Alternatives to Filebeat logo

Alternatives to Filebeat

Logstash, Fluentd, Rsyslog, Metricbeat, and Kafka are the most popular alternatives and competitors to Filebeat.
130
253
+ 1
0

What is Filebeat and what are its top alternatives?

Filebeat is a lightweight shipper for forwarding and centralizing logs and files. Key features include real-time data collection, built-in modules for popular logs and metrics, easy deployment and management, and integration with Elasticsearch and other Beats. However, some limitations include complex configuration for custom data formats and the need for additional resources for heavy log traffic.

  1. Logstash: Logstash is a flexible, open-source data collection, enrichment, and processing tool. It allows you to collect, parse, and transform data before sending it to a storage backend like Elasticsearch. Key features include a wide range of input plugins, filters, and output plugins. Pros include powerful transformation capabilities and community support, but cons include higher resource usage compared to Filebeat.

  2. Fluentd: Fluentd is an open-source data collector that allows you to unify data collection and consumption for better use and understanding of data. Key features include efficient log forwarding, flexible plugin system, and strong reliability. Pros include wide integration with various systems and frameworks, but cons include a steeper learning curve compared to Filebeat.

  3. Rsyslog: Rsyslog is a high-performance log processing system that can send logs to different destinations like Elasticsearch. Key features include modular architecture, support for a variety of log formats, and high throughput. Pros include customizable filtering and routing options, while cons include limited built-in integrations compared to Filebeat.

  4. Splunk: Splunk is a comprehensive platform for searching, monitoring, and analyzing machine-generated big data, including logs. Key features include real-time search and analysis, visualizations, and customizable dashboards. Pros include powerful search capabilities and extensive app ecosystem, but cons include high licensing costs compared to open-source alternatives.

  5. NXLog: NXLog is a versatile log management tool that can collect logs from various sources and forward them to multiple destinations. Key features include cross-platform support, high-performance log processing, and easy integration with SIEM tools. Pros include a lightweight footprint and support for multiple operating systems, but cons include a less intuitive configuration compared to Filebeat.

  6. Beats: Beats is a family of lightweight data shippers from Elastic that can send data to Elasticsearch or Logstash. Key features include simplicity, extensibility, and integration with the Elastic Stack. Pros include easy setup and configuration, while cons include limited processing capabilities compared to Logstash.

  7. Logagent: Logagent is an open-source, light-weight log shipper with out-of-the-box support for parsing and tagging logs. Key features include various input and output sources, parsing capabilities, and integration with different logging services. Pros include ease of use and powerful parsing capabilities, but cons include a smaller community compared to other alternatives.

  8. Flume: Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Key features include event-driven architecture, robust fault tolerance, and extensibility through custom plugins. Pros include high scalability and fault tolerance, but cons include a more complex setup compared to Filebeat.

  9. Vector: Vector is a high-performance, easy-to-setup observability data router. Key features include zero config data collection, real-time processing, and multi-datatype support. Pros include simplicity and efficiency, while cons include a smaller user base compared to more established tools.

  10. LogPilot: LogPilot is a lightweight log and metrics collection agent for Docker containers running on Kubernetes. Key features include automatic log parsing, metric collection, and real-time log streaming. Pros include seamless integration with Docker and Kubernetes, but cons include limited support for non-containerized environments.

Top Alternatives to Filebeat

  • 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. ...

  • 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. ...

  • Rsyslog
    Rsyslog

    It offers high-performance, great security features and a modular design. It is able to accept inputs from a wide variety of sources, transform them, and output to the results to diverse destinations. ...

  • Metricbeat
    Metricbeat

    Collect metrics from your systems and services. From CPU to memory, Redis to NGINX, and much more, It is a lightweight way to send system and service statistics. ...

  • Kafka
    Kafka

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

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

Filebeat alternatives & related posts

Logstash logo

Logstash

11.2K
8.6K
103
Collect, Parse, & Enrich Data
11.2K
8.6K
+ 1
103
PROS OF LOGSTASH
  • 69
    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 · 8.7M 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

Hi everyone. I'm trying to create my personal syslog monitoring.

