Alternatives to Logstash logo

Alternatives to Logstash

Fluentd, Splunk, Kafka, Beats, and Graylog are the most popular alternatives and competitors to Logstash.
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What is Logstash and what are its top alternatives?

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.
Logstash is a tool in the Log Management category of a tech stack.
Logstash is an open source tool with 12.4K GitHub stars and 3.2K GitHub forks. Here’s a link to Logstash's open source repository on GitHub

Top Alternatives to Logstash

  • Fluentd

    Fluentd

    Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure. ...

  • Splunk

    Splunk

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

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Beats

    Beats

    Beats is the platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. ...

  • Graylog

    Graylog

    Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information. ...

  • 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). ...

  • Filebeat

    Filebeat

    It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files. ...

  • Apache Flume

    Apache Flume

    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application. ...

Logstash alternatives & related posts

Fluentd logo

Fluentd

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Unified logging layer
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PROS OF FLUENTD
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    Open-source
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    Great for Kubernetes node container log forwarding
  • 9
    Lightweight
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    Easy
CONS OF FLUENTD
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    related Fluentd posts

    Splunk logo

    Splunk

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

    related Splunk posts

    Shared insights
    on
    Kibana
    Splunk
    Grafana

    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
    Kafka logo

    Kafka

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    Distributed, fault tolerant, high throughput pub-sub messaging system
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    PROS OF KAFKA
    • 120
      High-throughput
    • 114
      Distributed
    • 86
      Scalable
    • 79
      High-Performance
    • 64
      Durable
    • 35
      Publish-Subscribe
    • 18
      Simple-to-use
    • 14
      Open source
    • 10
      Written in Scala and java. Runs on JVM
    • 6
      Message broker + Streaming system
    • 4
      Avro schema integration
    • 2
      Suport Multiple clients
    • 2
      Robust
    • 2
      KSQL
    • 2
      Partioned, replayable log
    • 1
      Fun
    • 1
      Extremely good parallelism constructs
    • 1
      Simple publisher / multi-subscriber model
    • 1
      Flexible
    CONS OF KAFKA
    • 27
      Non-Java clients are second-class citizens
    • 26
      Needs Zookeeper
    • 7
      Operational difficulties
    • 2
      Terrible Packaging

    related Kafka posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M views

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

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    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    Beats logo

    Beats

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    Lightweight Data Shippers
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    PROS OF BEATS
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      CONS OF BEATS
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        Graylog logo

        Graylog

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        Open source log management that actually works
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        PROS OF GRAYLOG
        • 15
          Open source
        • 11
          Powerfull
        • 7
          Well documented
        • 5
          Flexibel query and parsing language
        • 5
          User authentification
        • 5
          Alerts
        • 2
          User management
        • 2
          Alerts and dashboards
        • 2
          Easy query language and english parsing
        • 1
          Manage users and permissions
        • 1
          Easy to install
        • 1
          A large community
        CONS OF GRAYLOG
        • 1
          Does not handle frozen indices at all

        related Graylog posts

        Elasticsearch logo

        Elasticsearch

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        Open Source, Distributed, RESTful Search Engine
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        PROS OF ELASTICSEARCH
        • 321
          Powerful api
        • 311
          Great search engine
        • 231
          Open source
        • 213
          Restful
        • 200
          Near real-time search
        • 96
          Free
        • 83
          Search everything
        • 54
          Easy to get started
        • 45
          Analytics
        • 26
          Distributed
        • 6
          Fast search
        • 5
          More than a search engine
        • 3
          Great docs
        • 3
          Awesome, great tool
        • 3
          Easy to scale
        • 2
          Intuitive API
        • 2
          Great piece of software
        • 2
          Fast
        • 2
          Nosql DB
        • 2
          Easy setup
        • 2
          Highly Available
        • 2
          Document Store
        • 2
          Great customer support
        • 1
          Reliable
        • 1
          Not stable
        • 1
          Potato
        • 1
          Open
        • 1
          Github
        • 1
          Elaticsearch
        • 1
          Actively developing
        • 1
          Responsive maintainers on GitHub
        • 1
          Ecosystem
        • 1
          Scalability
        • 0
          Easy to get hot data
        • 0
          Community
        CONS OF ELASTICSEARCH
        • 6
          Resource hungry
        • 6
          Diffecult to get started
        • 5
          Expensive
        • 3
          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 · 4.5M 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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        Filebeat logo

        Filebeat

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        A lightweight shipper for forwarding and centralizing log data
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        PROS OF FILEBEAT
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            Apache Flume logo

            Apache Flume

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            A service for collecting, aggregating, and moving large amounts of log data
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            PROS OF APACHE FLUME
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