The Automated Monolith

Marco Seifried (@marcoseifried)Tora Onaca (ro.linkedin.com/in/toravasilescu)Paul Vidrascu (ro.linkedin.com/in/paulvidrascu)

dev.haufe-lexware.com

github.com/Haufe-Lexware

@HaufeDev

Build, Deploy and Testing using Docker, Docker Compose, Docker Machine, go.cd and Azure

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No need to double check this change

list, if some problems remain the reviewer will

catch them

No need to look at this change list to closely, I‘m sure the author knows what he‘s doing

Haufe Strategy - Architecture Principles

Business value over technical strategy

Strategic goals over project-specific benefits

Composability over silos

Shared services over specific-purpose implementations

Evolutionary refinement over pursuit of initial perfection

Design for obsoleteness over building for eternity

Good enough over best of breed

Declarative processes over implicit knowledge

What do we want to achieve?

Speed

Business value in manual deployments?

Reduce Error

Prepare for change

How?

ContainerInfrastructure As Code

Immutable ServersBlue / Green Deployments

Project and Approach

Features: User-, License- & Subscription Management

Goal: Microservices, fully automated deployment, APIs

Start: Fully automated deployment

First iteration: Automated test environment per client on

Azure

How – Automation Tools and Technologies

Continuous Delivery // pipelines to run scripts // create docker images// deploy onto Azure

Bitbucket – repository for config

Deploy in Azure

Artifactory - Internal docker repository for images

Run anywhere, describe everything in dockerfiles

How – Automation Components and Flow

Break down – step 0

Independent on the pipeline execution, every time an artifact is built in TFS it is then pushed in the Haufe Artifactory.

Step 1 – building the Docker images

A new commit in the BitBucket repository triggers the pipeline.

The pipeline contains steps for building all the needed images (app server, db server, JMS server, logging, monitoring, test)

Step 1 – building the Docker images (cont.)

Logging

Docker logs not enoughAlternative – docker exec and accessing the log filesMap external volume and copy logs thereBest alternative: use what everyone else does and works like a charm: Kibana + ElasticSearch + Fluentd

Monitoring

See the status of the containers usingCadvisor influxDbGrafana

Step 2 – Deploy and Run

Summary

Two months effort to get to Azure, distributed teams across two countriesCreation of dev environment reduced from one week to 30 minutesDocker is constantly improving (improved networking, built in drivers, etc)Baseline for future improvements

What’s next:

Move this along to production Allow clients to choose the version of the images to useImprove some startup timesTry out different cloud solutions

MultumimThank You