Date Range Aggregation
The date range aggregation groups data into buckets based on specified date ranges. This is useful for comparing data across different time periods.
Example: Comparing CPU Usage in Different Time Ranges
POST /server_metrics/_search
{
"size": 0,
"aggs": {
"cpu_usage_ranges": {
"date_range": {
"field": "timestamp",
"ranges": [
{ "from": "2023-05-01T01:00:00Z", "to": "2023-05-01T03:00:00Z" },
{ "from": "2023-05-01T03:00:01Z", "to": "2023-05-01T05:00:00Z" }
]
},
"aggs": {
"average_cpu_usage": {
"avg": {
"field": "cpu_usage"
}
}
}
}
}
}
Output:
{
"aggregations": {
"cpu_usage_ranges": {
"buckets": [
{
"key": "2023-05-01T01:00:00.000Z-2023-05-01T03:00:00.000Z",
"from": 1682902800000,
"to": 1682910000000,
"doc_count": 2,
"average_cpu_usage": {
"value": 37.9
}
},
{
"key": "2023-05-01T03:00:01.000Z-2023-05-01T05:00:00.000Z",
"from": 1682910001000,
"to": 1682917200000,
"doc_count": 2,
"average_cpu_usage": {
"value": 67.6
}
}
]
}
}
}
This example compares CPU usage across two different time ranges, with the average CPU usage calculated for each range.
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.