path: root/src/third_party/wiredtiger/tools/optrack/find-latency-spikes.py

#!/usr/bin/env python
#
# Public Domain 2014-2019 MongoDB, Inc.
# Public Domain 2008-2014 WiredTiger, Inc.
#
# This is free and unencumbered software released into the public domain.
#
# Anyone is free to copy, modify, publish, use, compile, sell, or
# distribute this software, either in source code form or as a compiled
# binary, for any purpose, commercial or non-commercial, and by any
# means.
#
# In jurisdictions that recognize copyright laws, the author or authors
# of this software dedicate any and all copyright interest in the
# software to the public domain. We make this dedication for the benefit
# of the public at large and to the detriment of our heirs and
# successors. We intend this dedication to be an overt act of
# relinquishment in perpetuity of all present and future rights to this
# software under copyright law.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
#!/usr/bin/env python

import argparse
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, CustomJS, HoverTool, FixedTicker
from bokeh.models import  LabelSet, Legend, LegendItem
from bokeh.models import NumeralTickFormatter, OpenURL, Range1d, TapTool
from bokeh.models.annotations import Label
from bokeh.plotting import figure, output_file, reset_output, save, show
from bokeh.resources import CDN
import matplotlib
from multiprocessing import Process, Queue, Array
import multiprocessing
import numpy as np
import os
import pandas as pd
import sys
import statvfs
import traceback
import time

# A directory where we store cross-file plots for each bucket of the outlier
# histogram.
#
bucketDir = "BUCKET-FILES";

# A static list of available CSS colors
colorList = [];

# Codes for various colors for printing of informational and error messages.
#
class color:
    PURPLE = '\033[95m'
    CYAN = '\033[96m'
    DARKCYAN = '\033[36m'
    BLUE = '\033[94m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    RED = '\033[91m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    END = '\033[0m'

# A function name mapped to its corresponding color.
#
funcToColor = {};
lastColorUsed = 0;

# The smallest and the largest timestamps seen across all files.
#
firstTimeStamp = sys.maxsize;
lastTimeStamp = 0;

# A dictionary that holds function-specific threshold values telling
# us when the function is to be considered an outlier. These values
# would be read from a config file, if supplied by the user.
#
userDefinedLatencyThresholds = {};
userDefinedThresholdNames = {};

# A dictionary that holds a reference to the raw dataframe for each file.
#
perFileDataFrame = {};

# A dictionary that holds the intervals data per function.
#
perFuncDF = {};

# Data frames and largest stack depth for each file.
perFileDataFrame = {};
perFileLargestStackDepth = {};

# Each file has a timestamp indicating when the logging began
perFileTimeStamps = {};

plotWidth = 1200;
pixelsForTitle = 30;
pixelsPerHeightUnit = 30;
pixelsPerWidthUnit = 5;

# How many work units for perform in parallel.
targetParallelism = 0;

# The name of the time units that were used when recording timestamps.
# We assume that it's nanoseconds by default. Alternative units can be
# set in the configuration file.
#
timeUnitString = "nanoseconds";

# The percentile threshold. A function duration above that percentile
# is deemed an outlier.
#
PERCENTILE = 0.999;

def initColorList():

    global colorList;

    colorList = matplotlib.colors.cnames.keys();

    for color in colorList:
        # Some browsers break if you try to give them 'sage'
        if (color == "sage"):
            colorList.remove(color);
        # We reserve red to highlight occurrences of functions
        # that exceeded the user-defined latency threshold. Do
        # not use red for regular function colors.
        #
        elif (color == "red"):
            colorList.remove(color);

#
# Each unique function name gets a unique color.
# If we run out of colors, we repeat them from the
# beginning of the list.
#
def getColorForFunction(function):

    global colorList;
    global lastColorUsed;
    global funcToColor;

    if not funcToColor.has_key(function):
        funcToColor[function] = colorList[lastColorUsed % len(colorList)];
        lastColorUsed += 1;

    return funcToColor[function];

#
# An intervalEnd is a tuple of three items.
# item #0 is the timestamp,
# item #1 is the event type,
# item #2 is the function name.
#
def getIntervalData(intervalBeginningsStack, intervalEnd, logfile):

    errorOccurred = False;
    matchFound = False;

    if (intervalEnd[1] != 1):
        logfile.write(
            "getIntervaldata: only rows with event type 1 can be used.\n");
        logfile.write(str(intervalEnd) + "\n");
        return None;

    if (len(intervalBeginningsStack) < 1):
        logfile.write("Nothing on the intervalBeginningsStack. " +
                      "I cannot find the beginning for this interval.\n");
        logfile.write(str(intervalEnd) + "\n");
        return None;

    while (not matchFound):
        intervalBegin = intervalBeginningsStack.pop();
        if (intervalBegin is None):
            logfile.write("Could not find the matching operation begin record" +
                          " for the following operation end record: \n");
            logfile.write(str(intervalEnd) + "\n");
            return None;
        if (intervalBegin[2] != intervalEnd[2]):
            logfile.write("Operation end record does not match the available " +
                          "operation begin record. " +
                          "Your log file may be incomplete.\n" +
                          "Skipping the begin record.\n");
            logfile.write("Begin: " + str(intervalBegin) + "\n");
            logfile.write("End: " + str(intervalEnd) + "\n");
            errorOccurred = True;
        else:
            matchFound = True;

    return intervalBegin[0], intervalEnd[0], intervalEnd[2], errorOccurred;

def plotOutlierHistogram(dataframe, maxOutliers, func,
                         statisticalOutlierThreshold,
                         userLatencyThreshold,
                         averageDuration, maxDuration):

    global pixelsForTitle;
    global pixelsPerHeightUnit;
    global plotWidth;
    global timeUnitString;

    cds = ColumnDataSource(dataframe);

    figureTitle = "Occurrences of " + func + " that took longer than " \
                  + statisticalOutlierThreshold + ".";

    hover = HoverTool(tooltips = [
        ("interval start", "@lowerbound{0,0}"),
        ("interval end", "@upperbound{0,0}")]);

