forked from 3wordchant/capsul-flask
235 lines
6.8 KiB
Python
235 lines
6.8 KiB
Python
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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from functools import reduce
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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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max_value = reduce(lambda a, b: a if a > b else b, y, scale)
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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, max_value, 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) |