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Demo for scalable Elasticsearch setups with Frozen Indices, Index Lifecycle Management, and Rollups
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Scale Your Elasticsearch Cluster

Demo code for the Scale Your Elasticsearch Cluster talk.

The settings used here are for a short lived demo only. Do not use those for any development or production environments.


Start with a docker-compose up and explain the different parts while it is initializing.

Frozen Index

Trying out frozen indices including the recommended steps to use:

PUT frozen
  "settings": {
    "index.routing.allocation.include.size": "cold"

POST frozen/_doc
  "name": "Philipp"
POST frozen/_doc
  "name": "Nicolas"

GET frozen/_search

POST frozen/_forcemerge?max_num_segments=1

POST frozen/_freeze

GET frozen/_search

GET frozen/_search?ignore_throttled=false

GET _cat/indices/frozen?v&h=health,status,index,pri,rep,docs.count,store.size

GET _cat/thread_pool/search_throttled?v&h=node_name,name,active,rejected,queue,completed&s=node_name

GET frozen/_search?ignore_throttled=false&pre_filter_shard_size=1

POST frozen/_doc
  "name": "Abdon"

GET frozen/_settings?flat_settings=true

POST frozen/_unfreeze

POST frozen/_doc
  "name": "Abdon"

GET frozen/_search

# Import Kibana sample data (here the ecommerce one)
# Show the dashboard before and after freezing (and fix the setting in Kibana)
POST kibana_sample_data_ecommerce/_freeze

Index Lifecycle Management

Explain how node.attr.size is tying the architecture together. Show both setup/templace_metricbeat.json and setup/ilm.json, explain what is going on, and show the results in Index Management. Also load the ILM configuration in the Kibana UI, but explain why not all values are shown correctly.


Show the underlying setup/rollup.json and how you would build it in the Kibana UI. Show the underlying documents in Discover and build a visualization on the Docker network traffic (if there are a couple of documents at least).


Run docker-compose down -v to get rid of the entire setup again.


  • Integrate Frozen Indices better into the ILM demo
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