Data Monitoring Procedures
- 1 Saving Online Monitoring Data
- 2 Launching and Tracking Jobs
- 3 Running Over Data As It Comes In (DEPRECATED)
- 4 Extracting Summary Data
- 5 Data Versions
Saving Online Monitoring Data
The procedure for writing the data out is given in, e.g., Raid-to-Silo Transfer Strategy.
Once the DAQ writes out the data to the raid disk, cron jobs will copy the file to tape, and within ~20 min., we will have access to the file on tape at /mss/halld/$RUN_PERIOD/rawdata/RunXXXXXX.
All online monitoring plugins will be run as data is taken. They will be accessible within the counting house via RootSpy, and for each run and file, a ROOT file containing the histograms will be saved within a subdirectory for each run.
For immediate access to these files, the raid disk files may be accessed directly from the counting house, or the tape files will be available within ~20 min. of the file being written out.
Launching and Tracking Jobs
- This section details instructions on how to create and launch a set of jobs using the Hall-D job management system developed by Mark Ito. These instructions are generic: this system can be used for the weekly monitoring jobs, but can also be used for other sets of job launches as well.
Database Table Overview
- Job management database table (<project_name>): For each input file, keeps track of whether or not a job for it has been submitted, along with other optional fields.
- Job status database table (<project_name>Job (no space)): For each job, keeps track of the job-id, the job status, memory used, cpu & wall time, time taken to complete various stages (e.g. pending, dependency, active), and others.
Initialize Project Management
- Log into the ifarm machine with one of the gxproj accounts
ssh gxproj1@ifarm -Y
- Go to the project scripts folder and add the perl script directory to the current $PATH environment variable:
cd ~/halld/jproj/scripts/ source setup.csh
- Come up with a name for your job submission project. It will be a unique identifier for the current set of job submissions. For example, for the 10th pass over the 10/2014 data for the offline monitoring:
- However, the output file name format changed during the 10/2014 commissioning run (hd_raw_* --> hd_rawdata_*). Since these scripts assume a fixed file name format, for these runs an additional identifier should be used, e.g.:
- Copy and rename an existing set of project files to create new project files for your project(s). For example:
cd ~/halld/jproj/projects/ cp -r offmon_rp2014m10_v10_type1 offmon_rp2014m10_v11_type1 cp -r offmon_rp2014m10_v10_type2 offmon_rp2014m10_v11_type2
- For each project, descend into the new directory, and make changes to each file so that it will work for your project. These changes typically include:
- Changing the project name (e.g. offmon_rp2014m10_v10_type1 --> offmon_rp2014m10_v11_type1) in both the .jproj and .jsub file names, and in the contents of each file.
- If the project version number has changed, update it in the contents of the .jsub file.
- If the run period has changed, update it in the contents of each file (e.g. RunPeriod-2014-10 --> RunPeriod-2015-01).
- If the path or file name format for the input files have changed, update them in the .jproj and .jsub files.
- Any other changes to the execution script, environment variables, or job submission instructions can be made in the appropriate files.
Project File Overview
An overview of each project file:
- clear.sh: For the current project, deletes the job status and management database tables (if any), and creates new, empty ones.
- <project_name>.jproj: Contains the path and file name format for the input files for the jobs.
- <project_name>.jsub: The xml job submission script. The run number and file number variables are set during job submission for each input file.
- script.sh: The script that is executed during the job. If output job directories are not pre-created manually, they should be created in this script with the proper permissions:
mkdir -p -m 775 my_directory
- setup_jlab.csh: The environment that is sourced at the beginning of the job execution.
- status.sh: Updates the job status database table, and prints some of its columns to screen.