    TOOLS = [hover, "tap, reset"];

    p = figure(title = figureTitle, plot_width = plotWidth,
               plot_height = min(500, (max(5, (maxOutliers + 1)) \
                                       * pixelsPerHeightUnit + \
                                       pixelsForTitle)),
               x_axis_label = "Execution timeline (" + timeUnitString + ")",
               y_axis_label = "Number of outliers",
               tools = TOOLS, toolbar_location="above");

    y_ticker_max = p.plot_height / pixelsPerHeightUnit;
    y_ticker_step = max(1, (maxOutliers + 1)/y_ticker_max);
    y_upper_bound = (maxOutliers / y_ticker_step + 2) * y_ticker_step;

    p.yaxis.ticker = FixedTicker(ticks =
                                 range(0, y_upper_bound, y_ticker_step));
    p.ygrid.ticker = FixedTicker(ticks =
                                 range(0, y_upper_bound, y_ticker_step));
    p.xaxis.formatter = NumeralTickFormatter(format="0,");

    p.y_range = Range1d(0, y_upper_bound);

    p.quad(left = 'lowerbound', right = 'upperbound', bottom = 'bottom',
           top = 'height', color = funcToColor[func], source = cds,
           nonselection_fill_color= funcToColor[func],
           nonselection_fill_alpha = 1.0,
           line_color = "lightgrey",
           selection_fill_color = funcToColor[func],
           selection_line_color="grey"
    );

    p.x(x='markerX', y='markerY', size='markersize', color = 'navy',
        line_width = 1, source = cds);

    # Add an annotation to the chart
    #
    y_max = dataframe['height'].max();
    text = "Average duration: " + '{0:,.0f}'.format(averageDuration) + " " + \
           timeUnitString + \
           ". Maximum duration: " + '{0:,.0f}'.format(maxDuration) + " " + \
           timeUnitString + ". ";
    if (userLatencyThreshold is not None):
        text = text + \
               "An \'x\' shows intervals with operations exceeding " + \
               "a user-defined threshold of " + \
               userLatencyThreshold + ".";
    mytext = Label(x=0, y=y_upper_bound-y_ticker_step, text=text,
                   text_color = "grey", text_font = "helvetica",
                   text_font_size = "10pt",
                   text_font_style = "italic");
    p.add_layout(mytext);

    url = "@bucketfiles";
    taptool = p.select(type=TapTool);
    taptool.callback = OpenURL(url=url);

    return p;

# From all timestamps subtract the smallest observed timestamp, so that
# our execution timeline begins at zero.
# Cleanup the data to remove incomplete records and fix their effects.
#
def normalizeIntervalData():

    global firstTimeStamp;
    global perFileDataFrame;

    print(color.BLUE + color.BOLD + "Normalizing data..." + color.END);

    for file, df in perFileDataFrame.iteritems():
        df['origstart'] = df['start'];
        df['start'] = df['start'] - firstTimeStamp;
        df['end'] = df['end'] - firstTimeStamp;

def reportDataError(logfile, logfilename):

    if (logfile is not sys.stdout):
        print(color.BOLD + color.RED + "Your data may have errors. " +
              "Check the file " + logfilename + " for details." + color.END);
    return True;

#
# Go over all operation records in the dataframe and assign stack depths.
#
def assignStackDepths(dataframe):

    stack = [];

    df = dataframe.sort_values(by=['start']);
    df = df.reset_index(drop = True);

    for i in range(len(df.index)):

        myEndTime = df.at[i, 'end'];

        # Pop all items off stack whose end time is earlier than my
        # end time. They are not the callers on my stack, so I don't want to
        # count them.
        #
        while (len(stack) > 0 and stack[-1] < myEndTime):
            stack.pop();

        df.at[i, 'stackdepth'] = len(stack);
        stack.append(df.at[i, 'end']);

    return df;

def createCallstackSeries(data, logfilename):

    global firstTimeStamp;
    global lastTimeStamp;

    colors = [];
    beginIntervals = [];
    dataFrame = None;
    endIntervals = [];
    errorReported = False;
    functionNames = [];
    intervalBeginningsStack = [];
    largestStackDepth = 0;
    logfile = None;
    thisIsFirstRow = True;

