prometheus
Prometheus: Monitoring and Alerting for Cloud Native Apps
Overview:
Prometheus is an open-source monitoring and alerting system designed for cloud-native applications. It collects metrics and time-series data, allowing you to track application performance, health, and usage.
Key Concepts:
Metrics: Measurements of system behavior, such as CPU usage, request latency, or error counts.
Time-Series: Sequences of data points representing a metric over time.
Targets: Sources of metrics, such as application servers or databases.
Scrape: The process of periodically fetching metrics from targets.
Query Language (PromQL): A powerful language for analyzing metrics and creating alerts.
Alerting Rules: Define conditions to trigger alerts when specified metrics meet criteria.
Dashboard: Visual representations of metrics and alerts for quick monitoring.
Components:
Prometheus Server:
Collects metrics via scraping.
Stores time-series data in a highly optimized time-series database.
Exposes metrics for querying and visualization.
Exporters:
Agents that expose application metrics in a format compatible with Prometheus.
Collectors:
Components that gather metrics independently of Prometheus and can be integrated.
Alertmanager:
Manages and sends alerts based on Prometheus rules.
How it Works:
Scraping: Prometheus periodically scrapes metrics from targets using HTTP or other protocols.
Storage: Metrics are stored in a time-series database for efficient retrieval and querying.
Querying: Users can query metrics using PromQL to analyze data and generate visualizations.
Alerting: Rules are defined to trigger alerts when metrics meet specific conditions.
Notification: Alerts are routed through Alertmanager to email, chat, or other notification channels.
Code Examples:
Scraping a container:
Querying metrics:
Creating an alert rule:
Real-World Applications:
Application Performance Monitoring: Track metrics like CPU usage, memory consumption, and request latency to identify performance issues.
Cluster and Resource Management: Monitor Kubernetes clusters, virtual machines, and cloud resources to optimize workloads and detect utilization issues.
Event Monitoring: Collect and analyze events such as log messages, errors, and security events to detect anomalies and identify root causes.
Predictive Analytics: Use time-series data to build prediction models that forecast future metrics and identify potential problems.
Collaboration and Reporting: Share dashboards and alerts with team members to facilitate collaboration and provide visibility into system health.
Introduction to Prometheus
Prometheus is a free and open-source software used for monitoring and alerting in complex IT environments. It's designed to collect metrics from various sources, store them in a time series database, and provide a way to visualize and analyze the data.
Key Concepts in Prometheus
Metrics: Measurements that describe the state of a system, such as CPU usage, memory utilization, or network traffic.
Time Series: A collection of metrics over time. Each metric has a name, a set of labels (key-value pairs), and a sequence of values (timestamps with corresponding values).
PromQL (Prometheus Query Language): A DSL (Domain Specific Language) used to query, aggregate, and render metrics.
Grafana: A visualization tool that integrates with Prometheus to create interactive dashboards and graphs.
Components of Prometheus
Prometheus Server: Collects, stores, and serves metrics.
Exporters: Tools that collect metrics from specific sources, such as applications, operating systems, or cloud services.
Remote Storage: A way to store and manage long-term metrics data.
How Prometheus Works
Collect Metrics: Exporters gather metrics from various sources and send them to Prometheus.
Store Metrics: Prometheus stores metrics in a time series database.
Query and Analyze Metrics: Users can use PromQL to query and aggregate metrics for analysis.
Visualize Metrics: Prometheus can integrate with Grafana to create dashboards and graphs that visualize the metrics.
Alerting: Prometheus can trigger alerts based on predefined conditions on metric values.
Real-World Applications of Prometheus
Performance Monitoring: Tracking system metrics such as CPU usage, memory utilization, and response times to detect performance bottlenecks.
Availability Monitoring: Monitoring the availability of services and infrastructure to ensure high uptime.
Capacity Planning: Predicting future resource needs based on historical metric trends.
Troubleshooting: Identifying and diagnosing problems by analyzing metric patterns during incidents.
Cost Optimization: Monitoring cloud resource usage to optimize cost efficiency.
Example Code
Scrape Metrics from Linux System:
Create a PromQL Query to Calculate CPU Usage:
Example Dashboard in Grafana:
Prometheus
Prometheus is a monitoring system that collects metrics from your systems and applications and allows you to visualize and alert on them.
Installation
Topic 1: Installation on Linux
To install Prometheus on Linux, you can use the following steps:
Download the Prometheus binary:
Extract the binary:
Move the binary to a system directory:
Create a configuration file:
Add the following configuration:
Start Prometheus:
Topic 2: Installation on Windows
To install Prometheus on Windows, you can use the following steps:
Download the Prometheus binary:
Install the binary:
Create a configuration file:
Start Prometheus:
Topic 3: Configuration
The Prometheus configuration file is located at /etc/prometheus.yml on Linux and %PROGRAMDATA%\Prometheus\prometheus.yml on Windows. The following are some important configuration parameters:
scrape_interval: How often Prometheus scrapes metrics from targets.
scrape_configs: A list of scrape targets and configuration options.
Topic 4: Data Collection
Prometheus collects metrics from targets using exporters. Exporters are small programs that expose metrics over HTTP. Some common exporters include:
Node Exporter: Collects metrics from a Linux or Windows node.
mysqld_exporter: Collects metrics from MySQL.
redis_exporter: Collects metrics from Redis.
Topic 5: Visualization
You can visualize Prometheus metrics using the Prometheus web interface. The web interface is located at http://localhost:9090 by default.
Topic 6: Alerting
Prometheus can be used to create alerts based on metric values. Alerts can be sent to various destinations, such as email, SMS, or PagerDuty.
Real World Applications
Monitoring server performance: Prometheus can be used to monitor the performance of servers, such as CPU usage, memory usage, and disk I/O.
Monitoring application performance: Prometheus can be used to monitor the performance of web applications, such as response time, number of requests, and error rate.
Monitoring infrastructure: Prometheus can be used to monitor the health of your network devices, such as routers, firewalls, and load balancers.
Capacity planning: Prometheus can be used to identify when your infrastructure is reaching its capacity limits, so you can plan for future growth.
Prometheus: Monitoring for the Cloud Native Era
What is Prometheus?
Prometheus is a monitoring system that allows you to collect, store, and visualize metrics from your applications and infrastructure. It's designed to be:
Cloud-native: Works seamlessly with containerized environments like Docker and Kubernetes.
Extensible: Can collect data from a wide range of sources using various exporters.
Scalable: Can handle large volumes of data and monitor thousands of targets.
Open source: Free to use and modify under the Apache 2.0 license.
Getting Started
1. Install Prometheus
Download Prometheus from the official website: https://prometheus.io/download/
Follow the installation instructions for your operating system.
2. Configure Prometheus
Create a configuration file named
prometheus.yml.Specify the scrape targets (the applications and infrastructure you want to monitor).
Set up dashboards and alerts to visualize and react to metrics.
Example Configuration:
3. Start Prometheus
Run the following command to start Prometheus:
4. Access Prometheus
Visit the Prometheus web interface at
http://localhost:9090to view dashboards and metrics.
Real-World Applications:
Prometheus is used in many real-world applications, including:
Monitoring cloud-native applications: Cloud providers like AWS and Azure use Prometheus to monitor their cloud services.
Troubleshooting production issues: Engineers can use Prometheus to diagnose problems and identify root causes.
Capacity planning: Prometheus can help identify areas where resources are underutilized or overutilized.
Compliance reporting: Prometheus can be used to generate reports for compliance requirements.
Advanced Topics
1. Exporters
Exporters are software that collects metrics from various sources and sends them to Prometheus.
Prometheus provides a range of exporters for popular technologies like Kubernetes, Docker, and MySQL.
2. Alerting
Prometheus can send alerts when certain conditions are met (e.g., CPU usage exceeds a threshold).
Alerts can be configured using Prometheus's alert manager component.
3. Remote Storage
Prometheus can store metrics in remote storage providers like Amazon S3 or Google Cloud Storage.
This allows for long-term data retention and historical analysis.
Complete Code Implementation
Example Application with Prometheus Exporter
Prometheus Scrape Configuration
Prometheus Dashboard with Alert Rule
Next Steps
Explore Prometheus's official documentation: https://prometheus.io/docs/
Join the Prometheus community forum: https://groups.google.com/g/prometheus-users
Contribute to the Prometheus project: https://github.com/prometheus/prometheus
Prometheus Configuration
What is Prometheus?
Prometheus is a monitoring system that collects and stores metrics (measurements) from various sources.
Configuration File Overview
The Prometheus configuration file, usually named prometheus.yml, is where you define the settings for your Prometheus instance.
Main Sections
The config file has several main sections:
global: Overall settings for Prometheus itself.
scrape_configs: How Prometheus collects metrics from targets (e.g., servers).
rule_files: External files containing rules for generating alerts.
Global Section
scrape_interval: How often Prometheus scrapes targets for metrics (e.g., 1m = 1 minute).
scrape_timeout: How long to wait for a target to respond before marking it as failed.
evaluation_interval: How often Prometheus evaluates alert rules (e.g., 5m = 5 minutes).
Real-World Application: Optimizing scraping frequency for your environment to minimize network overhead and maximize data collection.
Code Example:
scrape_configs Section
job_name: Name for this set of scraping targets.
scrape_interval: How often to scrape targets in this job.
static_configs: List of specific targets to scrape.
relabel_configs: Rules for modifying metric labels before storage.
Real-World Application: Customizing scraping frequency and relabeling metric labels to match your organization's naming conventions.
Code Example:
rule_files Section
groups: List of alert rule groups.
rules: List of alert rules.
alert: Configuration for sending alerts.
Real-World Application: Generating email or PagerDuty alerts based on specific metric conditions.
Code Example:
Complete Configuration File Example
Potential Applications
Monitoring server performance (e.g., CPU usage, memory usage).
Alerting for incidents (e.g., high latency, low disk space).
Identifying trends and patterns in metric data.
Troubleshooting infrastructure issues (e.g., slowdowns, outages).
Configuration Overview
Prometheus is a monitoring system that collects metrics from various sources, stores them, and allows you to analyze them. To configure Prometheus, you need to create a configuration file, which contains settings for various aspects of the system.
1. Basic Configuration
scrape_configs: Specifies the targets to scrape metrics from.static_configs: Specifies statically defined targets.rule_files: Specifies files containing alerting and recording rules.storage: Specifies the storage mechanism for metrics.
2. Scraping
job_name: Identifies the scrape job.scrape_interval: How often to scrape targets (default: 15 seconds).scrape_timeout: Timeout for a single scrape (default: 10 seconds).relabel_configs: Relabel scraped metrics for filtering and grouping.
Example:
3. Alerting
alerting_rules: Defines alert rules.receiver: Specifies how to send alerts.alert_relabel_configs: Relabel alerts before sending.
Example:
4. Recording
record_rules: Defines recording rules.ruleGroups: Group recording rules.
Example:
5. Storage
type: Specifies the storage type (e.g., local, remote).path: Where to store metrics if using local storage.
Example:
Real-World Applications
System Monitoring: Monitor metrics like CPU usage, memory usage, and network traffic.
Application Monitoring: Monitor metrics like request volume, response time, and errors.
Cloud Monitoring: Monitor metrics from cloud services like AWS, Azure, and Google Cloud.
Anomaly Detection: Detect unusual patterns in metrics to identify potential problems.
Performance Optimization: Optimize applications and systems based on metric analysis.
Prometheus Server Configuration
Prometheus is an open-source monitoring and alerting system. It collects metrics from targets, stores them in a time series database, and provides a powerful query language to analyze the data.
Prometheus has a number of configuration options that allow you to customize its behavior. These options are organized into the following sections:
Global configuration
The global configuration section contains options that apply to the entire Prometheus server. These options include:
scrape_interval: The interval at which Prometheus scrapes targets for metrics.scrape_timeout: The timeout for scraping targets.evaluation_interval: The interval at which Prometheus evaluates rules and alerts.storage.tsdb.min-block-duration: The minimum duration of a block in the time series database.storage.tsdb.max-block-duration: The maximum duration of a block in the time series database.
Rule files
Rule files contain rules that Prometheus uses to evaluate metrics and generate alerts. Rules are written in a declarative language that allows you to specify conditions and actions.
