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How do you approach DevOps scaling practices for growing applications?
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Our user base is growing faster than expected and we need to improve our DevOps scaling practices. What approaches work best for scaling both infrastructure and processes?
I'm interested in horizontal vs vertical scaling decisions, auto-scaling configurations, database scaling strategies, and how you handle increased monitoring needs.
Also, how do you prepare your team for scaling? What skills or processes need to be in place before you start experiencing growth pains?
For DevOps scaling practices, we focus on horizontal scaling from the start. Even if we don't need multiple instances initially, we design services to be stateless and use external storage for state. This makes scaling out much easier when needed.
We use Kubernetes Horizontal Pod Autoscaler based on CPU and custom metrics. When traffic increases, pods automatically scale up. When it decreases, they scale down to save costs.
Database scaling is trickier. We use read replicas for PostgreSQL and sharding for MongoDB. The key is planning your data model for scalability early - denormalization, avoiding joins across shards, etc.
Our DevOps scaling practices include capacity planning exercises every quarter. We project growth, identify potential bottlenecks, and plan infrastructure upgrades before they become urgent.
We also implement rate limiting and circuit breakers at the API gateway level. This prevents one overwhelmed service from taking down the entire system during traffic spikes.
For team scaling, we document everything. Runbooks, architecture decisions, troubleshooting guides - all in a searchable wiki. New team members can get up to speed quickly without constantly interrupting experienced engineers.
One often overlooked aspect of DevOps scaling practices: monitoring and alerting scale too. As you add more services and instances, your monitoring system needs to handle the increased load.
We use Thanos with Prometheus for long-term metric storage and federation. Without this, our Prometheus instances would run out of memory.
Also, consider the cost of scaling. We use spot instances for stateless workloads and reserved instances for databases. Auto-scaling groups with mixed instance types help us get the best price/performance ratio.
For processes, we've found that smaller, focused teams scale better than large, general teams. Each team owns a bounded set of services and can move fast without coordination overhead.
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