For modern software teams, delivering at the rapid pace users expect demands streamlining developer workflows. But monolithic IT infrastructure often hampers velocity by creating development bottlenecks.
Shared servers strain under resource constraints with teams blocking each other. Inconsistent environments fail to mimic production realities. Manual configurations make spin-up delays agonizing.
These infrastructure headaches slow iteration speed through:
- Developers overwriting shared dependencies and crimping productivity
- New features built on inaccurate staging environments failing in production
- Hours to days wasted rebuilding suitable testing environments
The shift to cloud-based serverless architectures promises to eliminate these classic problems and unblock developer productivity. By fundamentally rethinking how we manage infrastructure…
What Makes Serverless Architectures Different
Before diving deeper, let‘s quickly ground ourselves on what serverless is and, more importantly, how it differs from traditional infrastructure:
Auto-Scaling: Serverless platforms intrinsically scale capacity up and down based on real workload instead of needing to predict demand.
Micro-Billing: Pay only for actual compute time used instead of renting servers by the hour resulting in overprovisioning waste.
Abstraction: Developers handle business logic while cloud platforms manage infrastructure reliability and maintenance.
By leveraging these attributes, serverless empowers teams to provision fully isolated development environments in seconds while paying only for what they use.
But just how much do constraints around shared resources impact developer velocity today?
According to recent surveys by CircleCI:
- 69% of developers lose 1-4 hours weekly waiting for access to development environments.
- 98% have experienced environment-related delays on projects at some point
- Over 75% say unreliable environments directly prevent them from trying out new ideas.
That translates to over a month of lost productivity yearly for most developers just from infrastructure delays even by conservative time estimates.
And the #1 trigger cited? Insufficient environment availability from resource contention across teams.
Serverless architectures can help here by making parallel, isolated development environments practical. The auto-scaling economics alleviate capacity constraints while usage-based billing ensures costs stay in check even with more environments to maximize productivity.
Designing Reliable Serverless Environments
However, to reap productivity gains and cost savings, serverless environments must provide reliable fidelity. They need to instill confidence that code behaving correctly in staging will operate similarly in production.
Let‘s explore core principles for creating trustworthy serverless environments:
Match Production Architecture
Begin by cloning the production infrastructure blueprint as the foundation. Reconstruct key components like:
- Virtual Private Cloud (VPC) with subnets
- Database instances
- Serverless compute services (Lambda, AppSync)
- Storage (S3 buckets, EFS)
- Security Groups
This production-equivalent architecture ensures accurate simulation of real-world dependencies for confidence.
And with infrastructure defined as code through templates, modifying environments to stay in sync requires only updating central code repositories versus complex manual reconfiguration.
# Sample CloudFormation template
Resources:
LambdaFunction:
Type: AWS::Lambda::Function
Properties:
Code: lambda.zip
AppSyncAPI:
Type: AWS::AppSync::GraphQLApi
Properties:
# AppSync configuration here
RDSDatabase:
Type: AWS::RDS::DBInstance
Properties:
# RDS configuration here
Regularly Sync Production Data
Accurate architecture alone is not enough if the datasets for testing get stale. Without a pulse on real user data flows, product breaks still surprise developers after new features get deployed.
Counter data drift by establishing regular production data syncs into environments:
Schemas: Extract updated DDL scripts weekly from production using Schema Conversion Tools to propagate structural DB changes.
Data Snapshots: Schedule periodic RDS or DynamoDB backups from production systems. Restore recent snapshots into dev databases to provide representative data sampling for code testing. Mask any sensitive information based on regulations.
Automate Process Flows
Even perfectly constructed standalone environments can slow productivity if developers waste time on manual management.
Automation is key here. Connect workflows across infrastructure, code, builds and testing through:
Infrastructure as Code
Define reusable CloudFormation templates for provisioning preconfigured environments using AWS best practices out of the box.
# Example preset CloudFormation environments
Templates:
Django_Environment:
# Full django stack
NodeJS_Environment:
# Preconfigured nodeJS compute env
Custom_VPC:
# Secure custom VPC
Git Version Control
Use Git repositories and branching strategies for developers to seamlessly contribute changes without stepping on each other. Enforce peer code reviews and testing before merging to master.
CI/CD Pipelines
Automatically build and deploy applications on every code change. Services like AWS CodePipeline let you connect repos to testing frameworks.
Also configure pipelines to cleanly dismantle environments once work completes. This optimizes costs by preventing unused environments from lingering.
Right-size Serverless Capacity
While auto-scaling reduces wasted capacity, proactively planning serverless resources still minimizes spend:
Schedule lambda timeouts: Enable outside business hours to release underlying container infrastructure without affecting availability during the workday.
Monitor usage metrics: Tune capacities based on peaks instead of overprovisioning for edge scenarios seen once an year.
Simulate load patterns: Model possible projections with tools like AWS Cost Explorer before deploying changes to validate cost impacts.
Governing Environments at Scale
As infrastructure self-service expands access, some guardrails help balance agility and governance:
Budget Alerts
Set CloudWatch billing alerts to notify teams when monthly spending trends higher indicating potential unused resource buildup.
Resource Limits
Define pre-approved quotas around maximum servers, cores, or GB memory individual developers can deploy. Prevents runaway launches.
Lock Down Permissions
Scope IAM roles to minimum access required reducing ability to inadvertently impact other environments.
Changing Team Dynamics Around Ownership
But tooling and automation alone cannot force change. The biggest environment breakthrough comes when teams embrace shared ownership mentalities.
In the serverless world, developers have unprecedented influence over their infrastructure flexibility. But with great power comes great responsibility.
Some ways to promote sustainable mindsets:
Incentive Usage Monitoring
Grant developers read-access to environment usage data and billing dashboards. Transparency around direct costs typically motivates more judicious resource usage.
Norms Training
Establish social team norms that value conservation. Small actions like peer appreciation for those shutting down environments promptly can influence wider behaviors.
Standards Enforcement
Implement linting on IaC scripts to validate things like mandatory auto-shutdown policies before allowing template merges. This pushes teams incrementally towards better practices.
Go Forth and Deploy, Developer!
Serverless platforms offer a golden chance to unburden your developers from infrastructure headaches. All ingredient are there – auto-scaling capacity, managed maintenance overhead, flexible architectures programmable as code.
The biggest question is how quickly you can start this journey to modernize development environments. How long before you aim to:
-
Reduce shared environment bottlenecks crushing productivity
-
Automate full-stack deployments in minutes through Infrastructure-as-Code
-
Govern costs and compliance through cloud best practices
The innovation race moves swiftly. The time for your developers to break free and soar ahead relies on how rapidly you transform infrastructure from bottleneck to multiplier. No pressure!
