glances

Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.

26,585

1,521

26,585

182

View on GitHub

Top Related Projects

netdata

70,358

Architected for speed. Automated for easy. Monitoring and troubleshooting, transformed!

htop

6,293

htop - an interactive process viewer

psutil

10,240

Cross-platform lib for process and system monitoring in Python

prometheus

54,803

The Prometheus monitoring system and time series database.

The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

Quick Overview

Glances is a cross-platform system monitoring tool written in Python. It provides a comprehensive overview of system resources including CPU, memory, disk, network, and processes, all displayed in a single terminal window. Glances can also be used as a web application or to export data to various databases and messaging systems.

Pros

Cross-platform compatibility (Linux, macOS, Windows)
Rich set of features including real-time monitoring, web interface, and REST API
Highly customizable with plugins and export capabilities
Low resource consumption compared to other monitoring tools

Cons

May require additional dependencies for certain features
Some advanced features might be overwhelming for casual users
Limited graphical representations compared to GUI-based monitoring tools
Occasional issues with specific system configurations or hardware

Getting Started

To install Glances using pip:

pip install glances

To run Glances in the terminal:

glances

To start Glances in web server mode:

glances -w

Then access the web interface at http://localhost:61208.

For more advanced usage, refer to the official documentation at https://glances.readthedocs.io/.

Competitor Comparisons

netdata

70,358

Architected for speed. Automated for easy. Monitoring and troubleshooting, transformed!

Pros of Netdata

More comprehensive and detailed real-time monitoring with a focus on performance analysis
Highly customizable dashboard with a wide range of plugins and integrations
Better suited for large-scale infrastructure monitoring

Cons of Netdata

Higher resource consumption, especially for CPU and memory
Steeper learning curve due to its extensive features and configuration options

Code Comparison

Netdata configuration example:

[global]
    update every = 1
    memory mode = ram
    history = 3600
    access log = none
    error log = syslog

Glances configuration example:

[global]
refresh=2
history_size=28800

Summary

Netdata offers more advanced monitoring capabilities and customization options, making it suitable for complex infrastructures. However, it comes with higher resource usage and a steeper learning curve. Glances, on the other hand, provides a simpler, lightweight monitoring solution with lower resource consumption, but may lack some of the advanced features and detailed analytics offered by Netdata. The choice between the two depends on the specific monitoring needs and available resources of the user's environment.

htop

6,293

htop - an interactive process viewer

Pros of htop

Lightweight and fast, with minimal system resource usage
Intuitive ncurses-based interface for easy navigation
Allows for interactive process management (kill, nice, etc.)

Cons of htop

Limited to system resource monitoring and process management
Lacks advanced features like network monitoring or disk I/O stats
No built-in support for remote monitoring or web interface

Code Comparison

htop (C):

static void Process_writeCommand(Process* this, int attr) {
    int baseattr = attr;
    RichString_begin(out);
    RichString_appendWide(&out, baseattr, this->comm);
    RichString_end(out);
}

Glances (Python):

def get_process_tree(self):
    tree = {}
    for proc in psutil.process_iter(['name', 'ppid']):
        try:
            tree[proc.pid] = (proc.info['name'], proc.info['ppid'])
        except (psutil.NoSuchProcess, psutil.AccessDenied):
            pass
    return tree

Glances offers a more comprehensive system monitoring solution with support for various platforms, remote monitoring, and a web interface. It provides detailed information on CPU, memory, network, disk I/O, and more. However, it has higher resource usage compared to htop and may be considered overkill for simple process monitoring tasks.

btop

20,379

A monitor of resources

Pros of btop

More visually appealing interface with customizable themes and layouts
Lower resource usage, especially on systems with many cores
Faster refresh rate for real-time monitoring

Cons of btop

Limited cross-platform support (primarily Linux and macOS)
Fewer built-in monitoring features compared to Glances
Less extensive documentation and community support

Code Comparison

btop (C++):

void Cpu::draw(int height, int width) {
    // Draw CPU usage graph and statistics
    drawCpuBox(height, width);
    drawCpuGraph(height, width);
    drawCpuStats(height, width);
}

Glances (Python):

def update_views(self):
    # Update all plugin views
    for plugin in self.plugins:
        plugin.update_stats()
        plugin.update_views()

Both projects aim to provide system monitoring capabilities, but they differ in implementation and focus. btop emphasizes a modern, customizable UI with efficient performance, while Glances offers a more comprehensive set of monitoring features and broader platform support. btop is written in C++, which contributes to its lower resource usage, while Glances uses Python, making it more accessible for contributions and extensions. The choice between the two depends on specific monitoring needs, system resources, and desired user experience.

