Alternatives to Lucene logo

Alternatives to Lucene

Solr, Elasticsearch, Sphinx, Apache Solr, and Hadoop are the most popular alternatives and competitors to Lucene.
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What is Lucene and what are its top alternatives?

Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
Lucene is a tool in the Search Engines category of a tech stack.

Top Alternatives to Lucene

  • 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. ...

  • Elasticsearch
    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). ...

  • Sphinx
    Sphinx

    It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server. ...

  • Apache Solr
    Apache Solr

    It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down. ...

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • MongoDB
    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. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

Lucene alternatives & related posts

Solr logo

Solr

809
641
126
A blazing-fast, open source enterprise search platform
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641
+ 1
126
PROS OF SOLR
  • 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
CONS OF SOLR
    Be the first to leave a con

    related Solr posts

    Ganesa Vijayakumar
    Full Stack Coder | Technical Lead · | 19 upvotes · 4.4M 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

    See more
    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
    Elasticsearch logo

    Elasticsearch

    34.7K
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    Open Source, Distributed, RESTful Search Engine
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    PROS OF ELASTICSEARCH
    • 326
      Powerful api
    • 315
      Great search engine
    • 230
      Open source
    • 214
      Restful
    • 199
      Near real-time search
    • 97
      Free
    • 84
      Search everything
    • 54
      Easy to get started
    • 45
      Analytics
    • 26
      Distributed
    • 6
      Fast search
    • 5
      More than a search engine
    • 3
      Highly Available
    • 3
      Awesome, great tool
    • 3
      Great docs
    • 3
      Easy to scale
    • 2
      Fast
    • 2
      Easy setup
    • 2
      Great customer support
    • 2
      Intuitive API
    • 2
      Great piece of software
    • 2
      Reliable
    • 2
      Potato
    • 2
      Nosql DB
    • 2
      Document Store
    • 1
      Not stable
    • 1
      Scalability
    • 1
      Open
    • 1
      Github
    • 1
      Elaticsearch
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
      Ecosystem
    • 1
      Easy to get hot data
    • 0
      Community
    CONS OF ELASTICSEARCH
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

    related Elasticsearch posts

    Tim Abbott

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

    See more
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Sphinx logo

    Sphinx

    1K
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    Open source full text search server, designed from the ground up with performance, relevance (aka search quality), and...
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    PROS OF SPHINX
    • 16
      Fast
    • 9
      Simple deployment
    • 6
      Open source
    • 1
      Lots of extentions
    CONS OF SPHINX
      Be the first to leave a con

      related Sphinx posts

      Apache Solr logo

      Apache Solr

      228
      91
      0
      An open source search platform
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      0
      PROS OF APACHE SOLR
        Be the first to leave a pro
        CONS OF APACHE SOLR
          Be the first to leave a con

          related Apache Solr posts

          Hadoop logo

          Hadoop

          2.7K
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          Open-source software for reliable, scalable, distributed computing
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          PROS OF HADOOP
          • 39
            Great ecosystem
          • 11
            One stack to rule them all
          • 4
            Great load balancer
          • 1
            Amazon aws
          • 1
            Java syntax
          CONS OF HADOOP
            Be the first to leave a con

