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import matplotlib.ticker as ticker |
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import matplotlib.pyplot as pyplot |
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import matplotlib.dates as mdates |
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import requests |
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#import json |
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from datetime import datetime |
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from io import BytesIO |
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from flask import Blueprint |
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from flask import current_app |
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from flask import session |
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from flask import render_template, make_response |
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from werkzeug.exceptions import abort |
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from capsulflask.db import get_model |
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from capsulflask.auth import account_required |
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bp = Blueprint("metrics", __name__, url_prefix="/metrics") |
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durations = dict( |
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_5m=[60*5, 15], |
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_1h=[60*60, 60], |
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_1d=[60*60*24, 60*20], |
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_30d=[60*60*24*30, 60*300] |
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) |
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sizes = dict( |
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s=[0.77, 0.23, 4], |
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m=[1, 1, 2], |
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l=[6, 4, 1], |
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) |
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green = (121/255, 240/255, 50/255) |
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blue = (70/255, 150/255, 255/255) |
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red = (255/255, 50/255, 8/255) |
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@bp.route("/html/<string:metric>/<string:capsulid>/<string:duration>") |
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@account_required |
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def display_metric(metric, capsulid, duration): |
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vm = get_model().get_vm_detail(session["account"], capsulid) |
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return render_template( |
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"display-metric.html", |
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vm=vm, |
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duration=duration, |
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durations=list(map(lambda x: x.strip("_"), durations.keys())), |
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metric=metric |
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) |
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@bp.route("/<string:metric>/<string:capsulid>/<string:duration>/<string:size>") |
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@account_required |
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def metric_png(metric, capsulid, duration, size): |
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result = get_plot_bytes(metric, capsulid, duration, size) |
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if result[0] != 200: |
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abort(result[0]) |
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response = make_response(result[1]) |
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response.headers.set('Content-Type', 'image/png') |
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return response |
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def get_plot_bytes(metric, capsulid, duration, size): |
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duration = f"_{duration}" |
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if duration not in durations: |
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return (404, None) |
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if size not in sizes: |
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return (404, None) |
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vm = get_model().get_vm_detail(session["account"], capsulid) |
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if not vm: |
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return (404, None) |
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now_unix = int(datetime.strftime(datetime.now(), "%s")) |
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duration_seconds = durations[duration][0] |
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interval_seconds = durations[duration][1] * sizes[size][2] |
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if interval_seconds < 30: |
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interval_seconds = 30 |
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metric_queries = dict( |
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cpu=f"irate(libvirtd_domain_info_cpu_time_seconds_total{{domain='{capsulid}'}}[30s])", |
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memory=f"libvirtd_domain_info_memory_usage_bytes{{domain='{capsulid}'}}", |
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network_in=f"rate(libvirtd_domain_interface_stats_receive_bytes_total{{domain='{capsulid}'}}[{interval_seconds}s])", |
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network_out=f"rate(libvirtd_domain_interface_stats_transmit_bytes_total{{domain='{capsulid}'}}[{interval_seconds}s])", |
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disk=f"rate(libvirtd_domain_block_stats_read_bytes_total{{domain='{capsulid}'}}[{interval_seconds}s])%2Brate(libvirtd_domain_block_stats_write_bytes_total{{domain='{capsulid}'}}[{interval_seconds}s])", |
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) |
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scales = dict( |
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cpu=vm["vcpus"], |
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memory=vm["memory_mb"]*1024*1024, |
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network_in=1024*1024*2, |
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network_out=1024*200, |
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disk=1024*1024*2, |
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) |
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if metric not in metric_queries: |
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return (404, None) |
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range_and_interval = f"start={now_unix-duration_seconds}&end={now_unix}&step={interval_seconds}" |
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prometheus_range_url = f"{current_app.config['PROMETHEUS_URL']}/api/v1/query_range" |
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#print(f"{prometheus_range_url}?query={metric_queries[metric]}&{range_and_interval}") |
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prometheus_response = requests.get(f"{prometheus_range_url}?query={metric_queries[metric]}&{range_and_interval}") |
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if prometheus_response.status_code >= 300: |
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return (502, None) |
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time_series_data = list(map( |
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lambda x: (datetime.fromtimestamp(x[0]), float(x[1])), |
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prometheus_response.json()["data"]["result"][0]["values"] |