Here is an example rule file:
This rule will generate an alert if the average request latency for the my-service service is greater than 1000 milliseconds.
Alertmanager configuration
The Alertmanager configuration section contains options that control how Prometheus sends alerts to Alertmanager. Alertmanager is a separate component that handles the delivery of alerts to various destinations, such as email, SMS, or Slack.
Here is an example Alertmanager configuration:
This configuration will route all alerts to the my-receiver receiver. The receiver will send alerts to the email address my-email@example.com.
Remote write
The remote write configuration section contains options that allow Prometheus to send metrics to a remote storage system. This can be useful for long-term storage or for replicating metrics to another system.
Here is an example remote write configuration:
This configuration will send metrics to the remote storage system at the URL http://example.com/api/v1/push.
Real-world applications
Prometheus is used by a variety of organizations to monitor their systems and applications. Here are a few examples of real-world applications:
Monitoring website performance: Prometheus can be used to monitor the performance of a website by collecting metrics such as response time, request rate, and error rate. This information can be used to identify performance bottlenecks and improve the user experience.
Monitoring application health: Prometheus can be used to monitor the health of applications by collecting metrics such as CPU usage, memory usage, and garbage collection time. This information can be used to identify potential problems and prevent them from affecting users.
Monitoring infrastructure: Prometheus can be used to monitor the infrastructure on which applications are running, such as servers, networks, and storage systems. This information can be used to identify potential problems and ensure that applications are running reliably.
Prometheus Scraping Configuration
Prometheus is a monitoring system that collects and stores metrics from targets. Targets can be any system or application that exposes metrics in a format that Prometheus can understand.
Scraping Configuration File
The scraping configuration file tells Prometheus which targets to scrape and how to scrape them. The file is located at /etc/prometheus/prometheus.yml by default.
Target Definition
A target definition specifies a target to scrape and the scrape interval. The interval is the amount of time between scrapes.
In this example, the my_job job will scrape the target example.com:9100 every 15 seconds. The instance label will be set to example.com. The namespace label will be set to the value of the __meta_kubernetes_namespace metadata label.
Metrics Relabeling
Metrics relabeling allows you to transform metrics before they are stored in Prometheus. You can use relabeling to:
Add labels to metrics
Remove labels from metrics
Modify the values of labels
In this example, the my_job job will scrape the target example.com:9100 every 15 seconds. The metric_name label will be set to the value of the __name__ metric label, prefixed with my_.
Real-World Example
Prometheus can be used to monitor a variety of systems and applications, including:
Servers
Databases
Applications
Containers
Kubernetes
Prometheus can be used to monitor the health and performance of these systems and applications. The data collected by Prometheus can be used to identify problems, troubleshoot issues, and improve performance.
Potential Applications
Prometheus can be used for a variety of tasks, including:
Monitoring the performance of a website
Troubleshooting issues with a database
Identifying performance bottlenecks in an application
Tracking the usage of a container
Monitoring the health of a Kubernetes cluster
Prometheus is a powerful tool that can be used to improve the reliability and performance of your systems and applications.
Prometheus Alerting Configuration
Imagine you have a child who plays in the backyard. You want to know if they're doing well and safe, so you set up a camera to keep an eye on them. Prometheus is like that camera, monitoring your systems and sounding an alarm if anything goes wrong. Alerting is how Prometheus tells you when something needs attention.
Alert Rules
Alert rules are like rules for the camera. They define when to sound the alarm and what to say. Here's a simple example:
alert: The name of the alert rule.
expr: The condition that triggers the alert. In this case, if the
temperaturemetric is greater than 90 degrees Celsius.for: How long the condition must be true before the alert is triggered.
labels: Additional labels to add to the alert.
annotations: Additional information to include in the alert message.
Notification Channels
Notification channels are how Prometheus sends you alerts. You can set up multiple channels to receive alerts in different ways, such as email, Slack, or PagerDuty. Here's an example of an email notification:
name: The name of the notification channel.
email_configs: Configuration for sending email notifications.
to: The email address to send alerts to.
send_resolved: Whether to also send notifications when the alert is resolved.
Alert Manager
Alert Manager is a separate service that manages alerts from Prometheus. It provides additional features like suppression, routing, and silencing. You can configure Alert Manager to handle alerts in a variety of ways, such as:
Grouping: Group similar alerts together to reduce the number of notifications.
De-duplication: Remove duplicate alerts caused by multiple data points.
Silencing: Temporarily suppress alerts that are not actionable at the moment.
Real-World Applications
Prometheus alerting is used in many real-world applications, including:
Monitoring website traffic: Alert on high traffic volumes or slow response times.
Tracking server performance: Alert on high CPU or memory usage, or unresponsive services.
Monitoring database activity: Alert on slow queries or excessive connections.
Verifying application uptime: Alert on failures or unavailability of critical services.
Detecting security breaches: Alert on suspicious network traffic or unauthorized access attempts.
Introduction to Prometheus Remote Storage Configuration
Prometheus is a monitoring system that collects metrics from various sources. These metrics can be stored locally or in a remote storage system. Remote storage allows for storing and retrieving metrics over extended periods of time, enabling long-term analysis and visualization.
Configuring Remote Storage
To configure remote storage in Prometheus, you need to specify the following options in the prometheus.yml configuration file:
Push Gateway
If you want to push metrics to the remote storage instead of scraping them, you can use the Prometheus Push Gateway:
Time Series Database (TSDB)
Prometheus supports storing metrics in a TSDB such as InfluxDB or OpenTSDB:
Real-World Applications
Long-Term Data Storage: Remote storage allows for storing metrics over long periods, enabling historical analysis and trend monitoring.
Scalability: By offloading storage to remote systems, Prometheus can handle larger metric volumes.
Data Replication: Remote storage provides data redundancy and failover capabilities, ensuring data availability even in case of server failures.
Centralized Metrics Storage: A remote storage system can aggregate metrics from multiple Prometheus instances, providing a single source of truth for monitoring data.
Code Examples
Storing Metrics in InfluxDB
Push Gateway Configuration
Reading Metrics from OpenTSDB
Prometheus Remote Write Configuration
Imagine you have a bunch of boxes that hold all your valuable data (metrics), like how many times a website was visited or how many errors occurred. Prometheus collects these metrics from your boxes and stores them in its own box. But sometimes, you may want to send these metrics to other boxes (remote storage) for backup or analysis. That's where Prometheus Remote Write Configuration comes in.
Enabling Remote Write
To tell Prometheus to write metrics to a remote storage, you need to add a remote_write section to your Prometheus configuration file (prometheus.yml).
url: The address of the remote endpoint you want to send metrics to.
timeout: How long Prometheus should wait before giving up on sending metrics.
tls_config: Optional TLS configuration for secure communication.
Writing Metrics to a Storage
Once you've configured Remote Write, Prometheus will start sending metrics to your specified endpoint. Here's an example of a storage endpoint:
Real-World Applications
Backup and redundancy: Remote Write allows you to store metrics in multiple locations, ensuring data safety.
Data analysis: By sending metrics to a specialized storage, you can perform in-depth analysis and derive insights.
Long-term storage: Remote Write can store metrics indefinitely, making historical data available for analysis.
Metrics monitoring: You can use Remote Write to send metrics to a monitoring system for alerting and visualization.
Remote Read Configuration
Prometheus can scrape metrics from remote targets via the remote read API. This can be used to collect metrics from targets that are not directly accessible by the Prometheus server, such as targets behind a firewall or in a different network.
Configuration
To enable remote read, you need to configure the remote_read section in your Prometheus configuration file. The following options are available:
url: The URL of the remote read endpoint.authorization: The authorization credentials to use when making the remote read request.read_recent: The maximum time to query for recent samples.query_interval: The interval at which to query the remote read endpoint.timeout: The timeout for the remote read request.scrape_interval: The interval at which to scrape the remote read endpoint.scrape_timeout: The timeout for the scrape request.
Here is an example remote read configuration:
Applications
Remote read can be used in a variety of applications, such as:
Monitoring remote systems: Prometheus can scrape metrics from remote systems, such as servers, virtual machines, and containers. This can be used to monitor the health and performance of these systems.
Cross-datacenter monitoring: Prometheus can scrape metrics from targets in different datacenters. This can be used to monitor the performance of applications and services that are deployed across multiple datacenters.
Multi-cluster monitoring: Prometheus can scrape metrics from targets in different Kubernetes clusters. This can be used to monitor the performance of applications and services that are deployed across multiple clusters.
Code Examples
Here is a code example that shows how to use the remote read API to scrape metrics from a remote target:
This code example shows how to use the remote read API to scrape metrics from a remote target that is discovered using the Kubernetes discovery provider.
This code example shows how to use the remote read API to scrape metrics from a remote target that is discovered using the Azure discovery provider.
Prometheus: Querying
Prometheus is a monitoring system that collects metrics from targets and stores them in time series. These time series can be queried using PromQL, a powerful query language.
Time Series
A time series is a sequence of data points, each with a timestamp and a value. Prometheus stores metrics in time series, which allows you to track changes in metrics over time.
PromQL
PromQL is a query language that allows you to retrieve specific time series from Prometheus. PromQL queries are written in a text-based format and are case-insensitive.
Basic PromQL Syntax
metric_name: The name of the metric you want to query.{...}: Braces enclose a set of key-value pairs that filter the results.[minmax avg sum ... ...]: Braces enclose a list of aggregation functions.offset 5m: Offset the results by a specified amount of time.group_left 10: Group the results by the first 10 lanes.
Examples
Get the average value of the
http_requests_totalmetric:Get the average value of the
http_requests_totalmetric for the last 5 minutes:Get the average value of the
http_requests_totalmetric for the last 5 minutes, grouped by themethodlabel:Get the average value of the
http_requests_totalmetric for the last 5 minutes, offset by 10 minutes:Get the average value of the
http_requests_totalmetric for the last 5 minutes, grouped by themethodlabel and offset by 10 minutes:
Applications
PromQL can be used to monitor the performance of your applications, identify trends, and troubleshoot issues. Here are a few real-world applications:
Track the number of requests per second to your web server.
Monitor the response time of your database.
Identify the most popular pages on your website.
Troubleshoot performance issues by comparing metrics over time.
Prometheus Querying
What is Prometheus?
Prometheus is a monitoring system that collects and stores metrics from your applications and systems. Metrics are measurements of different aspects of your systems, such as the number of requests processed or the average response time.
What is PromQL?
PromQL (Prometheus Query Language) is a query language specifically designed for querying Prometheus data. It allows you to extract and analyze metrics from your systems.
Simplified PromQL Topics
Time Range: Specify the time period you want to query. Example:
[5m],[1d].Metric Name: The metric you want to query. Example:
http_requests_total.Labels: Filters that restrict the results based on label values. Example:
{instance="server-1"}.Operators: Arithmetic and logical operators for combining metrics. Example:
+,>.Functions: Functions that transform metrics, such as
rate()oravg().Aggregations: Functions that summarize metrics, such as
sum()ormin().
Code Examples
Get the total number of HTTP requests in the past 5 minutes:
Get the average response time for HTTP requests from server-1 in the past hour:
Calculate the rate of successful HTTP requests in the past 10 minutes:
Real-World Applications
Monitor system performance: Track metrics like CPU usage, memory consumption, and network throughput to detect performance issues.
Alert on critical events: Set up alerts based on metric thresholds to notify you when important events occur, such as high server load or errors.
Identify trends and patterns: Analyze metrics over time to identify trends and patterns, which can help you optimize your systems and make data-driven decisions.
Prometheus Querying: Basic Queries
Simplify and Explain Each Topic:
Metrics: Prometheus collects data in the form of metrics. A metric is a name, value, and timestamp. Think of it like a thermometer measuring temperature (name), the value being the temperature reading, and the timestamp indicating when the reading was taken.
Query Language (PromQL): PromQL is the language used to query Prometheus and retrieve data. It's similar to SQL for databases, but specifically designed for querying time-series data (data that changes over time).
Simplified Code Examples:
Getting a Metric's Value:
This query returns the value of the "up" metric for the instance named "server1."