            related Hadoop posts

            Shared insights
            on
            KafkaKafkaHadoopHadoop
            at

            The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

            For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

            Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 2.9M views

            Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

            Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

            https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

            (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

            See more
            MongoDB logo

            MongoDB

            93K
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            4.1K
            The database for giant ideas
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            PROS OF MONGODB
            • 827
              Document-oriented storage
            • 593
              No sql
            • 553
              Ease of use
            • 464
              Fast
            • 410
              High performance
            • 257
              Free
            • 218
              Open source
            • 180
              Flexible
            • 145
              Replication & high availability
            • 112
              Easy to maintain
            • 42
              Querying
            • 39
              Easy scalability
            • 38
              Auto-sharding
            • 37
              High availability
            • 31
              Map/reduce
            • 27
              Document database
            • 25
              Easy setup
            • 25
              Full index support
            • 16
              Reliable
            • 15
              Fast in-place updates
            • 14
              Agile programming, flexible, fast
            • 12
              No database migrations
            • 8
              Easy integration with Node.Js
            • 8
              Enterprise
            • 6
              Enterprise Support
            • 5
              Great NoSQL DB
            • 4
              Support for many languages through different drivers
            • 3
              Drivers support is good
            • 3
              Aggregation Framework
            • 3
              Schemaless
            • 2
              Fast
            • 2
              Managed service
            • 2
              Easy to Scale
            • 2
              Awesome
            • 2
              Consistent
            • 1
              Good GUI
            • 1
              Acid Compliant
            CONS OF MONGODB
            • 6
              Very slowly for connected models that require joins
            • 3
              Not acid compliant
            • 1
              Proprietary query language

            related MongoDB posts

            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            Robert Zuber

            We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

            As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

            When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

            See more
            Redis logo

            Redis

            59.7K
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            Open source (BSD licensed), in-memory data structure store
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            PROS OF REDIS
            • 886
              Performance
            • 542
              Super fast
            • 513
              Ease of use
            • 444
              In-memory cache
            • 324
              Advanced key-value cache
            • 194
              Open source
            • 182
              Easy to deploy
            • 164
              Stable
            • 155
              Free
            • 121
              Fast
            • 42
              High-Performance
            • 40
              High Availability
            • 35
              Data Structures
            • 32
              Very Scalable
            • 24
              Replication
            • 22
              Great community
            • 22
              Pub/Sub
            • 19
              "NoSQL" key-value data store
            • 16
              Hashes
            • 13
              Sets
            • 11
              Sorted Sets
            • 10
              NoSQL
            • 10
              Lists
            • 9
              Async replication
            • 9
              BSD licensed
            • 8
              Bitmaps
            • 8
              Integrates super easy with Sidekiq for Rails background
            • 7
              Keys with a limited time-to-live
            • 7
              Open Source
            • 6
              Lua scripting
            • 6
              Strings
            • 5
              Awesomeness for Free
            • 5
              Hyperloglogs
            • 4
              Transactions
            • 4
              Outstanding performance
            • 4
              Runs server side LUA
            • 4
              LRU eviction of keys
            • 4
              Feature Rich
            • 4
              Written in ANSI C
            • 4
              Networked
            • 3
              Data structure server
            • 3
              Performance & ease of use
            • 2
              Dont save data if no subscribers are found
            • 2
              Automatic failover
            • 2
              Easy to use
            • 2
              Temporarily kept on disk
            • 2
              Scalable
            • 2
              Existing Laravel Integration
            • 2
              Channels concept
            • 2
              Object [key/value] size each 500 MB
            • 2
              Simple
            CONS OF REDIS
            • 15
              Cannot query objects directly
            • 3
              No secondary indexes for non-numeric data types
            • 1
              No WAL

            related Redis posts

            Robert Zuber

            We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

            As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

            When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

            See more

            I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

            We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

            Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

            We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

            Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

            See more
            Splunk logo

            Splunk

            753
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            20
            Search, monitor, analyze and visualize machine data
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            PROS OF SPLUNK
            • 3
              API for searching logs, running reports
            • 3
              Alert system based on custom query results
            • 2
              Dashboarding on any log contents
            • 2
              Custom log parsing as well as automatic parsing
            • 2
              Ability to style search results into reports
            • 2
              Query engine supports joining, aggregation, stats, etc
            • 2
              Splunk language supports string, date manip, math, etc
            • 2
              Rich GUI for searching live logs
            • 1
              Query any log as key-value pairs
            • 1
              Granular scheduling and time window support
            CONS OF SPLUNK
            • 1
              Splunk query language rich so lots to learn

            related Splunk posts

            Shared insights
            on
            KibanaKibanaSplunkSplunkGrafanaGrafana

            I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

            See more
            Shared insights
            on
            SplunkSplunkElasticsearchElasticsearch

            We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.

            See more