psutil

10,240

Cross-platform lib for process and system monitoring in Python

Pros of psutil

Lower-level library providing direct access to system information
Cross-platform support for major operating systems
Lightweight and focused on system and process utilities

Cons of psutil

Requires more coding to create a full monitoring solution
Less user-friendly for non-programmers
Lacks built-in visualization tools

Code Comparison

psutil:

import psutil

cpu_percent = psutil.cpu_percent()
memory = psutil.virtual_memory()
disk = psutil.disk_usage('/')

Glances:

from glances_api import GlancesApi

glances = GlancesApi()
cpu_percent = glances.getCpu()
memory = glances.getMem()
disk = glances.getFs()

Summary

psutil is a powerful, low-level library for accessing system and process information, offering cross-platform support and flexibility. It's ideal for developers building custom monitoring solutions or integrating system metrics into their applications.

Glances, on the other hand, provides a more comprehensive, out-of-the-box monitoring solution with a user-friendly interface and built-in visualization tools. It's better suited for system administrators or users who need a quick and easy way to monitor system performance without extensive coding.

While psutil offers more granular control and lower-level access, Glances provides a more complete monitoring solution with less setup required.

prometheus

54,803

The Prometheus monitoring system and time series database.

Pros of Prometheus

More scalable and designed for distributed systems monitoring
Powerful query language (PromQL) for complex data analysis
Extensive ecosystem with many exporters and integrations

Cons of Prometheus

Steeper learning curve and more complex setup
Requires additional components for long-term storage and visualization

Code Comparison

Glances (Python):

import psutil

cpu_percent = psutil.cpu_percent()
mem = psutil.virtual_memory()

Prometheus (Go):

import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promauto"
)

cpuTemp = promauto.NewGauge(prometheus.GaugeOpts{
    Name: "cpu_temperature_celsius",
    Help: "Current temperature of the CPU",
})

Summary

Glances is a lightweight, easy-to-use system monitoring tool written in Python, suitable for quick setups and individual system monitoring. It provides a comprehensive overview of system resources in a single interface.

Prometheus, on the other hand, is a more robust monitoring and alerting toolkit designed for complex, distributed systems. It offers powerful querying capabilities and scalability but requires more setup and infrastructure.

Choose Glances for simplicity and quick deployment on individual systems. Opt for Prometheus when dealing with large-scale, distributed environments that require advanced monitoring and alerting capabilities.

grafana

63,808

Pros of Grafana

More extensive visualization capabilities with a wide range of chart types and dashboards
Supports multiple data sources, allowing for integration with various databases and monitoring systems
Highly customizable and extensible through plugins and APIs

Cons of Grafana

Steeper learning curve and more complex setup compared to Glances
Requires additional configuration and maintenance for optimal performance
May be overkill for simple monitoring needs, especially on single systems

Code Comparison

Glances (Python):

import psutil

cpu_percent = psutil.cpu_percent()
mem = psutil.virtual_memory()
print(f"CPU: {cpu_percent}%, Memory: {mem.percent}%")

Grafana (JavaScript):

import { PanelCtrl } from 'app/plugins/sdk';

class CustomPanel extends PanelCtrl {
  onDataReceived(dataList) {
    // Process and visualize data
  }
}

Glances is a lightweight, easy-to-use system monitoring tool written in Python, while Grafana is a more comprehensive data visualization and monitoring platform. Glances excels in simplicity and quick setup for single-system monitoring, whereas Grafana offers powerful features for complex, multi-source data analysis and visualization across distributed systems.

Convert designs to code with AI

Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.

Try Visual Copilot

README

=============================== Glances - An eye on your system

.. |pypi| image:: https://img.shields.io/pypi/v/glances.svg :target: https://pypi.python.org/pypi/Glances

.. |starts| image:: https://img.shields.io/github/stars/nicolargo/glances.svg :target: https://github.com/nicolargo/glances/ :alt: Github stars

.. |docker| image:: https://img.shields.io/docker/pulls/nicolargo/glances :target: https://hub.docker.com/r/nicolargo/glances/ :alt: Docker pull

.. |pypistat| image:: https://pepy.tech/badge/glances/month :target: https://pepy.tech/project/glances :alt: Pypi downloads