Getting Multiple Metrics' Values:
This query returns the values of the "up" metric for both "web" and "database" jobs for the instance named "server1."
Filtering by Time Range:
This query returns the values of the "up" metric for the past hour for the instance named "server1."
Function Operators:
This query returns the sum of all "up" metric values for the instance named "server1."
Real-World Applications:
Monitoring server uptime and health: Querying the "up" metric can help identify server outages or performance issues.
Analyzing performance metrics: Querying metrics like CPU and memory usage can help pinpoint performance bottlenecks.
Detecting anomalies: Using query operators like "rate" and "derivative" can help detect sudden changes or spikes in metric values, indicating potential issues.
Capacity planning: Querying metrics over time ranges can help forecast future resource usage and plan for capacity expansion.
Complete Code Implementations:
Monitoring Server Uptime:
This configuration scrapes the metrics from "server1" every 30 seconds.
Querying Server Uptime:
This query returns the value of the "up" metric for the instance named "server1."
Potential Applications:
Setting up alerts to notify administrators of server outages.
Creating dashboards to visualize server uptime and performance over time.
Identifying and resolving performance issues through data analysis.
Prometheus Querying
Prometheus is a monitoring system that collects and stores metrics over time. You can query these metrics using the Prometheus Query Language (PromQL).
Basic Queries
To select a metric, use the metric name wrapped in curly braces:
To filter results, use the =, !=, <, >, <=, and >= operators:
Grouping and Aggregation
To group results by a specific label, use the by keyword:
To aggregate results, use the sum, avg, min, max, or stddev functions:
Range Queries
To specify a range of time, use the [start:end] syntax:
Binary Operators
To perform arithmetic operations on metrics, use the +, -, *, and / operators:
Comparison Operators
To compare metrics, use the ==, !=, <, >, <=, and >= operators:
Logical Operators
To combine queries using logical operators, use the and, or, and not operators:
Example Code Implementations
Get the average CPU usage over the last hour:
Group CPU usage by host and get the maximum value:
Compare CPU usage between two hosts:
Potential Applications in Real World
Monitoring system health and performance
Identifying performance bottlenecks
Detecting anomalies and errors
Configuring alerts and notifications
Analyzing data for trends and insights
Prometheus Querying Functions
Prometheus provides a range of functions to manipulate and analyze its data. Here's a simplified explanation and usage guide for some key functions:
Aggregate Functions
- sum(series-name): Sums the values of a given metric over time.
Example:
Explanation: This query calculates the total number of requests received.
- avg(series-name): Calculates the average of a metric's values over time.
Example:
Explanation: This query calculates the average response time for all requests.
- max(series-name): Returns the maximum value of a metric over time.
Example:
Explanation: This query finds the highest memory usage recorded at any point.
Time Range Functions
- range(series-name, duration): Computes the range (difference between maximum and minimum values) of a metric over a given duration.
Example:
Explanation: This query calculates the number of requests received in the last 5 minutes.
- rate(series-name, duration): Calculates the rate of change of a metric over a given duration.
Example:
Explanation: This query calculates the number of requests per minute in the last 5 minutes.
Mathematical Functions
- abs(series-name): Returns the absolute value (non-negative) of a metric.
Example:
Explanation: This query calculates the memory usage in absolute terms, regardless of whether it's positive or negative.
- ceil(series-name): Rounds a metric up to the nearest integer.
Example:
Explanation: This query rounds the average latency to the next whole second.
Logical Functions
- and(bool-series1, bool-series2): Returns a boolean series indicating if both input series are true.
Example:
Explanation: This query checks if both the "instance_up" and "healthy" metrics are true.
- or(bool-series1, bool-series2): Returns a boolean series indicating if either of the input series is true.
Example:
Explanation: This query checks if either the "instance_up" or "healthy" metrics are true.
Potential Applications
These functions have various real-world applications, including:
Monitoring system health: Summing and averaging metrics to track overall performance.
Trend analysis: Identifying patterns and anomalies by comparing metrics over time.
Capacity planning: Estimating resource needs based on maximum and average usage.
Incident response: Detecting and responding to issues by using logical functions to combine metrics and alerts.
Prometheus Querying Operators
Imagine Prometheus as a big box of data. To find specific information in this box, you can use operators, which are like tools that help you search and filter the data.
Mathematical Operators
These operators help you do math on your data.
+: Adds numbers. Example:rate(container_cpu_user_seconds_total[1m]) + rate(container_cpu_system_seconds_total[1m])calculates the total CPU usage.-: Subtracts numbers. Example:container_cpu_user_seconds_total - container_cpu_system_seconds_totalfinds the difference between user and system CPU usage.*: Multiplies numbers. Example:rate(container_cpu_user_seconds_total[1m]) * 100converts CPU usage from seconds to milliseconds./: Divides numbers. Example:rate(container_cpu_user_seconds_total[1m]) / rate(container_cpu_total_seconds_total[1m])calculates the percentage of CPU usage.
Logical Operators
These operators combine queries to create more complex searches.
and: Finds data that matches all the conditions. Example:container_cpu_user_seconds_total > 0 and container_cpu_system_seconds_total > 0finds containers with both user and system CPU usage above zero.or: Finds data that matches any of the conditions. Example:container_cpu_user_seconds_total > 0 or container_cpu_system_seconds_total > 0finds containers with either user or system CPU usage above zero.unless: Inverts the result of a query. Example:unless(container_cpu_user_seconds_total > 0)finds containers with user CPU usage equal to zero.
Comparison Operators
These operators compare data to a specific value.
==: Equals. Example:container_cpu_user_seconds_total == 0finds containers with no user CPU usage.!=: Not equals. Example:container_cpu_user_seconds_total != 0finds containers with any user CPU usage.<: Less than. Example:container_cpu_user_seconds_total < 10finds containers with user CPU usage below 10 seconds.<=: Less than or equal to. Example:container_cpu_user_seconds_total <= 10finds containers with user CPU usage less than or equal to 10 seconds.>: Greater than. Example:container_cpu_user_seconds_total > 10finds containers with user CPU usage above 10 seconds.>=: Greater than or equal to. Example:container_cpu_user_seconds_total >= 10finds containers with user CPU usage greater than or equal to 10 seconds.
Real World Applications
Monitoring CPU utilization:
rate(container_cpu_user_seconds_total[1m]) + rate(container_cpu_system_seconds_total[1m]) > 80identifies containers that are heavily utilizing the CPU.Detecting memory leaks:
(container_memory_usage_bytes - container_memory_cache - container_memory_swap) / container_memory_limit > 0.9detects containers that are dangerously close to exceeding their memory limits.Finding unusual network activity:
rate(container_network_receive_bytes_total[1m]) > 10000000flags containers that are sending or receiving an unusually high amount of network traffic.Identifying bottlenecks in applications:
http_request_duration_seconds{quantile="0.9"} > 0.1highlights endpoints that are responding slowly to 90% of requests.
Prometheus Querying
Prometheus is an open-source monitoring and alerting system that collects metrics from hosts, services, and applications. Once collected, these metrics can be queried using the PromQL language.
PromQL Syntax:
PromQL queries follow a simple syntax:
Metric Selector:
The metric selector specifies the metrics to be queried. It consists of a metric name and a set of key-value pairs to filter specific time series.
Aggregation:
Aggregation functions allow you to manipulate or summarize the selected metrics. Common aggregations include:
sum()min()max()avg()
Range:
The range specifies the time period over which metrics should be evaluated. It can be specified using relative time (e.g., 5m) or absolute time (e.g., [2020-01-01:10:00:00Z, 2020-01-01:11:00:00Z]).
Example Query:
This query calculates the sum of the HTTP request rate over the last 5 minutes.
Prometheus Recording Rules
Recording rules allow you to create new time series based on existing ones. This can be useful for transforming, filtering, or aggregating metrics.
Recording Rule Syntax:
New Metric:
The new metric specifies the name of the new time series to be created.
New Metric Expression:
The new metric expression defines how the new time series is calculated. It can include math operations, aggregations, and metric selectors.
Example Recording Rule:
This recording rule calculates the 90th percentile of the request duration over the last 5 minutes and stores it in a new time series called request_duration_avg.
Real-World Applications
Monitoring System Performance:
Query Prometheus metrics to monitor system metrics like CPU usage, memory usage, and network traffic.
Set up recording rules to calculate averages and percentiles of these metrics.
Tracking User Engagement:
Query metrics related to user behavior, such as page views, session duration, and conversion rates.
Use recording rules to create metrics that track key engagement metrics, such as daily active users.
Identifying Performance Bottlenecks:
Query metrics related to application response times and error rates.
Use recording rules to identify services or endpoints that are experiencing performance issues.
Predictive Analytics:
Query historical metrics to identify trends and patterns.
Use recording rules to create time series that predict future values based on these patterns.
Prometheus Alerting: Monitoring with Notifications
Simplified Explanation:
Imagine you have a car that you want to keep running smoothly. Prometheus is like a mechanic that checks the car's health. If anything goes wrong, like low tire pressure or a broken engine, Prometheus will send you an alert so you can fix it before it becomes a bigger problem.
Alert Definitions
Explanation: Alert definitions specify the conditions that trigger an alert. They are like rules that say, "If this happens, send an alert."
Example:
rule: Name of the rule.expr: Expression that checks the metric (e.g., CPU usage).labels: Additional information about the alert, like its severity.annotations: Human-readable descriptions of the alert.
Notification Channels
Explanation: Notification channels specify how alerts are sent. They can be email, Slack, PagerDuty, etc.
Example:
Configure an email notification channel:
to: Email address to send alerts to.from: Email address the alerts will come from.smart_host: SMTP server used to send emails.
Alert Groups
Explanation: Alert groups organize multiple alerts into a single notification. They allow you to group related alerts and send them together.
Example:
Create an alert group for all high-severity alerts:
name: Name of the alert group.rules: List of rules included in the group.interval: How often to send alerts from this group.
Alerting Rules
Explanation: Alerting rules combine alert definitions, notification channels, and alert groups to create complete alerting configurations. They specify when alerts are sent, who they are sent to, and how they are grouped.
Example:
Create an alerting rule that sends high-severity alerts to an email channel:
rule_files: Path to the YAML file that defines the alerting rules.
Real-World Applications
Potential Applications:
System Health Monitoring: Track the health of servers, databases, and other applications.
Performance Monitoring: Identify performance bottlenecks and areas for improvement.
Failure Detection: Receive alerts when components fail or experience errors.
Capacity Planning: Forecast future resource needs based on historical usage patterns.
Regulatory Compliance: Monitor systems to ensure compliance with industry regulations.
Alerting with Prometheus and Alertmanager
Imagine you have a garden with many plants. You want to monitor the health of your plants and send you alerts if any of them start to wilt or need attention.
Prometheus
Prometheus is a monitoring system that constantly collects metrics from your plants (e.g., temperature, water level). It's like a plant doctor that checks on your plants regularly.
Alertmanager
Alertmanager is a service that receives alerts from Prometheus. It's like a notification system that sends you alerts based on specific rules you define.
Alert Rules
Alert rules are configured in Prometheus to trigger alerts when specific conditions are met. For example, you can create a rule that alerts you if the temperature of a plant exceeds 30 degrees Celsius.
Receivers
Receivers are configured in Alertmanager to send alerts to specific channels. For example, you can configure a Slack receiver to send alerts to a specific Slack channel.
Real-World Applications
Monitoring Server Health: Monitor critical metrics like CPU usage, memory consumption, and disk space to identify potential issues.
IoT Device Monitoring: Track the status of connected devices, such as temperature sensors and motion detectors, to ensure they are functioning properly.
Website Monitoring: Monitor website availability, response time, and error rates to identify any performance problems.
Application Performance Monitoring: Identify performance bottlenecks and slowdowns in applications to improve user experience.
Financial Market Analysis: Track stock prices, economic indicators, and other financial data to detect trends and opportunities.
Prometheus Alerting Rules: Simplified and Explained
Introduction
Prometheus is a powerful monitoring system used to track metrics over time. It allows you to define alerts that notify you when specific conditions are met, such as high resource usage or application errors.
