Prometheus is one of the most popular monitoring and metrics collection tools available in the market. It has become a go-to solution for the DevOps community due to its ability to gather metrics from almost every system and service. Prometheus has its own querying language called PromQL, which allows users to query metrics and retrieve data in a highly optimized way. In this article, we will explore how to get all metrics with labels using Prometheus and provide some code examples.
What Are Labels in Prometheus?
Labels are key-value pairs that are associated with metrics in Prometheus. They can be thought of as metadata that adds more information about the metric. Labels allow users to easily track and filter metrics based on various parameters like server name, data center location, etc.
For example, let's say you want to monitor CPU usage across different servers. You can have a CPU metric for each server with a specific label like 'server_name'. So, for example, you could have CPU usage metrics like 'cpu_usage{server_name="server_01"}' and 'cpu_usage{server_name="server_02"}'. This way, you can easily group, filter and query metrics based on server_name.
How to Retrieve All Metrics with Labels
To retrieve all metrics with labels in Prometheus, we can use the PromQL query language. The following PromQL query retrieves all metrics that have at least one label:
{__name__, job, instance, <other label names>}
In this query, __name__, job, instance, and <other label names> are the label names of the desired metrics. The __name__ label is always present in Prometheus and contains the name of the metric.
For example, to retrieve all CPU usage metrics with their corresponding server names, we can use the following PromQL query:
cpu_usage{server_name}
This query retrieves all CPU usage metrics that have the server_name label. The result will contain all the CPU usage metrics along with their respective server names.
Code Examples
The following code examples demonstrate how to retrieve all metrics with labels using the Prometheus client libraries in various programming languages.
- Go
In Go, we can use the Prometheus Go client library to retrieve metrics. The following code demonstrates how to retrieve all metrics with labels using the Go client:
package main
import (
"fmt"
"net/http"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promhttp"
)
func main() {
// Register a new Counter metric with the server_name label
cpuUsage := prometheus.NewCounterVec(prometheus.CounterOpts{
Name: "cpu_usage",
Help: "CPU usage in percentage",
}, []string{"server_name"})
prometheus.MustRegister(cpuUsage)
// Start the HTTP server
http.Handle("/metrics", promhttp.Handler())
http.ListenAndServe(":8080", nil)
// Query all metrics with labels
metricFamilies, err := prometheus.DefaultGatherer.Gather()
if err != nil {
panic(fmt.Errorf("failed to gather metrics: %s", err))
}
for _, metricFamily := range metricFamilies {
for _, metric := range metricFamily.GetMetric() {
if len(metric.GetLabel()) > 0 {
fmt.Printf("Name: %s Labels: %v
", *metricFamily.Name, metric.GetLabel())
}
}
}
}
- Python
In Python, we can use the Prometheus Python client library to retrieve metrics. The following code demonstrates how to retrieve all metrics with labels using the Python client:
from prometheus_client import CollectorRegistry, Gauge, CONTENT_TYPE_LATEST, generate_latest
# Register a new Gauge metric with the server_name label
cpu_usage = Gauge("cpu_usage", "CPU usage in percentage", ["server_name"])
# Query all metrics with labels
registry = CollectorRegistry()
registry.register(cpu_usage)
metrics = generate_latest(registry)
for line in metrics.decode('utf-8').split('
'):
if len(line) > 0 and line[0] != '#':
parts = line.split('{')
metric_name = parts[0]
labels_parts = parts[1][:-2]
labels = dict([x.split('=') for x in labels_parts.split(',')])
if len(labels) > 0:
print("Name: %s Labels: %s" % (metric_name, labels))
Conclusion
In conclusion, retrieving all metrics with labels in Prometheus is an essential task for any DevOps engineer. Labels allow users to easily filter and query metrics based on specific parameters. With the examples provided in this article, developers can start retrieving all metrics with labels in their own applications using the Prometheus client libraries.
here's some additional information about Prometheus and its use of labels:
Prometheus is a highly powerful and configurable monitoring system that allows users to query metrics and retrieve data in real-time. It uses a flexible data model to represent data points, which allows users to filter and group data in multiple ways. The data model consists of metric names, labels, and values.
Labels are the key metadata associated with the metrics in Prometheus. They add information about the metric's context, such as the environment, instance, version, and other application-specific data. They make it easy to filter out metrics based on specific criteria and analyze them more efficiently.
Prometheus includes label operators that allow users to manipulate labels in queries. These operators include:
=: used to match an exact label value!=: used to exclude a label value=~: used to match a label value using regular expressions!~: used to exclude a label value using regular expressionsor: used to combine multiple label conditions with "or"and: used to combine multiple label conditions with "and"
Users can use these label operators in PromQL queries to filter, group, and calculate metrics based on specific criteria. For example, to group CPU usage metrics by server name, users can use the by clause in PromQL:
sum by (server_name) (cpu_usage)
This query returns the sum of CPU usage metrics grouped by their respective server names.
Prometheus also provides multiple client libraries that make it easy to integrate it with various programming languages and systems. These client libraries expose metrics that can be scraped by Prometheus server and queried using PromQL.
Overall, Prometheus and its use of labels make it a powerful tool for monitoring and analyzing system and application metrics. It allows DevOps engineers to quickly identify performance issues, troubleshoot problems, and make data-driven decisions.
Popular questions
- What are labels in Prometheus, and why are they important?
Labels in Prometheus are key-value pairs that are associated with metrics. They add additional contextual information about the metric, such as the environment, instance, version, etc. Labels are important because they make it easy to filter, group, and query metrics based on specific criteria. They help users gain more insights into their systems and applications' performance.
- How can you retrieve all metrics with labels in Prometheus?
To retrieve all metrics with labels in Prometheus, you can use the PromQL query language. The query to retrieve all metrics with labels is:
{__name__, job, instance, <other label names>}
In this query, __name__, job, instance, and <other label names> are the label names of the desired metrics.
- What are some label operators in PromQL, and how are they used?
Label operators in PromQL are used to manipulate labels in queries. Some commonly used label operators are:
=: used to match an exact label value!=: used to exclude a label value=~: used to match a label value using regular expressions!~: used to exclude a label value using regular expressionsor: used to combine multiple label conditions with "or"and: used to combine multiple label conditions with "and"
- How can you retrieve all metrics with labels using the Prometheus Go client library?
To retrieve all metrics with labels using the Prometheus Go client library, you can register the desired metrics and then gather all metrics using the prometheus.DefaultGatherer.Gather() function. Here's an example code snippet that demonstrates this:
metricFamilies, err := prometheus.DefaultGatherer.Gather()
if err != nil {
panic(fmt.Errorf("failed to gather metrics: %s", err))
}
for _, metricFamily := range metricFamilies {
for _, metric := range metricFamily.GetMetric() {
if len(metric.GetLabel()) > 0 {
fmt.Printf("Name: %s Labels: %v
", *metricFamily.Name, metric.GetLabel())
}
}
}
- What are some advantages of using Prometheus for monitoring and analysis?
Some key advantages of using Prometheus for monitoring and analysis are:
- Flexibility: Prometheus can monitor a wide range of systems and services.
- Querying: PromQL offers a powerful querying language to retrieve and analyze metrics.
- Alerting: Prometheus has built-in alerting capabilities to notify users of performance issues.
- Integrations: Prometheus has client libraries available for multiple programming languages and systems.
- Open-source: Prometheus is an open-source project with an active community, making it easy to contribute and get help when needed.
Tag
PromQL