    # Let's open the log file.
    try:
        logfile = open(logfilename, "w");
    except:
        logfile = sys.stdout;

    for row in data.itertuples():
        # row[0] is the timestamp, row[1] is the event type,
        # row[2] is the function name.
        #
        if (row[1] == 0):
            intervalBeginningsStack.append(row);
        elif (row[1] == 1):
            try:
                intervalBegin, intervalEnd, function, error\
                    = getIntervalData(intervalBeginningsStack, row, logfile);
                if (error and (not errorReported)):
                    errorReported = reportDataError(logfile, logfilename);
            except:
                if (not errorReported):
                    errorReported = reportDataError(logfile, logfilename);
                continue;

            if (intervalBegin < firstTimeStamp):
                firstTimeStamp =  intervalBegin;
            if (intervalEnd > lastTimeStamp):
                lastTimeStamp = intervalEnd;

            colors.append(getColorForFunction(function));
            beginIntervals.append(intervalBegin);
            endIntervals.append(intervalEnd);
            functionNames.append(function);

        else:
            print("Invalid event in this line:");
            print(str(row[0]) + " " + str(row[1]) + " " + str(row[2]));
            continue;

    if (len(intervalBeginningsStack) > 0):
        logfile.write(str(len(intervalBeginningsStack)) + " operations had a " +
                      "begin record, but no matching end records. " +
                      "Please check that your operation tracking macros " +
                      "are properly inserted.\n");
        if (not errorReported):
            errorReported = reportDataError(logfile, logfilename);
        intervalBeginningsStack = [];

    dict = {};
    dict['color'] = colors;
    dict['start'] = beginIntervals;
    dict['end'] = endIntervals;
    dict['function'] = functionNames;
    dict['stackdepth'] = [0] * len(beginIntervals);

    dataframe = pd.DataFrame(data=dict);
    dataframe = assignStackDepths(dataframe);

    dataframe['durations'] = dataframe['end'] - dataframe['start'];
    dataframe['stackdepthNext'] = dataframe['stackdepth'] + 1;

    return dataframe;

# For each function we only show the legend once. In this dictionary we
# keep track of colors already used.
#
colorAlreadyUsedInLegend = {};

def createLegendFigure(legendDict):

    global pixelsForTitle;
    global plotWidth;

    FUNCS_PER_ROW = 5;
    HSPACE_BETWEEN_FUNCS = 10;
    VSPACE_BETWEEN_FUNCS = 1;

    funcs = [];
    colors = [];
    x_coords = [];
    y_coords = [];
    pixelsForLegendItem = 20;

    # Get a sorted list of functions and their
    # corresponding colors.
    #
    for func in sorted(legendDict.keys()):
        funcs.append(func);
        colors.append(legendDict[func]);

    # Figure out the coordinates of functions on the plot
    #
    for i in range(len(funcs)):

        x_coord = i % (FUNCS_PER_ROW) + 1;
        x_coord += i % (FUNCS_PER_ROW) *  HSPACE_BETWEEN_FUNCS;
        x_coords.append(x_coord);

        y_coord = (i/FUNCS_PER_ROW) + 1;
        y_coord += (i/FUNCS_PER_ROW) *  VSPACE_BETWEEN_FUNCS;
        y_coords.append(y_coord);

    data = {};
    data['func'] = funcs;
    data['color'] = colors;
    data['left'] = x_coords;
    data['bottom'] = y_coords;

    df = pd.DataFrame(data=data);

    max_ycoord = df['bottom'].max();
    df['bottom'] = (max_ycoord + 1) - df['bottom'];

    df['right'] = df['left'] + 1;
    df['top'] = df['bottom'] + 1;

    cds = ColumnDataSource(df);

    p = figure(title="TRACKED FUNCTIONS",
               plot_width=plotWidth,
               plot_height = max((max_ycoord + 2) * pixelsForLegendItem, 90),
               tools = [], toolbar_location="above",
               x_range = (0, (FUNCS_PER_ROW + 1)* HSPACE_BETWEEN_FUNCS),
               y_range = (0, max_ycoord + 2),
               x_axis_label = "",
               y_axis_label = "");

    p.title.align = "center";
    p.title.text_font_style = "normal";

    p.xaxis.axis_line_color = "lightgrey";
    p.xaxis.major_tick_line_color = None;
    p.xaxis.minor_tick_line_color = None;
    p.xaxis.major_label_text_font_size = '0pt';

    p.yaxis.axis_line_color = "lightgrey";
    p.yaxis.major_tick_line_color = None;
    p.yaxis.minor_tick_line_color = None;
    p.yaxis.major_label_text_font_size = '0pt';

    p.xgrid.grid_line_color = None;
    p.ygrid.grid_line_color = None;

    p.outline_line_width = 1
    p.outline_line_alpha = 1
    p.outline_line_color = "lightgrey"

    p.quad(left = 'left', right = 'right', bottom = 'bottom',
           top = 'top', color = 'color', line_color = "lightgrey",
           line_width = 0.5, source=cds);

    labels = LabelSet(x='right', y='bottom', text='func', level='glyph',
                      text_font_size = "10pt",
                      x_offset=3, y_offset=0, source=cds, render_mode='canvas');
    p.add_layout(labels);

    return p;

def generateBucketChartForFile(figureName, dataframe, y_max, x_min, x_max):

    global funcToColor;
    global plotWidth;
    global timeUnitString;

    colorAlreadyUsedInLegend = {};

    MAX_ITEMS_PER_LEGEND = 10;
    numLegends = 0;
    legendItems = {};
    pixelsPerStackLevel = 30;
    pixelsPerLegend = 60;
    pixelsForTitle = 30;

    cds = ColumnDataSource(dataframe);

    hover = HoverTool(tooltips=[
        ("function", "@function"),
        ("duration", "@durations{0,0}"),
        ("log file begin timestamp", "@origstart{0,0}")
    ]);