Creating Alerting Rules
Topic: Alert Configuration
Alerting rules define the conditions under which alerts are generated. They consist of three main parts:
1. Expression: Specifies the metric(s) and condition(s) to monitor (e.g., "CPU usage exceeds 80%").
2. Duration: Defines the time range over which the condition must be met (e.g., "for 5 minutes").
3. Labels: Optional tags to categorize the alert (e.g., "server_name" or "application").
Code Example:
Alerting Notifications
Topic: Receivers
Prometheus uses receivers to send alerts to specific destinations (e.g., email, Slack, PagerDuty).
Code Example:
Alerting Routing
Topic: Grouping and Silencing
Grouping combines related alerts into a single notification. Silencing suppresses alerts based on certain criteria (e.g., time of day).
Code Example:
Real-World Applications
Potential Applications:
Monitoring server uptime and performance
Detecting application errors and failures
Notifying on resource shortages or capacity issues
Alerting on security events or unauthorized access
Example Implementation:
Suppose you want to receive an alert whenever the CPU usage of a specific server exceeds 80% for more than 5 minutes.
This rule will generate an alert with the severity "critical" and an "instance" label identifying the affected server. The notification will include a summary and a detailed description of the alert condition.
Prometheus Alerting
Prometheus is a monitoring and alerting system that collects time series data from various sources. Alerts can be configured to trigger when certain conditions are met, such as when a metric exceeds a threshold or when there is a sudden change in the value of a metric.
Alerting Rule Basics
An alerting rule is a combination of:
Expression: A PromQL expression that evaluates to a boolean value.
For: How long the expression must evaluate to true before an alert is triggered.
Labels: Key-value pairs that provide additional information about the alert.
Alerting Rule Examples
Threshold Alert
Triggers an alert when a metric value exceeds a threshold:
Rate Change Alert
Triggers an alert when the rate of change of a metric exceeds a threshold:
Alert Notification
Prometheus can send alerts to various destinations, such as email, Slack, and PagerDuty.
To configure a notification channel:
To configure an alert notification:
Real-World Applications
Alerting is crucial for monitoring the health and performance of systems. Some real-world applications include:
Monitoring website uptime and performance
Detecting anomalies in user behavior
Identifying potential hardware failures
Notifying on security events
Enhancing operational efficiency by automating incident responses
Prometheus Alerting/Notification Templates
Introduction
Prometheus is a monitoring system that collects and analyzes metrics from various sources. When certain conditions are met, Prometheus can generate alerts to notify you of potential issues. Notifications can be sent to different platforms, such as email, Slack, or pager.
Alerting Basics
Alert Rule: Defines the conditions under which an alert is triggered, such as a metric exceeding a threshold or a service being unavailable.
Alert Group: Groups related alerts together for easier management and notification preferences.
Alert Receiver: Specifies the platform and destination where alerts are sent.
Notification Templates
Notification templates determine how alerts are formatted and sent. They define the content and layout of the alerts, as well as the sender and recipient information.
Creating Notification Templates
Edit the
prometheus.ymlconfiguration file.Add a new section named
notification_templates.Specify the template name and its properties, such as:
name: A unique name for the template.content: A text template using the Go text/template syntax.receiver: The name of the alert receiver to associate with this template.
Example Notification Template
Potential Applications
Email Notifications: Send alerts as emails to a specific recipient list.
Slack Notifications: Post alerts to a designated Slack channel for real-time updates.
Pager Notifications: Trigger alerts on pagers for urgent notifications that require immediate attention.
Custom Notifications: Create your own notification channels using custom alert receivers.
Real-World Implementation Example
Sending Email Notifications
This template formats alerts as emails with information about the firing or resolved alerts, rule name, annotations, and a link to the Prometheus UI for further investigation.
Sending Slack Notifications
This template creates Slack messages containing the alert title, text, and a link to the Prometheus UI.
Prometheus: Alerting and Silences
Alerting
Prometheus monitors your systems and generates alerts when specified conditions are met. An alert is a notification that something is wrong or requires attention.
How it Works:
You create a rule that defines the conditions for an alert, such as "If the CPU usage is above 80% for 5 minutes."
Prometheus continuously evaluates metrics and checks if any rules match the current metric values.
If a match is found, Prometheus triggers an alert and sends it to your configured notification channels, such as email or Slack.
Example Rule:
This rule creates an alert called "HighCPUUsage" that triggers when the average CPU usage over the last 5 minutes is greater than 80%. It remains active for 5 minutes.
Silences
Silences allow you to temporarily suppress alerts that you don't want to receive at the moment.
How it Works:
You create a silence that specifies a time period and a set of matching criteria for alerts.
When an alert is triggered, Prometheus checks if it matches any of the active silences.
If a match is found, the alert is suppressed and will not be sent to notification channels.
Example Silence:
This silence suppresses all alerts with the "HighCPUUsage" alertname that are triggered between midnight and 6am on March 8, 2023.
Real-World Applications
Alerting:
Detecting high disk usage to prevent data loss
Monitoring website availability to ensure user access
Notifying DevOps teams of application errors
Silences:
Suppressing alerts during scheduled maintenance periods
Ignoring alerts for non-critical events
Reducing alert fatigue by only receiving essential notifications
Code Examples
Complete Implementation:
Potential Applications:
System monitoring: Monitor performance metrics of servers, networks, and applications.
Website monitoring: Check website availability, response times, and errors.
Application monitoring: Detect errors, performance issues, and security threats in applications.
Prometheus Instrumentation
Overview
Prometheus is a popular open-source monitoring system that collects and aggregates metrics from various sources. Instrumentation refers to the process of adding code to your application to expose these metrics to Prometheus.
Metrics
Metrics are quantitative measurements that provide insights into the performance and behavior of your application. They are typically collected in the form of time series, which represent a value over time.
Types of Metrics:
Counter: Counts how many times an event occurs. Example: Number of HTTP requests received.
Gauge: Measures the current value of a metric. Example: Memory usage.
Histogram: Collects data on the distribution of values. Example: Response times of HTTP requests.
Exporters
Exporters are libraries or tools that translate the metrics collected by your application into a format that Prometheus can understand. There are exporters for various programming languages and frameworks, such as Java, Python, and Node.js.
Scrapers
Scrapers are used by Prometheus to periodically pull metrics from your application. They can be configured to scrape specific endpoints or use service discovery mechanisms like Kubernetes.
Alerting
Prometheus can be used to define alerts based on the collected metrics. For example, you can create an alert to notify you if the memory usage of your application exceeds a certain threshold.
Potential Applications
Prometheus instrumentation can be used in various real-world applications, including:
Performance monitoring: Track metrics such as response times, CPU utilization, and memory usage to identify performance bottlenecks.
Service health monitoring: Monitor the availability and health of your services by tracking metrics such as uptime, error counts, and latency.
Capacity planning: Forecast future resource requirements based on historical metric data.
Compliance monitoring: Ensure compliance with industry regulations or internal SLAs by monitoring key performance indicators.
Prometheus Instrumentation: Client Libraries
Overview
Prometheus is a monitoring system that collects metrics (measurements of system state) from various sources and makes them available for querying and alerting. Client libraries allow applications to export metrics to Prometheus in a standardized way.
Exporters
Exporters are programs that extract metrics from applications and send them to Prometheus. There are client libraries available in various programming languages that make it easy to create exporters:
Python: prometheus_client
Java: micrometer
Go: promhttp
Code Example: Creating an Exporter
Metric Types
Client libraries support various metric types that represent different types of system measurements:
Counter: A non-decreasing value that represents the number of events that have occurred.
Gauge: A current value that represents the state of the system at a given point in time.
Histogram: A distribution of values that represents the frequency of occurrence of measured values.
Summary: A statistical summary of quantiles and aggregates of measured values.
Labels
Labels are key-value pairs that provide additional context to metrics. They can be used to identify specific instances of a metric or to group related metrics:
Applications
Prometheus client libraries are used in various real-world applications, including:
Monitoring the performance and availability of web applications
Tracking resource utilization (CPU, memory, disk space)
Diagnosing system issues and bottlenecks
Creating custom metrics for specific business use cases
Prometheus Instrumentation and Exporters
What is Prometheus?
Prometheus is like a superhero that helps us keep track of how well our systems are running. It uses metrics, which are like little pieces of information, to tell us how many people are visiting our website, how long it takes our database to process queries, and other important things.
What are Exporters?
Exporters are like special messengers that take metrics from our systems and send them to Prometheus. They act like translators, converting metrics into the language that Prometheus understands.
Metrics
Metrics are like measurements that tell us how our systems are doing. They can measure things like:
The number of requests our website receives
The amount of memory our application is using
The temperature of our servers
Types of Exporters
There are many different types of exporters, each designed to collect metrics from different sources. Some common exporters include:
Client library exporters: These are built into our applications and send metrics directly to Prometheus.
Service mesh exporters: These collect metrics from a service mesh, which is a layer of software that helps manage communications between our services.
Host exporters: These run on our servers and collect metrics from the operating system and other applications running on the server.
How to Use Exporters
Using exporters is pretty straightforward. Here's a simplified example:
This script will start a simple HTTP server that exposes Prometheus metrics. Exporters can be used in a similar way to collect metrics from other sources.
Real-World Applications
Prometheus and exporters are used in many real-world applications to monitor and improve the performance of systems. For example, they can be used to:
Identify performance bottlenecks in web applications
Monitor the health of cloud infrastructure
Track the usage of microservices in a microservices architecture
Prometheus Instrumentation Libraries
Overview
Prometheus is a monitoring system that collects and stores time-series metrics. Instrumentation libraries are client-side libraries that allow you to expose metrics from your application to Prometheus.
Client Libraries
Prometheus provides client libraries for various programming languages, including:
Python (client_python)
Go (client_golang)
Java (client_java)
Node.js (client_nodejs)
Metrics Types
Prometheus supports four main types of metrics:
Counter: A metric that monotonically increases, such as the number of requests processed.
Gauge: A metric that can increase or decrease, such as the current memory usage.
Histogram: A metric that measures the distribution of values, such as the response time of requests.
Summary: A metric that measures the quantiles of a distribution, such as the 90th percentile response time.
Metric Creation
To create a metric using a client library, you first need to define the metric's name, help text (description), and labels (key-value pairs to identify the metric in a specific context).
For example, in Python:
Metric Labels
Labels allow you to identify and group metrics based on specific dimensions, such as HTTP status code or request method.
For example, to create a counter for HTTP requests that tracks the status code, you would add a label named status_code to the metric definition:
Metric Collection
Prometheus collects metrics by scraping metrics endpoints (typically port 9090) on the host where the instrumentation library is running.
To enable scraping, you can start the Prometheus server:
Real-World Applications
Prometheus with instrumentation libraries is used in various real-world applications, such as:
Monitoring the performance of web applications
Identifying bottlenecks in distributed systems
Tracking user behavior on websites and mobile apps
Alerting on critical metrics
Prometheus: Instrumentation/Third-Party Integrations
Introduction
Prometheus is a monitoring and alerting system that collects metrics from systems and services. To monitor more complex systems, Prometheus can be integrated with third-party tools that provide specialized monitoring capabilities.
Topics
1. OpenTelemetry (OTel)
OpenTelemetry is a unified API and set of tools for generating, collecting, and exporting telemetry data. Prometheus can receive and process OTel data.
Integration Method:
Install the OpenTelemetry SDK in your application.
Configure the SDK to send data to a Prometheus endpoint.
Enable the OTel collector component in Prometheus.
Example:
Potential Application:
Monitor complex applications that require standardized telemetry data collection and analytics across multiple services.
2. Jaeger
Jaeger is a distributed tracing system. Prometheus can collect trace data from Jaeger and generate metrics for trace duration, error rates, and latency.
Integration Method:
Enable the Jaeger Prometheus exporter.
Configure the exporter to send data to a Prometheus endpoint.
Enable the Jaeger tracing system and send traces to the exporter.
Example:
Potential Application:
Monitor and analyze the performance of distributed systems by tracing individual requests and identifying bottlenecks.
3. Grafana
Grafana is a visualization and dashboarding tool. Prometheus can integrate with Grafana to provide interactive visualizations of metrics.
Integration Method:
Install the Grafana plugin for Prometheus.
Configure the plugin to connect to a Prometheus endpoint.
