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

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126
A blazing-fast, open source enterprise search platform
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+ 1
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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 · 4.7M 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
    SolrSolrPHPPHPJavaJavaMySQLMySQL
    at

    One of the earliest public references to Slack’s stack comes from a Twitter conversation. The Slack account states that “the messaging server is java, the app is php, db is mysql and solr for search,” and that uploaded files are “Stored on S3, but private files require authentication so requests go through the app.”

    See more
    Elasticsearch logo

    Elasticsearch

    34.1K
    26.6K
    1.6K
    Open Source, Distributed, RESTful Search Engine
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    PROS OF ELASTICSEARCH
    • 328
      Powerful api
    • 315
      Great search engine
    • 231
      Open source
    • 214
      Restful
    • 200
      Near real-time search
    • 98
      Free
    • 85
      Search everything
    • 54
      Easy to get started
    • 45
      Analytics
    • 26
      Distributed
    • 6
      Fast search
    • 5
      More than a search engine
    • 4
      Great docs
    • 4
      Awesome, great tool
    • 3
      Highly Available
    • 3
      Easy to scale
    • 2
      Potato
    • 2
      Document Store
    • 2
      Great customer support
    • 2
      Intuitive API
    • 2
      Nosql DB
    • 2
      Great piece of software
    • 2
      Reliable
    • 2
      Fast
    • 2
      Easy setup
    • 1
      Open
    • 1
      Easy to get hot data
    • 1
      Github
    • 1
      Elaticsearch
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
      Ecosystem
    • 1
      Not stable
    • 1
      Scalability
    • 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 · 8.3M 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.

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    Sphinx logo

    Sphinx

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    32
    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
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      related Sphinx posts

      Apache Solr logo

      Apache Solr

      134
      91
      0
      An open source search platform
      134
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      + 1
      0
      PROS OF APACHE SOLR
        Be the first to leave a pro
        CONS OF APACHE SOLR
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          related Apache Solr posts

          Hadoop logo

          Hadoop

          2.5K
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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 · 3M 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

            91.8K
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            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
              Schemaless
            • 3
              Aggregation Framework
            • 3
              Drivers support is good
            • 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

            Shared insights
            on
            Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

            I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

            For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

            1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

            2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

            3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

            See more
            Vaibhav Taunk
            Team Lead at Technovert · | 31 upvotes · 3.9M views

            I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

            See more
            Redis logo

            Redis

            58.4K
            44.9K
            3.9K
            Open source (BSD licensed), in-memory data structure store
            58.4K
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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

            Russel Werner
            Lead Engineer at StackShare · | 32 upvotes · 2.2M views

            StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

            Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

            #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

            See more
            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Splunk logo

            Splunk

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            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
            SplunkSplunkDjangoDjango

            I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)

            Is there any better or standard approach? Thanks in advance.

            See more
            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