    TOOLS = [hover];

    p = figure(title=figureName, plot_width=plotWidth,
               x_range = (x_min, x_max),
               y_range = (0, y_max+1),
               x_axis_label = "Time (" + timeUnitString + ")",
               y_axis_label = "Stack depth",
               tools = TOOLS, toolbar_location="above");

    # No minor ticks or labels on the y-axis
    p.yaxis.major_tick_line_color = None;
    p.yaxis.minor_tick_line_color = None;
    p.yaxis.major_label_text_font_size = '0pt';
    p.yaxis.ticker = FixedTicker(ticks = range(0, y_max+1));
    p.ygrid.ticker = FixedTicker(ticks = range(0, y_max+1));

    p.xaxis.formatter = NumeralTickFormatter(format="0,");
    p.title.text_font_style = "bold";

    p.quad(left = 'start', right = 'end', bottom = 'stackdepth',
           top = 'stackdepthNext', color = 'color', line_color = "lightgrey",
           line_width = 0.5, source=cds);

    for func, fColor in funcToColor.iteritems():

        # If this function is not present in this dataframe,
        # we don't care about it.
        #
        boolVec = (dataframe['function'] == func);
        fDF = dataframe[boolVec];
        if (fDF.size == 0):
            continue;

        # If we already added a color to any legend, we don't
        # add it again to avoid redundancy in the charts and
        # in order not to waste space.
        #
        if (colorAlreadyUsedInLegend.has_key(fColor)):
            continue;
        else:
            colorAlreadyUsedInLegend[fColor] = True;

        legendItems[func] = fColor;

    # Plot height is the function of the maximum call stack and the number of
    # legends
    p.plot_height =  max((y_max+1) * pixelsPerStackLevel, 100) + pixelsForTitle;

    return p, legendItems;

def generateEmptyDataset():

    dict = {};
    dict['color'] = [0];
    dict['durations'] = [0];
    dict['start'] = [0];
    dict['end'] = [0];
    dict['function'] = [""];
    dict['stackdepth'] = [0];
    dict['stackdepthNext'] = [0];

    return pd.DataFrame(data=dict);

#
# Here we generate plots that span all the input files. Each plot shows
# the timelines for all files, stacked vertically. The timeline shows
# the function callstacks over time from this file.
#
# Since a single timeline is too large to fit on a single screen, we generate
# a separate HTML file with plots for bucket "i". A bucket is a vertical slice
# across the timelines for all files. We call it a bucket, because it
# corresponds to a bucket in the outlier histogram.
#
def generateCrossFilePlotsForBucket(i, lowerBound, upperBound, navigatorDF,
                                    retFilename):

    global bucketDir;
    global timeUnitString;

    aggregateLegendDict = {};
    figuresForAllFiles = [];
    fileName = bucketDir + "/bucket-" + str(i) + ".html";

    reset_output();

    intervalTitle = "Interval #" + str(i) + ". {:,}".format(lowerBound) + \
                    " to " + "{:,}".format(upperBound) + \
                    " " + timeUnitString + ".";

    # Generate a navigator chart, which shows where we are in the
    # trace and allows moving around the trace.
    #
    navigatorFigure = generateNavigatorFigure(navigatorDF, i, intervalTitle);
    figuresForAllFiles.append(navigatorFigure);

    # Select from the dataframe for this file the records whose 'start'
    # and 'end' timestamps fall within the lower and upper bound.
    #
    for fname in sorted(perFileDataFrame.keys()):

        fileDF = perFileDataFrame[fname];

        # Select operations whose start timestamp falls within
        # the current interval, delimited by lowerBound and upperBound.
        #
        startInBucket = fileDF.loc[(fileDF['start'] >= lowerBound)
                                   & (fileDF['start'] < upperBound)];

        # Select operations whose end timestamp falls within
        # the current interval, delimited by lowerBound and upperBound.
        #
        endInBucket = fileDF.loc[(fileDF['end'] > lowerBound)
                                   & (fileDF['end'] <= upperBound)];

        # Select operations that begin before this interval and end after
        # this interval, but continue throughout this interval. The interval
        # is delimited by lowerBound and upperBound.
        #
        spanBucket = fileDF.loc[(fileDF['start'] < lowerBound)
                                   & (fileDF['end'] > upperBound)];

        frames = [startInBucket, endInBucket, spanBucket];
        bucketDF = pd.concat(frames).drop_duplicates().reset_index(drop=True);

        if (bucketDF.size == 0):
            continue;

        # If the end of the function is outside the interval, let's pretend
        # that it is within the interval, otherwise we won't see any data about
        # it when we hover. This won't have the effect of showing wrong
        # data to the user.
        #
        mask = bucketDF.end >= upperBound;
        bucketDF.loc[mask, 'end'] = upperBound-1;

        # Same adjustment as above if the start of the operation falls outside
        # the interval's lower bound.
        #
        mask = bucketDF.start < lowerBound;
        bucketDF.loc[mask, 'start'] = lowerBound;

        largestStackDepth = bucketDF['stackdepthNext'].max();
        figureTitle = fname;

        figure, legendDict = generateBucketChartForFile(figureTitle, bucketDF,
                                                        largestStackDepth,
                                                        lowerBound, upperBound);
        aggregateLegendDict.update(legendDict);
        figuresForAllFiles.append(figure);