- Delete (if any) and create the database table(s) for the current set of job submissions:
- Search for input files matching the string in the .jproj file, and create a row for each in the job management database table (called <project_name>). You can test by adding an optional argument at the end, which only selects files with a specific file number:
jproj.pl <project_name> update <optional_file_number>
- Confirm that the job management database is accurate by printing it's contents to screen:
mysql -hhalldweb1 -ufarmer farming -e "select * from <project_name>"
- ONLY if a mistake was made, to delete the tables from the database and recreate new, empty ones, run:
- Submit the unsubmitted jobs in the job management database, and add their job ids to the job status database:
jproj.pl <project_name> submit
- To look at the status of the submitted jobs, first query auger and update the job status database:
- The job status can then be viewed by submitting a query to the job status database (called <project_name>Job (no space in between)):
mysql -hhalldweb1 -ufarmer farming -e "select id,run,file,jobId,hostname,status,timeSubmitted,timeActive,walltime,cput,timeComplete,result,error from <project_name>Job"
- These last two commands can instead be executed simultaneously by running:
Handy mysql Instructions
- Handy mysql instructions:
mysql -hhalldweb1 -ufarmer farming # Enter the "farming" mysql database on "halldweb1" as user "farmer" quit; # Exit mysql show tables; # Show a list of the tables in the current database show columns from <project_name>; # show all of the columns for the given table select * from <project_name>; # show the contents of all rows from the given table
Running Over Data As It Comes In (DEPRECATED)
A special user gxproj1 will have a cron job set up to run the plugins as new data appears on /mss. During the week, gxproj1 will submit offline plugin jobs with the same setup as the weekly jobs run the previous Friday. The procedure for this is shown below.
Setting up the environment
is sourced through .tcshrc. This file is the same as what is linked to by
except HALLD_HOME, HDDS_HOME, and JANA_CALIB_URL are set separately so that this user can have a separate build.
To obtain the builds from the previous Friday's runs, execute
/home/gxproj1/halld/monitoring/newruns/setup_previous.sh [year] [month] [day]
The build revisions from the previous Friday are archived in files
and the script will build libraries based on those stored revision numbers.
Running the cron job
To run the cron job go to
To check whether the cron job is running, do
To remove the cron job do
The cron job will run the script scan_for_jobs.sh, which runs generatejobs_plugins_rawdata.sh for any new runs that it had not seen before. All previous runs are recorded in the file filelists/files_current.txt so clear this to run over runs, or set the parameters MINRUN and MAXRUN which will set the range of runs submitted.
Extracting Summary Data
For high-level monitoring, we save images of selected histograms and store time series of selected quantities in a database, which are then displayed on a web page. This section describes how to generate the monitoring images and database information.
The scripts used to generate this summary data are currently kept in /u/home/gluex/halld/monitoring/process Note that these scripts currently have some parameters which must be periodically set by hand.
The default python version on most JLab machine does not have the modules to allow these scripts to connect to the MySQL database. To run these scripts, load the environment with the following command
There are two scripts for running over the monitoring data generated by the online system and offline reconstruction. The online script can be run with either of the following commands:
./check_new_runs.py OR ./check_new_runs.csh
The shell script sets up the environment properly to run the python script. To connect to the monitoring database on the JLab CUE, modules continued in the installation of python >= 2.7 are needed. The shell script is appropriate to use in a cron job.
The online monitoring system copies a ROOT file containing the results of the online monitoring, and other configuration files into a directory accessible outside the counting house. This python script automatically checks for new ROOT files, which it will then automatically process. It contains several configuration variables that must be correctly set, which contains the location of input/output directories, etc...
Note that while this script is current run as a cronjob, the processing of online ROOT files is currently disabled, so its only function it to update the run_info database.
After the data is run over, the results should be processed, so that summary data is entered into the monitoring database and plots are made for the monitoring webpages. Currently, this processing is controlled by a cronjob that runs the following script:
This script checks for new ROOT files, and only runs over those it hasn't processed yet. Since one monitoring ROOT file is produced for each EVIO file, whenever a new file is produced, the plots for the corresponding run are recreated and all the ROOT files for a run are combined into one file. Information is stored in the database on a per-file basis.
