def _start_workflows_and_record_start_time(
workflow_name: str,
workflow_range: (int, int),
workers: int = WORKERS_DEFAULT_COUNT) -> pd.DataFrame:
logger.info(f"Starting {workflow_range} workflows...")
df = create_empty_dataframe_for_started_results()
with concurrent.futures.ThreadPoolExecutor(
max_workers=workers) as executor:
futures = [
executor.submit(_start_single_workflow,
build_extended_workflow_name(workflow_name, i))
for i in range(workflow_range[0], workflow_range[1] + 1)
]
for future in concurrent.futures.as_completed(futures):
try:
workflow_name, asked_to_start_datetime = future.result()
df = df.append(
{
"name": workflow_name,
"asked_to_start_date": asked_to_start_datetime,
},
ignore_index=True,
)
except Exception as e:
logger.error(e)
return df
def monitor(workflow: str, sleep: int) -> None:
"""Start periodically collect defined metrics and save them to JSON file.
This function is blocking.
"""
_print_metrics()
logger.info("Starting monitoring...")
all_metrics = {}
metrics_parameters = {
"workflow": workflow,
}
try:
while True:
# if metrics will take, for example, couple of seconds to collect monitored_date will be less accurate
monitored_date = get_utc_now_timestamp()
collected_metrics = _collect_metrics(metrics_parameters)
all_metrics[monitored_date] = collected_metrics
_save_metrics(workflow, all_metrics)
time.sleep(sleep)
except KeyboardInterrupt:
logger.info("Stopping monitoring...")
finally:
_save_metrics(workflow, all_metrics)
def _save_plots(plots: List[Tuple[str, Figure]], workflow: str,
workflow_range: Tuple[int, int]) -> None:
logger.info("Saving plots...")
for base_name, figure in plots:
path = Path(
f"{workflow}_{base_name}_{workflow_range[0]}_{workflow_range[1]}.png"
)
figure.savefig(path)
def _merge_workflows_and_started_results(
workflows: pd.DataFrame,
started_results: pd.DataFrame) -> pd.DataFrame:
"""Merge workflows status results and recorded started results.
Required columns: name (workflow_name)
"""
logger.info("Merging workflows and started results...")
return workflows.merge(started_results, on=["name"], how="left")
def start(workflow_name: str, workflow_range: (int, int),
workers: int) -> None:
"""Start already submitted workflows."""
started_results = _start_workflows_and_record_start_time(
workflow_name, workflow_range, workers)
started_results = _append_to_existing_started_results(
workflow_name, started_results)
_save_started_results(workflow_name, started_results)
logger.info("Finished starting workflows.")
def _append_to_existing_started_results(
workflow_name: str, new_results: pd.DataFrame) -> pd.DataFrame:
"""Append new started results to existing started results and return them."""
results_path = build_started_results_path(workflow_name)
existing_results = pd.DataFrame()
if results_path.exists():
logger.info("Loading existing started results. Appending...")
existing_results = pd.read_csv(results_path)
return existing_results.append(new_results, ignore_index=True)
def _build_plots(df: pd.DataFrame,
plot_parameters: Dict) -> List[Tuple[str, Figure]]:
logger.info("Building plots...")
plots = []
for build_plot in [
_build_execution_progress_plot,
_build_execution_status_plot,
_build_total_time_histogram,
_build_runtime_histogram,
_build_pending_time_histogram,
]:
plot_base_name, figure = build_plot(df, plot_parameters)
plots.append((plot_base_name, figure))
return plots
def _create_and_upload_workflows(
workflow: str,
workflow_range: (int, int),
file: Optional[str] = None,
workers: int = WORKERS_DEFAULT_COUNT,
) -> None:
logger.info(f"Creating and uploading {workflow_range} workflows...")
with concurrent.futures.ThreadPoolExecutor(
max_workers=workers) as executor:
futures = [
executor.submit(
_create_and_upload_single_workflow,
build_extended_workflow_name(workflow, i),
file,
) for i in range(workflow_range[0], workflow_range[1] + 1)
]
for future in concurrent.futures.as_completed(futures):
# collect results, in case of exception, it will be raised here
future.result()
def _clean_results(df: pd.DataFrame) -> pd.DataFrame:
logger.info("Cleaning results...")
# fix "-" values for created status
df.loc[df["status"] == "created", "started"] = None
df.loc[df["status"] == "created", "ended"] = None
df["asked_to_start_date"] = df.apply(
lambda row: None
if pd.isna(row["asked_to_start_date"]) else row["asked_to_start_date"],
axis=1,
)
# fix "-" values for running, pending, queued statuses
df.loc[df["status"] == "running", "ended"] = None
df.loc[df["status"] == "pending", "started"] = None
df.loc[df["status"] == "pending", "ended"] = None
df.loc[df["status"] == "queued", "started"] = None
df.loc[df["status"] == "queued", "ended"] = None
return df
def collect(workflow_prefix: str, force: bool) -> None: # noqa: D103
results_path = build_started_results_path(workflow_prefix)
if results_path.exists():
started_results = pd.read_csv(results_path)
else:
logger.warning("Started results are not found.")
started_results = create_empty_dataframe_for_started_results()
workflows = _get_workflows(workflow_prefix)
if _workflows_finished(workflows) or force:
results = _merge_workflows_and_started_results(workflows,
started_results)
results = _clean_results(results)
collect_datetime = get_utc_now_timestamp()
results["collected_date"] = [collect_datetime] * len(results)
_save_collected_results(workflow_prefix, results)
logger.info(f"Collected {len(results)} workflows. Finished.")
else:
logger.info(
"Not collecting. Workflows are still running. Use -f option to force collect."
)
def _derive_metrics(df: pd.DataFrame) -> pd.DataFrame:
logger.info("Deriving metrics...")
df["workflow_number"] = df.apply(
lambda row: _get_workflow_number_from_name(row["name"]), axis=1)
def _calculate_difference(row: pd.Series, start_column: str,
end_column: str) -> Optional[int]:
"""Calculate difference between two date times in string format."""
start_date = row[start_column]
end_date = row[end_column]
start_date_exists = not pd.isna(start_date)
end_date_exists = not pd.isna(end_date)
if start_date_exists and end_date_exists:
return _convert_str_date_to_epoch(
end_date) - _convert_str_date_to_epoch(start_date)
return None
df["pending_time"] = df.apply(
partial(
_calculate_difference,
start_column="asked_to_start_date",
end_column="started",
),
axis=1,
)
df["runtime"] = df.apply(
partial(_calculate_difference,
start_column="started",
end_column="ended"),
axis=1,
)
return df
def submit(workflow_prefix: str, workflow_range: (int, int), file: str,
workers: int) -> None:
"""Submit multiple workflows, do not start them."""
_create_and_upload_workflows(workflow_prefix, workflow_range, file,
workers)
logger.info("Finished creating and uploading workflows.")
def _save_started_results(workflow_name: str, df: pd.DataFrame) -> None:
logger.info("Saving started results...")
results_path = build_started_results_path(workflow_name)
df.to_csv(results_path, index=False)
def _print_metrics() -> None:
logger.info("Following metrics will be collected:")
for m in METRICS:
logger.info(f"- {m.name}")
def _save_collected_results(workflow: str, df: pd.DataFrame):
logger.info("Saving collected results...")
results_path = build_collected_results_path(workflow)
df.to_csv(results_path, index=False)
