Background: Heroku's Abstractions and Their Consequences
The 12-Factor Alignment
Heroku's design heavily aligns with the 12-Factor methodology: stateless processes, ephemeral storage, and configuration via environment variables. While this simplifies initial deployment, it creates friction for workloads that assume persistent state, shared disks, or long-running daemons.
Enterprise Misalignments
Enterprises often deploy mixed workloads—APIs, background jobs, batch ETL, and WebSocket-heavy services. The assumptions Heroku enforces (ephemeral dynos, connection caps on managed databases, routing latency) can stress these workloads in unexpected ways.
Architectural Implications
Dyno Lifecycle and Ephemeral Filesystem
Heroku dynos restart at least once every 24 hours. Any in-memory state, background tasks, or files written to the ephemeral filesystem vanish. This disrupts naive designs that assume long-lived caches or durable queues.
Database Connection Saturation
Each dyno opens its own pool of database connections. Scaling web dynos horizontally often exceeds Postgres connection limits. Without pooling, the database thrashes, causing timeouts and transaction failures.
Routing Mesh and Request Queues
Heroku's router introduces queueing when dynos are saturated. Requests exceeding a 30-second timeout are dropped. Applications with uneven traffic or spiky latency patterns see intermittent 503s unless carefully tuned.
Diagnostics
1. Detecting Dyno Crashes and Restarts
Use heroku logs --tail to identify forced restarts. For systematic monitoring, export Heroku's release and restart events into a metrics system (Datadog, Prometheus). Spikes often correlate with memory leaks or dependency issues.
2. Identifying Database Bottlenecks
Enable pg:diagnose and pg:outliers to trace long-running queries. Monitor connection counts via pg:info. If connections approach the limit, introduce a connection pooler like PgBouncer.
# Check connection stats heroku pg:info --app my-app heroku pg:diagnose --app my-app
3. Debugging Router Queues
Heroku provides router metrics including requests in queue and request wait time. Export them and set alerts. If requests routinely wait more than 100ms, add dynos or optimize request handlers.
Common Pitfalls
Ephemeral Storage Misuse
Applications that write to /tmp or rely on uploaded files persisting beyond a request window fail unpredictably. Files disappear after dyno restarts, breaking image processing or report generation flows.
Over-Provisioned Web Dynos
Scaling web dynos without considering DB connections overloads Postgres. Horizontal scaling must be balanced with connection pooling.
Ignoring Worker Isolation
Background workers share the same dyno model. If a worker crashes due to OOM, it may silently restart mid-job, leading to duplicate processing unless idempotency is enforced.
Autoscaling Surprises
Autoscaling rules that react to request latency can trigger oscillations—scaling up during transient traffic spikes, then scaling down too aggressively. This drives costs while reducing stability.
Step-by-Step Fixes
1. Durable Storage Strategy
Never depend on Heroku's filesystem for persistence. Use S3 or similar object stores for uploads and caching layers like Redis for transient state.
2. Database Connection Pooling
Introduce PgBouncer as a buildpack or sidecar to reduce active connections per dyno. Configure your ORM (ActiveRecord, Sequelize) to use fewer pooled connections.
# Example: Rails database.yml production: adapter: postgresql pool: 5 timeout: 5000
3. Queue-Based Background Processing
Use Resque, Sidekiq, or RabbitMQ with retry and idempotency guarantees. Store job state in Redis or Postgres. Design jobs to tolerate restarts and deduplicate where necessary.
4. Router Queue Management
Break long requests into background jobs. Stream results progressively or implement webhooks instead of synchronous long-polling. Keep web dyno work under 500ms where possible.
5. Autoscaling Discipline
Set autoscaling policies based on throughput rather than latency alone. Add hysteresis to prevent thrashing. Regularly review scaling logs to ensure cost efficiency.
Best Practices
- Treat dynos as cattle, not pets—design for restart at any time.
- Implement structured logging and forward to ELK or Datadog.
- Use feature flags to test scaling changes incrementally.
- Encrypt all environment config and rotate regularly.
- Schedule load tests that simulate dyno restarts mid-traffic.
Conclusion
Heroku abstracts away infrastructure complexity, but those abstractions hide operational trade-offs that only surface under scale. By understanding dyno lifecycles, database connection limits, routing constraints, and autoscaling behaviors, enterprises can design systems that remain reliable and cost-efficient. Long-term success on Heroku requires treating its constraints as architectural boundaries, not implementation details, and building resilience into every layer of the stack.
FAQs
1. Why do dynos restart every 24 hours?
This is part of Heroku's platform contract to ensure fairness and to apply underlying system updates. You cannot disable it; instead, design applications to survive restarts gracefully.
2. How can I avoid Postgres connection limits when scaling?
Introduce a connection pooler like PgBouncer and configure minimal per-dyno pools. Consider upgrading to higher-tier databases with larger connection limits if pooling is insufficient.
3. What's the best way to handle file uploads on Heroku?
Send uploads directly to cloud storage (S3, GCS) rather than persisting to the dyno filesystem. This ensures durability across restarts and supports horizontal scaling.
4. How should I design background jobs for Heroku?
Use queue systems with retries and idempotency. Ensure jobs can be retried safely after dyno restarts and use persistent storage for intermediate state if necessary.
5. Why does Heroku autoscaling sometimes increase costs unexpectedly?
Autoscaling based purely on latency may respond too aggressively to transient spikes. Add smoothing windows or base rules on request throughput to prevent oscillations and cost blowouts.