slurm2sql 0.9.0

Import Slurm accounting database from sacct to sqlite3 database

This contains one utility, slurm2sql, which uses the Slurm workload manager’s sacct, to export all statistics from jobs and load them to a well-formed sqlite3 file. This file can then be queried for analytics much more easily than the raw database or your own exports.

Installation

There is only a single file with no dependencies. Python greater than 2.7 is required.

Usage

Sample usage:

slurm2sql.py [output_db] -- [sacct selection options]

For example, to get all data from July and August (-S) for all users (-a):

slurm2sql.py sincejuly.sqlite3 -- -S 2019-07-1 -a

To get the data from the last N days. This will, day by day, get each of these history and cumulatively update the database. This updates a database by default, so that it can be used every day in order to efficiently keep a running database. The -u option means “don’t delete existing database” (jobs with the same JobID get updated, not duplicated):

slurm2sql.py --history-days=N -u sincejuly.sqlite3 -- -a

The --history-start=YYYY-MM-DD option can do a similar thing starting from a certain day, and --history=DD-HH:MM:SS starts collecting from a given interval of time ago (the time format is as in Slurm).

It can also be used from Python as what is essentially a glorified parser:

db = sqlite3.connect(':memory:')
slurm2sql.slurm2sql(db, ['-S', '2019-08-26'])

# For example, you can then convert to a dataframe:
import pandas as pd
df = pd.read_sql('SELECT * FROM slurm', db)

Database format

There is one table with name slurm.

There is one row for each item returned by sacct.

In general, there is one column for each item returned by sacct, but some of them are converted into a more useful form. Some columns are added by re-processing other columns. In general, just use the source. See COLUMNS in slurm2sql.py for details. Extra columns can easily be added.

There are two types of converter functions: easy ones, which map one slurm column directly to a database column via a function, and line functions, which take the whole row and can do arbitrary remixing of the data.

Columns

All column values are converted to standard units: bytes (not MB, KB, etc), seconds, fraction 0.0-1.0 for things like percentages.

Below are some notable columns which do not exist in sacct. It’s good to verify that any of our custom columns make sense before trusting them. For other columns, check man sacct.

• Time: approximation of last active time of a job. The first of these that exists: End, Start, Submitted. This is intended to be used when you need to classify a job by when it ran, but you don’t care to be that specific. (Only the Time column is indexed by default, not the other times)

• Submit, Start, End: like the sacct equivalents, but unixtime. Assume that the sacct timestamps are in localtime of the machine doing the conversion. (slurm2sql.unixtime converts slurm-format timestamp to unixtime)

• Job IDs. Slurm Job ID is by default of format JobID.JobStep or ArrayJobID_ArrayTaskID.JobStep. Furthermore, each array job has a “Raw JobID” (different for each job, and is an actual JobID) in addition to the “ArrayJobID” which is the same for all jobs in an array. We split all of these different IDs into the following fields:

  • JobID: Only the integer Job ID, without the trailing array tasks or job IDs. For array jobs, this is the “Raw JobID” as described above, use ArrayJobID to filter jobs that are the same. Integer

  • ArrayJobID: The common array ID for all jobs in an array - only. For non-array jobs, same as JobID. Integer or null.

  • ArrayTaskID: As used above. Integer on null.

  • JobStep: Job step - only. If you SQL filter for StepID is null you get only the main allocations. String.

  • JobIDSlurm: The raw output from sacct JobID field, including . and _. String.

• ReqMem: The raw slurm value in a format like “5Gn”. Instead of parsing this, you probably want to use one of the other values below.

• ReqMemNode, ReqMemCPU: Requested memory per node or CPU, either taken from ReqMem (if it matches) or computed (you might want to check our logic if you rely on this). In Slurm, you can request memory either per-node or per-core, and this calculates the other one for you.

• ReqMemType: c if the user requested mem-per-core originally, n if mem-per-node. Extracted from ReqMem.

• ReqMemRaw: The numeric value of the ReqMem, whether it is c or n.

• ReqGPU: Number of GPUs requested. Extracted from ReqGRES.

• GPU information. At Aalto we store GPU usage information in the Comment field in JSON of the form {"gpu_util": NN.NN, "gpu_max_mem": NN, "ngpu": N}. This extracts information from that.

  • GPUMem: Max amount of memory used from any GPU. Note: all GPU stats require a separate Aalto-developed script.

  • GPUEff: Percent usage of the GPUs (0.0-1.0).

  • NGPU: Number of GPUs. Should be the same as ReqGPU, but who knows.

• MemEff: Memory efficiency (0.0-1.0). Like in seff. We compute it ourselves, so it could be wrong. Test before trusting! There can still be corner cases, job steps may be off, etc. This also relies on memory reporting being correct, which may not be the case…

• CPUEff: CPU efficiency (0.0-1.0). All the same caveats as above apply: test before trusting.

Quick reference of the other most important columns from the accounting database:

• Elapsed: Wall clock time

• CPUTime: Reserved CPU time (Elapsed * number of CPUs). CPUEff ≈ TotalCPU/CPUTime = TotalCPU/(NCPUs x Elapsed)

• TotalCPU: SystemCPU + TotalCPU, seconds of productive work.

Development and maintenance

This could be considered functional alpha right or almost beta now.

Release process:

python setup.py sdist bdist_wheel
twine upload [--repository-url https://test.pypi.org/legacy/] dist/*0.9.0*

Originally developed at Aalto University, Finland.