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commonHelpers
commonHelpers
 
 
data
data
 
 
maps
maps
 
 
pandaserver
pandaserver
 
 
scrapers
scrapers
 
 
test
test
 
 
writers
writers
 
 
.gitignore
.gitignore
 
 
.gitlab-ci.yml
.gitlab-ci.yml
 
 
Client.py
Client.py
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
downsample.py
downsample.py
 
 
getJobAttributes.py
getJobAttributes.py
 
 
requirements.txt
requirements.txt
 
 
run.sh
run.sh
 
 
run_1h.sh
run_1h.sh
 
 
run_downsample_1d.sh
run_downsample_1d.sh
 
 
run_downsample_1h.sh
run_downsample_1h.sh
 
 
run_scrapers.py
run_scrapers.py
 
 
writeDatadiskInfo.py
writeDatadiskInfo.py
 
 
writeDerivedQuantities.py
writeDerivedQuantities.py
 
 
writeJobStats.py
writeJobStats.py
 
 
writeQueueInfoMYSQL.py
writeQueueInfoMYSQL.py
 
 

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CI status Python 2.7 MIT license GitHub release GitHub tag Code style: black

Queue Monitoring

Low-latency PanDA queue monitoring

This repository gathers the various scripts and tools that are necessary for QMonit — a lightweight monitoring tool for PanDA queues with a latency of only 10 minutes.

Table of contents

  1. Introduction
  2. Setup
  3. How To Run
  4. Example Likelihood

Introduction

The data in the InfluxDB instance is being visualised in MONIT Grafana.

At the moment, there are three main dashboards that are being fed by QMonit:

  • Jobs Monitoring : Low-latency monitoring of all ATLAS sites based on queue-level information from PanDA.
  • Suspicious sites : Built on top of the same low-latency information from PanDA, showing you sites where the current number of jobs varies drastically from their moving average.
  • DAOD distribution : Are we placing DAODs optimally on the grid? This dashboard compares analysis jobs running against each datadisk with the amount of DAODs placed on the datadisks.

Below is a figure of the rough technical setup of this project. The green blob cron jobs contains all the code that is tracked in this repository and is responsible for uploading, downloading and downsampling data as well as computing derived quantities that need to be readily available.

The cron jobs can be run on any machine, provided that you have the MySQL and InfluxDB python clients available, as well as a connection to the PanDA server (more on this below).

Technical details

Setup

You don't need to. This setup runs on an ADC Service Account on lxplus, so you don't have to deal with any of this. If you want to run it anyway, or if you want to work on this project, here is a couple of things you'll need.

Clone the repository:

git clone ssh://[email protected]:7999/eschanet/QMonit.git

Get the InfluxDB and MySQL python clients

pip install influxdb
pip install mysql-connector

Add the necessary cron jobs to your crontab. This is a bit hacky because CERN IT doesn't allow you to have jobs that run more than once an hour. That's why we run the same jobs 6 times in equidistant time intervals of 10 minutes ...

# Getting my actual data for InfluxDB   
0 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1  
10 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1  
20 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1
30 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1
40 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1
50 * * * * lxplus.cern.ch <path_to_QMonit_base>/run.sh >/dev/null 2>&1

# I also need to write some datadisk stuff once in a while. Once every hour is fine.
5 * * * * lxplus.cern.ch <path_to_QMonit_base>/run_1h.sh >/dev/null 2>&1

# Downsampling using Influx's CQs isn't really working for this usecase, so lets do it manually
15 * * * * lxplus.cern.ch <path_to_QMonit_base>/run_downsample_1h.sh >/dev/null 2>&1
6 0 * * * lxplus.cern.ch <path_to_QMonit_base>/run_downsample_1d.sh >/dev/null 2>&1

Last but not least, you'll need credentials to the databases. These are of course not included in this repository, but should, in general, be provided using a config.cfg file, placed at the base of the QMonit directory and providing the following:

[credentials]
password = <idb_and_mysql_pwd>
username = <idb_and_mysql_user>
database = <idb_and_mysql_db>

[credentials_elasticsearch]
password = <es_pwd>
username = <es_user>
host = <es_host>

[credentials_monit_grafana]
token = <token>
url = <url>

Feel free to get in touch if you want to help with this project and I'll provide the necessary credentials to you.

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Low latency computing queue monitoring for ATLAS

Topics

anomaly-detectionatlascerngrafanagrid-computinginfluxdblow-latencymonitoringsqlwlcg

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