Deploying Code with Confidence - Using AWS Services to Automate and Simplify Your Deployments
Back then, we did most deployments completely manually – connecting to servers via SSH or remote desktop sessions, executing convoluted bash scripts or PowerShell commands, and hoping fervently that nothing broke along the way.
But often, despite extensive preparation, something would still go awry in production that our testing environments didn’t uncover. Perhaps server configurations had gradually drifted over time so assumptions made during development no longer applied. Or maybe a key dependency got updated by another team without documentation.
The Perils of Mutable Infrastructure
This common phenomenon highlights one of the biggest risks with legacy deployment setups – mutable infrastructure. In plain terms, that means servers that can be manually changed, reconfigured, upgraded etc. by engineers or external factors over time.
When you first set up a server – whether physical, virtualized, or in the cloud – often great care goes into documenting component versions, dependencies installed, security hardening status, and all manner of other details. But as time progresses, configuration drift sets in.
Maybe another team updates the default database version your code relies on. Or perhaps you apply a security patch to underlying Linux packages that breaks obscure application functionality. Without meticulous change tracking, your infrastructure becomes a snowflake – unique and undocumented.
Now this server mutability problem matters because it directly impacts new code deployments. Sure, you can test application changes extensively on local environments or staging servers. But mutable infrastructure means your production systems may differ in totally unexpected ways!
So that carefully built new feature might work flawlessly in pre-production testing but completely fail in production or cause strange bugs for users. Ops teams waste countless hours debugging why nearly identical servers give different outputs for the same inputs.
The sad truth is you can never confidently deploy changes if infrastructure varies significantly from development through production environments. We clearly need a better approach for consistent, predictable code deployments. And that brings us to immutable infrastructure...
Introducing Immutable Infrastructure
The essential idea behind immutable infrastructure is that servers become static and read-only in their optimized production state. Need to deploy updates or modifications? No problem – just launch completely new servers from a common image master instead!
This approach relies on automation tools to rapidly rebuild consistent server infrastructure on demand. Updates get baked into a shared immutable template that any environment launches fresh instances from on each deployment. Drift gets eliminated since every provisioned system starts identical.
For example, take a simple architecture of a single EC2 instance running our application code. In traditional mutable models, we might SSH directly into this server and run scripts to deploy recent changes.
But any human errors or environmental differences could easily take down the live production system! That would disrupt all active users and potentially require tricky manual rollbacks to recover a working state.
An immutable approach drastically reduces this deployment risk profile. We'd start by creating a golden image template known as an Amazon Machine Image (AMI) that includes the OS, dependencies, app runtime, libraries, configs etc. needed to launch properly configured instances.
Then to release updates, we simply boot up a separate identical EC2 instance from this AMI. Run integration tests to validate the new server works as expected, cut over any DNS entries or load balancer rules to point at the new instance, and terminate the old instance once confirmed healthy.
Now your production environment always remains consistent since fresh instances come online regularly from the same baseline image. No more snowflake servers drifting into unique configurations over time!
Auto Scaling Groups Streamline Instance Management
Managing individual instances as immutable resources offers big reliability improvements for deployments over legacy servers. But most real-world applications require running multiple distributed instances to provide adequate capacity, availability and fault tolerance.
Recreating complete fleets of instances manually for each deployment starts getting extremely tedious and error-prone. Maintaining separate AMIs per instance class or keeping server counts in sync during scaling events represents painful overhead too.
Here AWS Auto Scaling groups shine for simplifying all facets of startup, shutdown and updating batches of identical instances according to rules you define. Groups launch or terminate instances based monitoring metrics like CPU usage to maintain steady application performance.
An auto scaling group requires a configuration template for launching the EC2 instances needed. This launch template defines important details like the AMI ID to use, instance type, VPC settings, storage options, IAM roles, and so forth.
Combining auto scaling groups with immutable infrastructure provides huge deployment advantages. Since your instances always initialize from consistent AMIs, replacing auto scaled nodes comes with minimal disruption. Need more capacity? Just define rules to scale out. Ready to deploy? Trivially update the launch config's AMI ID to instantly roll updates across the environment's instances.
