Alternatives to SumoLogic logo

Alternatives to SumoLogic

Kibana, Grafana, OpenSSL, Logstash, and Prometheus are the most popular alternatives and competitors to SumoLogic.
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What is SumoLogic and what are its top alternatives?

The Sumo Logic platform helps you make data-driven decisions and reduce the time to investigate security and operational issues so you can free up resources for more important activities.
SumoLogic is a tool in the Monitoring Tools category of a tech stack.

Top Alternatives to SumoLogic

  • Kibana
    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • Grafana
    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • OpenSSL
    OpenSSL

    It is a robust, commercial-grade, and full-featured toolkit for the Transport Layer Security (TLS) and Secure Sockets Layer (SSL) protocols. It is also a general-purpose cryptography library. ...

  • 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. ...

  • Prometheus
    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • 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. ...

  • 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. ...

SumoLogic alternatives & related posts

Kibana logo

Kibana

18.1K
14.4K
257
Visualize your Elasticsearch data and navigate the Elastic Stack
18.1K
14.4K
+ 1
257
PROS OF KIBANA
  • 88
    Easy to setup
  • 62
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 8
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 3
    Dev Tools
  • 3
    More "user-friendly"
  • 3
    Can build dashboards
  • 2
    Easy to drill-down
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 1
    Up and running
CONS OF KIBANA
  • 6
    Unintuituve
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
  • 3
    Works on top of elastic only

related Kibana posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.5M 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.

See more
Patrick Sun
Software Engineer at Stitch Fix · | 11 upvotes · 546.2K views

Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

See more
Grafana logo

Grafana

14.5K
11.7K
412
Open source Graphite & InfluxDB Dashboard and Graph Editor
14.5K
11.7K
+ 1
412
PROS OF GRAFANA
  • 87
    Beautiful
  • 68
    Graphs are interactive
  • 57
    Free
  • 56
    Easy
  • 34
    Nicer than the Graphite web interface
  • 25
    Many integrations
  • 18
    Can build dashboards
  • 10
    Easy to specify time window
  • 10
    Can collaborate on dashboards
  • 9
    Dashboards contain number tiles
  • 5
    Open Source
  • 5
    Click and drag to zoom in
  • 5
    Integration with InfluxDB
  • 4
    Authentification and users management
  • 4
    Threshold limits in graphs
  • 3
    Alerts
  • 3
    Simple and native support to Prometheus
  • 3
    It is open to cloud watch and many database
  • 2
    You can visualize real time data to put alerts
  • 2
    Great community support
  • 2
    You can use this for development to check memcache
  • 0
    Grapsh as code
  • 0
    Plugin visualizationa
CONS OF GRAFANA
  • 1
    No interactive query builder

related Grafana posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.3M views

Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

https://eng.uber.com/m3/

(GitHub : https://github.com/m3db/m3)

See more
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 15 upvotes · 323.6K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
OpenSSL logo

OpenSSL

11.6K
5.7K
0
Full-featured toolkit for the Transport Layer Security and Secure Sockets Layer protocols
11.6K
5.7K
+ 1
0
PROS OF OPENSSL
    Be the first to leave a pro
    CONS OF OPENSSL
      Be the first to leave a con

      related OpenSSL posts

      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 29 upvotes · 5M 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
      Logstash logo

      Logstash

      10.4K
      7.7K
      102
      Collect, Parse, & Enrich Data
      10.4K
      7.7K
      + 1
      102
      PROS OF LOGSTASH
      • 68
        Free
      • 18
        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
      • 4
        Memory-intensive
      • 1
        Documentation difficult to use

      related Logstash posts

      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 5.5M 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.

      See more
      Tanya Bragin
      Product Lead, Observability at Elastic · | 10 upvotes · 681.1K 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.

      See more
      Prometheus logo

      Prometheus

      3.3K
      3.5K
      239
      An open-source service monitoring system and time series database, developed by SoundCloud
      3.3K
      3.5K
      + 1
      239
      PROS OF PROMETHEUS
      • 47
        Powerful easy to use monitoring
      • 38
        Flexible query language
      • 32
        Dimensional data model
      • 27
        Alerts
      • 23
        Active and responsive community
      • 22
        Extensive integrations
      • 19
        Easy to setup
      • 12
        Beautiful Model and Query language
      • 7
        Easy to extend
      • 6
        Nice
      • 3
        Written in Go
      • 2
        Good for experimentation
      • 1
        Easy for monitoring
      CONS OF PROMETHEUS
      • 12
        Just for metrics
      • 6
        Bad UI
      • 6
        Needs monitoring to access metrics endpoints
      • 4
        Not easy to configure and use
      • 3
        Supports only active agents
      • 2
        Written in Go
      • 2
        TLS is quite difficult to understand
      • 2
        Requires multiple applications and tools
      • 1
        Single point of failure

      related Prometheus posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.3M views

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more
      Matt Menzenski
      Senior Software Engineering Manager at PayIt · | 15 upvotes · 323.6K views

      Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

      See more
      Serilog logo

      Serilog

      2.3K
      93
      0
      A portable and structured logging framework to record diagnostic logs
      2.3K
      93
      + 1
      0
      PROS OF SERILOG
        Be the first to leave a pro
        CONS OF SERILOG
          Be the first to leave a con

          related Serilog posts

          SLF4J logo

          SLF4J

          2.1K
          57
          0
          Simple logging facade for Java
          2.1K
          57
          + 1
          0
          PROS OF SLF4J
            Be the first to leave a pro
            CONS OF SLF4J
              Be the first to leave a con

              related SLF4J posts

              Logback logo

              Logback

              2K
              63
              0
              A logging framework for Java applications
              2K
              63
              + 1
              0
              PROS OF LOGBACK
                Be the first to leave a pro
                CONS OF LOGBACK
                  Be the first to leave a con

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