Introduce task statistics script

scripts/statistics/task_statistics.py

@@ -0,0 +1,306 @@
+#!/usr/bin/env python3
+
+# -------------------------------------------------------------------------
+#
+#  Part of the CodeChecker project, under the Apache License v2.0 with
+#  LLVM Exceptions. See LICENSE for license information.
+#  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+#
+# -------------------------------------------------------------------------
+
+import argparse
+import dataclasses
+import json
+import sys
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+from typing import Optional
+
+
+def print_dict(d: dict):
+    max_key_length = max(map(len, d.keys()))
+    for k, v in d.items():
+        print(f"{k + ':':<{max_key_length + 2}}{v}")
+
+
+def parse_date(date: str) -> datetime:
+    return datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
+
+
+def date_diff(a: datetime, b: datetime) -> float:
+    return (b - a).total_seconds()
+
+
+@dataclass
+class DataStats:
+    min: float = 0
+    max: float = 0
+    avg: float = 0
+
+
+class EventType(Enum):
+    WAIT_START = 1
+    PROCESS_START = 2
+    COMPLETED = 3
+
+
+@dataclass
+class TaskEvent:
+    date: datetime
+    type: EventType
+    duration: float = 0
+
+
+@dataclass
+class DataPoint:
+    date: datetime
+    waiting_count: int = 0
+    processing_count: int = 0
+    completed_count: int = 0
+    waiting_stats: Optional[DataStats] = None
+    processing_stats: Optional[DataStats] = None
+
+
+def calc_stats(float_list: list[float]) -> DataStats:
+    return DataStats(
+            min=min(float_list),
+            max=max(float_list),
+            avg=round(sum(float_list) / len(float_list), 2))
+
+
+@dataclass
+class ProcessingResult:
+    total_status_count: dict = field(default_factory=dict)
+    total_waiting_stats: Optional[DataStats] = None
+    total_processing_stats: Optional[DataStats] = None
+    intervals: list[DataPoint] = field(default_factory=list)
+
+
+def check(data):
+    print("Checking JSON file ...")
+
+    if not isinstance(data, list):
+        print("Error: Invalid JSON file!")
+
+    if len(data) == 0:
+        sys.exit("Error: No task data is available!")
+
+    required_fields = ["status", "enqueuedAt", "startedAt", "completedAt"]
+
+    for e in data:
+        for r in required_fields:
+            if r not in e:
+                print("Error: Invalid JSON file!")
+                print(f"Field '{r}' is missing in item {e}")
+                sys.exit(1)
+
+    print("JSON file is valid.")
+
+
+def process(data, interval_duration: int) -> ProcessingResult:
+    check(data)
+
+    # Convert date strings to datetime objects.
+    for e in data:
+        for k in ["enqueuedAt", "startedAt", "completedAt"]:
+            e[k] = parse_date(e[k]) if e[k] else None
+
+    events: list[TaskEvent] = []
+    total_waiting_durations: list[float] = []
+    total_process_durations: list[float] = []
+    status_count: dict[str, int] = {}
+
+    for e in data:
+        status: str = e["status"]
+        enqueued_at: datetime = e["enqueuedAt"]
+        started_at: datetime = e["startedAt"]
+        completed_at: datetime = e["completedAt"]
+
+        if status not in status_count:
+            status_count[status] = 1
+        else:
+            status_count[status] += 1
+
+        if status == "COMPLETED":
+            waiting_dur: float = date_diff(enqueued_at, started_at)
+            processing_dur: float = date_diff(started_at, completed_at)
+
+            total_waiting_durations.append(waiting_dur)
+            total_process_durations.append(processing_dur)
+
+            events.append(TaskEvent(date=enqueued_at,
+                                    type=EventType.WAIT_START,
+                                    duration=waiting_dur))
+
+            events.append(TaskEvent(date=started_at,
+                                    type=EventType.PROCESS_START,
+                                    duration=processing_dur))
+
+            events.append(TaskEvent(date=completed_at,
+                                    type=EventType.COMPLETED))
+
+    events.sort(key=lambda e: e.date)
+
+    intervals: list[DataPoint] = [DataPoint(date=events[0].date)]
+    counter_waiting: int = 0
+    counter_processing: int = 0
+    waiting_durations: list[float] = []
+    processing_durations: list[float] = []
+    last: DataPoint = intervals[-1]
+
+    for e in events:
+        if (interval_duration != 0 and
+                date_diff(last.date, e.date) > interval_duration):
+
+            # Closing last interval
+            last.waiting_count = counter_waiting
+            last.processing_count = counter_processing
+            last.waiting_stats = calc_stats(waiting_durations) \
+                if waiting_durations else None
+            last.processing_stats = calc_stats(processing_durations) \
+                if processing_durations else None
+            waiting_durations = []
+            processing_durations = []
+
+            # Create new interval
+            intervals.append(DataPoint(date=e.date))
+            last = intervals[-1]
+
+        if e.type == EventType.WAIT_START:
