102 lines
3.1 KiB
Python
Executable File
102 lines
3.1 KiB
Python
Executable File
#!/bin/python3
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"""
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This script performs a build status analysis on the outputs of the workflow
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to generate tables that can then be plotted by another program.
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"""
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import argparse
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import csv
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import os
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import datetime
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def analysis(input_table):
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"""
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Analyzes the given build status table to count the results of the building
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of the Dockerfile for each category.
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Parameters
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----------
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input_table: str
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Table to analyse.
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Returns
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-------
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dict
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Output table of the analysis in the form of a dict with headers as keys.
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"""
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# All build status, initialized to 0.
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# This is required to make the column of the result table deterministic,
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# so they can be determined without the header in the CSV file.
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buildstatus = {"success":0, "package_install_failed":0, "baseimage_unavailable":0, "artifact_unavailable":0, "dockerfile_not_found":0, "script_crash":0, "job_time_exceeded":0, "unknown_error":0}
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for row in input_table:
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# Third column is the result:
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buildstatus[row[2]] += 1
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return buildstatus
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def main():
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# Command line arguments parsing:
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parser = argparse.ArgumentParser(
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prog = "buildstatus_analysis",
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description =
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"""
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This script performs a build status analysis on the outputs of the
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workflow to generate tables that can then be plotted by another program.
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The generated table gives the amount of images that have been
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built successfully, and the amount of images that failed to build,
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for each category of error.
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"""
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)
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parser.add_argument(
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"-v", "--verbose",
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action = "store_true",
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help = "Shows more details on what is being done."
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)
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parser.add_argument(
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"-i", "--input",
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action = "append",
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nargs = "+",
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help =
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"""
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The CSV file used as input for the analysis function. Multiple files
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can be specified at once by separating them with a space.
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All the input files must be build status logs generated by ECG.
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""",
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required = True
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)
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parser.add_argument(
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"-o", "--output",
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help =
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"""
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Path to the output CSV file that will be created by the analysis function.
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""",
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required = True
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)
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args = parser.parse_args()
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inputs = args.input
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output_path = args.output
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# Parsing the input files:
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input_table = []
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for i in inputs:
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for path in i:
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input_file = open(path)
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input_table += list(csv.reader(input_file))
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input_file.close()
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# Analyzing the inputs:
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output_dict = analysis(input_table)
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# Adding the current time to every row:
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now = datetime.datetime.now()
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timestamp = str(datetime.datetime.timestamp(now))
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output_dict["timestamp"] = timestamp
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# Writing analysis to output file:
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output_file = open(output_path, "w+")
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dict_writer = csv.DictWriter(output_file, fieldnames=output_dict.keys())
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# dict_writer.writeheader()
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dict_writer.writerow(output_dict)
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output_file.close()
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if __name__ == "__main__":
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main() |