    # Create the legend for this file and insert it after the navigator figure
    if (len(aggregateLegendDict) > 0):
        legendFigure = createLegendFigure(aggregateLegendDict);
        figuresForAllFiles.insert(1, legendFigure);

    save(column(figuresForAllFiles), filename = fileName,
         title=intervalTitle, resources=CDN);

    retFilename.value = fileName;


# Generate a plot that shows a view of the entire timeline in a form of
# intervals. By clicking on an interval we can navigate to that interval.
#
def generateNavigatorFigure(dataframe, i, title):

    global pixelsForTitle;
    global pixelsPerHeightUnit;
    global plotWidth;

    # Generate the colors, such that the current interval is shown in a
    # different color than the rest.
    #
    numIntervals = dataframe['intervalnumber'].size;
    color = ["white" for x in range(numIntervals)];
    color[i] = "salmon";
    dataframe['color'] = color;

    cds = ColumnDataSource(dataframe);

    title = title + " CLICK TO NAVIGATE";

    hover = HoverTool(tooltips = [
        ("interval #", "@intervalnumber"),
        ("interval start", "@intervalbegin{0,0}"),
        ("interval end", "@intervalend{0,0}")]);

    TOOLS = [hover, "tap"];

    p = figure(title = title, plot_width = plotWidth,
               x_range = (0, numIntervals),
               plot_height =  2 * pixelsPerHeightUnit + pixelsForTitle,
               x_axis_label = "",
               y_axis_label = "", tools = TOOLS,
               toolbar_location="above");

    # No minor ticks or labels on the y-axis
    p.yaxis.major_tick_line_color = None;
    p.yaxis.minor_tick_line_color = None;
    p.yaxis.major_label_text_font_size = '0pt';
    p.yaxis.ticker = FixedTicker(ticks = range(0, 1));
    p.ygrid.ticker = FixedTicker(ticks = range(0, 1));

    p.xaxis.formatter = NumeralTickFormatter(format="0,");

    p.title.align = "center";
    p.title.text_font_style = "normal";

    p.quad(left = 'intervalnumber', right = 'intervalnumbernext',
           bottom = 0, top = 2, color = 'color', source = cds,
           nonselection_fill_color='color',
           nonselection_fill_alpha = 1.0,
           line_color = "aliceblue",
           selection_fill_color = "white",
           selection_line_color="lightgrey"
    );

    url = "@bucketfiles";
    taptool = p.select(type=TapTool);
    taptool.callback = OpenURL(url=url);

    return p;


# Create a dataframe describing all time intervals, which will later be used
# to generate a plot allowing us to navigate along the execution by clicking
# on different intervals.
#
def createIntervalNavigatorDF(numBuckets, timeUnitsPerBucket):

    global bucketDir;

    bucketFiles = [];
    bucketID = [];
    intervalBegin = [];
    intervalEnd = [];

    for i in range(numBuckets):

        lBound = i * timeUnitsPerBucket;
        uBound = (i+1) * timeUnitsPerBucket;
        fileName = "bucket-" + str(i) + ".html";

        bucketID.append(i);
        intervalBegin.append(lBound);
        intervalEnd.append(uBound);
        bucketFiles.append(fileName);

    data = {};
    data['bucketfiles'] = bucketFiles;
    data['intervalbegin'] =  intervalBegin;
    data['intervalend'] =  intervalEnd;
    data['intervalnumber'] = bucketID;

    dataframe = pd.DataFrame(data=data);
    dataframe['intervalnumbernext'] = dataframe['intervalnumber'] + 1;
    return dataframe;


def waitOnOneProcess(runningProcesses):

    success = False;
    for i, p in runningProcesses.items():
        if (not p.is_alive()):
            del runningProcesses[i];
            success = True;

    # If we have not found a terminated process, sleep for a while
    if (not success):
        time.sleep(1);

# Update the UI message showing what percentage of work done by
# parallel processes has completed.
#
def updatePercentComplete(runnableProcesses, runningProcesses,
                          totalWorkItems, workName):

    percentComplete = float(totalWorkItems - len(runnableProcesses) \
                        - len(runningProcesses)) / float(totalWorkItems) * 100;
    print(color.BLUE + color.BOLD + " " + workName),
    sys.stdout.write(" %d%% complete  \r" % (percentComplete) );
    sys.stdout.flush();

# Generate plots of time series slices across all files for each bucket
# in the outlier histogram. Save each cross-file slice to an HTML file.
#
def generateTSSlicesForBuckets():

    global firstTimeStamp;
    global lastTimeStamp;
    global plotWidth;
    global pixelsPerWidthUnit;
    global targetParallelism;

    bucketFilenames = [];
    runnableProcesses = {};
    returnValues = {};
    spawnedProcesses = {};

    numBuckets = plotWidth / pixelsPerWidthUnit;
    timeUnitsPerBucket = (lastTimeStamp - firstTimeStamp) / numBuckets;

    navigatorDF = createIntervalNavigatorDF(numBuckets, timeUnitsPerBucket);

    print(color.BLUE + color.BOLD +
        "Will process " + str(targetParallelism) + " work units in parallel."
        + color.END);

    for i in range(numBuckets):
        retFilename = Array('c', os.statvfs('/')[statvfs.F_NAMEMAX]);
        lowerBound = i * timeUnitsPerBucket;
        upperBound = (i+1) * timeUnitsPerBucket;

        p = Process(target=generateCrossFilePlotsForBucket,
                    args=(i, lowerBound, upperBound,
                          navigatorDF, retFilename));
        runnableProcesses[i] = p;
        returnValues[i] = retFilename;

    while (len(runnableProcesses) > 0):
        while (len(spawnedProcesses) < targetParallelism
               and len(runnableProcesses) > 0):

            i, p = runnableProcesses.popitem();
            p.start();
            spawnedProcesses[i] = p;

