Metadata-Version: 2.1
Name: slurm2sql
Version: 0.9.0
Summary: Import Slurm accounting database from sacct to sqlite3 database
Home-page: https://github.com/NordicHPC/slurm2sql
Author: Richard Darst
License: UNKNOWN
Keywords: slurm sqlite3
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: System Administrators
Classifier: Topic :: Database
Classifier: Topic :: System :: Clustering
Classifier: Topic :: System :: Distributed Computing
Requires-Python: >= 2.7, >=3.4

Convert Slurm accounting database to sqlite3 file
=================================================

This contains one utility, ``slurm2sql``, which uses the `Slurm
<https://slurm.schedmd.com/overview>`__ 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.


