Alternatives to Librato logo

Alternatives to Librato

Datadog, New Relic, Hosted Graphite, Grafana, and Prometheus are the most popular alternatives and competitors to Librato.
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What is Librato and what are its top alternatives?

Librato provides a complete solution for monitoring and understanding the metrics that impact your business at all levels of the stack. We provide everything you need to visualize, analyze, and actively alert on the metrics that matter to you.
Librato is a tool in the Performance Monitoring category of a tech stack.

Top Alternatives to Librato

  • 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! ...

  • New Relic
    New Relic

    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...

  • Hosted Graphite
    Hosted Graphite

    Hosted Graphite is the powerful open-source application metrics system used by hundreds of companies. We take away the headaches of scaling, maintenance, and upgrades and let you do what you do best - write great software. ...

  • 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. ...

  • 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. ...

  • 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. ...

Librato alternatives & related posts

Datadog logo

Datadog

9.2K
7.9K
860
Unify logs, metrics, and traces from across your distributed infrastructure.
9.2K
7.9K
+ 1
860
PROS OF DATADOG
  • 139
    Monitoring for many apps (databases, web servers, etc)
  • 107
    Easy setup
  • 87
    Powerful ui
  • 84
    Powerful integrations
  • 70
    Great value
  • 54
    Great visualization
  • 46
    Events + metrics = clarity
  • 41
    Notifications
  • 41
    Custom metrics
  • 39
    Flexibility
  • 19
    Free & paid plans
  • 16
    Great customer support
  • 15
    Makes my life easier
  • 10
    Adapts automatically as i scale up
  • 9
    Easy setup and plugins
  • 8
    Super easy and powerful
  • 7
    In-context collaboration
  • 7
    AWS support
  • 6
    Rich in features
  • 5
    Docker support
  • 4
    Cute logo
  • 4
    Source control and bug tracking
  • 4
    Monitor almost everything
  • 4
    Cost
  • 4
    Full visibility of applications
  • 4
    Simple, powerful, great for infra
  • 4
    Easy to Analyze
  • 4
    Best than others
  • 4
    Automation tools
  • 3
    Best in the field
  • 3
    Free setup
  • 3
    Good for Startups
  • 3
    Expensive
  • 2
    APM
CONS OF DATADOG
  • 19
    Expensive
  • 4
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated

related Datadog posts

Noah Zoschke
Engineering Manager at Segment · | 30 upvotes · 269.1K views

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

For the newest round of APIs we adopted the GRPC service #framework.

The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

For the API gateway and RPC we adopted the Envoy service proxy.

The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

See more
Robert Zuber

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve 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.

See more
New Relic logo

New Relic

20.7K
8.5K
1.9K
New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
20.7K
8.5K
+ 1
1.9K
PROS OF NEW RELIC
  • 415
    Easy setup
  • 344
    Really powerful
  • 244
    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 107
    Free tier
  • 80
    Great tool for insights
  • 66
    Heroku Integration
  • 55
    Market leader
  • 49
    Peace of mind
  • 21
    Push notifications
  • 20
    Email notifications
  • 17
    Heroku Add-on
  • 16
    Error Detection and Alerting
  • 13
    Multiple language support
  • 11
    Server Resources Monitoring
  • 11
    SQL Analysis
  • 9
    Transaction Tracing
  • 8
    Azure Add-on
  • 8
    Apdex Scores
  • 7
    Detailed reports
  • 7
    Analysis of CPU, Disk, Memory, and Network
  • 6
    Application Response Times
  • 6
    Performance of External Services
  • 6
    Application Availability Monitoring and Alerting
  • 6
    Error Analysis
  • 5
    JVM Performance Analyzer (Java)
  • 5
    Most Time Consuming Transactions
  • 4
    Top Database Operations
  • 4
    Easy to use
  • 4
    Browser Transaction Tracing
  • 3
    Application Map
  • 3
    Weekly Performance Email
  • 3
    Custom Dashboards
  • 3
    Pagoda Box integration
  • 2
    App Speed Index
  • 2
    Easy to setup
  • 2
    Background Jobs Transaction Analysis
  • 1
    Time Comparisons
  • 1
    Access to Performance Data API
  • 1
    Super Expensive
  • 1
    Team Collaboration Tools
  • 1
    Metric Data Retention
  • 1
    Metric Data Resolution
  • 1
    Worst Transactions by User Dissatisfaction
  • 1
    Real User Monitoring Overview
  • 1
    Real User Monitoring Analysis and Breakdown
  • 1
    Free
  • 1
    Best of the best, what more can you ask for
  • 1
    Best monitoring on the market
  • 1
    Rails integration
  • 1
    Incident Detection and Alerting
  • 0
    Cost
  • 0
    Exceptions
  • 0
    Price
  • 0
    Proce
CONS OF NEW RELIC
  • 20
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
  • 7
    Expensive
  • 7
    Visualizations aren't very helpful
  • 5
    Hard to understand why things in your app are breaking

related New Relic posts

Cooper Marcus
Director of Ecosystem at Kong Inc. · | 17 upvotes · 110.6K views
Shared insights
on
New RelicNew RelicGitHubGitHubZapierZapier
at

I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.