Create dashboards using Prometheus data sources.
Example:
Potential Application:
Monitor and visualize key metrics from Prometheus using interactive dashboards and visualizations.
4. Alertmanager
Alertmanager is a notification system for alerts generated by Prometheus. Prometheus can integrate with Alertmanager to send alerts and manage their state.
Integration Method:
Install Alertmanager.
Configure Prometheus to send alerts to Alertmanager.
Configure Alertmanager to route alerts to desired receivers (e.g., email, Slack, PagerDuty).
Example:
Potential Application:
Monitor systems and services and receive timely notifications about critical issues or health changes.
5. VictorOps
VictorOps is an incident management and alert notification tool. Prometheus can integrate with VictorOps to send alerts and receive incident updates.
Integration Method:
Install the VictorOps plugin for Prometheus.
Configure the plugin to connect to a VictorOps instance.
Configure Prometheus to send alerts to VictorOps.
Example:
Potential Application:
Monitor systems and services and receive real-time incident updates and on-call notifications.
Introduction to Prometheus
Prometheus is a monitoring and alerting platform that:
Collects metrics from different sources (e.g., servers, applications, databases).
Stores and aggregates these metrics over time.
Alerts you when metrics reach certain thresholds or patterns.
Simplify: Prometheus is like a dashboard that keeps track of how your systems are doing.
Key Concepts
Metrics: Measurements about your systems (e.g., CPU usage, memory consumption).
Time series: A collection of metrics over time.
Labels: Metadata associated with metrics (e.g., server name, application version).
Getting Started with Prometheus
1. Install Prometheus:
2. Configure Prometheus:
3. Run Prometheus:
4. Install Node Exporter on Your Target Servers:
5. Access Prometheus Web Interface:
Querying Metrics
PromQL (Prometheus Query Language): Language for querying time series.
Example: Find all servers with CPU usage above 20%:
Creating Alerts
Alert rule: Defines conditions and actions for alerts.
Example: Alert if CPU usage exceeds 80% for 5 minutes:
Potential Applications
System monitoring: Track performance metrics of servers, applications, and databases.
Capacity planning: Forecast future resource needs based on historical data.
Troubleshooting: Identify performance issues and isolate their root causes.
Observability: Gain insights into how your systems work and how they interact with each other.
Prometheus Metrics Naming Best Practices
Prometheus is a popular monitoring and alerting system used to collect and store metrics. Metrics in Prometheus are uniquely identified by their name, so it's important to adhere to best practices when naming them.
Metric Name Syntax
A metric name consists of four parts, separated by underscores:
Namespace: Typically the team or service that owns the metric.
Subsystem: A more specific grouping within the namespace.
Name: The main identifier of the metric.
Label Name: Optional. A subcomponent or attribute of the metric.
For example, a metric named http_request_duration_seconds might track the duration of HTTP requests, and could have a label name endpoint to specify which endpoint the request was made to.
Best Practices
1. Use Descriptive Names:
Choose names that clearly describe what the metric measures. Avoid using generic names like "monitor" or "metric".
2. Follow a Naming Hierarchy:
Use namespaces and subsystems to group related metrics. This helps organize and navigate metrics.
3. Use Verbs to Indicate Measurement:
Most metrics are measurements, so names should use verbs to indicate the action being measured. For example, http_request_duration_seconds instead of http_request_duration.
4. Use Singular Form:
Use the singular form for metric names, even if the metric measures a collection of items.
5. Use Consistent Labels:
Use consistent label names and values across metrics to allow for aggregation and comparison.
Code Examples
Namespace and Subsystem:
Descriptive Name:
Label Name:
Real-World Applications
Monitoring a Web Application:
Namespace:
my_web_applicationSubsystem:
http_serverMetric Name:
http_request_duration_secondsLabel Name:
endpoint
This metric tracks the duration of HTTP requests to different endpoints in the application.
Monitoring a Database:
Namespace:
my_databaseSubsystem:
storageMetric Name:
database_size_bytesLabel Name:
database
This metric tracks the size of different databases in the system.
Labeling in Prometheus
What is Labeling?
Imagine Prometheus as a giant box of data. Labeling is like adding sticky notes to the data points inside the box. These sticky notes help you organize and filter the data so that you can easily find what you need.
Benefits of Labeling:
Organization: Keep track of related data points by grouping them with labels.
Filtering: Narrow down the data to specific areas of interest by applying filters based on labels.
Grouping: Combine data points with similar characteristics into groups for analysis.
Types of Labels:
Key-Value Labels: A simple way to label data points with a key (a name) and a value (a description). Example:
app=myappMulti-Labels: Allow you to attach multiple labels to a single data point. Example:
component=backend,env=productionLabel Selectors: Define specific rules to match and filter data points based on their labels. Example:
{component=backend,env=production}
Code Examples:
Key-Value Labels:
Multi-Labels:
Label Selectors:
Real-World Applications:
Monitoring Application Performance: Label your metrics with information such as application, environment, component, and version to identify performance bottlenecks and areas for improvement.
Tracking Resource Usage: Label your metrics with information such as host, instance, and type to monitor resource utilization and identify potential issues.
Troubleshooting and Root Cause Analysis: Use labels to filter through data and isolate specific incidents or trends that may require further investigation.
Metric Cardinality
Imagine you have a website with 100 users. You want to track how many times each user visits the site.
Scalar Metric: You could create a scalar metric called
user_visits. This metric would have a single value that represents the total number of visits by all users.Vector Metric: Instead, you could create a vector metric called
user_visits_by_user. This metric would have a separate value for each user.
Vector metrics are more useful when you want to track data that varies across different dimensions. For example, you could create a vector metric to track the number of visits by each user, by each region, or by each device type.
High Cardinality
A metric with a high cardinality is a metric that has a large number of unique values. For example, the user_visits_by_user metric would have a high cardinality because there are 100 unique users.
High cardinality metrics can be difficult to store and process. Prometheus uses a special data structure called a time series database to store its metrics. Time series databases are optimized for storing high cardinality data.
Low Cardinality
A metric with a low cardinality is a metric that has a small number of unique values. For example, the user_visits metric would have a low cardinality because there is only one unique value.
Low cardinality metrics are easy to store and process. Prometheus can store low cardinality metrics in memory.
Cardinality Considerations
When designing your metrics, you should consider the cardinality of the metric. High cardinality metrics can be difficult to store and process. Low cardinality metrics are easy to store and process.
Here are some tips for reducing the cardinality of your metrics:
Use labels. Labels are key-value pairs that can be used to identify different instances of a metric. For example, you could add a
user_idlabel to theuser_visits_by_usermetric. This would reduce the cardinality of the metric because the same user would only have one instance of the metric.Use histograms. Histograms are a type of metric that can be used to track data that is distributed over a range of values. For example, you could create a histogram to track the response times of your website. This would reduce the cardinality of the metric because the same response time would only have one instance of the metric.
Use summaries. Summaries are a type of metric that can be used to track data that is summarized over a period of time. For example, you could create a summary to track the average response time of your website. This would reduce the cardinality of the metric because the same average response time would only have one instance of the metric.
Real-World Applications
Here are some real-world applications of metric cardinality:
Tracking user behavior. You can use vector metrics to track the behavior of individual users on your website. For example, you could track the number of pages each user visits, the amount of time each user spends on your site, and the devices each user uses to access your site.
Monitoring performance. You can use vector metrics to monitor the performance of your servers and applications. For example, you could track the response times of your web servers, the CPU usage of your servers, and the memory usage of your applications.
Predicting demand. You can use vector metrics to predict demand for your products and services. For example, you could track the number of searches for a particular product on your website, the number of orders for a particular product, and the number of support requests for a particular product.
Code Examples
Here is a code example of a Prometheus metric that has a high cardinality:
This metric has a high cardinality because there are 3 unique values for the user_id label.
Here is a code example of a Prometheus metric that has a low cardinality:
This metric has a low cardinality because there is only one unique value.
PromQL Queries
Introduction: PromQL (Prometheus Query Language) is a powerful language used to query and analyze time series data stored in Prometheus. It allows you to retrieve, aggregate, and visualize data in various ways.
Topics:
1. Aggregations
Explanation: Aggregations combine multiple time series into a single, representative time series. Common aggregations include:
sum: Adds up the values from multiple series.
avg: Calculates the average value across multiple series.
min: Returns the minimum value across multiple series.
max: Returns the maximum value across multiple series.
Code Example:
This query sums up all values for the metric time series where label1 is equal to value1 and label2 is equal to value2.
Applications:
Calculating total traffic or revenue across multiple servers.
Finding the average response time for all database requests.
2. Label Filters
Explanation: Label filters select time series based on their labels. Labels are key-value pairs associated with time series. Filters allow you to narrow down the results to only those series that meet specific criteria.
Code Example:
This query selects all time series with the label label1 set to value1.
Applications:
Filtering out metrics from a specific environment or region.
Isolating data for a particular user or application.
3. Time Ranges
Explanation: Time ranges specify the interval of data to be queried. They can be absolute (e.g., [10m]) or relative to the current time (e.g., [1h:30m]).
Code Example:
This query fetches the values of the metric time series for the past 10 minutes.
Applications:
Analyzing historical data over a specific timeframe.
Identifying trends or patterns over time.
4. Transformations
Explanation: Transformations modify the values in a time series. Some common transformations include:
rate: Calculates the rate of change over time.
irate: Calculates the instantaneous rate of change.
histogram_quantile: Returns a specific quantile of a histogram time series.
Code Example:
This query calculates the rate of change for the metric time series over the past 10 minutes.
Applications:
Monitoring the rate of incoming requests or errors.
Identifying bottlenecks or performance issues.
5. Subqueries
Explanation: Subqueries allow you to nest queries within other queries. This can be useful for creating more complex filters or aggregations.
Code Example:
This query counts the number of time series for each unique value of the label1 label.
Applications:
Grouping data by labels and analyzing distributions.
Creating dashboards with multiple views of the data.
Scraping and Instrumenting
Prometheus is a monitoring and alerting system that collects metrics from applications and services. These metrics can be used to track the performance and health of your systems.
There are two main ways to get metrics into Prometheus: scraping and instrumenting.
Scraping
Scraping is the process of collecting metrics from applications and services by making HTTP requests to them. Prometheus uses a special type of HTTP request called the Prometheus scrape request.
The Prometheus scrape request includes a list of metric names that Prometheus is interested in. The application or service responds with a list of metrics and their values.
Here is an example of a Prometheus scrape request:
Here is an example of a response to a Prometheus scrape request:
Prometheus can scrape metrics from a variety of applications and services, including:
Web servers
Database servers
Cloud services
Operating systems
Hardware devices
Instrumenting
Instrumenting is the process of adding code to your applications and services to explicitly expose metrics to Prometheus. This can be done using a Prometheus client library.
Prometheus client libraries are available for a variety of programming languages, including:
Go
Python
Java
C++
Here is an example of how to instrument a Go application using the Prometheus client library:
Real-World Applications
Scraping and instrumenting are essential for monitoring and alerting in production environments. By collecting metrics from your applications and services, you can:
Track the performance of your systems
Identify bottlenecks
Detect errors
Set up alerts to notify you of potential problems
Prometheus is a powerful tool that can help you to improve the reliability and performance of your systems. By using scraping and instrumenting, you can gain valuable insights into the health of your applications and services.
Prometheus Scaling
Introduction
Prometheus is a monitoring and alerting system that lets you track metrics from various sources and triggers alerts based on pre-defined conditions. As your system grows, you may need to scale Prometheus to handle the increasing load.
Horizontal Scaling
Sharding
Sharding involves splitting your time series data into smaller chunks and distributing them across multiple Prometheus instances. Each instance handles a subset of the data.
Example:
Benefits:
Improves scalability by distributing the load
Reduces the failure impact on a single instance
Downsides:
More complex to manage multiple instances
Requires coordination for querying and alert generation
Federation
Federation allows multiple Prometheus instances to be combined into a single logical namespace. Queries and alerts are directed to the appropriate instances based on a predefined mapping.
