Build memory trendline

README.md

1. Record perfetto traces using the instruction. Make sure phone has some available memory on disk. Increase max trace size and length. Record only memory-related activities to reduce trace file size. You can stop recording and start a new one. Put all tracess from the session to one directory on your computer.
2. install Pyton3 brew install python3
3. pip3 install pandas perfetto
4. Run ./analyse.py pathToTraceFolder applicationId. Pass directory with traces and application id a parameter, for example if you have traces in directory testTraces and you want to build memory threndline for an application com.mapbox.navigation.examples, run ./analyse.py testTraces com.mapbox.navigation.examples

If analyse.py fails because random port allocation fails, try running it one more time.

Result of the trace is a chart with memory usage (blue) + trend line(red). It additionally output cooficient of the trenline linear function to command line, like rss trendline has coefficient -0.00034122302415542287. Positive coeficient means that value is groving, negative that memory usage is slowly decreasing, ideal value is close to 0.

analyse.py

#!/usr/bin/env python3

from cProfile import label
from datetime import timedelta
import sys
import pandas
import glob
from perfetto.batch_trace_processor.api import BatchTraceProcessor
import matplotlib.pyplot as plt
import numpy as np

# trendline https://www.statology.org/matplotlib-trendline/
def drawTrendLine(dataFrame, xKey, yKey, color, label):
  ts = pandas.to_numeric(dataFrame[xKey])
  mem = pandas.to_numeric(dataFrame[yKey])
  coefficients = np.polyfit(ts, mem, 1)
  b = coefficients[0]
  print(f'{label} has coefficient {b:.20f}')
  p = np.poly1d(coefficients)
  plt.plot(ts, p(ts), color=color, label=label)
  plt.legend()

tracesFolder = sys.argv[1]
packageName = sys.argv[2] if len(sys.argv) > 3 else 'com.mapbox.navigation.examples'

files = glob.glob(f'{tracesFolder}/*.perfetto-trace')
if (len(files) == 0):
    print(f"no trace files found in {tracesFolder}")
    exit()

print("loading:" + ' '.join(files))
with BatchTraceProcessor(files) as btp:
  rssMemorySets = btp.query(f"select c.ts / 1000000 as timestamp, c.value / 1000 as rss from counter as c left join process_counter_track as t on c.track_id = t.id left join process as p using (upid) where t.name like 'mem.rss' and p.name like '{packageName}' order by c.ts")
  rssMemory = pandas.concat(rssMemorySets)
  rssMemory.sort_values(by=['timestamp'], inplace=True)
  print(rssMemory.head())
  rssMemory.plot(x='timestamp', y='rss')
  drawTrendLine(rssMemory, xKey='timestamp', yKey='rss', color="red", label="rss trendline")
  
  startOfTheTrace = rssMemory['timestamp'].iloc[0]
  endOfTheTrace = rssMemory['timestamp'].iloc[-1]
  traceDurationMilliseconds = (endOfTheTrace - startOfTheTrace)
  delta = timedelta(milliseconds=traceDurationMilliseconds)
  print("trace duration " + str(delta))



#   heapSizeMemorySets = btp.query("select c.ts / 1000000 as timestamp, c.value / 1000 as heap from counter as c left join process_counter_track as t on c.track_id = t.id left join process as p using (upid) where t.name like 'Heap size (KB)' and p.name like 'com.mapbox.navigation.examples' order by c.ts")
#   heapSize = pandas.concat(heapSizeMemorySets)
#   heapSize.sort_values(by=['timestamp'], inplace=True)
#   print(heapSize.head())
#   heapSize.plot(x='timestamp', y='heap')
#   drawTrendLine(heapSize, xKey='timestamp', yKey='heap', color="yellow", label="heap trendline")

  plt.show()