mirror of
https://github.com/evennia/evennia.git
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111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
"""
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Script that saves memory and idmapper data over time.
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Data will be saved to game/logs/memoryusage.log. Note that
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the script will append to this file if it already exists.
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Call this module directly to plot the log (requires matplotlib and numpy).
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"""
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from __future__ import division
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import os
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import sys
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import time
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# TODO!
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#sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
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#os.environ['DJANGO_SETTINGS_MODULE'] = 'game.settings'
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import evennia
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from evennia.utils.idmapper import models as _idmapper
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LOGFILE = "logs/memoryusage.log"
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INTERVAL = 30 # log every 30 seconds
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class Memplot(evennia.DefaultScript):
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"""
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Describes a memory plotting action.
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"""
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def at_script_creation(self):
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"Called at script creation"
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self.key = "memplot"
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self.desc = "Save server memory stats to file"
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self.start_delay = False
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self.persistent = True
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self.interval = INTERVAL
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self.db.starttime = time.time()
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def at_repeat(self):
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"Regularly save memory statistics."
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pid = os.getpid()
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rmem = float(os.popen('ps -p %d -o %s | tail -1' % (pid, "rss")).read()) / 1000.0 # resident memory
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vmem = float(os.popen('ps -p %d -o %s | tail -1' % (pid, "vsz")).read()) / 1000.0 # virtual memory
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total_num, cachedict = _idmapper.cache_size()
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t0 = (time.time() - self.db.starttime) / 60.0 # save in minutes
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with open(LOGFILE, "a") as f:
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f.write("%s, %s, %s, %s\n" % (t0, rmem, vmem, int(total_num)))
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if __name__ == "__main__":
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# plot output from the file
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from matplotlib import pyplot as pp
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import numpy
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data = numpy.genfromtxt("../../../game/" + LOGFILE, delimiter=",")
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secs = data[:, 0]
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rmem = data[:, 1]
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vmem = data[:, 2]
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nobj = data[:, 3]
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# calculate derivative of obj creation
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#oderiv = (0.5*(nobj[2:] - nobj[:-2]) / (secs[2:] - secs[:-2])).copy()
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#oderiv = (0.5*(rmem[2:] - rmem[:-2]) / (secs[2:] - secs[:-2])).copy()
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fig = pp.figure()
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ax1 = fig.add_subplot(111)
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ax1.set_title("1000 bots (normal accounts with light building)")
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ax1.set_xlabel("Time (mins)")
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ax1.set_ylabel("Memory usage (MB)")
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ax1.plot(secs, rmem, "r", label="RMEM", lw=2)
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ax1.plot(secs, vmem, "b", label="VMEM", lw=2)
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ax1.legend(loc="upper left")
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ax2 = ax1.twinx()
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ax2.plot(secs, nobj, "g--", label="objs in cache", lw=2)
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#ax2.plot(secs[:-2], oderiv/60.0, "g--", label="Objs/second", lw=2)
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#ax2.plot(secs[:-2], oderiv, "g--", label="Objs/second", lw=2)
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ax2.set_ylabel("Number of objects")
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ax2.legend(loc="lower right")
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ax2.annotate("First 500 bots\nconnecting", xy=(10, 4000))
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ax2.annotate("Next 500 bots\nconnecting", xy=(350, 10000))
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#ax2.annotate("@reload", xy=(185,600))
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# # plot mem vs cachesize
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# nobj, rmem, vmem = nobj[:262].copy(), rmem[:262].copy(), vmem[:262].copy()
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#
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# fig = pp.figure()
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# ax1 = fig.add_subplot(111)
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# ax1.set_title("Memory usage per cache size")
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# ax1.set_xlabel("Cache size (number of objects)")
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# ax1.set_ylabel("Memory usage (MB)")
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# ax1.plot(nobj, rmem, "r", label="RMEM", lw=2)
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# ax1.plot(nobj, vmem, "b", label="VMEM", lw=2)
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#
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# empirical estimate of memory usage: rmem = 35.0 + 0.0157 * Ncache
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# Ncache = int((rmem - 35.0) / 0.0157) (rmem in MB)
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#
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# rderiv_aver = 0.0157
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# fig = pp.figure()
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# ax1 = fig.add_subplot(111)
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# ax1.set_title("Relation between memory and cache size")
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# ax1.set_xlabel("Memory usage (MB)")
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# ax1.set_ylabel("Idmapper Cache Size (number of objects)")
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# rmem = numpy.linspace(35, 2000, 2000)
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# nobjs = numpy.array([int((mem - 35.0) / 0.0157) for mem in rmem])
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# ax1.plot(rmem, nobjs, "r", lw=2)
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pp.show()
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