Example:
Benefits:
Centralized monitoring of multiple clusters
Simplifies querying and alerting across clusters
Downsides:
Requires coordination and synchronization between instances
Can introduce query latency due to inter-server communication
Vertical Scaling
Increasing Resources
To handle higher load, you can increase resources allocated to your Prometheus instance, such as CPU, memory, and storage.
Example:
Benefits:
Simplest and quickest scaling method
No changes to the Prometheus setup required
Downsides:
Limited scalability as resources are finite
Can be expensive to acquire more hardware
Multi-tenancy
Multi-tenancy allows you to isolate data and queries for different users or teams. Each tenant has their own view of Prometheus, but the underlying data is stored centrally.
Example:
Benefits:
Enforces data separation and security
Improves scalability by reducing cross-tenant interference
Downsides:
Requires additional configuration and management
Can introduce performance overhead due to tenant isolation
Practical Applications
Monitoring a Large Kubernetes Cluster
Use sharding to distribute time series data across multiple Prometheus instances.
Federate those instances to provide a centralized monitoring dashboard.
Monitoring a Global Infrastructure
Use multi-tenancy to isolate data for different regions or teams.
Scale vertically by increasing resources on each Prometheus instance as needed.
Conclusion
Scaling Prometheus is crucial for handling increasing load and ensuring its availability. There are various scaling options available, and the best approach depends on your specific requirements. By carefully considering the trade-offs, you can optimize your Prometheus setup for efficient monitoring and alerting in large-scale environments.
Horizontally Scaling Prometheus
Prometheus is a monitoring system that collects, stores, and visualizes metrics from various sources. Horizontally scaling Prometheus means distributing the load across multiple instances of Prometheus to handle large-scale monitoring requirements.
Benefits of Horizontal Scaling
Improved performance: Distributing the load reduces the burden on individual Prometheus instances, leading to faster query execution and overall system responsiveness.
High availability: Multiple Prometheus instances enhance fault tolerance. If one instance fails, the others will continue to collect and store metrics.
Scalability: It allows Prometheus to handle a growing number of metrics and targets without performance degradation.
Components Involved
Prometheus server: Collects metrics from targets and stores them in a time-series database.
Remote write receivers: Used by Prometheus servers to send collected metrics to other Prometheus servers (peers).
Query API: Endpoint used by Grafana, dashboards, and other applications to query metrics.
Configuration
To enable horizontal scaling, configurePrometheus endpoints and remote write receivers in each Prometheus instance as follows:
Code Example
Suppose you have three Prometheus instances in a cluster:
Each Prometheus instance will be configured to send metrics to its two peers.
Prometheus-1 configuration:
Prometheus-2 configuration:
Prometheus-3 configuration:
Real-World Application
Horizontally scaling Prometheus is essential for large-scale infrastructure, particularly in environments with high metric cardinality or query traffic. It allows for the monitoring of hundreds of thousands of targets and the storage of trillions of time-series data points.
Examples include:
Monitoring in cloud-native environments, such as Kubernetes and OpenShift, with numerous containers and microservices.
Scaling for monitoring large-scale distributed systems like Hadoop, HBase, and Cassandra.
Providing high availability and fault tolerance for critical monitoring infrastructure.
Vertical Scaling of Prometheus
Imagine Prometheus as a rocket that launches your monitoring data. Vertical scaling is like adding more engines to the rocket to make it fly higher and faster. By adding more resources to a single Prometheus instance, you can handle larger workloads and store more data.
Benefits of Vertical Scaling
Increased Throughput: Can handle a higher volume of data from more targets.
Improved Storage Capacity: Can store more metrics and data points for longer periods.
Reduced Latency: Queries and alerts can be processed faster.
Drawbacks of Vertical Scaling
Increased Cost: More resources (CPU, memory, etc.) come with a higher price tag.
Single Point of Failure: All data is stored on a single instance, making it vulnerable to crashes or outages.
Limited Scalability: There is a limit to how much data a single instance can handle efficiently.
Best Practices for Vertical Scaling
Determine your resource requirements based on the expected workload.
Monitor resource usage and adjust as needed.
Consider using a managed Prometheus service to simplify resource management.
Code Example:
Real-World Example:
A large e-commerce website experiences a surge in traffic during peak season. To accommodate the increased load, they vertically scale their Prometheus instance to handle the additional data and ensure that their monitoring system remains reliable.
Horizontal Scaling of Prometheus
Instead of adding engines to a single rocket (vertical scaling), horizontal scaling creates multiple rockets (Prometheus instances) to spread the load. This approach reduces the risk of single points of failure and provides greater scalability.
Benefits of Horizontal Scaling
Improved Reliability: Multiple instances provide redundancy, reducing the impact of outages.
Increased Scalability: Can handle exponentially more data by adding more instances.
Reduced Cost: Can be more cost-effective than vertical scaling in the long run.
Drawbacks of Horizontal Scaling
Increased Complexity: Managing multiple instances requires more effort and infrastructure.
Potential Data Loss: Data may not be perfectly consistent across all instances.
Configuration Management: Ensuring that all instances are configured consistently can be challenging.
Best Practices for Horizontal Scaling
Use a load balancer to distribute traffic across instances.
Enable remote read and write for data sharing between instances.
Establish a consistent data retention policy across all instances.
Code Example:
Real-World Example:
A multi-national bank has a large number of customer accounts to monitor. To provide a comprehensive monitoring solution, they horizontally scale their Prometheus deployment across multiple data centers, ensuring that data is available and accessible even in the event of a regional outage.
Prometheus Scaling and Federation
Overview
Prometheus is a monitoring system that collects and stores metrics. However, when you have a large number of metrics or need to monitor a large number of servers, a single Prometheus instance may not be enough. That's where scaling and federation come in.
Scaling
Scaling means increasing the capacity of Prometheus to handle more load. This can be done by adding more Prometheus instances or by using a managed service like Prometheus Operator.
Prometheus instances can be configured to scrape data from different targets. For example, one instance could scrape data from all your web servers, while another instance could scrape data from all your database servers. This can help distribute the load and make your monitoring system more reliable.
Federation
Federation allows you to combine multiple Prometheus instances into a single system. This can be useful for aggregating metrics from different sources or for creating a global view of your infrastructure.
Federated Prometheus instances can communicate with each other using a gossip protocol. This allows them to share information about the targets they are monitoring and the metrics they are collecting. This information can be used to create a global view of your infrastructure and to identify trends and patterns.
Applications in the Real World
Scaling and federation can be used in a variety of real-world applications, such as:
Monitoring a large number of servers: If you have a large number of servers to monitor, you can use scaling and federation to distribute the load and make your monitoring system more reliable.
Aggregating metrics from different sources: If you have multiple sources of metrics, such as web servers, database servers, and network devices, you can use federation to aggregate the metrics into a single system. This can give you a global view of your infrastructure and help you identify trends and patterns.
Creating a global view of your infrastructure: If you have a global infrastructure, you can use federation to create a single view of all your metrics. This can help you identify trends and patterns that would not be visible if you were only monitoring a single region.
Code Examples
Here are some code examples that show how to scale and federate Prometheus:
Scaling
Federation
Remote Storage
Prometheus is a monitoring system that collects and stores metrics from various sources. By default, Prometheus stores metrics in its local database. However, for long-term storage and scalability, it's recommended to use a remote storage solution.
Benefits of Remote Storage
Scalability: Remote storage allows Prometheus to store large amounts of data without compromising performance.
Durability: Remote storage provides redundancy and ensures that metrics are not lost in case of hardware failures.
Cost-effectiveness: External storage services like Amazon S3 offer cost-efficient solutions for long-term storage.
Types of Remote Storage
Prometheus supports two main types of remote storage:
1. Block Storage:
Stores a time-series database in a persistent data block, such as a file or disk.
Examples: Google Cloud Storage, Amazon S3, Azure Blob Storage
2. Time Series Database (TSDB):
A specialized database designed for storing time-series data.
Examples: VictoriaMetrics, TimescaleDB, InfluxDB
Configuring Remote Storage
To configure remote storage for Prometheus, you need to edit the prometheus.yml file and add the following block:
name: A unique name for the remote storage configuration.type: The type of remote storage (e.g., filesystem, gcs, azure_blob).path: The path to the remote storage directory (for block storage) or the database endpoint (for TSDB).
Code Examples
Example 1: Using Google Cloud Storage (Block Storage)
Example 2: Using VictoriaMetrics (TSDB)
Real-World Applications
Long-term data retention: Store metrics for extended periods (e.g., months or years) for historical analysis.
Disaster recovery: Backup metrics to a remote location to ensure data availability in case of infrastructure failures.
Scaling to high volumes: Handle large amounts of metric data by leveraging the scalability of remote storage services.
Cost optimization: Utilize cost-effective remote storage solutions to reduce infrastructure costs.
Prometheus
What is it?
A system for monitoring and alerting on the performance of your software and infrastructure.
Collects metrics (like CPU usage, memory consumption, etc.) and stores them in a time series database.
Can alert you when certain thresholds are exceeded or when unusual behavior is detected.
Why use it?
Ensures your systems are running reliably and efficiently.
Helps you identify and troubleshoot issues quickly.
Can improve your overall system performance.
Documentation:
1. Metrics
What are they?
Measurements of different aspects of your system, such as CPU usage, memory consumption, and request latency.
How to collect them?
Prometheus provides a library for collecting metrics from your applications and infrastructure.
Can also use third-party agents to collect metrics from specific services or systems.
Example:
2. Targets
What are they?
The sources of your metrics.
Can be your applications, infrastructure components, or external systems.
How to configure them?
Specify the targets in Prometheus's configuration file.
Example:
3. Alerts
What are they?
Rules that define when Prometheus should trigger an alert.
How to create them?
Use the Prometheus Alert Manager to create and manage alert rules.
Example:
4. Dashboard
What is it?
A graphical interface for visualizing your metrics and alerts.
How to use it?
Use a tool like Grafana to create and customize dashboards.
Example:
Real-World Applications:
Website monitoring: Track metrics like request latency and uptime to ensure your website is running smoothly.
Server monitoring: Monitor CPU usage, memory consumption, and disk space to identify potential performance issues.
Application performance management: Track metrics like response time and error rate to understand how your application is performing.
Infrastructure monitoring: Monitor resources like network bandwidth and storage capacity to ensure your infrastructure is operating optimally.
Cloud monitoring: Monitor metrics from your cloud provider to optimize resource utilization and reduce costs.
Topic 1: Prometheus Backups
Simplified Explanation:
Imagine Prometheus as a big box full of important data (metrics) that help you track the health of your systems. A backup is like making a copy of this box and storing it somewhere safe, so that if the original box gets lost or damaged, you still have a backup to recover your data from.
Code Example:
To create a backup of your Prometheus data, you can use the tsdb command. For example:
This will create a compressed backup file (.snappy) at the specified path /path/to/backup.snappy.
Real-World Application:
Backing up your Prometheus data is essential for protecting against data loss in case of system failures, hardware issues, or accidental deletions. This allows you to restore your metrics and continue monitoring your systems without any disruptions.
Topic 2: Remote Storage
Simplified Explanation:
Remote storage is like having an extra box or folder outside of your house (Prometheus server) where you can store your backups. This way, even if your house burns down (Prometheus server crashes), your backups are still safe and accessible from the remote storage.
Code Example:
To configure Prometheus to use remote storage for backups, you can specify the storage.tsdb remote write endpoint in your Prometheus configuration file (prometheus.yml):
Real-World Application:
Storing backups in remote storage provides additional security and reliability, especially for large-scale deployments where data loss can have significant consequences. It also allows for easier disaster recovery and data migration between different systems.
Topic 3: Restores
Simplified Explanation:
Imagine if your original box (Prometheus data) gets lost or corrupted. A restore is the process of using the backup you made earlier to recreate the original box and recover your data.
Code Example:
To restore your Prometheus data from a backup, you can use the tsdb command's restore subcommand. For example:
This will restore the metrics data from the specified backup file (backup.snappy) into your Prometheus database.
Real-World Application:
Restoring from backups is crucial for data recovery after system failures or accidental deletions. It allows you to quickly restore your metrics and resume monitoring your systems without losing any valuable data.
Prometheus Reliability and Disaster Recovery
Overview
Prometheus, a metrics monitoring system, offers mechanisms for ensuring reliability and minimizing data loss in the event of failures or disasters.
