Date Histogram Aggregation
The date histogram aggregation groups data into buckets based on a specified interval (e.g., hourly, daily). This is useful for visualizing trends over time.
Example: Hourly Aggregation of CPU Usage
POST /server_metrics/_search
{
"size": 0,
"aggs": {
"hourly_cpu_usage": {
"date_histogram": {
"field": "timestamp",
"calendar_interval": "hour"
},
"aggs": {
"average_cpu_usage": {
"avg": {
"field": "cpu_usage"
}
}
}
}
}
}
Output:
{
"aggregations": {
"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": "2023-05-01T02:00:00.000Z",
"key": 1682906400000,
"doc_count": 1,
"average_cpu_usage": {
"value": 45.3
}
},
{
"key_as_string": "2023-05-01T03:00:00.000Z",
"key": 1682910000000,
"doc_count": 1,
"average_cpu_usage": {
"value": 50.1
}
},
{
"key_as_string": "2023-05-01T04:00:00.000Z",
"key": 1682913600000,
"doc_count": 1,
"average_cpu_usage": {
"value": 75.0
}
},
{
"key_as_string": "2023-05-01T05:00:00.000Z",
"key": 1682917200000,
"doc_count": 1,
"average_cpu_usage": {
"value": 60.2
}
}
]
}
}
}
In this example, the CPU usage is aggregated hourly, and the average CPU usage for each hour is calculated.
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.