Skip to main content

Allocate Shards of Indices to Specific Elasticsearch Nodes

Hands-On Lab


Photo of

Training Architect





A great way to save money when sizing compute and storage for an Elasticsearch cluster is by using hot-warm architectures. Especially for time series data, hot-warm architectures allow you to allocate your most relevant data (hot data) on your fastest nodes (hot nodes) and your less relevant data (warm data) on your slower nodes (warm nodes). This allows you to scale data retention with cheaper compute and storage and only pay a premium for your hot nodes. All this is made possible in Elasticsearch through the use of node attributes and allocation filtering, which can be used in any case where you want your index allocation to meet specific requirements. In this hands-on lab, you are given the opportunity to exercise the following:

  • Apply node attributes to each data node in a cluster
  • Configure indexes to allocate to specific nodes based on their attributes
What are Hands-On Labs?

Hands-On Labs are scenario-based learning environments where learners can practice without consequences. Don't compromise a system or waste money on expensive downloads. Practice real-world skills without the real-world risk, no assembly required.