chrome-input-latency

chrome-input-latency.ipynb

chrome-input-latency.py

# coding: utf-8

# ---
# title: "Chrome Input Latency"
# authors:
# - bsmedberg
# created_at: 2017-04-13
# ---

# In[1]:

import ujson as json
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py

from plotly.graph_objs import *
from moztelemetry import get_pings_properties, get_one_ping_per_client
from moztelemetry.dataset import Dataset
from moztelemetry.histogram import Histogram
from operator import add

from datetime import date, timedelta

get_ipython().magic(u'matplotlib inline')


# In[2]:

sc.defaultParallelism


# In[7]:

def ping_filter(p):
    if p.get("environment/system/os/name", None) != "Windows_NT":
        return False
    if p.get("payload/info/subsessionLength", 0) <= 0:
        return False
    if p.get("environment/settings/e10sEnabled", False) != True:
        return False
    addons = p.get("environment/addons/activeAddons", {}) or {}
    for a in addons.itervalues():
        if a.get("isSystem", False) != True:
            return False
    return True

def print_histogram(histogram, processtype, hours):
    print "{} process:".format(processtype)
    stotal = 0
    for cutoff, count in histogram.buckets.sort_index(ascending=False).iteritems():
        if cutoff < 150:
            break
        stotal = stotal + count
        print "  {:5}ms: {:0.3f} hours".format(cutoff, float(hours) / stotal)    

def input_latency_mtbf(startdate, days=7, channel="nightly"):
    sdstring = startdate.strftime("%Y%m%d")
    edstring = (startdate + timedelta(days=days)).strftime("%Y%m%d")
    
    pings = Dataset.from_source("telemetry")         .where(docType='main')         .where(submissionDate=lambda d: sdstring <= d <= edstring)         .where(appUpdateChannel=channel)         .records(sc)

    data = get_pings_properties(pings, [
        "environment/system/os/name",
        "environment/settings/e10sEnabled",
        "environment/addons/activeAddons",
        "payload/histograms/INPUT_EVENT_RESPONSE_MS",
        "payload/info/subsessionLength"], with_processes=True) \
        .filter(ping_filter).cache()

    total_session_length_hours = data         .map(lambda p: p["payload/info/subsessionLength"])         .reduce(add) / 60 / 60

    print "Input jank MTBF for {} channel, {} for {} days:".format(channel, startdate.strftime("%Y-%m-%d"), days)
    print "Usage hours total: {}".format(total_session_length_hours)

    aggregated_input_latency_chrome = data         .map(lambda p: p.get("payload/histograms/INPUT_EVENT_RESPONSE_MS_parent", None))         .filter(lambda v: v is not None)         .reduce(add)
    chrome_histogram = Histogram("INPUT_EVENT_RESPONSE_MS", aggregated_input_latency_chrome)
    print_histogram(chrome_histogram, "chrome", total_session_length_hours)

    aggregated_input_latency_content = data         .map(lambda p: p.get("payload/histograms/INPUT_EVENT_RESPONSE_MS_children", None))         .filter(lambda v: v is not None)         .reduce(add)
    content_histogram = Histogram("INPUT_EVENT_RESPONSE_MS", aggregated_input_latency_content)
    print_histogram(content_histogram, "content", total_session_length_hours)
    
    print


# Caculate mean time between failure (MTBF) of "hangs at least as long as X ms". Cut off at 150ms because for now that's not a failure case.

# In[8]:

input_latency_mtbf(date(2017, 4, 2), channel="nightly")


# In[9]:

input_latency_mtbf(date(2017, 4, 2), channel="beta")