This auto scaling approach massively lowers engineering overhead for managing large server clusters. The AWS service handles provisioning, updating, monitoring and healing instances without overwhelming manual upkeep. Teams can focus innovation efforts on application functionality rather than infrastructure maintenance.
Automating Code Deployments with AWS CodeDeploy
Alright, so now we know that immutable infrastructure plus auto scaling groups enable easy rollouts across large server clusters. But how do we actually conduct code deployments in a safe, controlled fashion? Trying to directly update running production instances poses all sorts of risks we want to avoid generally.
Here AWS CodeDeploy delivers an extremely robust managed service specially designed for consistent, reliable application deployments. Rather than handling intricate update orchestration manually, CodeDeploy introduces deployment automation for your entire infrastructure footprint.
Its intuitive UI and API surface simplifies pointing CodeDeploy at application revisions for release across one or thousands of instances behind load balancers. CodeDeploy coordinates updating batches of servers gracefully based on settings you define while proactively monitoring health. If any issues emerge, automatic or manual rollbacks quickly restore working states.
Getting Started with CodeDeploy
One of the best aspects of CodeDeploy lies in its incredible ease of integration. Setting up basic deployment pipelines requires little specialized cloud or DevOps knowledge - perfect for lean teams focused on application functionality over infrastructure management.
The service distills down to just a few components:
CodeDeploy Agent: This is a small process installed on each EC2 instance that handles pulling down revisions from S3 when starting deployment tasks as well as coordinating with CodeDeploy's orchestration signals. Installation takes just a single command on Linux or Windows servers!
Deployment Group: The deployment group logically associates together a CodeDeploy application revision with the fleet of EC2 instances to update. Really this linking of code artifacts to compute resources. Configure grouping rules like tagging schemas for dynamic groups covering Auto Scaling instances that evolve over time.
Application Revisions: Each deployment group connects to application revisions, which point at S3 artifacts that contain your application code, configs, scripts etc. These fully describe a releasable set of updates created from your CI/CD pipeline runs. CodeDeploy pulls down these packages across your server group on deployments.
Compute Platforms: CodeDeploy support deployments across both EC2 and even on-premise servers. This flexibility allows gradually transitioning legacy infrastructure under automated management without disruptive rearchitecting. Integrate deployment automation seamlessly with containers or serverless too!
With these basic pieces wired together, CodeDeploy handles all the intricate coordination logic like health monitoring, batch instance coordination, validation checks, rollbacks and more! Very little specialized deployment knowledge needed - the AWS service encapsulates best practices allowing you to focus innovation efforts on application functionality exclusively.
CodeDeploy Deployment Configuration Strategies
Beyond raw deployment mechanics, one of CodeDeploy's major strengths lies in configuration options that balance release agility vs stability. Teams can tailor update workflows matching risk tolerance and infrastructure constraints through:
- Deployment Configuration
- EC2 Auto Scaling Integration
- Validation & Automated Rollback Checks
CodeDeploy Deployment Configurations
CodeDeploy provides ample levers so you don't blindly push untested changes instantly across production environments. Configure controlled deployment workflows that update incrementally while running validations between batches. Popular options include:
One At A Time: Updates (and optionally tests) a single isolated instance before advancing gradually across an ASG. Fast rollbacks if any server shows issues.
Half At A Time: Updates 50% of the ASG capacity in batches to balance stability and speed.
Linear: Deploys in consistent increments you define across fleets for gradual rollout pacing. Customizable by percentage.
All At Once: For less critical apps, directly update entire ASGs simultaneously for fast iteration.
Picking the right approach depends heavily on your goals and constraints. Modify batch percentages too if the defaults work but need fine tuning for your scale.
Don't forget deployment strategies like blue/green (covered later) for advanced patterns!
