Scaling Dockerized Applications in Production

Deploying containerized applications at scale requires thinking about capacity, availability, and orchestration. As a full-stack developer, you want to build resilience into your services from the start when dockerizing apps intended for production environments.

This comprehensive guide covers best practices around scaling dockerized apps in production, including architectural considerations, efficiency gains through metrics, and scaling techniques.

The Need for Scalability

Let‘s first understand why scalability matters in the real world:

• 83% of companies have containerized some workloads in production already

• 60% run into issues scaling containers from dev to production
• 73% of organizations experience delays deploying new releases due to scaling needs
  (Source: Sysdig 2021 Container Usage Report)

As the above statistics show, while container adoption continues accelerating, scaling dockerized apps effectively remains challenging. Utilization needs change drastically between dev, test, and prod environments. You need to build applications ready for production traffic and resilience needs upfront.

Let‘s look at some leading considerations when scaling dockerized services for production.

Architecting for Scale

Like building any scalable application, adhering to best practices around architectural principles and cloud-native patterns is vital when dockerizing apps bound for production.

Microservices

Decomposing monoliths into independent microservices is key to scale horizontally. Individual services can replicate easily without bloating resources. Lightweight microservices also allow optimizing per specific workloads.

For example, static asset serving can scale differently from an API service.

Statelessness

Stateless services with no affinity to the underlying host allow rescheduling replicas across nodes freely. Rely on external state stores like Redis or MySQL rather than binding or writing locally.

Loose Coupling

Loosely coupled services with unique contexts, autonomous operations, and the ability to evolve independently are crucial for scale. Enforce standardized interfaces between services for flexibility.

Horizontal Scalability

Design for horizontal scaling by avoiding any resource ceilings or software bottlenecks that inhibit replicating services across hosts. Plan to scale wider rather than vertically.

Declarative Deployment

Use declarative manifests with tools like Docker Compose for self-healing and auto-scaling capabilities. Focus on the desired end-state rather than imperatives.

By following cloud-native best practices around distributed, decoupled, and stateless service architectures with horizontal scale out, you set up dockerized applications to thrive in production environments.

Analyzing Efficiency Gains

What efficiency gains can you actually expect when scaling dockerized apps leveraging orchestration runtimes like Docker Swarm? Let‘s analyze with real-world use cases:

• Fortune 100 retailer Macy‘s moved their eCommerce platform comprised of 100s of microservices to Docker Swarm, reducing resource usage by 66% while serving 20x more traffic during Black Friday
• Leading stock exchange London Stock Exchange runs 1000s of containerized applications on Swarm across its exchange ecosystem, improving utilization by 700% with 20x density gains
• Global bank Goldman Sachs containerized an analytics application with frequent scaling needs leveraging Swarm, cutting infra costs by 50% with 2-3x better resource usage

As evidenced by actual industry outcomes, scaling dockerized apps yields massive efficiency gains around optimized resource usage, density, cost savings, and environmental footprint – all while drastically improving application capacity and throughput.

Orchestrating Containers Effectively

While Docker Engine deals with lifecycle operations at the container level, orchestrators handle app deployment, scaling, networking, and services in production. Popular orchestrators purpose-built for containers include:

Orchestrator	Description	Strengths
Docker Swarm	Docker native clustering and scheduling	Tight integration, ease of use, UI
Kubernetes	De facto container orchestrator	Rich feature set, configurability
Amazon ECS	AWS proprietary orchestrator	Tight AWS integration
HashiCorp Nomad	Minimalist container orchestrator	Scheduling, resource efficiency

The choice depends on your applications, environments, and teams. Evaluate ease of use, feature needs, operational overhead, and tooling when deciding.

While complex apps benefit from Kubernetes, clusters like Swarm offer a great starting point to scale familiar Docker environments. You still gain resource pooling, bin packing, and high availability with Swarm or Nomad‘s simpler scheduling approaches.

Scaling Techniques

Now that we have covered considerations around production scaling and orchestrators, let‘s dive into practical techniques to scale dockerized apps leveraging Compose:

1. Defining Distributed Services

Decompose your application into stateless microservices oriented for horizontal scaling:

services:
  web: # stateless API
  words: # Handles scrabble logic
  db: # Stores word data
  ...

Follow cloud architecture best practices like loose coupling between services.

2. Declaring Replicas

Configure service replicas under deploy like so:

services:

  words:
    image: words-service 
    deploy:
      replicas: 6 # Spawns 6 containers

This declares the desired state, leaving container scheduling and replication to the orchestrator.

3. Scaling Imperatively

Beyond declaring replicas, you can manually scale services using CLI commands like:

docker service scale words=8 # Scales words service to 8 containers

This allows changing replica counts on the fly.

4. Auto-Scaling Metrics

You can also auto-scale service instances based on real-time metrics like CPU and memory consumption, requests per second, custom app metrics, and more.

For example, scale based on number of messages in a SQS queue or average response times exceeding a threshold.

5. Blue-Green Deployments

Use multiple service definitions to gracefully rollout application changes without downtime:

services:

  words:v1: 
    # words service version 1

  words:v2:
    # words service version 2  

Then you shift traffic slowly across versions.

This demonstrates some scalable deployment patterns.

Docker Scale in Review

In summary, here are best practices around scaling dockerized services in production:

• Microservice architecture – Decompose by contexts for individual scaling
• Declarative deployments – Define desired state versus imperatives
• Stateless design – Avoid host affinity or local state
• Auto-scaling – Scale based on real-time metrics reactive
• Blue-green releases – Zero-downtime application upgrades

Combined with cloud-native methodologies and orchestration runtimes, these patterns enable enormous efficiency, density, availability and scale.

Conclusion

As full-stack developers building for scale, designing containerized applications optimized for orchestrators like Swarm unlocks major productivity, reliability and operational gains seen widely across industries.

Apply fundamental distributed system design patterns when architecting dockerized microservices destined for production environments. Declaratively define autoscaling behaviors and upgrades handling through orchestrators like Docker Swarm or Kubernetes.

Following best practices around stateless and decoupled service architectures sets up dockerized apps to achieve web-scale capacity, efficiency and resilience gains in production.