Putting It All Together: A Real-World Scenario
Let’s create a time series index for monitoring server metrics (CPU usage) and apply the tuning strategies discussed above.
Step 1: Create an Index Template
PUT /_template/server_metrics_template
{
"index_patterns": ["server-metrics-*"],
"settings": {
"number_of_shards": 1,
"number_of_replicas": 1,
"refresh_interval": "30s",
"index.lifecycle.name": "metrics_policy"
},
"mappings": {
"_source": { "enabled": false },
"properties": {
"timestamp": { "type": "date" },
"cpu_usage": { "type": "float" },
"server_id": { "type": "keyword" }
}
}
}
Step 2: Define an ILM Policy
PUT _ilm/policy/metrics_policy
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover": {
"max_size": "50gb",
"max_age": "30d"
}
}
},
"delete": {
"min_age": "90d",
"actions": {
"delete": {}
}
}
}
}
}
Step 3: Create the Initial Index
PUT /server-metrics-000001
{
"settings": {
"index.lifecycle.name": "metrics_policy",
"index.lifecycle.rollover_alias": "server-metrics"
}
}
Tuning Elasticsearch for Time Series Data
Elasticsearch is a powerful and versatile tool for handling a wide variety of data types, including time series data. However, optimizing Elasticsearch for time series data requires specific tuning and configuration to ensure high performance and efficient storage. This article will delve into various strategies and best practices for tuning Elasticsearch for time series data, complete with examples and outputs to illustrate the concepts.