queries. Mirage is a modern, open-source web based query explorer for Elasticsearch. ...

  • Jina
    Jina

    It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience. ...

  • Searchkit
    Searchkit

    Searchkit is a suite of React components that communicate directly with your Elasticsearch cluster. Each component is built in React and is fully customisable to your needs. ...

Vulcanizer alternatives & related posts

Vulcan logo

Vulcan

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DigitalOcean's API-compatible alternative to Prometheus
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PROS OF VULCAN
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    CONS OF VULCAN
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      related Vulcan posts

      Algolia logo

      Algolia

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      Developer-friendly API and complete set of tools for building search
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      695
      PROS OF ALGOLIA
      • 125
        Ultra fast
      • 95
        Super easy to implement
      • 73
        Modern search engine
      • 71
        Excellent support
      • 70
        Easy setup, fast and relevant
      • 46
        Typos handling
      • 40
        Search analytics
      • 31
        Designed to search records, not pages
      • 30
        Multiple datacenters
      • 30
        Distributed Search Network
      • 10
        Smart Highlighting
      • 9
        Search as you type
      • 8
        Instantsearch.js
      • 8
        Multi-attributes
      • 6
        Super fast, easy to set up
      • 5
        Amazing uptime
      • 5
        Database search
      • 4
        Realtime
      • 4
        Great documentation
      • 4
        Highly customizable
      • 4
        Github-awesome-autocomple
      • 3
        Powerful Search
      • 3
        Beautiful UI
      • 3
        Places.js
      • 2
        Integrates with just about everything
      • 2
        Awesome aanltiycs and typos hnadling
      • 1
        Fast response time
      • 1
        Smooth platform
      • 1
        Github integration
      • 1
        Developer-friendly frontend libraries
      CONS OF ALGOLIA
      • 10
        Expensive

      related Algolia posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.5M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 16 upvotes · 325.9K views

      Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

      Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

      Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

      #SearchAsAService

      See more
      Solr logo

      Solr

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      125
      A blazing-fast, open source enterprise search platform
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      PROS OF SOLR
      • 35
        Powerful
      • 22
        Indexing and searching
      • 20
        Scalable
      • 19
        Customizable
      • 13
        Enterprise Ready
      • 5
        Apache Software Foundation
      • 5
        Restful
      • 4
        Great Search engine
      • 2
        Security built-in
      CONS OF SOLR
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        related Solr posts

        Ganesa Vijayakumar
        Full Stack Coder | Technical Lead · | 19 upvotes · 2.8M views

        I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

        I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

        As per my work experience and knowledge, I have chosen the followings stacks to this mission.

        UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

        Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

        Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

        Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

        Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

        Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

        Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

        Happy Coding! Suggestions are welcome! :)

        Thanks, Ganesa

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        Shared insights
        on
        SolrSolrLuceneLucene
        at

        "Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.

        Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."

        See more
        Elastic logo

        Elastic

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        Elasticsearch client for Go
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        + 1
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        PROS OF ELASTIC
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            related Elastic posts

            Dejavu logo

            Dejavu

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            A modern, open-source data browser for Elasticsearch
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            PROS OF DEJAVU
            • 2
              Available as a chrome app
            • 2
              Open-source (MIT License)
            • 1
              Clean and modern data browsing UI
            • 1
              Available as a docker image
            CONS OF DEJAVU
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              Mirage logo

              Mirage

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              GUI for writing Elasticsearch queries
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              PROS OF MIRAGE
              • 1
                Clean GUI
              CONS OF MIRAGE
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                Jina logo

                Jina

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                An easier way to build neural search on the cloud
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                PROS OF JINA
                • 1
                  Local and cloud friendly
                • 1
                  Support for all kinds of data
                CONS OF JINA
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                  Searchkit logo

                  Searchkit

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                  React UI Components for Elasticsearch
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                  PROS OF SEARCHKIT
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                      related Searchkit posts