  1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

  2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

I would like to know... Which is a cheaper and scalable solution?

Or even if there is a better way to do it.

See more
Fluentd logo

Fluentd

600
688
37
Unified logging layer
600
688
+ 1
37
PROS OF FLUENTD
  • 11
    Open-source
  • 9
    Great for Kubernetes node container log forwarding
  • 9
    Lightweight
  • 8
    Easy
CONS OF FLUENTD
    Be the first to leave a con

    related Fluentd posts

    Rsyslog logo

    Rsyslog

    37
    74
    0
    A high-performance system for log processing
    37
    74
    + 1
    0
    PROS OF RSYSLOG
      Be the first to leave a pro
      CONS OF RSYSLOG
        Be the first to leave a con

        related Rsyslog posts

        Metricbeat logo

        Metricbeat

        49
        125
        3
        A Lightweight Shipper for Metrics
        49
        125
        + 1
        3
        PROS OF METRICBEAT
        • 2
          Simple
        • 1
          Easy to setup
        CONS OF METRICBEAT
          Be the first to leave a con

          related Metricbeat posts

          Sunil Chaudhari

          Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

          Is it OK to use Metricbeat for Linux server or can we use Prometheus?

          What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

          Regards, Sunil.

          See more
          Kafka logo

          Kafka

          23.2K
          21.8K
          607
          Distributed, fault tolerant, high throughput pub-sub messaging system
          23.2K
          21.8K
          + 1
          607
          PROS OF KAFKA
          • 126
            High-throughput
          • 119
            Distributed
          • 92
            Scalable
          • 86
            High-Performance
          • 66
            Durable
          • 38
            Publish-Subscribe
          • 19
            Simple-to-use
          • 18
            Open source
          • 12
            Written in Scala and java. Runs on JVM
          • 9
            Message broker + Streaming system
          • 4
            KSQL
          • 4
            Avro schema integration
          • 4
            Robust
          • 3
            Suport Multiple clients
          • 2
            Extremely good parallelism constructs
          • 2
            Partioned, replayable log
          • 1
            Simple publisher / multi-subscriber model
          • 1
            Fun
          • 1
            Flexible
          CONS OF KAFKA
          • 32
            Non-Java clients are second-class citizens
          • 29
            Needs Zookeeper
          • 9
            Operational difficulties
          • 5
            Terrible Packaging

          related Kafka posts

          Nick Rockwell
          SVP, Engineering at Fastly · | 46 upvotes · 3.5M views

          When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

          So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

          React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

          Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

          See more
          Ashish Singh
          Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