.. |test| image:: https://github.com/nicolargo/glances/actions/workflows/ci.yml/badge.svg?branch=develop :target: https://github.com/nicolargo/glances/actions :alt: Linux tests (GitHub Actions)

.. |contributors| image:: https://img.shields.io/github/contributors/nicolargo/glances :target: https://github.com/nicolargo/glances/issues?q=is%3Aissue+is%3Aopen+label%3A%22needs+contributor%22 :alt: Contributors

.. |quality| image:: https://scrutinizer-ci.com/g/nicolargo/glances/badges/quality-score.png?b=develop :target: https://scrutinizer-ci.com/g/nicolargo/glances/?branch=develop :alt: Code quality

.. |sponsors| image:: https://img.shields.io/github/sponsors/nicolargo :target: https://github.com/sponsors/nicolargo :alt: Sponsors

.. |twitter| image:: https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40nicolargo :target: https://twitter.com/nicolargo :alt: @nicolargo

Summary

Glances is an open-source system cross-platform monitoring tool. It allows real-time monitoring of various aspects of your system such as CPU, memory, disk, network usage etc. It also allows monitoring of running processes, logged in users, temperatures, voltages, fan speeds etc. It also supports container monitoring, it supports different container management systems such as Docker, LXC. The information is presented in an easy to read dashboard and can also be used for remote monitoring of systems via a web interface or command line interface. It is easy to install and use and can be customized to show only the information that you are interested in.

.. image:: https://raw.githubusercontent.com/nicolargo/glances/develop/docs/_static/glances-summary.png

In client/server mode, remote monitoring could be done via terminal, Web interface or API (XML-RPC and RESTful). Stats can also be exported to files or external time/value databases, CSV or direct output to STDOUT.

.. image:: https://raw.githubusercontent.com/nicolargo/glances/develop/docs/_static/glances-responsive-webdesign.png

Glances is written in Python and uses libraries to grab information from your system. It is based on an open architecture where developers can add new plugins or exports modules.

Project sponsorship

You can help me to achieve my goals of improving this open-source project or just say "thank you" by:

sponsor me using one-time or monthly tier Github sponsors_ page
send me some pieces of bitcoin: 185KN9FCix3svJYp7JQM7hRMfSKyeaJR4X
buy me a gift on my wishlist_ page

Any and all contributions are greatly appreciated.

Requirements

python>=3.8 (use Glances 3.4.x for lower Python version)
psutil (better with latest version)
defusedxml (in order to monkey patch xmlrpc)
packaging (for the version comparison)
orjson (an optimized alternative to the standard json module)

Note for Python 2 users

Glances version 4 or higher do not support Python 2 (and Python 3 < 3.8). Please uses Glances version 3.4.x if you need Python 2 support.

Optional dependencies:

batinfo (for battery monitoring)
bernhard (for the Riemann export module)
cassandra-driver (for the Cassandra export module)
chevron (for the action script feature)
docker (for the Containers Docker monitoring support)
elasticsearch (for the Elastic Search export module)
FastAPI and Uvicorn (for Web server mode)
graphitesender (For the Graphite export module)
hddtemp (for HDD temperature monitoring support) [Linux-only]
influxdb (for the InfluxDB version 1 export module)
influxdb-client (for the InfluxDB version 2 export module)
jinja2 (for templating, used under the hood by FastAPI)
kafka-python (for the Kafka export module)
netifaces (for the IP plugin)
nvidia-ml-py (for the GPU plugin)
pycouchdb (for the CouchDB export module)
pika (for the RabbitMQ/ActiveMQ export module)
podman (for the Containers Podman monitoring support)
potsdb (for the OpenTSDB export module)
prometheus_client (for the Prometheus export module)
pygal (for the graph export module)
pymdstat (for RAID support) [Linux-only]
pymongo (for the MongoDB export module)
pysnmp-lextudio (for SNMP support)
pySMART.smartx (for HDD Smart support) [Linux-only]
pyzmq (for the ZeroMQ export module)
requests (for the Ports, Cloud plugins and RESTful export module)
sparklines (for the Quick Plugin sparklines option)
statsd (for the StatsD export module)
wifi (for the wifi plugin) [Linux-only]
zeroconf (for the autodiscover mode)

Installation

There are several methods to test/install Glances on your system. Choose your weapon!

PyPI: Pip, the standard way

Glances is on PyPI. By using PyPI, you will be using the latest stable version.

To install Glances, simply use the pip command line.

