2.9K
2K
+ 1
95

What is 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.
Logstash is a tool in the Log Management category of a tech stack.
Logstash is an open source tool with 10.9K GitHub stars and 2.9K GitHub forks. Here’s a link to Logstash's open source repository on GitHub

Who uses Logstash?

Companies
791 companies reportedly use Logstash in their tech stacks, including Airbnb, reddit, and Typeform.

Developers
2041 developers on StackShare have stated that they use Logstash.

Logstash Integrations

Elasticsearch, Rollbar, PagerDuty, OpsGenie, and ElasticBox are some of the popular tools that integrate with Logstash. Here's a list of all 16 tools that integrate with Logstash.

Why developers like Logstash?

Here’s a list of reasons why companies and developers use Logstash
Logstash Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Logstash in their tech stack.

Tymoteusz Paul
Tymoteusz Paul
Devops guy at X20X Development LTD · | 15 upvotes · 372.3K views
Vagrant
Vagrant
VirtualBox
VirtualBox
Ansible
Ansible
Elasticsearch
Elasticsearch
Kibana
Kibana
Logstash
Logstash
TeamCity
TeamCity
Jenkins
Jenkins
Slack
Slack
Apache Maven
Apache Maven
Vault
Vault
Git
Git
Docker
Docker
CircleCI
CircleCI
LXC
LXC
Amazon EC2
Amazon EC2

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
Tanya Bragin
Product Lead, Observability at Elastic · | 10 upvotes · 93.9K views
atElasticElastic
Elasticsearch
Elasticsearch
Logstash
Logstash
Kibana
Kibana

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
StackShare Editors
StackShare Editors
Kibana
Kibana
Grafana
Grafana
Elasticsearch
Elasticsearch
Logstash
Logstash
Graphite
Graphite
Icinga
Icinga

One size definitely doesn’t fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O’Reilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company’s use cases.

At the time, Slack used a few well-known monitoring tools since it’s Technical Operations team wasn’t large enough to build an in-house solution for all of these. Nor did the team think it’s sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack’s needs.

On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they’ve tried to build usability into Grafana that reflects the team’s mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

See more
StackShare Editors
StackShare Editors
| 4 upvotes · 105.8K views
atUber TechnologiesUber Technologies
Kafka
Kafka
Kibana
Kibana
Elasticsearch
Elasticsearch
Logstash
Logstash
Hadoop
Hadoop

With interactions across each other and mobile devices, logging is important as it is information for internal cases like debugging and business cases like dynamic pricing.

With multiple Kafka clusters, data is archived into Hadoop before expiration. Data is ingested in realtime and indexed into an ELK stack. The ELK stack comprises of Elasticsearch, Logstash, and Kibana for searching and visualization.

See more
Prometheus
Prometheus
Logstash
Logstash
nginx
nginx
OpenResty
OpenResty
Lua
Lua
Go
Go

At Kong while building an internal tool, we struggled to route metrics to Prometheus and logs to Logstash without incurring too much latency in our metrics collection.

We replaced nginx with OpenResty on the edge of our tool which allowed us to use the lua-nginx-module to run Lua code that captures metrics and records telemetry data during every request’s log phase. Our code then pushes the metrics to a local aggregator process (written in Go) which in turn exposes them in Prometheus Exposition Format for consumption by Prometheus. This solution reduced the number of components we needed to maintain and is fast thanks to NGINX and LuaJIT.

See more
React
React
LoopBack
LoopBack
Node.js
Node.js
ExpressJS
ExpressJS
Elasticsearch
Elasticsearch
Kibana
Kibana
Logstash
Logstash
Sequelize
Sequelize
Mocha
Mocha
Chai
Chai
Visual Studio Code
Visual Studio Code

React LoopBack Node.js ExpressJS Elasticsearch Kibana Logstash Sequelize Mocha Chai Visual Studio Code are the combo of technologies being used by me to build BestPrice Extension with all its micro-services & Web-based fragments

See more

Logstash's Features

  • Centralize data processing of all types
  • Normalize varying schema and formats
  • Quickly extend to custom log formats
  • Easily add plugins for custom data source

Logstash Alternatives & Comparisons

What are some alternatives to Logstash?
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 Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
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 is the platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch.
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.
See all alternatives

Logstash's Followers
1964 developers follow Logstash to keep up with related blogs and decisions.
Dmitry Kovalenko
Antoine DAVID
Marcus K.
Shadow Sun
Ahrenn Sivananthan
Sunil Adhikari
Aendenne X
Lalit Nayyar
xiangdejun
Pooja Palkar