+            counter_waiting += 1
+            waiting_durations.append(e.duration)
+        elif e.type == EventType.PROCESS_START:
+            counter_waiting -= 1
+            counter_processing += 1
+            processing_durations.append(e.duration)
+        elif e.type == EventType.COMPLETED:
+            counter_processing -= 1
+
+    return ProcessingResult(
+        total_status_count=status_count,
+        total_waiting_stats=calc_stats(total_waiting_durations)
+        if total_waiting_durations else None,
+        total_processing_stats=calc_stats(total_process_durations)
+        if total_process_durations else None,
+        intervals=intervals)
+
+
+def present(result: ProcessingResult,
+            plot: bool, plot_sum: str,
+            plot_engine: str):
+    print("--- Total Status Count ---")
+    print_dict(result.total_status_count)
+    print("--- Total Waiting in Queue Time Statistics ---")
+    print_dict({k: str(v) + " secs"
+               for k, v in vars(result.total_waiting_stats).items()})
+    print("--- Total Processing Time Statistics ---")
+    print_dict({k: str(v) + " secs"
+               for k, v in vars(result.total_processing_stats).items()})
+
+    if plot:
+        import matplotlib
+        import matplotlib.pyplot as plt
+
+        matplotlib.use(plot_engine)
+
+        iv = result.intervals
+        fig, axs = plt.subplots(2, 2)
+        fig.suptitle("Serverside Task Statistics")
+
+        x = [i.date for i in iv]
+        y = [i.waiting_count for i in iv]
+        axs[0, 0].step(x, y, 'y')
+        axs[0, 0].set_ylabel("Number of tasks waiting in queue")
+
+        x = [i.date for i in iv]
+        y = [i.processing_count for i in iv]
+        axs[1, 0].step(x, y)
+        axs[1, 0].set_ylabel("Number of tasks being processed")
+
+        iv_wait = list(filter(lambda e: e.waiting_stats, iv))
+        x = [i.date for i in iv_wait]
+        y = [getattr(i.waiting_stats, plot_sum) for i in iv_wait]
+        axs[0, 1].step(x, y, 'y')
+        axs[0, 1].set_ylabel(
+            f"{plot_sum.upper()} waiting in queue time (secs)")
+
+        iv_process = list(filter(lambda e: e.processing_stats, iv))
+        x = [i.date for i in iv_process]
+        y = [getattr(i.processing_stats, plot_sum) for i in iv_process]
+        axs[1, 1].step(x, y)
+        axs[1, 1].set_ylabel(
+            f"{plot_sum.upper()} task processing time (secs)")
+
+        try:
+            plt.show()
+        except KeyboardInterrupt:
+            pass
+
+
+def get_plot_sum_choices():
+    return list(map(lambda e: e.name, dataclasses.fields(DataStats)))
+
+
+class CustomArgFormatter(argparse.RawTextHelpFormatter,
+                         argparse.ArgumentDefaultsHelpFormatter):
+    pass
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(formatter_class=CustomArgFormatter,
+                                     description="""
+This script can be used to display useful metrics about the server-side tasks.
+The required input is a JSON file, which can be generated using the
+\"CodeChecker cmd serverside-tasks\" command. Using the --plot option,
+you can generate graphs showing various statistics. This requires matplotlib
+to be installed.""")
+
+    parser.add_argument("json_file", help="""
+JSON file containg a list of serverside tasks.
+This file can be acquired by running the command below:
+CodeChecker cmd serverside-tasks --url <server_url> --enqueued-after <date1>
+    --enqueued-before <date2> --output json > out.json""")
+
+    parser.add_argument('-i', '--interval',
+                        dest="interval_duration",
+                        type=int,
+                        required=False,
+                        default=60,
+                        help="""
+Interval duration in seconds. Task events are grouped into intervals of this
+duration to compute various metrics, calculated separately for each
+interval.""")
+
+    parser.add_argument('--plot',
+                        dest="plot",
+                        action="store_true",
+                        required=False,
+                        default=False,
+                        help="""
+Displays the statistics plot. This requires matplotlib to be installed.""")
+
+    parser.add_argument('--plot-summary',
+                        dest="plot_sum",
+                        type=str,
+                        required=False,
+                        choices=get_plot_sum_choices(),
+                        default="avg",
+                        help="""
+Specifies the statistics shown in the right-hand plots.""")
+
+    parser.add_argument('--plot-engine',
+                        dest="plot_engine",
+                        type=str,
+                        required=False,
+                        default="Qt5Agg",
+                        help="""
+Defines which rendering engine matplotlib uses.""")
+
+    args = parser.parse_args()
+    with open(args.json_file, encoding="utf-8") as f:
+        json_data = json.load(f)
+        present(process(json_data, args.interval_duration),
+                args.plot,
+                args.plot_sum,
+                args.plot_engine)