Plots for the monitoring web page can be made from single histograms or multiple histograms using RootSpy macros. If you want to change the list of plots made, you must modify one of the following files:
- histograms_to_monitor - specify either the name of the histogram or its the full ROOT path
- macros_to_monitor - specify the full path to the RootSpy macro .C file
When a new monitoring run is started, or the conditions are changed, the following steps should be taken to process the new files:
- Add a new data version, as described below:
- Change the following parameters in check_monitoring_data.csh:
- JOBDATE should correspond to the ouptut date used by the job submission script
- OUTPUTDIR should correspond to the directory corresponding to the run period and revision corresponding to the new version you just submitted. Presumably, this directory will be empty at the beginning.
- Once you create a new data version as defined below, you should pass the needed information as a command line option. Currently this is done by the ARGS variable. For example, the argument "-v RunPeriod-2014-10,8" tells the monitoring scripts to look up the version corresponding to revision 8 of RunPeriod-2014-10 in the monitoring DB and to use to store the results.
Example configuration parameters: set JOBDATE=2015-01-09 set INPUTDIR=/volatile/halld/RunPeriod-2014-10/offline_monitoring set OUTPUTDIR=/w/halld-scifs1a/data_monitoring/RunPeriod-2014-10/ver08 set ARGS=" -v RunPeriod-2014-10,8 "
If you want to process the results manually, the data is processed using the following script:
./process_new_offline_data.py <input directory> <output directory> EXAMPLE: ./process_new_offline_data.py 2014-11-14 /volatile/halld/RunPeriod-2014-10/offline_monitoring/ /w/halld-scifs1a/data_monitoring/RunPeriod-2014-10/ver02
The python script takes several options to enable/disable various steps in the processing. Of interest is the "--force" option, which will run over all monitoring ROOT files, whether or not they've been previously identified.
Every time a new reconstruction pass is performed, a new version number must be generated. To do this, prepare a version file as described below. Then run the register_new_version.py script to store the information in the database. The script will return a version number, which then should be set by hand in process_new_offline_data.py - future versions of the script will streamline this part of the procedure. An example of how to generate a new version is:
./register_new_version.py add /u/home/gluex/halld/monitoring/process/versions/vers_RunPeriod-2014-10_pass1.txt
Currently the run_info database is being updated by Sean by hand. Note that this must be done inside the counting house. If you want to do this yourself, check out the monitoring scripts on a gluon machine
svn co https://halldsvn.jlab.org/repos/trunk/scripts/monitoring/process/
In the process/get_conds directory, run the process_runlog_files.py script with the maximum and minimum run number that you want to process, e.g.
./process_runlog_files.py -b 2200 -e 2260
To document the conditions of the monitoring data that is created, for the sake of reproducability and further analysis we save several pieces of information. The format is intended to be comprehensive enough to document not just monitoring data, but versions of raw and reconstructed data, so that this database table can be used for the event database as well.
We store one record per pass through one run period, with the following structure:
|data_type||The level of data we are processing. For the purposes of monitoring, "rawdata" is the online monitoring, "recon" is the offline monitoring|
|run_period||The run period of the data|
|revision||An integer specifying which pass through the run period this data corresponds to|
|software_version||The name of the XML file that specifies the different software versions used|
|jana_config||The name of the text file that specifies which JANA options were passed to the reconstruction program|
|ccdb_context||The value of JANA_CALIB_CONTEXT, which specifies the version of calibration constants that were used|
|production_time||The data at which monitoring/reconstruction began|
|dataVersionString||A convenient string for identifying this version of the data|
An example file used as as input to ./register_new_version.py is:
data_type = recon run_period = RunPeriod-2014-10 revision = 1 software_version = soft_comm_2014_11_06.xml jana_config = jana_rawdata_comm_2014_11_06.conf ccdb_context = calibtime=2014-11-10 production_time = 2014-11-10 dataVersionString = recon_RunPeriod-2014-10_20141110_ver01