Nested Aggregations
Nested aggregations allow us to perform more complex analyses by nesting one aggregation within another. This is useful for breaking down data further based on additional criteria.
Example: Aggregating CPU Usage by Server and Hour
POST /server_metrics/_search
{
"size": 0,
"aggs": {
"by_server": {
"terms": {
"field": "server_id"
},
"aggs": {
"hourly_cpu_usage": {
"date_histogram": {
"field": "timestamp",
"calendar_interval": "hour"
},
"aggs": {
"average_cpu_usage": {
"avg": {
"field": "cpu_usage"
}
}
}
}
}
}
}
}
Output:
{
"aggregations": {
"by_server": {
"buckets": [
{
"key": "server1",
"doc_count": 3,
"hourly_cpu_usage": {
"buckets": [
{
"key_as_string": "2023-05-01T01:00:00.000Z",
"key": 1682902800000,
"doc_count": 1,
"average_cpu_usage": {
"value": 30.5
}
},
{
"key_as_string": "2023-05-01T03:00:00.000Z",
"key": 1682910000000,
"doc_count": 1,
"average_cpu_usage": {
"value": 50.1
}
},
{
"key_as_string": "2023-05-01T05:00:00.000Z",
"key": 1682917200000,
"doc_count": 1,
"average_cpu_usage": {
"value": 60.2
}
}
]
}
},
{
"key": "server2",
"doc_count": 2,
"hourly_cpu_usage": {
"buckets": [
{
"key_as_string": "2023-05-01T02:00:00.000Z",
"key": 168290640000
Performing Time Series Analysis with Date Aggregation in Elasticsearch
Time series analysis is a crucial technique for analyzing data collected over time, such as server logs, financial data, and IoT sensor data. Elasticsearch, with its powerful aggregation capabilities, is well-suited for performing such analyses. This article will explore how to perform time series analysis using date aggregation in Elasticsearch, with detailed examples and outputs to illustrate the concepts.
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