EC2 ASG Integration Features
For essential availability, CodeDeploy integrates natively with EC2 Auto Scaling groups including:
- Launch configurations for bootstrapping new instances
- Multiple rollback strategies for failed deployments
- Event hook coordination during scale events
- Dynamic membership for keeping sync
- Replacement of failed nodes post-deployment
Together these capabilities ensure your ASG-backed applications continue running smoothly through the entire release lifecycle without infrastructure hiccups.
Automated Validation & Rollback Checks
Beyond deployment configuration, CodeDeploy also helps run automated post-deployment validation checks before considering releases successful. Common approaches include:
Script Execution - Run integration/smoke tests natively on deployed instances to verify application health. CodeDeploy integrates easily with frameworks like Selenium to emulate user workflows.
CloudWatch Alarms - Set up CloudWatch alarms to monitor key application or business metrics. Breaching thresholds automatically triggers rollback workflows until addressed.
AWS Lambda Functions - Call Lambda functions from deployment lifecycle event hooks to validate production state. Check log analysis, audit events, billing anomalies, etc.
Custom Health Checks - Define specific HTTP endpoints that must return success codes for deployments to finalize. This confirms critical infrastructure integrates correctly.
Adding checks like these builds confidence that major user-facing flows work post-deployment or catch issues before customers notice problems. Telemetry monitoring is great but nothing replaces actual validation that primary use cases function properly in production.
Canary and Blue/Green Deployment Strategies
Beyond deployment configuration and health checks, implementing canary or blue/green release patterns adds further protection against poorly validated changes reaching all users simultaneously. Let's explore both approaches.
Canary Deployments
Canary releases mean pushing code changes to a small portion of infrastructure first before rolling out more widely. This technique takes its name from the historical practice where miners used caged canary birds to detect dangerous gases before entering areas themselves.
In software contexts, we route a small percentage of production traffic to canary servers after deployments. With live usage generating telemetry, closely monitor application health metrics and user workflow performance against baselines.
If all signals remain within expected ranges, gradually shift more production traffic towards the canary fleet until reaching 100%. However, if issues emerge, the small canary group limits impact while you investigate root causes. Achieve greater safety without significant rollout delays.
Specialized tools like LaunchDarkly or Impulse provide advanced canary analysis and release automation capabilities as well. But even manual inspection of metrics dashboards works smoothly thanks to CodeDeploy's traffic shifting support.
Blue/Green Deployments
Similar to canaries, blue-green deployments reduce risk by creating separate environments for updates. The "blue" servers run the current production application version while an equal capacity "green" group stages the proposed updates.
Once the isolated green deployment succeeds internally, user traffic begins shifting gradually from blue over to green through CodeDeploy orchestration. If any issues emerge, transactions rollback to blue instantly to minimize disruption.
This approach prevents new versions from impacting existing applications directly throughout the release process. It does require roughly double the peak resource capacity but simplifies rollbacks, facilitates A/B testing, and more.
Many variations exist like red/black, rainbow, etc but the concepts share separating stable and unvalidated application versions via distinct infrastructure during updates.
Recap & Closing Thoughts
The days of manual deployments directly onto mutable production servers rightly ended long ago for most modern engineering teams. Primitive bash scripts and SSH just can't provide the reliability, safety and confidence needed for frequent updates to critical business applications.
Instead, SaaS services like AWS CodeDeploy introduce deployment automation across infrastructure environments by relying on key architectural patterns:
- Immutable infrastructure prevents configuration drift by launching disposable application instances from common AMI master images after each update.
- Auto Scaling groups manage capacity variability in these homogeneous instance clusters.
- CodeDeploy handles orchestrating controlled, graceful rollouts while providing ample options for customization matching risk tolerance levels through deployment configurations, health checks, and more.
- Advanced techniques like canary analysis and blue/green deployments isolate pre-production systems from impacted users until thoroughly validation passes.
Adopting solutions like these helps even small teams punch above their weight class delivering sophisticated, robust applications that enterprises demand. Remove release headaches so you can focus innovation efforts on depth within your solution's core domain.
What other deployment best practices have you found valuable through your experiences? Please share your thoughts in the comments section below!