Welcome to Infpyng !

Introduction

Infpyng is a powerful python script which use fping to probe endpoint through ICMP and parsing the output to Influxdb. The result can then be visualize easily through Grafana.

• Infpyng is perhaps your alternative to SmokePing
• You can add dynamic hosts without restarting script
• Custom Polling time configuration
• Low resource consumption
• Docker compatibility

Benchmark

Those tests were performed from CentOS 8 with 1 CPU and 2 GB of memory

IP to ping	IP reachable	Finished in
474	454	11 seconds
1299	1197	13 seconds
2653	2552	28 seconds
3388	3262	32 seconds

Screenshots

Requirements

• Grafana 6.7.x
  • Plugin : grafana-multibar-graph-panel
• Influxdb 1.8.x

Installation

You must bind the configuration files with the container during the run for Infpyng to work normally, otherwise an error will appear in the systemd log.

Docker usage

1. Pull the image from hub.docker

   # docker pull oijkn/infpyng

2. Run the container to add your config/hosts files

   docker run -d \
       --name infpyng \
       --hostname docker-infpyng \
       --restart unless-stopped \
       --mount src=/dir/from/host/infpyng/config,target=/infpyng/config,type=bind \
       --mount src=/dir/from/host/infpyng.log,target=/infpyng/infpyng.log,type=bind \
       --log-driver=journald \
       --log-opt tag="{{.Name}}" \
       --env TZ=Europe/Paris \
   oijkn/infpyng
   

   Note: You must have config files on your host and edit them according to your environment and adapt the TZ=Europe/Paris depending on your location.

SSH to Infpyng Docker

The command started using docker exec only runs while the container’s is running, and it is not restarted if the container is restarted.

1. Retrieve container id

   # docker ps -a

   CONTAINER ID	IMAGE	COMMAND	CREATED	STATUS	PORTS	NAMES
   5591dbd111e5	oijkn/infpyng:1.0.0	"python infpyng.py"	13 seconds ago	Up 11 seconds		infpyng

2. Retrieve container id

   # docker exec -it 5591dbd111e5 sh

3. Show log file

   /app/infpyng # cat /var/log/infpyng.log
   2020-05-28 08:54:16 root INFO :: Settings loaded successfully
   2020-05-28 08:54:16 root INFO :: Init InfluxDB successfully
   2020-05-28 08:54:16 root INFO :: Starting Infpyng Multiprocessing v1.0.0
   2020-05-28 08:54:16 root INFO :: Polling time every 300s
   2020-05-28 08:54:16 root INFO :: Total of targets : 5
   2020-05-28 08:54:16 root INFO :: Multiprocessing : 40
   2020-05-28 08:54:16 root INFO :: Buckets : 5
   2020-05-28 08:54:20 root INFO :: Targets alive : 5
   2020-05-28 08:54:20 root INFO :: Targets unreachable : 0
   2020-05-28 08:54:20 root INFO :: Data written to DB successfully
   2020-05-28 08:54:20 root INFO :: Finished in : 4.44 seconds
   2020-05-28 08:54:20 root INFO :: ---------------------------------------
   

Docker Compose usage (Stack)

Multi-container Docker app built from the following services:

• InfluxDB - time series database
• Chronograf - admin UI for InfluxDB
• Grafana - visualization UI for InfluxDB

Useful for quickly setting up a monitoring stack for performance testing. Please refer to this link Infpyng-stack to create a performance testing environment in minutes.

Github usage

1. Download Infpyng project

   # cd /dir/from/host/
# git clone https://github.com/oijkn/infpyng.git
# pip install -r requirements.txt

2. Ensure correct permission on *.py files

   # chmod -R +x /somewhere/in/your/host/infpyng/*.py

3. Edit your custom settings (conf + hosts)

   # vi /dir/from/host/infpyng/config/config.toml
# vi /dir/from/host/infpyng/config/hosts.toml

4. Run Infpyng python script

   # python /dir/from/host/infpyng/infpyng.py &

Grafana

Grafana allows you to query and visualize metrics stored in InfluxDB.

You can use my dashboard example or you can create your own.

Logger

By default the Infpyng logs are located in /var/log/infpyng.log

2020-05-26 09:19:41 root INFO :: Settings loaded successfully
2020-05-26 09:19:41 root INFO :: Init InfluxDB successfully
2020-05-26 09:19:41 root INFO :: Starting Infpyng Multiprocessing v1.0.0
2020-05-26 09:19:41 root INFO :: Polling time every 300s
2020-05-26 09:19:41 root INFO :: Total of targets : 1883  
2020-05-26 09:19:41 root INFO :: Multiprocessing : 40  
2020-05-26 09:19:41 root INFO :: Buckets : 47  
2020-05-26 09:19:51 root INFO :: Targets alive : 1883  
2020-05-26 09:19:51 root INFO :: Targets unreachable : 0  
2020-05-26 09:19:51 root INFO :: Data written to DB successfully  
2020-05-26 09:19:51 root INFO :: Finished in : 9.94 seconds  

Metrics

Format

• infpyng
  • tags:
    • host (host name)
    • target
  • fields:
    • packets_transmitted (integer)
    • packets_received (integer)
    • percent_packets_loss (float)
    • average_response_ms (float)
    • minimum_response_ms (float)
    • maximum_response_ms (float)

Example Output

infpyng,country=de,host=TIG,server=germany,target=facebook.de average_response_ms=21.2,maximum_response_ms=21.8,minimum_response_ms=20.7,packets_received=2i,packets_transmitted=2i,percent_packet_loss=0i 1589193188000000000  

Github contributors lib

• High performance ping tool
• Python lib for TOML
• Python client for InfluxDB

Licensing

This project is released under the terms of the MIT Open Source License. View LICENSE file for more information.