Setting Up Elasticsearch for Time Series Analysis
Before diving into aggregations, let’s set up an index with sample time series data.
Creating an Index
We will create an index called server_metrics to store our time series data, which includes CPU usage metrics from different servers.
PUT /server_metrics
{
"mappings": {
"properties": {
"timestamp": { "type": "date" },
"cpu_usage": { "type": "float" },
"server_id": { "type": "keyword" }
}
}
}
Ingesting Sample Data
Next, we’ll ingest some sample data into the server_metrics index.
POST /server_metrics/_bulk
{ "index": {} }
{ "timestamp": "2023-05-01T01:00:00Z", "cpu_usage": 30.5, "server_id": "server1" }
{ "index": {} }
{ "timestamp": "2023-05-01T02:00:00Z", "cpu_usage": 45.3, "server_id": "server2" }
{ "index": {} }
{ "timestamp": "2023-05-01T03:00:00Z", "cpu_usage": 50.1, "server_id": "server1" }
{ "index": {} }
{ "timestamp": "2023-05-01T04:00:00Z", "cpu_usage": 75.0, "server_id": "server2" }
{ "index": {} }
{ "timestamp": "2023-05-01T05:00:00Z", "cpu_usage": 60.2, "server_id": "server1" }
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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