Skip to main content

A package for processing PBS Pro accounting records

Project description

pbsparse

A library for loading and processing PBS Professional accounting records.

Details

The PBS Professional scheduler stores event records for each created job in plain-text files called accounting logs. While these records do not contain all of the information presented in the qstat command, they provide a large subset and are useful for gauging system usage patterns, debugging job issues, and more.

PBS Pro does provide the tracejob command to query the accounting logs on the command line.

This pbsparse library allows for easy loading of PBS Pro accounting records into Python scripts for analysis. All of the difficult work of robustly parsing each accounting record is handled for you.

Installation

Simply install from PyPI using pip:

$ python3 -m pip install pbsparse

Usage

The main interface to load records is the get_pbs_records function. For example, if you wanted to get all jobs by their start record, you could use the following:

from pbsparse import get_pbs_records

job_starts = get_pbs_records("/pbs/accounting/20250301", type_filter = "S")

This function allows for extensive filtering options.

You can also use the PbsRecord class directly:

from pbsparse import PbsRecord

with open("/pbs/accounting/20250301", "r") as pbs_file:
    for line in pbs_file:
        # Read data into object and process metadata
        record = PbsRecord(record, True)
        print(record)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pbsparse-0.2.6.tar.gz (10.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pbsparse-0.2.6-py3-none-any.whl (10.9 kB view details)

Uploaded Python 3

File details

Details for the file pbsparse-0.2.6.tar.gz.

File metadata

  • Download URL: pbsparse-0.2.6.tar.gz
  • Upload date:
  • Size: 10.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for pbsparse-0.2.6.tar.gz
Algorithm Hash digest
SHA256 a69a80b956a1c85f76797773aa4bc33962c90bc8746d0b062c2cf7ff98fcbb4d
MD5 4f9224fdf6892dc3ac74206f75a4d52d
BLAKE2b-256 f07b7ab2ec15f00b636e4abba3cf8d279ea89fc6884e377a325474d5099b2311

See more details on using hashes here.

Provenance

The following attestation bundles were made for pbsparse-0.2.6.tar.gz:

Publisher: publish-to-pypi.yml on NCAR/pbsparse

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pbsparse-0.2.6-py3-none-any.whl.

File metadata

  • Download URL: pbsparse-0.2.6-py3-none-any.whl
  • Upload date:
  • Size: 10.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for pbsparse-0.2.6-py3-none-any.whl
Algorithm Hash digest
SHA256 83ae0a1cb2789fd02a31c01c341390ee0d230d36c9a8fdb1c2ec96ad4b14074c
MD5 bf59e16590907944d1be05b6848b32fd
BLAKE2b-256 2d9028f44cc514bb5d47a94756f6d662e9a689bfacea567988b972861f3c903d

See more details on using hashes here.

Provenance

The following attestation bundles were made for pbsparse-0.2.6-py3-none-any.whl:

Publisher: publish-to-pypi.yml on NCAR/pbsparse

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page