High Availability
Topic: Sharding
Prometheus can be split into multiple independent shards, each responsible for monitoring a subset of targets.
This allows for scaling and resilience, as if one shard fails, the others can continue operating.
Code Example:
Application: Scaling Prometheus to monitor a large number of targets, reducing the impact of single-point failures.
Data Retention and Recovery
Topic: Storage Mechanisms
Prometheus stores time series data in various storage mechanisms, such as local disk or remote systems.
Different mechanisms offer different durability and availability characteristics.
Code Examples:
Local disk storage (not recommended for high availability):
Remote storage (e.g., S3, GCS):
Application: Balancing storage durability and performance based on specific requirements.
Replication
Topic: Remote Write Backend
Prometheus can replicate time series data to remote instances or storage systems using the "remote write" backend.
This creates multiple copies of the data, enhancing resilience.
Code Example:
Application: Setting up disaster recovery or high-availability solutions.
Monitoring and Alerting
Topic: Exporter and Alertmanager
Prometheus uses exporters to collect metrics from targets.
Alertmanager monitors metrics and sends alerts when thresholds are exceeded.
By monitoring the health of Prometheus and its components, failures or outages can be detected promptly.
Code Example:
Application: Creating a complete monitoring stack that ensures the reliability and availability of Prometheus.
Best Practices
Implement high availability using sharding, replication, or remote storage.
Choose an appropriate storage mechanism based on data retention and recovery requirements.
Monitor Prometheus and its components to detect failures promptly.
Establish disaster recovery plans and test them regularly.
Prometheus Reliability and High Availability
1. Introduction
Prometheus is a monitoring and alerting system that collects and stores data from various sources (e.g., servers, containers, applications). To ensure its reliability and high availability, it's important to have multiple Prometheus instances working together.
2. Replication
Replication ensures that all Prometheus instances have the same data. This is achieved by having one instance act as the "primary" and other instances as "secondaries". The primary stores the original data, while the secondaries receive a copy of the data and also accept queries.
Code Example:
Real-World Application:
Ensures continuous data availability even if the primary instance fails.
Provides load balancing by distributing queries across multiple instances.
3. Remote Write and Read
Remote write and read allow data to be sent from one Prometheus instance to another. Remote write is used to push data from a "source" instance to a "receiver" instance. Remote read is used to query data from a "source" instance by a "receiver" instance.
Code Examples:
Remote Write:
Remote Read:
Real-World Applications:
Centralizes data from multiple sources into a single instance for analysis.
Distributes data for performance and scalability.
4. Alert Manager
Alert Manager is a separate component that handles alerts from Prometheus. It can route alerts to different teams, deduplicate alerts, and perform actions (e.g., send notifications).
Code Example:
Real-World Applications:
Centralizes alert handling.
Reduces alert fatigue.
Automates alert escalation and response.
5. HAProxy
HAProxy is a load balancer that can be used to distribute traffic across multiple Prometheus instances. It can also provide failover in case an instance becomes unavailable.
Code Example:
Real-World Applications:
Improves scalability by distributing load.
Enhances reliability by providing failover mechanisms.
6. Monitoring Prometheus Itself
It's crucial to monitor Prometheus itself to ensure its health and availability. Common metrics include:
prometheus_tsdb_head_series: Number of time series in the database.prometheus_target_errors: Errors encountered when scraping targets.prometheus_web_request_duration_seconds: Time taken to handle web requests.
Code Example:
Real-World Applications:
Identifies potential issues early.
Allows for proactive maintenance.
Prometheus Overview
Imagine Prometheus as a super-smart robot that keeps an eye on your servers and applications. It's like a doctor who constantly monitors your systems to make sure they're healthy.
Topics
Metrics
Metrics are like measurements of your systems. They tell Prometheus things like how many users are on your website or how much memory your servers are using.
Targets
Targets are the systems or applications that Prometheus monitors. It could be a web server, a database, or any other component in your infrastructure.
Scraping
Imagine Prometheus as a vacuum cleaner that sucks up metrics from your targets. Scraping is the process of fetching metrics from targets at regular intervals.
Storing Data
Prometheus stores metrics in its database, called a time series database. It's like a wardrobe where Prometheus keeps all the measurements it has collected over time.
Alerting
Imagine Prometheus as a watchdog that barks when something goes wrong. Alerting is the process of setting up rules to trigger alarms if certain metric values exceed or fall below specified thresholds.
Grafana
Imagine Grafana as a dashboard that shows you all the metrics and alerts that Prometheus has collected. It's like a control panel that gives you a visual representation of your systems' health.
Applications
Performance Monitoring: Track metrics like CPU usage, memory, and response times to identify performance bottlenecks.
Availability Monitoring: Ensure systems are always up and running by monitoring uptime and error rates.
Capacity Planning: Forecast future resource requirements by analyzing historical usage patterns.
Security Monitoring: Detect suspicious activity and identify security vulnerabilities by monitoring log files and system events.
Prometheus: Reliability and Security
Reliability
Redundancy
Prometheus servers are designed to be highly redundant, meaning multiple instances can run concurrently and provide data in the event of a failure. This can be achieved by:
Federation: Running multiple Prometheus servers and combining their data into a single view.
Remote Write: Sending data from one Prometheus server to multiple others for backup.
Remote Read: Querying data from multiple Prometheus servers to ensure availability even if one server fails.
Data durability
Prometheus stores data on a local disk, which can be configured for redundancy using:
Snapshots: Periodically taking backups of the data and storing them on a separate machine or cloud storage.
WAL (Write-Ahead Logging): Ensuring that all writes to the data are logged before the actual data is updated, reducing the risk of data loss during a server crash.
Security
Prometheus offers several security features to protect data and prevent unauthorized access:
Authentication and Authorization
Basic authentication: Require users to provide a username and password to access Prometheus.
SSO (Single Sign-On): Integrate Prometheus with an existing authentication system for centralized user management.
Role-Based Access Control (RBAC): Define permissions to limit what specific users can access and modify within Prometheus.
Data Encryption
TLS (Transport Layer Security): Encrypt communication between Prometheus servers, clients, and remote storage.
Data encryption at rest: Encrypt data stored on disk to protect it from unauthorized access.
Audit Logging
Prometheus can log all activities performed by users, including:
Logins and logouts
Data modifications
API requests
Error messages
This log can help identify any unauthorized access or suspicious behavior.
Code Examples
Redundancy
Data Durability
Security
Real-World Applications
Redundancy
High availability: Ensure continuous monitoring even during maintenance or server outages.
Disaster recovery: Provide a backup plan in case of data loss or catastrophic events.
Data Durability
Data loss prevention: Protect against accidental data deletion or hardware failures.
Compliance: Meet regulatory requirements for data retention and backup.
Security
Data protection: Prevent unauthorized access to sensitive metrics or configuration.
Access control: Limit user permissions based on their roles and responsibilities.
Audit trail: Track user activities for security audits and investigations.
Upgrading Prometheus
Overview
Upgrading Prometheus involves updating the Prometheus binary and potentially making changes to the configuration. The upgrade process should be planned and executed carefully to minimize downtime and data loss.
Preparing for the Upgrade
Backup data: Create a backup of the Prometheus database (e.g., TimescaleDB, InfluxDB) to prevent data loss in case of any issues.
Review configuration: Check the Prometheus configuration file for any changes that may be required after the upgrade.
Plan downtime: Schedule a maintenance window to minimize service disruption during the upgrade.
Updating the Binary
Download new binary: Obtain the latest Prometheus binary from the official website.
Stop Prometheus: Gracefully stop the running Prometheus instance.
Replace binary: Copy the new Prometheus binary to the appropriate location (e.g., /usr/bin/prometheus).
Start Prometheus: Start the new Prometheus instance.
Configuration Changes
Prometheus configuration may need to be updated to support new features or address any breaking changes.
Check for breaking changes: Review the Prometheus documentation for any changes that may affect your configuration.
Update configuration: Make necessary changes to the Prometheus configuration file based on the breaking changes.
Validate configuration: Use the
prometheus --config.file=<path>command to validate the configuration before applying it.
Code Example
Real-World Applications
Rolling upgrades: Safely upgrade Prometheus without significant downtime by updating individual targets in a rolling fashion.
Configuration adjustments: Adjust Prometheus configuration to optimize performance, data retention, or alerting rules based on specific requirements.
Bug fixes and security updates: Apply bug fixes and security updates to ensure the stability and security of Prometheus.
What is Prometheus?
Prometheus is a monitoring system that collects metrics from various sources (like servers, databases, and applications) and stores them in a time-series database. These metrics can then be used to create dashboards, alerts, and other insights into the performance of your systems.
Prometheus Client Libraries
Prometheus client libraries are software packages that allow you to easily add metrics from your applications to Prometheus. These libraries provide an API for creating and exposing metrics, as well as collecting and exporting them to Prometheus.
Creating Metrics
The most common type of metric is a counter, which measures how many times something has happened. For example, you might create a counter to track the number of requests processed by your web server. To create a counter, you would use the following code:
Exposing Metrics
Once you have created some metrics, you need to expose them to Prometheus so that it can collect and store them. To do this, you can use the start_http_server function provided by the Prometheus client library. This function creates an HTTP server that listens on a specified port and exposes all the metrics you have created. For example:
Collecting and Exporting Metrics
The Prometheus client library also provides a collector for collecting metrics from your application and exporting them to Prometheus. This collector can be used to collect metrics from various sources, such as the Python standard library, third-party libraries, and custom code. For example:
Real-World Applications
Prometheus client libraries are used in a wide variety of real-world applications, including:
Monitoring the performance of web servers and applications
Tracking the usage of cloud resources
Identifying performance bottlenecks
Creating dashboards and alerts to monitor system health
Automating the scaling of systems based on metrics
Conclusion
Prometheus client libraries are a powerful tool for collecting and exposing metrics from your applications. They can be used to gain insights into the performance of your systems and to create dashboards, alerts, and other tools to help you manage your infrastructure.
Intro to Prometheus
Prometheus is like a smart kid that keeps an eye on everything. It watches your computer systems and makes sure they're running smoothly. When something goes wrong, Prometheus knows about it right away and tells you.
Client Libraries
Client libraries are like messengers that help Prometheus talk to your computer. They let Prometheus know what's going on with your systems.
Go Client Library
The Go client library is a special messenger that works with Go, a programming language. It lets you easily build programs that use Prometheus.
How to Use the Go Client Library
Import the library:
Create a Counter:
Register the Counter:
Increment the Counter:
Complete Code Example
Real-World Applications
Monitoring website traffic: Track the number of visitors to your website.
Tracking server performance: Monitor CPU usage, memory usage, and response times.
Troubleshooting performance issues: Identify problems quickly and easily.
Prometheus Java Client Library
Introduction
Prometheus is a monitoring and alerting system that collects metrics from various sources and stores them in a time-series database. The Java client library allows you to send metrics to Prometheus from your Java applications.
Getting Started
1. Add the dependency to your project
2. Create a client
3. Start the client
Metrics Types
Prometheus supports different types of metrics:
Counter: Measures the cumulative count of events.
Gauge: Measures the current value of a metric.
Histogram: Measures the distribution of values over time.
Summary: Measures the statistical summary of values over time.
Creating Metrics
To create a metric, you use a builder pattern. The builder allows you to specify the name, help text, and labels for the metric.
Counter:
Gauge:
Histogram:
Summary:
Setting Metric Values
Once you have created a metric, you can set its value.
Counter:
Gauge:
Histogram:
Summary:
Exposing Metrics
To expose the metrics to Prometheus, you need to start a Prometheus server. The server listens on a port and serves the metrics in a format that Prometheus can understand.
Real-World Applications
Prometheus is used in a wide variety of applications, including:
Monitoring the performance of web applications
Tracking the number of errors in a system
Measuring the latency of API calls
Visualizing the distribution of values over time
Prometheus Client Library for Python
What is Prometheus?
Prometheus is a monitoring system that collects metrics from various sources, including servers, applications, and infrastructure. These metrics can be used to monitor performance, identify issues, and track trends.
What is the Prometheus Client Library for Python?