        # Find at least one terminated process
        waitOnOneProcess(spawnedProcesses);
        updatePercentComplete(runnableProcesses, spawnedProcesses,
                              numBuckets, "Generating timeline charts");

    # Wait for all processes to terminate
    while (len(spawnedProcesses) > 0):
        waitOnOneProcess(spawnedProcesses);
        updatePercentComplete(runnableProcesses, spawnedProcesses,
                              numBuckets, "Generating timeline charts");

    for i, fname in returnValues.items():
        bucketFilenames.append(str(fname.value));
    print(color.END);

    return bucketFilenames;

#
# After we have cleaned up the data by getting rid of incomplete function
# call records (e.g., a function begin record is presend but a function end
# is not or vice versa), we optionally dump this clean data into a file, so
# it can be re-processed by other visualization tools. The output format is
#
# <0/1> <funcname> <timestamp>
#
# We use '0' if it's a function entry, '1' if it's a function exit.
#
def dumpCleanData(fname, df):

    enterExit = [];
    timestamps = [];
    functionNames = [];

    outfile = None;
    fnameParts = fname.split(".txt");
    newfname = fnameParts[0] + "-clean.txt";

    for index, row in df.iterrows():
        # Append the function enter record:
        enterExit.append(0);
        timestamps.append(row['start']);
        functionNames.append(row['function']);

        # Append the function exit record:
        enterExit.append(1);
        timestamps.append(row['end']);
        functionNames.append(row['function']);

    newDF = pd.DataFrame({'enterExit' : enterExit, 'timestamp' : timestamps,
                          'function' : functionNames});
    newDF = newDF.set_index('timestamp', drop=False);
    newDF.sort_index(inplace = True);

    print("Dumping clean data to " + newfname);
    newDF.to_csv(newfname, sep=' ', index=False, header=False,
                 columns = ['enterExit', 'function', 'timestamp']);

def checkForTimestampAndGetRowSkip(fname):

    global perFileTimeStamps;

    with open(fname) as f:
        firstLine = f.readline();

        firstLine = firstLine.strip();
        words = firstLine.split(" ");

        if (len(words) == 1):
            try:
                perFileTimeStamps[fname] = long(words[0]);
            except ValueError:
                print(color.BOLD + color.RED +
                      "Could not parse seconds since Epoch on first line" +
                      + color.END);
            return 1;
        else:
            return 0;

def processFile(fname, dumpCleanDataBool):

    global perFileDataFrame;
    global perFuncDF;

    skipRows = checkForTimestampAndGetRowSkip(fname);

    rawData = pd.read_csv(fname,
                          header=None, delimiter=" ",
                          index_col=2,
                          names=["Event", "Function", "Timestamp"],
                          dtype={"Event": np.int32, "Timestamp": np.int64},
                          thousands=",", skiprows = skipRows);

    print(color.BOLD + color.BLUE +
          "Processing file " + str(fname) + color.END);
    iDF = createCallstackSeries(rawData, "." + fname + ".log");

    if (dumpCleanDataBool):
        dumpCleanData(fname, iDF);


    perFileDataFrame[fname] = iDF;

    for func in funcToColor.keys():

        funcDF = iDF.loc[lambda iDF: iDF.function == func, :];
        funcDF = funcDF.drop(columns = ['function']);

        if (not perFuncDF.has_key(func)):
            perFuncDF[func] = funcDF;
        else:
            perFuncDF[func] = pd.concat([perFuncDF[func], funcDF]);


#
# For each function, split the timeline into buckets. In each bucket
# show how many times this function took an unusually long time to
# execute.
#
def createOutlierHistogramForFunction(func, funcDF, bucketFilenames):

    global firstTimeStamp;
    global lastTimeStamp;
    global plotWidth;
    global pixelsPerWidthUnit;
    global timeUnitString;
    global PERCENTILE;

    statisticalOutlierThreshold = 0;
    statisticalOutlierThresholdDescr = "";
    userLatencyThreshold = sys.maxint;
    userLatencyThresholdDescr = None;


    #
    # funcDF is a list of functions along with their start and end
    # interval and durations. We need to create a new dataframe where
    # we separate the entire timeline into a fixed number of periods
    # and for each period compute how many outlier durations were
    # observed. Then we create a histogram from this data.