          To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

          Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

          We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

          Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

          Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

          #BigData #AWS #DataScience #DataEngineering

          See more
          JavaScript logo

          JavaScript

          352.6K
          268.3K
          8.1K
          Lightweight, interpreted, object-oriented language with first-class functions
          352.6K
          268.3K
          + 1
          8.1K
          PROS OF JAVASCRIPT
          • 1.7K
            Can be used on frontend/backend
          • 1.5K
            It's everywhere
          • 1.2K
            Lots of great frameworks
          • 897
            Fast
          • 745
            Light weight
          • 425
            Flexible
          • 392
            You can't get a device today that doesn't run js
          • 286
            Non-blocking i/o
          • 237
            Ubiquitousness
          • 191
            Expressive
          • 55
            Extended functionality to web pages
          • 49
            Relatively easy language
          • 46
            Executed on the client side
          • 30
            Relatively fast to the end user
          • 25
            Pure Javascript
          • 21
            Functional programming
          • 15
            Async
          • 13
            Full-stack
          • 12
            Setup is easy
          • 12
            Future Language of The Web
          • 12
            Its everywhere
          • 11
            Because I love functions
          • 11
            JavaScript is the New PHP
          • 10
            Like it or not, JS is part of the web standard
          • 9
            Expansive community
          • 9
            Everyone use it
          • 9
            Can be used in backend, frontend and DB
          • 9
            Easy
          • 8
            Most Popular Language in the World
          • 8
            Powerful
          • 8
            Can be used both as frontend and backend as well
          • 8
            For the good parts
          • 8
            No need to use PHP
          • 8
            Easy to hire developers
          • 7
            Agile, packages simple to use
          • 7
            Love-hate relationship
          • 7
            Photoshop has 3 JS runtimes built in
          • 7
            Evolution of C
          • 7
            It's fun
          • 7
            Hard not to use
          • 7
            Versitile
          • 7
            Its fun and fast
          • 7
            Nice
          • 7
            Popularized Class-Less Architecture & Lambdas
          • 7
            Supports lambdas and closures
          • 6
            It let's me use Babel & Typescript
          • 6
            Can be used on frontend/backend/Mobile/create PRO Ui
          • 6
            1.6K Can be used on frontend/backend
          • 6
            Client side JS uses the visitors CPU to save Server Res
          • 6
            Easy to make something
          • 5
            Clojurescript
          • 5
            Promise relationship
          • 5
            Stockholm Syndrome
          • 5
            Function expressions are useful for callbacks
          • 5
            Scope manipulation
          • 5
            Everywhere
          • 5
            Client processing
          • 5
            What to add
          • 4
            Because it is so simple and lightweight
          • 4
            Only Programming language on browser
          • 1
            Test
          • 1
            Hard to learn
          • 1
            Test2
          • 1
            Not the best
          • 1
            Easy to understand
          • 1
            Subskill #4
          • 1
            Easy to learn
          • 0
            Hard 彤
          CONS OF JAVASCRIPT
          • 22
            A constant moving target, too much churn
          • 20
            Horribly inconsistent
          • 15
            Javascript is the New PHP
          • 9
            No ability to monitor memory utilitization
          • 8
            Shows Zero output in case of ANY error
          • 7
            Thinks strange results are better than errors
          • 6
            Can be ugly
          • 3
            No GitHub
          • 2
            Slow

          related JavaScript posts

          Zach Holman

          Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

          But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

          But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

          Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

          How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

          Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

          Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

          https://eng.uber.com/distributed-tracing/

          (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

          Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

          See more
          Git logo

          Git

          291.6K
          174.9K
          6.6K
          Fast, scalable, distributed revision control system
          291.6K
          174.9K
          + 1
          6.6K
          PROS OF GIT
          • 1.4K
            Distributed version control system
          • 1.1K
            Efficient branching and merging
          • 959
            Fast
          • 845
            Open source
          • 726
            Better than svn
          • 368
            Great command-line application
          • 306
            Simple
          • 291
            Free
          • 232
            Easy to use
          • 222
            Does not require server
          • 27
            Distributed
          • 22
            Small & Fast
          • 18
            Feature based workflow
          • 15
            Staging Area
          • 13
            Most wide-spread VSC
          • 11
            Role-based codelines
          • 11
            Disposable Experimentation
          • 7
            Frictionless Context Switching
          • 6
            Data Assurance
          • 5
            Efficient
          • 4
            Just awesome
          • 3
            Github integration
          • 3
            Easy branching and merging
          • 2
            Compatible
          • 2
            Flexible
          • 2
            Possible to lose history and commits
          • 1
            Rebase supported natively; reflog; access to plumbing
          • 1
            Light
          • 1
            Team Integration
          • 1
            Fast, scalable, distributed revision control system
          • 1
            Easy
          • 1
            Flexible, easy, Safe, and fast
          • 1
            CLI is great, but the GUI tools are awesome
          • 1
            It's what you do
          • 0
            Phinx
          CONS OF GIT
          • 16
            Hard to learn
          • 11
            Inconsistent command line interface
          • 9
            Easy to lose uncommitted work
          • 7
            Worst documentation ever possibly made
          • 5
            Awful merge handling
          • 3
            Unexistent preventive security flows
          • 3
            Rebase hell
          • 2
            When --force is disabled, cannot rebase
          • 2
            Ironically even die-hard supporters screw up badly
          • 1
            Doesn't scale for big data

          related Git posts

          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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
          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 8.7M 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
          GitHub logo