I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.

Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.

Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.

#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics

See more
Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

See more
Hosted Graphite logo

Hosted Graphite

22
28
12
Graphite as a service
22
28
+ 1
12
PROS OF HOSTED GRAPHITE
  • 4
    Great customer support
  • 2
    Custom metrics
  • 2
    Grafana dashboards
  • 1
    Event annotations
  • 1
    Alerting with lots of notification options
  • 1
    Great value
  • 1
    Lots of Integrations
CONS OF HOSTED GRAPHITE
    Be the first to leave a con

    related Hosted Graphite posts

    Grafana logo

    Grafana

    17.5K
    14K
    415
    Open source Graphite & InfluxDB Dashboard and Graph Editor
    17.5K
    14K
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    415
    PROS OF GRAFANA
    • 89
      Beautiful
    • 68
      Graphs are interactive
    • 57
      Free
    • 56
      Easy
    • 34
      Nicer than the Graphite web interface
    • 26
      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
      Integration with InfluxDB
    • 5
      Click and drag to zoom in
    • 4
      Authentification and users management
    • 4
      Threshold limits in graphs
    • 3
      Alerts
    • 3
      It is open to cloud watch and many database
    • 3
      Simple and native support to Prometheus
    • 2
      Great community support
    • 2
      You can use this for development to check memcache
    • 2
      You can visualize real time data to put alerts
    • 0
      Grapsh as code
    • 0
      Plugin visualizationa
    CONS OF GRAFANA
    • 1
      No interactive query builder

    related Grafana posts

    Matt Menzenski
    Senior Software Engineering Manager at PayIt · | 16 upvotes · 995.7K 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
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M 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
    Prometheus logo

    Prometheus

    4.2K
    3.8K
    239
    An open-source service monitoring system and time series database, developed by SoundCloud
    4.2K
    3.8K
    + 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

    Matt Menzenski
    Senior Software Engineering Manager at PayIt · | 16 upvotes · 995.7K 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
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M 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
    JavaScript logo

    JavaScript

    350.9K
    267.2K
    8.1K
    Lightweight, interpreted, object-oriented language with first-class functions
    350.9K
    267.2K
    + 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
    • 896
      Fast
    • 745
      Light weight
    • 425
      Flexible
    • 392
      You can't get a device today that doesn't run js
    • 286
      Non-blocking i/o
    • 236
      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
      Its everywhere
    • 12
      Future Language of The Web
    • 11
      JavaScript is the New PHP
    • 11
      Because I love functions
    • 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
      Easy to hire developers
    • 8
      No need to use PHP
    • 8
      For the good parts
    • 8
      Can be used both as frontend and backend as well
    • 8
      Powerful
    • 8
      Most Popular Language in the World
    • 7
      Popularized Class-Less Architecture & Lambdas
    • 7
      It's fun
    • 7
      Nice
    • 7
      Versitile
    • 7
      Hard not to use
    • 7
      Its fun and fast
    • 7
      Agile, packages simple to use
    • 7
      Supports lambdas and closures
    • 7
      Love-hate relationship
    • 7
      Photoshop has 3 JS runtimes built in
    • 7
      Evolution of C
    • 6
      1.6K Can be used on frontend/backend
    • 6
      Client side JS uses the visitors CPU to save Server Res
    • 6
      It let's me use Babel & Typescript
    • 6
      Easy to make something
    • 6
      Can be used on frontend/backend/Mobile/create PRO Ui
    • 5
      Promise relationship
    • 5
      Stockholm Syndrome
    • 5
      Function expressions are useful for callbacks
    • 5
      Scope manipulation
    • 5
      Everywhere
    • 5
      Client processing
    • 5
      Clojurescript
    • 5
      What to add
    • 4
      Because it is so simple and lightweight
    • 4
      Only Programming language on browser
    • 1
      Test2
    • 1
      Easy to learn
    • 1
      Easy to understand
    • 1
      Not the best
    • 1
      Hard to learn
    • 1
      Subskill #4
    • 1
      Test
    • 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 · 10.1M 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

    289.9K
    174.2K
    6.6K
    Fast, scalable, distributed revision control system
    289.9K
    174.2K
    + 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 · 9.2M 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 · 8.3M 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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