In this lesson, you will learn how to perform rolling updates on your cache clusters to upgrade software or change configurations without causing a service outage for your users.
When you need to upgrade a cache engine—like applying a patch or modifying memory allocation—restarting the entire cluster at once creates a "cold start" problem. The database suddenly receives a massive influx of requests as the cache misses, leading to potential latency spikes or, in extreme cases, a cascading failure of your downstream services.
To avoid this, we use a rolling update strategy. Instead of taking the whole cluster offline, we update nodes one by one.
The Rolling Update Process
- Drain/Shift Traffic: Before updating a specific node, remove it from the load balancer rotation or the client-side consistent hashing ring.
- Upgrade Node: Apply the upgrade to the isolated node.
- Warm-up: Before putting the node back into production, "warm" it by pre-populating it with common keys to avoid cache misses.
- Rejoin: Add the node back into the cluster.
If your cache uses a cluster-aware client, you can automate this process. Here is a conceptual view of how you might manage a node status before an update:
-- Conceptual: Flagging a node for maintenance in your routing metadata
UPDATE cache_nodes
SET status = 'DRAINING'
WHERE node_id = 'cache-node-01';
-- After successful upgrade and warm-up:
UPDATE cache_nodes
SET status = 'ACTIVE'
WHERE node_id = 'cache-node-01';
By ensuring that the majority of your cluster remains ACTIVE while a small percentage is undergoing maintenance, you maintain high availability. This pattern relies heavily on your previous knowledge of Designing for Partial Failure; if one node is briefly unavailable during an update, the system should gracefully route around it or handle the slight increase in latency without collapsing. Always ensure your Capacity Over-Provisioning is sufficient to handle the temporary increase in load on the remaining nodes while one is being upgraded.
Key Takeaway: Perform cache upgrades by isolating nodes one-by-one to maintain constant availability and prevent "cache stampede" events during the transition.