Warning: on modern Linux operating systems, you may have an externally-managed-environment error message when you try to use pip. In this case, go to the the PipX section below.

.. code-block:: console

pip install --user glances

Note: Python headers are required to install psutil_, a Glances dependency. For example, on Debian/Ubuntu the simplest is apt install python3-psutil or alternatively need to install first the python-dev package and gcc (python-devel on Fedora/CentOS/RHEL). For Windows, just install psutil from the binary installation file.

By default, Glances is installed without the Web interface dependencies. To install it, use the following command:

.. code-block:: console

pip install --user 'glances[web]'

For a full installation (with all features):

.. code-block:: console

pip install --user 'glances[all]'

To upgrade Glances to the latest version:

.. code-block:: console

pip install --user --upgrade glances

The current develop branch is published to the test.pypi.org package index. If you want to test the develop version (could be instable), enter:

.. code-block:: console

pip install --user -i https://test.pypi.org/simple/ Glances

PyPI: PipX, the alternative way

Install PipX on your system (apt install pipx on Ubuntu).

Install Glances (with all features):

.. code-block:: console

pipx install 'glances[all]'

The glances script will be installed in the ~/.local/bin folder.

Docker: the cloudy way

Glances Docker images are availables. You can use it to monitor your server and all your containers !

Get the Glances container:

.. code-block:: console

docker pull nicolargo/glances:latest-full

The following tags are availables:

latest-full for a full Alpine Glances image (latest release) with all dependencies
latest for a basic Alpine Glances (latest release) version with minimal dependencies (FastAPI and Docker)
dev for a basic Alpine Glances image (based on development branch) with all dependencies (Warning: may be instable)
ubuntu-latest-full for a full Ubuntu Glances image (latest release) with all dependencies
ubuntu-latest for a basic Ubuntu Glances (latest release) version with minimal dependencies (FastAPI and Docker)
ubuntu-dev for a basic Ubuntu Glances image (based on development branch) with all dependencies (Warning: may be instable)

Run last version of Glances container in console mode:

.. code-block:: console

docker run --rm -e TZ="${TZ}" -v /var/run/docker.sock:/var/run/docker.sock:ro -v /run/user/1000/podman/podman.sock:/run/user/1000/podman/podman.sock:ro --pid host --network host -it nicolargo/glances:latest-full

By default, the /etc/glances/glances.conf file is used (based on docker-compose/glances.conf).

Additionally, if you want to use your own glances.conf file, you can create your own Dockerfile:

.. code-block:: console

FROM nicolargo/glances:latest
COPY glances.conf /root/.config/glances/glances.conf
CMD python -m glances -C /root/.config/glances/glances.conf $GLANCES_OPT

Alternatively, you can specify something along the same lines with docker run options (notice the GLANCES_OPT environment variable setting parameters for the glances startup command):

.. code-block:: console

docker run -e TZ="${TZ}" -v $HOME/.config/glances/glances.conf:/glances.conf:ro -v /var/run/docker.sock:/var/run/docker.sock:ro -v /run/user/1000/podman/podman.sock:/run/user/1000/podman/podman.sock:ro --pid host -e GLANCES_OPT="-C /glances.conf" -it nicolargo/glances:latest-full

Where $HOME/.config/glances/glances.conf is a local directory containing your glances.conf file.

Run the container in Web server mode:

.. code-block:: console

docker run -d --restart="always" -p 61208-61209:61208-61209 -e TZ="${TZ}" -e GLANCES_OPT="-w" -v /var/run/docker.sock:/var/run/docker.sock:ro -v /run/user/1000/podman/podman.sock:/run/user/1000/podman/podman.sock:ro --pid host nicolargo/glances:latest-full

For a full list of options, see the Glances Docker_ documentation page.

GNU/Linux package

Glances is available on many Linux distributions, so you should be able to install it using your favorite package manager. Be aware that when you use this method the operating system package_ for Glances may not be the latest version and only basics plugins are enabled.

Note: The Debian package (and all other Debian-based distributions) do not include anymore the JS statics files used by the Web interface (see issue2021). If you want to add it to your Glances installation, follow the instructions: issue2021comment. In Glances version 4 and higher, the path to the statics file is configurable (see issue2612).

FreeBSD

To install the binary package:

.. code-block:: console

# pkg install py39-glances

To install Glances from ports:

.. code-block:: console

# cd /usr/ports/sysutils/py-glances/
# make install clean

macOS

If you do not want to use the glancesautoinstall script, follow this procedure.

macOS users can install Glances using Homebrew or MacPorts.

