Alternatives to ELK logo

Alternatives to ELK

Datadog, Splunk, Graylog, Logstash, and Papertrail are the most popular alternatives and competitors to ELK.
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What is ELK and what are its top alternatives?

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server鈥憇ide data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.
ELK is a tool in the Log Management category of a tech stack.

Top Alternatives to ELK

  • Datadog

    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • Splunk

    Splunk

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

  • 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. ...

  • 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. ...

  • Papertrail

    Papertrail

    Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs. ...

  • 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. ...

  • Serilog

    Serilog

    It provides diagnostic logging to files, the console, and elsewhere. It is easy to set up, has a clean API, and is portable between recent .NET platforms. ...

  • Logentries

    Logentries

    Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users. ...

ELK alternatives & related posts

Datadog logo

Datadog

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Unify logs, metrics, and traces from across your distributed infrastructure.
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PROS OF DATADOG
  • 132
    Monitoring for many apps (databases, web servers, etc)
  • 104
    Easy setup
  • 84
    Powerful ui
  • 81
    Powerful integrations
  • 67
    Great value
  • 52
    Great visualization
  • 43
    Events + metrics = clarity
  • 40
    Custom metrics
  • 39
    Notifications
  • 37
    Flexibility
  • 17
    Free & paid plans
  • 14
    Great customer support
  • 13
    Makes my life easier
  • 8
    Easy setup and plugins
  • 8
    Adapts automatically as i scale up
  • 6
    Super easy and powerful
  • 5
    AWS support
  • 5
    In-context collaboration
  • 4
    Rich in features
  • 3
    Docker support
  • 3
    Cost
  • 3
    Best than others
  • 2
    Free setup
  • 2
    Monitor almost everything
  • 2
    Automation tools
  • 2
    Source control and bug tracking
  • 2
    Simple, powerful, great for infra
  • 2
    Full visibility of applications
  • 2
    Good for Startups
  • 2
    Cute logo
  • 2
    Expensive
  • 2
    Easy to Analyze
  • 1
    Best in the field
CONS OF DATADOG
  • 15
    Expensive
  • 2
    No errors exception tracking
  • 1
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated

related Datadog posts

Robert Zuber

Our primary source of monitoring and alerting is Datadog. We鈥檝e got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We鈥檝e definitely scaled past the point where managing dashboards is easy, but we haven鈥檛 had time to invest in using features like Anomaly Detection. We鈥檝e started using Honeycomb for some targeted debugging of complex production issues and we are liking what we鈥檝e seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

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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
  • 1
    Query engine supports joining, aggregation, stats, etc
  • 1
    Ability to style search results into reports
  • 1
    Query any log as key-value pairs
  • 1
    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
    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.

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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
  • 14
    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
    Easy to install
  • 1
    Manage users and permissions
  • 1
    A large community
CONS OF GRAYLOG
  • 1
    Does not handle frozen indices at all

related Graylog posts

Logstash logo

Logstash

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Collect, Parse, & Enrich Data
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PROS OF LOGSTASH
  • 66
    Free
  • 17
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Great to meet GDPR goals
  • 1
    Well Documented
CONS OF LOGSTASH
  • 3
    Memory-intensive
  • 1
    Documentation difficult to use

related Logstash posts

Tymoteusz Paul
Devops guy at X20X Development LTD | 21 upvotes 路 4.3M 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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Tanya Bragin
Product Lead, Observability at Elastic | 10 upvotes 路 589.2K 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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Papertrail logo

Papertrail

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Hosted log management for servers, apps, and cloud services
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PROS OF PAPERTRAIL
  • 86
    Log search
  • 43
    Easy log aggregation across multiple machines
  • 43
    Integrates with Heroku
  • 37
    Simple interface
  • 26
    Backup to S3
  • 19
    Easy setup, independent of existing logging setup
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    Heroku add-on
  • 3
    Command line interface
  • 1
    Alerting
  • 1
    Good for Startups
CONS OF PAPERTRAIL
  • 2
    Expensive
  • 1
    External Network Goes Down You Wont Be Logging

related Papertrail posts

Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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Fluentd logo

Fluentd

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Unified logging layer
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PROS OF FLUENTD
  • 7
    Open-source
  • 6
    Lightweight
  • 6
    Great for Kubernetes node container log forwarding
  • 5
    Easy
CONS OF FLUENTD
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    related Fluentd posts

    Serilog logo

    Serilog

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    A portable and structured logging framework to record diagnostic logs
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    PROS OF SERILOG
      Be the first to leave a pro
      CONS OF SERILOG
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        related Serilog posts

        Logentries logo

        Logentries

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        Real-time log management and analytics built for the cloud
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        PROS OF LOGENTRIES
        • 34
          Log search
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          Live logs
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          Easy setup
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          Heroku Add-on
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          Backup to S3
        • 2
          Easy setup, independent of existing logging setup
        • 2
          Free
        • 2
          Search/query with regex
        • 0
          E
        CONS OF LOGENTRIES
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          related Logentries posts

          Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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