The Prometheus Client Library for Python is a library that allows you to easily add Prometheus metrics to your Python applications. This allows you to track metrics such as request count, response time, and errors.
How to Use the Prometheus Client Library for Python
To use the client library, you first need to install it:
Then, you can import the library into your Python code:
The client library provides a number of classes and functions for creating and managing metrics. The most important classes are:
Counter: A counter that tracks the number of times an event has occurred.
Gauge: A gauge that tracks the current value of a metric.
Histogram: A histogram that tracks the distribution of values for a metric.
To create a metric, you first need to create a metric family. A metric family is a collection of related metrics. For example, you might have a metric family for all the requests to your application.
Once you have created a metric family, you can create individual metrics within that family. For example, you might create a counter to track the number of successful requests.
Here is an example of how to create a metric family and a counter:
To expose the metrics to Prometheus, you need to create a metrics endpoint in your application. The endpoint will be queried by Prometheus to collect the metrics.
Here is an example of how to create a metrics endpoint:
Potential Applications
The Prometheus Client Library for Python can be used in a variety of ways, such as:
Monitoring the performance of web applications.
Tracking the usage of cloud services.
Monitoring the health of infrastructure.
Identifying performance bottlenecks.
Troubleshooting issues.
Prometheus Client Libraries/Ruby
Prometheus is a monitoring system that collects metrics from applications and stores them in a time-series database.
Client Libraries
Client libraries allow your application to interact with Prometheus and expose metrics for collection.
Topics:
1. Instrumentation
Counters: Measure events that occur, such as API requests.
Gauges: Measure current values, such as memory usage.
Histograms: Measure the distribution of values, such as response times.
Summaries: Similar to histograms, but with additional quantile calculations.
2. Exposing Metrics
Exporter: Collects metrics from the client library and exposes them via HTTP.
3. Configuration
Labels: Associate additional information with metrics, such as API endpoint or region.
Buckets: Define the boundaries for histogram and summary buckets.
Applications:
Monitoring system health and performance
Identifying performance bottlenecks
Tracking user behavior and usage patterns
Detecting anomalies and issues in real time
Prometheus
Metrics:
Like measurements in other domains (e.g., bytes, milliseconds).
Three basic metric types:
Counter: Increments over time, like traffic count.
Gauge: A point-in-time measure, like temperature.
Histogram: Aggregates values into buckets, like HTTP response times.
Code Example (Go):
Applications:
Monitor system performance (CPU, memory usage)
Track business metrics (sales, customer satisfaction)
Client Libraries:
Overview:
Libraries that help you easily integrate Prometheus with different programming languages and frameworks.
Go Library:
client_golanglibrary:Simplifies metric collection and exposition
Provides client for sending data to Prometheus server
Code Example (Go):
Other Libraries:
Java:
simpleclientPython:
prometheus-clientRuby:
prometheusNode.js:
prom-client
Applications:
Enable metric collection in applications written in different languages
Centralize monitoring across diverse systems and technologies
Others:
Exporters:
Tools that convert metrics from different sources into Prometheus format.
E.g., MySQL exporter, Kafka exporter
Code Example (Go):
Applications:
Collect metrics from sources that don't natively support Prometheus
Enable monitoring of heterogeneous systems
Alertmanager:
Tool for creating and managing alerts based on Prometheus metrics.
E.g., send email or SMS when a metric crosses a threshold.
Code Example (YAML):
Applications:
Proactively notify stakeholders of critical events
Respond to system issues before they impact users
Prometheus
What is Prometheus?
A monitoring and alerting system that collects and analyzes metrics from systems and applications.
Like a watchdog that keeps an eye on your IT infrastructure.
Key Concepts:
Metrics: Measurements of system or application behavior, such as CPU usage or request latency.
Time Series: Sequences of metrics over time, allowing for trend analysis.
PromQL: A query language for retrieving and filtering metrics from Prometheus.
Alerts: Rules that trigger when certain metrics exceed predefined thresholds.
Getting Started
Installation:
Download and install Prometheus from the official website.
Configuration:
Create a configuration file (
prometheus.yml) to specify where to scrape metrics from.
Metrics Collection:
Prometheus scrapes metrics from targets using exporters (e.g., Node Exporter for system metrics).
Querying Metrics
PromQL:
Use PromQL to query and filter metrics.
Example:
Query for CPU usage over the last 10 minutes:
Alerting
Rules:
Define alert rules to trigger when metrics exceed thresholds.
Example:
Alert if CPU usage is above 80% for 5 minutes:
Real-World Applications
Monitoring IT infrastructure:
Track performance metrics (CPU, memory, network) to identify bottlenecks and performance issues.
Troubleshooting:
Analyze historical metrics to identify the root cause of problems.
Capacity planning:
Predict future resource needs based on historical usage patterns.
SLA monitoring:
Track metrics to ensure that applications meet performance targets.
Security monitoring:
Monitor metrics related to login attempts, firewall events, and intrusion detection.
Prometheus Kubernetes Integration
Prometheus is a monitoring system that collects and stores time-series data. Kubernetes is a container orchestration system that automates the deployment, management, and scaling of containerized applications.
Kubernetes Discovery
Prometheus needs to know about the Kubernetes resources it wants to monitor. There are several ways to discover these resources:
kube-state-metrics: A DaemonSet that exposes Kubernetes metrics as Prometheus metrics.
Prometheus operator: A Kubernetes operator that deploys and manages Prometheus and its components.
Explicit configuration: Manually configuring Prometheus to scrape specific Kubernetes resources.
Example:
Metrics Collection
Once Prometheus knows about the Kubernetes resources, it can start collecting metrics from them. Metrics are exposed through the /metrics endpoint of Kubernetes components.
Example:
Alerting
Prometheus can be used to create alerts based on the metrics it collects. Alerts can be configured to notify the user when certain conditions are met.
Example:
Real-World Applications
The Prometheus Kubernetes integration can be used to:
Monitor the performance of Kubernetes clusters
Detect and troubleshoot issues with Kubernetes deployments
Create alerts to notify the user of potential problems
Optimize the resource utilization of Kubernetes clusters
Prometheus Service Discovery
Prometheus can automatically discover targets for monitoring based on various service discovery mechanisms. This allows Prometheus to monitor dynamic environments, where services can come and go.
Static Configuration
The simplest form of service discovery is to manually configure the target endpoints in the prometheus.yml file.
This configuration tells Prometheus to scrape the metrics from two endpoints: example.com:8080 and example2.com:9090.
DNS Service Discovery
Prometheus can discover targets based on DNS SRV records. This allows Prometheus to monitor services that are registered in DNS.
This configuration tells Prometheus to scrape the metrics from all services that have an HTTP SRV record with the domain example.com and port 8080.
Kubernetes Service Discovery
Prometheus can discover targets within a Kubernetes cluster. This allows Prometheus to monitor Kubernetes pods, services, and deployments.
This configuration tells Prometheus to scrape the metrics from all Kubernetes pods.
Real-World Applications
Service discovery is essential for monitoring dynamic environments. It allows Prometheus to automatically discover and monitor new services as they are deployed.
Some potential applications of service discovery include:
Monitoring microservices: Microservices are often deployed in dynamic environments, where services can come and go frequently. Service discovery allows Prometheus to monitor these services automatically.
Monitoring Kubernetes clusters: Kubernetes is a container orchestration platform that allows users to deploy and manage applications in a containerized environment. Service discovery allows Prometheus to monitor all the services running in a Kubernetes cluster.
Monitoring cloud-native applications: Cloud-native applications are designed to be deployed and run in a cloud environment. Service discovery allows Prometheus to monitor these applications automatically, regardless of where they are deployed.
Prometheus Exporters
Prometheus exporters are tools that collect and expose metrics from various systems and applications. These metrics can include things like CPU usage, memory consumption, network traffic, and application-specific performance data.
How do Exporters Work?
Exporters work by periodically scraping data from the target system or application. The data is then formatted into a format that Prometheus can understand and exposed on a specific port. Prometheus can then scrape the metrics from the exporter and store them in its own database.
Types of Exporters
There are many different types of exporters available, each designed to collect metrics from a specific system or application. Some popular exporters include:
Node.js exporter: Collects metrics from Node.js applications.
MySQL exporter: Collects metrics from MySQL databases.
Apache HTTP Server exporter: Collects metrics from Apache HTTP Server.
Kubernetes exporter: Collects metrics from Kubernetes clusters.
How to Use Exporters
To use an exporter, you need to:
Install the exporter on the system or application you want to monitor.
Configure the exporter to scrape the desired metrics.
Start the exporter.
Add the exporter's URL to your Prometheus configuration file.
Code Examples
Node.js Exporter
This Node.js exporter exposes two metrics: cpu_usage and memory_usage.
Prometheus Configuration File
This Prometheus configuration file scrapes metrics from the Node.js exporter running on localhost:9100.
Real-World Applications
Exporters are used to monitor a wide range of systems and applications, including:
Servers
Databases
Cloud services
Web applications
Mobile applications
By monitoring these systems, you can gain insights into their performance and identify potential issues before they cause major disruptions.
Prometheus and Third-Party Systems
Prometheus is a monitoring system that collects data from various sources and stores it in a time-series database. This data can then be used to create alerts, dashboards, and other monitoring tools.
Prometheus can be integrated with a variety of third-party systems, including:
Databases: Prometheus can collect data from databases such as MySQL, PostgreSQL, and MongoDB. This data can include metrics such as the number of queries per second, the average query time, and the number of connections.
Web servers: Prometheus can collect data from web servers such as Apache and Nginx. This data can include metrics such as the number of requests per second, the average response time, and the number of errors.
Cloud providers: Prometheus can collect data from cloud providers such as AWS, Azure, and GCP. This data can include metrics such as the number of instances running, the amount of CPU and memory being used, and the number of network requests.
Custom applications: Prometheus can also collect data from custom applications. This data can include metrics such as the number of users, the average session length, and the number of transactions.
Benefits of Integrating Prometheus with Third-Party Systems
There are many benefits to integrating Prometheus with third-party systems, including:
Increased visibility: Prometheus can provide a single pane of glass into all of your monitoring data. This makes it easier to identify problems and trends, and to make informed decisions about your infrastructure.
Improved performance: Prometheus can help you to identify inefficiencies in your infrastructure. This information can be used to improve performance and reduce costs.
Enhanced security: Prometheus can help you to detect and respond to security threats. This information can be used to protect your data and systems from attack.
How to Integrate Prometheus with Third-Party Systems
There are a few different ways to integrate Prometheus with third-party systems. The most common method is to use an exporter. An exporter is a piece of software that translates data from a third-party system into a format that Prometheus can understand.
There are many different exporters available for different third-party systems. For example, there is an exporter for MySQL, an exporter for Nginx, and an exporter for AWS.
Once you have installed an exporter, you can configure Prometheus to scrape data from it. Prometheus will periodically poll the exporter and collect the data that it exposes.
Real-World Examples of Prometheus Integrations
Prometheus is used by a wide variety of organizations to monitor their infrastructure. Here are a few examples of real-world Prometheus integrations:
Google: Google uses Prometheus to monitor its entire infrastructure, which includes over 10 million servers. Prometheus helps Google to identify problems early, improve performance, and reduce costs.
Netflix: Netflix uses Prometheus to monitor its video streaming service. Prometheus helps Netflix to ensure that its service is always available and performing optimally.
Spotify: Spotify uses Prometheus to monitor its music streaming service. Prometheus helps Spotify to identify problems early, improve performance, and reduce costs.
Potential Applications of Prometheus Integrations
Prometheus integrations can be used in a variety of ways to improve your monitoring and observability. Here are a few potential applications:
Monitoring your entire infrastructure: Prometheus can be used to monitor all of your servers, applications, and cloud resources. This gives you a single pane of glass into all of your monitoring data, making it easier to identify problems and trends.
Improving performance: Prometheus can help you to identify inefficiencies in your infrastructure. This information can be used to improve performance and reduce costs.
Detecting security threats: Prometheus can help you to detect and respond to security threats. This information can be used to protect your data and systems from attack.
Automating tasks: Prometheus can be used to automate tasks such as alerting, scaling, and provisioning. This can save you time and effort, and help you to keep your infrastructure running smoothly.