Use Cases and Examples
InfluxDB for Time Series Analysis
Example: Monitoring CPU Usage
Let’s consider an example where we monitor CPU usage on multiple servers.
Schema Setup:
- measurement: cpu_usage
- tags: server_id, location
- fields: usage
- timestamp: automatically assigned
Inserting Data:
- cpu_usage,server_id=server1,location=us-west usage=55.3 1672531200000
- cpu_usage,server_id=server2,location=us-east usage=47.6 1672534800000
- cpu_usage,server_id=server1,location=us-west usage=60.1 1672538400000
Querying Data:
Using InfluxQL:
SELECT MEAN(usage) FROM cpu_usage
WHERE time >= '2023-01-01T00:00:00Z'
AND time <= '2023-01-01T23:59:59Z'
GROUP BY time(1h), server_id
Output:
time server_id usage_mean
2023-01-01T00:00:00Z server1 57.7
2023-01-01T01:00:00Z server2 47.6
InfluxDB vs Elasticsearch for Time Series Analysis
Time series analysis is a crucial component in many fields, from monitoring server performance to tracking financial markets. Two of the most popular databases for handling time series data are InfluxDB and Elasticsearch. Both have their strengths and weaknesses and understanding these can help you choose the right tool for your specific needs.
In this article, we will explore InfluxDB and Elasticsearch in detail, focusing on their capabilities for time series analysis, with examples and outputs to illustrate their usage.