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)) |
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plot_bytes = draw_plot_bytes(time_series_data, scale=scales[metric], size_x=sizes[size][0], size_y=sizes[size][1]) |
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return (200, plot_bytes) |
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def draw_plot_bytes(data, scale, size_x=3, size_y=1): |
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pyplot.style.use("seaborn-dark") |
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fig, my_plot = pyplot.subplots(figsize=(size_x, size_y)) |
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# x=range(1, 15) |
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# y=[1,4,6,8,4,5,3,2,4,1,5,6,8,7] |
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divide_by = 1 |
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unit = "" |
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if scale > 1024 and scale < 1024*1024*1024: |
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divide_by = 1024*1024 |
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unit = "MB" |
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if scale > 1024*1024*1024: |
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divide_by = 1024*1024*1024 |
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unit = "GB" |
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scale /= divide_by |
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if scale > 10: |
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my_plot.get_yaxis().set_major_formatter( ticker.FuncFormatter(lambda x, p: "{}{}".format(int(x), unit)) ) |
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elif scale > 1: |
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my_plot.get_yaxis().set_major_formatter( ticker.FuncFormatter(lambda x, p: "{:.1f}{}".format(x, unit)) ) |
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else: |
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my_plot.get_yaxis().set_major_formatter( ticker.FuncFormatter(lambda x, p: "{:.2f}{}".format(x, unit)) ) |
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x=list(map(lambda x: x[0], data)) |
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y=list(map(lambda x: x[1]/divide_by, data)) |
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minutes = float((x[len(x)-1] - x[0]).total_seconds())/float(60) |
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hours = minutes/float(60) |
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days = hours/float(24) |
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day_locator = mdates.WeekdayLocator() |
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minute_locator = mdates.MinuteLocator() |
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ten_minute_locator = mdates.MinuteLocator(interval=10) |
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hour_locator = mdates.HourLocator(interval=6) |
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hour_minute_formatter = mdates.DateFormatter('%H:%M') |
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day_formatter = mdates.DateFormatter('%b %d') |
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if minutes < 10: |
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my_plot.xaxis.set_major_locator(minute_locator) |
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my_plot.xaxis.set_major_formatter(hour_minute_formatter) |
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elif hours < 2: |
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my_plot.xaxis.set_major_locator(ten_minute_locator) |
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my_plot.xaxis.set_major_formatter(hour_minute_formatter) |
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elif days < 2: |
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my_plot.xaxis.set_major_locator(hour_locator) |
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my_plot.xaxis.set_major_formatter(hour_minute_formatter) |
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else: |
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my_plot.xaxis.set_major_locator(day_locator) |
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my_plot.xaxis.set_major_formatter(day_formatter) |
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average=(sum(y)/len(y))/scale |
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average=average*1.25+0.1 |
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bg_color=color_gradient(average) |
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average -= 0.1 |
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fill_color=color_gradient(average) |
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highlight_color=lerp_rgb_tuples(fill_color, (1,1,1), 0.5) |
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my_plot.fill_between( x, scale, color=bg_color, alpha=0.13) |
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my_plot.fill_between( x, y, color=highlight_color, alpha=0.3) |
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my_plot.plot(x, y, 'r-', color=highlight_color) |
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my_plot.patch.set_facecolor('red') |
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my_plot.patch.set_alpha(0.5) |
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if size_y < 4: |
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my_plot.set_yticks([0, scale*0.5, scale]) |
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my_plot.set_ylim(0, scale) |
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my_plot.xaxis.label.set_color(highlight_color) |
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my_plot.tick_params(axis='x', colors=highlight_color) |
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my_plot.yaxis.label.set_color(highlight_color) |
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my_plot.tick_params(axis='y', colors=highlight_color) |
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if size_x < 4: |
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my_plot.set_xticklabels([]) |
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if size_y < 1: |
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my_plot.set_yticklabels([]) |
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image_binary = BytesIO() |
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fig.savefig(image_binary, transparent=True, bbox_inches="tight", pad_inches=0.05) |
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return image_binary.getvalue() |
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def lerp_rgb_tuples(a, b, lerp): |
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if lerp < 0: |
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lerp = 0 |
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if lerp > 1: |
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lerp = 1 |
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return ( |
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a[0]*(1.0-lerp)+b[0]*lerp, |
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a[1]*(1.0-lerp)+b[1]*lerp, |
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a[2]*(1.0-lerp)+b[2]*lerp |
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) |
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def color_gradient(value): |
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if value < 0: |
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value = 0 |
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if value > 1: |
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value = 1 |
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if value < 0.5: |
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return lerp_rgb_tuples(green, blue, value*2) |
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else: |
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return lerp_rgb_tuples(blue, red, (value-0.5)*2) |
