- provide access to the data.
"""
+import multiprocessing
+import os
import re
import pandas as pd
import logging
-import xml.etree.ElementTree as ET
from robot.api import ExecutionResult, ResultVisitor
from robot import errors
from string import replace
from os import remove
+from input_data_files import download_and_unzip_data_file
+
class ExecutionChecker(ResultVisitor):
"""Class to traverse through the test suite structure.
REGEX_MRR = re.compile(r'MaxReceivedRate_Results\s\[pkts/(\d*)sec\]:\s'
r'tx\s(\d*),\srx\s(\d*)')
- def __init__(self, **metadata):
+ def __init__(self, metadata):
"""Initialisation.
:param metadata: Key-value pairs to be included in "metadata" part of
self._version = str(re.search(self.REGEX_VERSION, msg.message).
group(2))
self._data["metadata"]["version"] = self._version
+ self._data["metadata"]["generated"] = msg.timestamp
self._msg_type = None
- logging.info(" VPP version: {0}".format(self._version))
-
def _get_vat_history(self, msg):
"""Called when extraction of VAT command history is required.
self._cfg = spec
# Data store:
- self._input_data = None
+ self._input_data = pd.Series()
@property
def data(self):
return self.data[job][build]["tests"]
@staticmethod
- def _parse_tests(job, build):
+ def _parse_tests(job, build, log):
"""Process data from robot output.xml file and return JSON structured
data.
:param job: The name of job which build output data will be processed.
:param build: The build which output data will be processed.
+ :param log: List of log messages.
:type job: str
:type build: dict
+ :type log: list of tuples (severity, msg)
:returns: JSON data structure.
:rtype: dict
"""
- tree = ET.parse(build["file-name"])
- root = tree.getroot()
- generated = root.attrib["generated"]
+ metadata = {
+ "job": job,
+ "build": build
+ }
with open(build["file-name"], 'r') as data_file:
try:
result = ExecutionResult(data_file)
except errors.DataError as err:
- logging.error("Error occurred while parsing output.xml: {0}".
- format(err))
+ log.append(("ERROR", "Error occurred while parsing output.xml: "
+ "{0}".format(err)))
return None
- checker = ExecutionChecker(job=job, build=build, generated=generated)
+ checker = ExecutionChecker(metadata)
result.visit(checker)
return checker.data
- def read_data(self):
- """Parse input data from input files and store in pandas' Series.
+ def _download_and_parse_build(self, pid, data_queue, job, build, repeat):
+ """Download and parse the input data file.
+
+ :param pid: PID of the process executing this method.
+ :param data_queue: Shared memory between processes. Queue which keeps
+ the result data. This data is then read by the main process and used
+ in further processing.
+ :param job: Name of the Jenkins job which generated the processed input
+ file.
+ :param build: Information about the Jenkins build which generated the
+ processed input file.
+ :param repeat: Repeat the download specified number of times if not
+ successful.
+ :type pid: int
+ :type data_queue: multiprocessing.Manager().Queue()
+ :type job: str
+ :type build: dict
+ :type repeat: int
+ """
+
+ logs = list()
+
+ logging.info(" Processing the job/build: {0}: {1}".
+ format(job, build["build"]))
+
+ logs.append(("INFO", " Processing the job/build: {0}: {1}".
+ format(job, build["build"])))
+
+ state = "failed"
+ success = False
+ data = None
+ do_repeat = repeat
+ while do_repeat:
+ success = download_and_unzip_data_file(self._cfg, job, build, pid,
+ logs)
+ if success:
+ break
+ do_repeat -= 1
+ if not success:
+ logs.append(("ERROR", "It is not possible to download the input "
+ "data file from the job '{job}', build "
+ "'{build}', or it is damaged. Skipped.".
+ format(job=job, build=build["build"])))
+ if success:
+ logs.append(("INFO", " Processing data from the build '{0}' ...".
+ format(build["build"])))
+ data = InputData._parse_tests(job, build, logs)
+ if data is None:
+ logs.append(("ERROR", "Input data file from the job '{job}', "
+ "build '{build}' is damaged. Skipped.".
+ format(job=job, build=build["build"])))
+ else:
+ state = "processed"
+
+ try:
+ remove(build["file-name"])
+ except OSError as err:
+ logs.append(("ERROR", "Cannot remove the file '{0}': {1}".
+ format(build["file-name"], err)))
+ logs.append(("INFO", " Done."))
+
+ result = {
+ "data": data,
+ "state": state,
+ "job": job,
+ "build": build,
+ "logs": logs
+ }
+ data_queue.put(result)
+
+ def download_and_parse_data(self, repeat=1):
+ """Download the input data files, parse input data from input files and
+ store in pandas' Series.