          GitHub

          280.6K
          244.9K
          10.3K
          Powerful collaboration, review, and code management for open source and private development projects
          280.6K
          244.9K
          + 1
          10.3K
          PROS OF GITHUB
          • 1.8K
            Open source friendly
          • 1.5K
            Easy source control
          • 1.3K
            Nice UI
          • 1.1K
            Great for team collaboration
          • 867
            Easy setup
          • 504
            Issue tracker
          • 486
            Great community
          • 483
            Remote team collaboration
          • 451
            Great way to share
          • 442
            Pull request and features planning
          • 147
            Just works
          • 132
            Integrated in many tools
          • 121
            Free Public Repos
          • 116
            Github Gists
          • 112
            Github pages
          • 83
            Easy to find repos
          • 62
            Open source
          • 60
            It's free
          • 60
            Easy to find projects
          • 56
            Network effect
          • 49
            Extensive API
          • 43
            Organizations
          • 42
            Branching
          • 34
            Developer Profiles
          • 32
            Git Powered Wikis
          • 30
            Great for collaboration
          • 24
            It's fun
          • 23
            Clean interface and good integrations
          • 22
            Community SDK involvement
          • 20
            Learn from others source code
          • 16
            Because: Git
          • 14
            It integrates directly with Azure
          • 10
            Standard in Open Source collab
          • 10
            Newsfeed
          • 8
            It integrates directly with Hipchat
          • 8
            Fast
          • 8
            Beautiful user experience
          • 7
            Easy to discover new code libraries
          • 6
            Smooth integration
          • 6
            Cloud SCM
          • 6
            Nice API
          • 6
            Graphs
          • 6
            Integrations
          • 6
            It's awesome
          • 5
            Quick Onboarding
          • 5
            Reliable
          • 5
            Remarkable uptime
          • 5
            CI Integration
          • 5
            Hands down best online Git service available
          • 4
            Uses GIT
          • 4
            Version Control
          • 4
            Simple but powerful
          • 4
            Unlimited Public Repos at no cost
          • 4
            Free HTML hosting
          • 4
            Security options
          • 4
            Loved by developers
          • 4
            Easy to use and collaborate with others
          • 3
            Ci
          • 3
            IAM
          • 3
            Nice to use
          • 3
            Easy deployment via SSH
          • 2
            Easy to use
          • 2
            Leads the copycats
          • 2
            All in one development service
          • 2
            Free private repos
          • 2
            Free HTML hostings
          • 2
            Easy and efficient maintainance of the projects
          • 2
            Beautiful
          • 2
            Easy source control and everything is backed up
          • 2
            IAM integration
          • 2
            Very Easy to Use
          • 2
            Good tools support
          • 2
            Issues tracker
          • 2
            Never dethroned
          • 2
            Self Hosted
          • 1
            Dasf
          • 1
            Profound
          CONS OF GITHUB
          • 54
            Owned by micrcosoft
          • 38
            Expensive for lone developers that want private repos
          • 15
            Relatively slow product/feature release cadence
          • 10
            API scoping could be better
          • 9
            Only 3 collaborators for private repos
          • 4
            Limited featureset for issue management
          • 3
            Does not have a graph for showing history like git lens
          • 2
            GitHub Packages does not support SNAPSHOT versions
          • 1
            No multilingual interface
          • 1
            Takes a long time to commit
          • 1
            Expensive

          related GitHub posts

          Johnny Bell

          I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

          I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

          I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

          Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

          Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

          With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

          If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

          See more
          Russel Werner
          Lead Engineer at StackShare · | 32 upvotes · 2.5M views

          StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

          Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

          #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

          See more