    # Subtract the smallest timestamp from all the interval data.
    funcDF['start'] = funcDF['start'] - firstTimeStamp;
    funcDF['end'] = funcDF['end'] - firstTimeStamp;

    funcDF = funcDF.sort_values(by=['start']);

    averageDuration = funcDF['durations'].mean();
    maxDuration = funcDF['durations'].max();

    # There are two things that we want to capture on the
    # outlier charts: statistical outliers and functions exceeding the
    # user-defined latency threshold. An outlier is a function
    # whose duration is in the 99.9th percentile. For each
    # time period we will show a bar whose height corresponds
    # to the number of outliers observed during this exection
    # period.
    #
    # Not all outliers are indicative of performance problems.
    # To highlight real performance problems (as defined by the user)
    # we will highlight those bars that contain operations whose
    # duration exceeded the user-defined threshold.
    #
    if (userDefinedLatencyThresholds.has_key(func)):
        userLatencyThreshold = userDefinedLatencyThresholds[func];
        userLatencyThresholdDescr = userDefinedThresholdNames[func];
    elif (userDefinedLatencyThresholds.has_key("*")):
        userLatencyThreshold = userDefinedLatencyThresholds["*"];
        userLatencyThresholdDescr = userDefinedThresholdNames["*"];

    statisticalOutlierThreshold = funcDF['durations'].quantile(PERCENTILE);
    statisticalOutlierThresholdDescr = \
                            '{0:,.0f}'.format(statisticalOutlierThreshold) \
                            + " " + timeUnitString + \
                            " (" + str(PERCENTILE * 100) + \
                            "th percentile)";


    numBuckets = plotWidth / pixelsPerWidthUnit;
    timeUnitsPerBucket = (lastTimeStamp - firstTimeStamp) / numBuckets;

    bucketHeights = [];
    markers = [];
    lowerBounds = [];
    maxOutliers = 0;
    upperBounds = [];

    for i in range(numBuckets):
        markerSize = 0;
        lowerBound = i * timeUnitsPerBucket;
        upperBound = (i+1) * timeUnitsPerBucket;

        # Find out how many statistical outliers we have in the
        # current period.
        bucketDF = funcDF.loc[(funcDF['start'] >= lowerBound)
                              & (funcDF['start'] < upperBound)
                              & (funcDF['durations'] >=
                                 statisticalOutlierThreshold)];

        # The number of statistical outliers is the height of the bar
        numOutliers = bucketDF.size;
        if (numOutliers > maxOutliers):
            maxOutliers = numOutliers;

        # Find out whether we have any functions whose duration exceeded
        # the user-defined threshold.
        if (userLatencyThresholdDescr is not None):
            bucketDF = funcDF.loc[(funcDF['start'] >= lowerBound)
                                  & (funcDF['start'] < upperBound)
                                  & (funcDF['durations'] >=
                                     userLatencyThreshold)];

            # If there is at least one element in this dataframe, then the
            # operations that exceeded the user defined latency threshold are
            # present in this period. Highlight this bucket with a bright color.
            if (bucketDF.size > 0):
                markerSize = 6;

        lowerBounds.append(lowerBound);
        upperBounds.append(upperBound);
        bucketHeights.append(numOutliers);
        markers.append(markerSize);

    if (maxOutliers == 0):
        return None;

    dict = {};
    dict['lowerbound'] = lowerBounds;
    dict['upperbound'] = upperBounds;
    dict['height'] = bucketHeights;
    dict['bottom'] = [0] * len(lowerBounds);
    dict['bucketfiles'] = bucketFilenames;
    dict['markersize'] = markers;

    dataframe = pd.DataFrame(data=dict);
    dataframe['markerX'] = dataframe['lowerbound'] +  \
                           (dataframe['upperbound'] -
                            dataframe['lowerbound']) / 2 ;
    dataframe['markerY'] = dataframe['height'] + 0.2;

    return plotOutlierHistogram(dataframe, maxOutliers, func,
                                statisticalOutlierThresholdDescr,
                                userLatencyThresholdDescr,
                                averageDuration,
                                maxDuration);

#
# Return the string naming the time units used to measure time stamps,
# depending on how many time units there are in a second.
#
def getTimeUnitString(unitsPerSecond):

    if unitsPerSecond == 1000:
        return "milliseconds";
    elif unitsPerSecond == 1000000:
        return "microseconds";
    elif unitsPerSecond == 1000000000:
        return "nanoseconds";
    else:
        return "CPU cycles";

#
# The configuration file tells us which functions should be considered
# outliers. All comment lines must begin with '#'.
#
# The first non-comment line of the file must tell us how to interpret
# the measurement units in the trace file. It must have a single number
# telling us how many time units are contained in a second. This should
# be the same time units used in the trace file. For example, if the trace
# file contains timestamps measured in milliseconds, the number would be 1000,
# it the timestamp is in nanoseconds, the number would be 1000000000.
# If timestamps were measured in clock cycles, the number
# must tell us how many times the CPU clock ticks per second on the processor
# where the trace was gathered.
#
# The remaining lines must have the format:
#       <func_name> <outlier_threshold> [units]
#
# For example, if you would like to flag as outliers all instances of
# __cursor_row_search that took longer than 200ms, you would specify this as:
#
#        __cursor_row_search 200 ms
#
# You can use * as the wildcard for all function. No other wildcard options are
# supported at the moment.
#
# Acceptable units are:
#
# s -- for seconds
# ms -- for milliseconds
# us -- for microseconds
# ns -- for nanoseconds
# stdev -- for standard deviations.
#
# If no units are supplied, the same unit as the one used for the timestamp
# in the trace files is assumed.
#
# If there is a valid configuration file, but the function does not appear in
# it, we will not generate an outlier histogram for this function. Use the
# wildcard symbol to include all functions.
#
def parseConfigFile(fname):

    global userDefinedLatencyThresholds;
    global userDefinedThresholdNames;
    global timeUnitString;

