Alternatives to Log4j logo

Alternatives to Log4j

SLF4J, Logback, Logstash, Loki, and Seq are the most popular alternatives and competitors to Log4j.
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What is Log4j and what are its top alternatives?

It is an open source logging framework. With this tool – logging behavior can be controlled by editing a configuration file only without touching the application binary and can be used to store the Selenium Automation flow logs.
Log4j is a tool in the Logging Tools category of a tech stack.
Log4j is an open source tool with 1.2K GitHub stars and 731 GitHub forks. Here’s a link to Log4j's open source repository on GitHub

Top Alternatives to Log4j

  • SLF4J

    SLF4J

    It is a simple Logging Facade for Java (SLF4J) serves as a simple facade or abstraction for various logging frameworks allowing the end user to plug in the desired logging framework at deployment time. ...

  • Logback

    Logback

    It is intended as a successor to the popular log4j project. It is divided into three modules, logback-core, logback-classic and logback-access. The logback-core module lays the groundwork for the other two modules, logback-classic natively implements the SLF4J API so that you can readily switch back and forth between logback and other logging frameworks and logback-access module integrates with Servlet containers, such as Tomcat and Jetty, to provide HTTP-access log functionality. ...

  • Logstash

    Logstash

    Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana. ...

  • Loki

    Loki

    Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate, as it does not index the contents of the logs, but rather a set of labels for each log stream. ...

  • Seq

    Seq

    Seq is a self-hosted server for structured log search, analysis, and alerting. It can be hosted on Windows or Linux/Docker, and has integrations for most popular structured logging libraries. ...

  • CocoaLumberjack

    CocoaLumberjack

    CocoaLumberjack is a fast & simple, yet powerful & flexible logging framework for Mac and iOS. ...

  • uno

    uno

    We built uno, a small tool similar to uniq (the UNIX CLI tool that removes duplicates) - but with fuzziness. uno considers two lines to be equal if their edit distance is less than a specified threshold, by default set to 30%. It reads from stdin and prints the deduplicated lines to stdout. ...

  • Bunyan

    Bunyan

    It is a simple and fast JSON logging module for node.js services. It has extensible streams system for controlling where log records go (to a stream, to a file, log file rotation, etc.) ...

Log4j alternatives & related posts

SLF4J logo

SLF4J

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Simple logging facade for Java
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      Logback logo

      Logback

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      A logging framework for Java applications
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      PROS OF LOGBACK
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          Logstash logo

          Logstash

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          Collect, Parse, & Enrich Data
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          PROS OF LOGSTASH
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            Free
          • 17
            Easy but powerful filtering
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            Scalable
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            Kibana provides machine learning based analytics to log
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            Great to meet GDPR goals
          • 1
            Well Documented
          CONS OF LOGSTASH
          • 3
            Memory-intensive
          • 1
            Documentation difficult to use

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          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 22 upvotes · 4.4M 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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          Tanya Bragin
          Product Lead, Observability at Elastic · | 10 upvotes · 596.5K views

          ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

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          Loki logo

          Loki

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          Like Prometheus, but for logs (by the makers of Grafana)
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          PROS OF LOKI
          • 2
            Near real-time search
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            REST Api
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            Low resource footprint
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            Perfect fit for k8s
          • 1
            Smart way of tagging
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            Very fast ingestion
          • 1
            Opensource
          CONS OF LOKI
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            Seq logo

            Seq

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            Log search, analysis, and alerting server built for modern structured log data
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            PROS OF SEQ
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              Easy to install and configure
            • 3
              Easy to use
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              Flexible query language
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              Free unlimited one-person version
            • 2
              Beautiful charts and dashboards
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              Extensive plug-ins and integrations
            CONS OF SEQ
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              CocoaLumberjack logo

              CocoaLumberjack

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              A fast & simple, yet powerful & flexible logging framework for Mac and iOS
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                  uno logo

                  uno

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                  A uniq like CLI tool for log data
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                      Bunyan logo

                      Bunyan

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                      A logging module for node.js services
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