thongonary / cmsspark

CMSSpark

Set of spark scripts to parse and extract useful aggregated info from various CMS data streams on HDFS. So far, it supports DBS, PhEDEx, AAA, EOS, CMSSW data parsing on HDFS.

Here are few examples how to get various stats:

# First set proper paths (or run setup.sh instead) 
export PYTHONPATH=$PYTHONPATH:$PWD/src/python
export PATH=$PWD/bin:$PATH

# DBS+PhEDEx
apatterns="*BUNNIES*,*Commissioning*,*RelVal*"
hdir=hdfs:///cms/users/qnguyen/datasets
run_spark dbs_phedex.py --fout=$hdir --antipatterns=$apatterns --yarn --verbose

# Send data to CERN MONIT, user must provide stomppy egg and AMQ JSON broker file
run_spark cern_monit.py --hdir=$hdir --stomp=static/stomp.py-4.1.15-py2.7.egg --amq=amq_broker.json

# DBS+CMSSW
run_spark dbs_cmssw.py --verbose --yarn --fout=hdfs:///cms/users/qnguyen/cmssw --date=20170411

# DBS+AAA
run_spark dbs_aaa.py --verbose --yarn --fout=hdfs:///cms/users/qnguyen/aaa --date=20170411

# DBS+EOS
run_spark dbs_eos.py --verbose --yarn --fout=hdfs:///cms/users/qnguyen/eos --date=20170411

# WMArchive examples:
run_spark wmarchive.py --fout=hdfs:///cms/users/qnguyen/wma --date=20170411
run_spark wmarchive.py --fout=hdfs:///cms/users/qnguyen/wma --date=20170411,20170420 --yarn
run_spark wmarchive.py --fout=hdfs:///cms/users/qnguyen/wma --date=20170411-20170420 --yarn

# To list the output files in HDFS:
hadoop fs -ls /cms/users/qnguyen/

# Copy to local:
hadoop fs -get /cms/users/qnguyen/wma .

Please note: in order to run cern_monit.py script user must supply two additional parameters. The StompAMQ library file and AMQ credentials. The former is located in static are of this package. The later contains CERN MONIT end-point parameters and should be individually obtained from CERN MONIT team. For example

run_spark cern_monit.py --hdir=/cms/users/qnguyen/datasets --amq=amq_broker.json --stomp=/path/stomp.py-4.1.15-py2.7.egg

CMS metadata

CMS metadata are stored in the following location on HDFS and accessing from analytix cluster:

• DBS: /project/awg/cms/CMS_DBS3_PROD_GLOBAL/ (full DB dump in CSV data-format)
• CMSSW: /project/awg/cms/cmssw-popularity (daily snapshots in avro data-format)
• JobMonitoring: /project/awg/cms/jm-data-popularity (daily snapshots in avro data-format)
• JobMonitoring: /project/awg/cms/job-monitoring (daily snapshots in avro data-format)
• PhedexReplicas: /project/awg/cms/phedex/block-replicas-snapshots (daily snapshots in CSV data-format)
• PhedexCatalog: /project/awg/cms/phedex/catalog (daily snapshots in CSV data-format)
• AAA: /project/monitoring/archive/xrootd (daily snapshots in JSON data-format)
• EOS: /project/monitoring/archive/eos (daily snapshots in JSON data-format)
• WMArchive: /cms/wmarchive/avro (daily snapshots in Avro data-format)

Aggregating data and preparing reports

It is possible to aggregate PhEDEx and DBS data and prepare reports with tables and plots visualizing the data.

Initialization

Before running the scripts please run ./src/bash/reports_init to clone wiki repository which contains reports. If you don't intend to submit reports to github wiki this step is not necessary.

Aggregating by data tier

Please run ./src/bash/report_tiers/aggregate to download PhEDEx and DBS data aggregated by data tier.

Then run python src/python/CMSSpark/reports/visualize_tiers.py to generate the report which will be available here: src/bash/CERNTasks.wiki/CMS_Tier_Reports.md

Scripts should be ran from this directory: src/bash/report_tiers

Aggregating by campaign

Please run ./src/bash/report_campaigns/aggregate download PhEDEx and DBS data aggregated by campaign.

Then run python src/python/CMSSpark/reports/visualize_campaigns.py to generate the report which will be available here: src/bash/CERNTasks.wiki/CMS_Campaign_Reports.md

Scripts should be ran from this directory: src/bash/report_campaigns

Aggregating leftover datasets

Please follow instructions these instructions.