Uses of Prometheus metrics
Prometheus metrics can be used in several ways:
## Monitoring
Prometheus scrapes metrics from targets and stores them in a time series database. This allows you to:
- Visualize metrics over time using dashboards like Grafana
- Set alerts based on metric thresholds
- Troubleshoot issues by analyzing historical metric data
Prometheus makes it easy to define the metrics you want to monitor by exposing HTTP endpoints. It then periodically scrapes these endpoints to collect the data.
## Instrumentation
Prometheus client libraries allow you to instrument your applications to expose metrics. You can expose:
- Counter metrics to track things like request counts and error rates
- Gauge metrics for measured values like memory usage and number of connections
- Histogram metrics to track distributions like response times and sizes
By instrumenting your applications, you can gain insights into their performance and behavior.
## Aggregation
Prometheus lets you aggregate metrics using its PromQL query language. You can:
- Calculate rates, averages, minimums, and maximums
- Group metrics by labels
- Join metrics from different scrape jobs
This allows you to derive higher-level metrics from your raw instrumentation data.
## Alerting
By defining alerting rules in Prometheus, you can trigger alerts when certain conditions are met. For example, you can alert:
- When error rates exceed a threshold
- If the number of available servers falls below a minimum
- When response times increase significantly
Prometheus can send these alerts to tools like PagerDuty, Slack, or email to notify operators.
In summary, Prometheus metrics allow you to:
- Gain insights into your systems by instrumenting and monitoring key metrics
- Detect issues early through the use of alerts
- Troubleshoot performance problems by analyzing historical metric data
# Types of Prometheus Metrics
Prometheus has four main types of metrics:
## Counters
Counters are used for cumulative metrics that only increase over time. They are useful for counting events like requests, errors, etc. An example counter metric is:
```
http_requests_total{api="add_product"} 4633433
```
This means the `add_product` API has been called 4,633,433 times.
Counters are used with functions like `rate()` to calculate the per-second change and `increase()` to calculate the total change over a time period.
## Gauges
Gauges represent a single numerical value that can go up and down. They are used for measured values like temperatures, memory usage, number of connections, etc.
The actual gauge value is meaningful on its own. Functions like `max_over_time()`, `min_over_time()` and `avg_over_time()` are useful for gauges.
An example gauge metric:
```
node_memory_used_bytes{hostname="host1.domain.com"} 943348382
```
This indicates the host is using around 900MB of memory.
## Histograms
Histograms divide a range of measurements into buckets and count how many measurements fall into each bucket. They are useful for measuring distributions like request durations and response sizes.
A histogram metric includes:
- A `_count` counter with the total number of measurements
- A `_sum` counter with the sum of all measurements
- `_bucket` counters for each bucket
For example:
```
http_request_duration_seconds_bucket{le="0.05"} 1672
```
This bucket counts measurements below 0.05 seconds.
The `histogram_quantile()` function can be used to calculate percentiles from histograms.
Histograms allow aggregation across multiple time series.
## Summaries
Summaries are similar to histograms but calculate quantiles instead of buckets. They provide more accurate quantiles but are more expensive to calculate.
A summary metric includes:
- A `_count` counter
- A `_sum` counter
- Quantile gauges
For example:
```
http_request_duration_seconds{quantile="0.99"} 2.829188272
```
This quantile represents the 99th percentile.
However, summaries cannot be aggregated across time series, making them less useful in distributed systems.
# Prometheus Metrics Use Cases
Prometheus metrics can be used for a variety of real-life use cases, including:
- Monitoring application performance
Prometheus metrics can be used to monitor key performance indicators of applications, such as request latency, error rates, throughput, and resource utilization. This allows you to identify bottlenecks, anomalies, and potential issues.
- Tracking resource usage
Prometheus can collect metrics related to resource usage, such as CPU, memory, disk, and network. This helps optimize resource allocation and avoid capacity issues.
- Alerting on metric thresholds
Prometheus allows you to define alerting rules based on metric thresholds. This enables you to be notified when certain conditions are met, such as CPU usage exceeding 80% or error rates going above 5%.
- Visualizing metrics over time
The Prometheus UI and integrations like Grafana allow you to visualize metrics as graphs over time. This helps identify trends, anomalies, and seasonal patterns in your data.
- Monitoring distributed systems
Prometheus is well-suited for monitoring distributed systems due to its dimensional data model and ability to aggregate metrics from multiple sources. This allows you to monitor microservices architectures, containerized applications, and cloud environments.
- Custom metric collection
Prometheus provides a flexible framework for collecting custom metrics using client libraries, exporters, and integrations. This allows you to track application-specific metrics and gain insights into system behavior.
Here is an example of instrumenting a Go application to expose a counter metric using the Prometheus client library:
```go
opsProcessed.Inc()
```
This metric can then be visualized, queried, and alerted on within the Prometheus ecosystem.
That's a wrap.......