Homebrew


.. code-block:: console

    $ brew install glances

MacPorts

.. code-block:: console

$ sudo port install glances

Windows

Install Python_ for Windows (Python 3.4+ ship with pip) and then run the following command:

.. code-block:: console

$ pip install glances

Android

You need a rooted device and the Termux_ application (available on the Google Play Store).

Start Termux on your device and enter:

.. code-block:: console

$ apt update
$ apt upgrade
$ apt install clang python
$ pip install fastapi uvicorn jinja2
$ pip install glances

And start Glances:

.. code-block:: console

$ glances

You can also run Glances in server mode (-s or -w) in order to remotely monitor your Android device.

Source

To install Glances from source:

.. code-block:: console

$ wget https://github.com/nicolargo/glances/archive/vX.Y.tar.gz -O - | tar xz
$ cd glances-*
# python setup.py install

Note: Python headers are required to install psutil.

Chef

An awesome Chef cookbook is available to monitor your infrastructure: https://supermarket.chef.io/cookbooks/glances (thanks to Antoine Rouyer)

Puppet

You can install Glances using Puppet: https://github.com/rverchere/puppet-glances

Ansible

A Glances Ansible role is available: https://galaxy.ansible.com/zaxos/glances-ansible-role/

Usage

For the standalone mode, just run:

.. code-block:: console

$ glances

For the Web server mode, run:

.. code-block:: console

$ glances -w

and enter the URL http://<ip>:61208 in your favorite web browser.

For the client/server mode, run:

.. code-block:: console

$ glances -s

on the server side and run:

.. code-block:: console

$ glances -c <ip>

on the client one.

You can also detect and display all Glances servers available on your network or defined in the configuration file:

.. code-block:: console

$ glances --browser

You can also display raw stats on stdout:

.. code-block:: console

$ glances --stdout cpu.user,mem.used,load
cpu.user: 30.7
mem.used: 3278204928
load: {'cpucore': 4, 'min1': 0.21, 'min5': 0.4, 'min15': 0.27}
cpu.user: 3.4
mem.used: 3275251712
load: {'cpucore': 4, 'min1': 0.19, 'min5': 0.39, 'min15': 0.27}
...

or in a CSV format thanks to the stdout-csv option:

.. code-block:: console

$ glances --stdout-csv now,cpu.user,mem.used,load
now,cpu.user,mem.used,load.cpucore,load.min1,load.min5,load.min15
2018-12-08 22:04:20 CEST,7.3,5948149760,4,1.04,0.99,1.04
2018-12-08 22:04:23 CEST,5.4,5949136896,4,1.04,0.99,1.04
...

or in a JSON format thanks to the stdout-json option (attribute not supported in this mode in order to have a real JSON object in output):

.. code-block:: console

$ glances --stdout-json cpu,mem
cpu: {"total": 29.0, "user": 24.7, "nice": 0.0, "system": 3.8, "idle": 71.4, "iowait": 0.0, "irq": 0.0, "softirq": 0.0, "steal": 0.0, "guest": 0.0, "guest_nice": 0.0, "time_since_update": 1, "cpucore": 4, "ctx_switches": 0, "interrupts": 0, "soft_interrupts": 0, "syscalls": 0}
mem: {"total": 7837949952, "available": 2919079936, "percent": 62.8, "used": 4918870016, "free": 2919079936, "active": 2841214976, "inactive": 3340550144, "buffers": 546799616, "cached": 3068141568, "shared": 788156416}
...

and RTFM, always.

Documentation

For complete documentation have a look at the readthedocs_ website.

If you have any question (after RTFM!), please post it on the official Q&A forum_.

Gateway to other services

Glances can export stats to: CSV file, JSON file, InfluxDB, Cassandra, CouchDB, OpenTSDB, Prometheus, StatsD, ElasticSearch, RabbitMQ/ActiveMQ, ZeroMQ, Kafka, Riemann, Graphite and RESTful server.

How to contribute ?

If you want to contribute to the Glances project, read this wiki_ page.

There is also a chat dedicated to the Glances developers:

.. image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/nicolargo/glances?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge

Author

Nicolas Hennion (@nicolargo) nicolas@nicolargo.com

.. image:: https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40nicolargo :target: https://twitter.com/nicolargo

License

Glances is distributed under the LGPL version 3 license. See COPYING for more details.

Top Related Projects

Convert designs to code with AI

Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.

Try Visual Copilot