+
+ :param repeat: Repeat the download specified number of times if not
+ successful.
+ :type repeat: int
"""
- logging.info("Parsing input files ...")
+ logging.info("Downloading and parsing input files ...")
+
+ work_queue = multiprocessing.JoinableQueue()
+
+ manager = multiprocessing.Manager()
+
+ data_queue = manager.Queue()
+
+ cpus = multiprocessing.cpu_count()
+ workers = list()
+ for cpu in range(cpus):
+ worker = Worker(work_queue,
+ data_queue,
+ self._download_and_parse_build)
+ worker.daemon = True
+ worker.start()
+ workers.append(worker)
+ os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
+ format(cpu, worker.pid))
- job_data = dict()
for job, builds in self._cfg.builds.items():
- logging.info(" Extracting data from the job '{0}' ...'".
- format(job))
- builds_data = dict()
for build in builds:
- if build["status"] == "failed" \
- or build["status"] == "not found":
- continue
- logging.info(" Extracting data from the build '{0}'".
- format(build["build"]))
- logging.info(" Processing the file '{0}'".
- format(build["file-name"]))
-
- data = InputData._parse_tests(job, build)
-
- logging.info(" Removing the file '{0}'".
- format(build["file-name"]))
- try:
- remove(build["file-name"])
- build["status"] = "processed"
- except OSError as err:
- logging.error(" Cannot remove the file '{0}': {1}".
- format(build["file-name"], err))
- if data is None:
- logging.error("Input data file from the job '{job}', build "
- "'{build}' is damaged. Skipped.".
- format(job=job, build=build["build"]))
- continue
+ work_queue.put((job, build, repeat))
+
+ work_queue.join()
+
+ logging.info("Done.")
+
+ while not data_queue.empty():
+ result = data_queue.get()
+ job = result["job"]
+ build_nr = result["build"]["build"]
+
+ if result["data"]:
+ data = result["data"]
build_data = pd.Series({
"metadata": pd.Series(data["metadata"].values(),
index=data["metadata"].keys()),
"suites": pd.Series(data["suites"].values(),
index=data["suites"].keys()),
"tests": pd.Series(data["tests"].values(),
- index=data["tests"].keys()),
- })
- builds_data[str(build["build"])] = build_data
- logging.info(" Done.")
+ index=data["tests"].keys())})
+
+ if self._input_data.get(job, None) is None:
+ self._input_data[job] = pd.Series()
+ self._input_data[job][str(build_nr)] = build_data
+
+ self._cfg.set_input_file_name(job, build_nr,
+ result["build"]["file-name"])
+
+ self._cfg.set_input_state(job, build_nr, result["state"])
+
+ for item in result["logs"]:
+ if item[0] == "INFO":
+ logging.info(item[1])
+ elif item[0] == "ERROR":
+ logging.error(item[1])
+ elif item[0] == "DEBUG":
+ logging.debug(item[1])
+ elif item[0] == "CRITICAL":
+ logging.critical(item[1])
+ elif item[0] == "WARNING":
+ logging.warning(item[1])
+
+ del data_queue
- job_data[job] = pd.Series(builds_data.values(),
- index=builds_data.keys())
- logging.info(" Done.")
+ # Terminate all workers
+ for worker in workers:
+ worker.terminate()
+ worker.join()
- self._input_data = pd.Series(job_data.values(), index=job_data.keys())
logging.info("Done.")
@staticmethod
merged_data[ID] = item_data
return merged_data
+
+
+class Worker(multiprocessing.Process):
+ """Worker class used to download and process input files in separate
+ parallel processes.
+ """
+
+ def __init__(self, work_queue, data_queue, func):
+ """Initialization.
+
+ :param work_queue: Queue with items to process.
+ :param data_queue: Shared memory between processes. Queue which keeps
+ the result data. This data is then read by the main process and used
+ in further processing.
+ :param func: Function which is executed by the worker.
+ :type work_queue: multiprocessing.JoinableQueue
+ :type data_queue: multiprocessing.Manager().Queue()
+ :type func: Callable object
+ """
+ super(Worker, self).__init__()
+ self._work_queue = work_queue
+ self._data_queue = data_queue
+ self._func = func
+
+ def run(self):
+ """Method representing the process's activity.
+ """
+
+ while True:
+ try:
+ self.process(self._work_queue.get())
+ finally:
+ self._work_queue.task_done()
+
+ def process(self, item_to_process):
+ """Method executed by the runner.
+
+ :param item_to_process: Data to be processed by the function.
+ :type item_to_process: tuple
+ """
+ self._func(self.pid, self._data_queue, *item_to_process)
Code Review / csit.git / blobdiff