    configFile = None;
    firstNonCommentLine = True;
    unitsPerSecond = -1;
    unitsPerMillisecond = 0.0;
    unitsPerMicrosecond = 0.0;
    unitsPerNanosecond = 0.0;

    try:
        configFile = open(fname, "r");
    except:
        print(color.BOLD + color.RED +
              "Could not open " + fname + " for reading." + color.END);
        return False;

    for line in configFile:

        if (line[0] == "#"):
            continue;
        elif (firstNonCommentLine):
            try:
                unitsPerSecond = int(line);
                unitsPerMillisecond = unitsPerSecond / 1000;
                unitsPerMicrosecond = unitsPerSecond / 1000000;
                unitsPerNanosecond  = unitsPerSecond / 1000000000;

                timeUnitString = getTimeUnitString(unitsPerSecond);

                firstNonCommentLine = False;
            except ValueError:
                print(color.BOLD + color.RED +
                      "Could not parse the number of measurement units " +
                      "per second. This must be the first value in the " +
                      "config file." + color.END);
                return False;
        else:
            func = "";
            number = 0;
            threshold = 0.0;
            units = "";

            words = line.split();
            try:
                func = words[0];
                number = int(words[1]);
                units = words[2];
            except ValueError:
                print(color.BOLD + color.RED +
                      "While parsing the config file, could not understand " +
                      "the following line: " + color.END);
                print(line);
                continue;

            # Now convert the number to the baseline units and record in the
            # dictionary.
            #
            if (units == "s"):
                threshold = unitsPerSecond * number;
            elif (units == "ms"):
                threshold = unitsPerMillisecond * number;
            elif (units == "us"):
                threshold = unitsPerMicrosecond * number;
            elif (units == "ns"):
                threshold = unitsPerNanosecond * number;
            elif (units == "stdev"):
                threshold = -units;
                # We record it as negative, so that we know
                # this is a standard deviation. We will compute
                # the actual value once we know the average.
            else:
                print(color.BOLD + color.RED +
                      "While parsing the config file, could not understand " +
                      "the following line: " + color.END);
                print(line);
                continue;

            userDefinedLatencyThresholds[func] = threshold;
            userDefinedThresholdNames[func] = str(number) + " " + units;

    # We were given an empty config file
    if (firstNonCommentLine):
        return False;

    return True;


def main():

    global arrowLeftImg;
    global arrowRightImg;
    global bucketDir;
    global perFuncDF;
    global targetParallelism;

    configSupplied = False;
    figuresForAllFunctions = [];

    # Set up the argument parser
    #
    parser = argparse.ArgumentParser(description=
                                 'Visualize operation log');
    parser.add_argument('files', type=str, nargs='*',
                        help='log files to process');
    parser.add_argument('-c', '--config', dest='configFile', default='');
    parser.add_argument('-d', '--dumpCleanData', dest='dumpCleanData',
                        default=False, action='store_true',
                        help='Dump clean log data. Clean data will \
                        not include incomplete function call records, \
                        e.g., if there is a function begin record, but\
                        no function end record, or vice versa.');
    parser.add_argument('-j', dest='jobParallelism', type=int,
                        default='0');

    args = parser.parse_args();

    if (len(args.files) == 0):
        parser.print_help();
        sys.exit(1);

    # Determine the target job parallelism
    if (args.jobParallelism > 0):
        targetParallelism = args.jobParallelism;
    else:
        targetParallelism = multiprocessing.cpu_count() * 2;

    # Get names of standard CSS colors that we will use for the legend
    initColorList();

    # Read the configuration file, if supplied.
    if (args.configFile != ''):
        configSupplied = parseConfigFile(args.configFile);

    if (not configSupplied):
        pluralSuffix = "";

        print(color.BLUE + color.BOLD +
              "Will deem as outliers all function instances whose runtime " +
              "was higher than the " + str(PERCENTILE * 100) +
              "th percentile for that function."
              + color.END);


    # Create a directory for the files that display the data summarized
    # in each bucket of the outlier histogram. We call these "bucket files".
    #
    if not os.path.exists(bucketDir):
        os.makedirs(bucketDir);

    # Parallelize this later, so we are working on files in parallel.
    for fname in args.files:
        processFile(fname, args.dumpCleanData);

    # Normalize all intervals by subtracting the first timestamp.
    normalizeIntervalData();

    # Generate plots of time series slices across all files for each bucket
    # in the outlier histogram. Save each cross-file slice to an HTML file.
    #
    fileNameList = generateTSSlicesForBuckets();

    totalFuncs = len(perFuncDF.keys());
    i = 0;
    # Generate a histogram of outlier durations
    for func in sorted(perFuncDF.keys()):
        funcDF = perFuncDF[func];
        figure = createOutlierHistogramForFunction(func, funcDF, fileNameList);
        if (figure is not None):
            figuresForAllFunctions.append(figure);

        i += 1;
        percentComplete = float(i) / float(totalFuncs) * 100;
        print(color.BLUE + color.BOLD + " Generating outlier histograms... "),
        sys.stdout.write("%d%% complete  \r" % (percentComplete) );
        sys.stdout.flush();

    print(color.END);
    reset_output();
    output_file(filename = "WT-outliers.html", title="Outlier histograms");
    show(column(figuresForAllFunctions));

if __name__ == '__main__':
    main()