Best practices for scaling Elasticsearch clusters
- Use metrics from inside Elasticsearch for accuracy
- Scale in larger increments to reduce noise
- Force index rotation to evenly spread load across nodes
- Judge cluster size based on disk usage and search latency
- Use local SSDs for better I/O latency
- Consider hot-warm-cold architecture for data management
When scaling up an Elasticsearch cluster, it's important to evenly distribute the load across all nodes. This can be achieved by forcing index rotation, which creates a new index that is spread out throughout the cluster. However, when scaling down, it's important to properly drain nodes before shutting them down to avoid an imbalanced cluster. Additionally, it's important to consider search latency and disk usage when judging the size of a cluster, and to use local SSDs for better I/O latency. Finally, the hot-warm-cold architecture can be useful for managing data as it becomes less relevant over time.