pax_global_header 0000666 0000000 0000000 00000000064 11717730045 0014517 g ustar 00root root 0000000 0000000 52 comment=59aa65e54ba6046cef3381b8d36ef7ec2f71907b
ruby-aggregate-0.2.2/ 0000775 0000000 0000000 00000000000 11717730045 0014425 5 ustar 00root root 0000000 0000000 ruby-aggregate-0.2.2/LICENSE 0000664 0000000 0000000 00000002044 11717730045 0015432 0 ustar 00root root 0000000 0000000 Copyright (c) 2009 Joseph Ruscio
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
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 OR COPYRIGHT
HOLDERS 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.
ruby-aggregate-0.2.2/README.textile 0000664 0000000 0000000 00000020611 11717730045 0016762 0 ustar 00root root 0000000 0000000 h1. Aggregate
By Joseph Ruscio
Aggregate is an intuitive ruby implementation of a statistics aggregator
including both default and configurable histogram support. It does this
without recording/storing any of the actual sample values, making it
suitable for tracking statistics across millions/billions of sample
without any impact on performance or memory footprint. Originally
inspired by the Aggregate support in "SystemTap.":http://sourceware.org/systemtap
h2. Getting Started
Aggregates are easy to instantiate, populate with sample data, and then
inspect for common aggregate statistics:
#After instantiation use the << operator to add a sample to the aggregate:
stats = Aggregate.new
loop do
# Take some action that generates a sample measurement
stats << sample
end
# The number of samples
stats.count
# The average
stats.mean
# Max sample value
stats.max
# Min sample value
stats.min
# The standard deviation
stats.std_dev
h2. Histograms
Perhaps more importantly than the basic aggregate statistics detailed above
Aggregate also maintains a histogram of samples. For anything other than
normally distributed data are insufficient at best and often downright misleading
37Signals recently posted a terse but effective "explanation":http://37signals.com/svn/posts/1836-the-problem-with-averages of the importance of histograms.
Aggregates maintains its histogram internally as a set of "buckets".
Each bucket represents a range of possible sample values. The set of all buckets
represents the range of "normal" sample values.
h3. Binary Histograms
Without any configuration Aggregate instance maintains a binary histogram, where
each bucket represents a range twice as large as the preceding bucket i.e.
[1,1], [2,3], [4,5,6,7], [8,9,10,11,12,13,14,15]. The default binary histogram
provides for 128 buckets, theoretically covering the range [1, (2^127) - 1]
(See NOTES below for a discussion on the effects in practice of insufficient
precision.)
Binary histograms are useful when we have little idea about what the
sample distribution may look like as almost any positive value will
fall into some bucket. After using binary histograms to determine
the coarse-grained characteristics of your sample space you can
configure a linear histogram to examine it in closer detail.
h3. Linear Histograms
Linear histograms are specified with the three values low, high, and width.
Low and high specify a range [low, high) of values included in the
histogram (all others are outliers). Width specifies the number of
values represented by each bucket and therefore the number of
buckets i.e. granularity of the histogram. The histogram range
(high - low) must be a multiple of width:
#Want to track aggregate stats on response times in ms
response_stats = Aggregate.new(0, 2000, 50)
The example above creates a linear histogram that tracks the
response times from 0 ms to 2000 ms in buckets of width 50 ms. Hopefully
most of your samples fall in the first couple buckets!
h3. Histogram Outliers
An Aggregate records any samples that fall outside the histogram range as
outliers:
# Number of samples that fall below the normal range
stats.outliers_low
# Number of samples that fall above the normal range
stats.outliers_high
h3. Histogram Iterators
Once a histogram is populated Aggregate provides iterator support for
examining the contents of buckets. The iterators provide both the
number of samples in the bucket, as well as its range:
#Examine every bucket
@stats.each do |bucket, count|
end
#Examine only buckets containing samples
@stats.each_nonzero do |bucket, count|
end
h3. Histogram Bar Chart
Finally Aggregate contains sophisticated pretty-printing support to generate
ASCII bar charts. For any given number of columns >= 80 (defaults to 80) and
sample distribution the to_s method properly sets a marker weight based on the
samples per bucket and aligns all output. Empty buckets are skipped to conserve
screen space.
# Generate and display an 80 column histogram
puts stats.to_s
# Generate and display a 120 column histogram
puts stats.to_s(120)
This code example populates both a binary and linear histogram with the same
set of 65536 values generated by rand to produce the
two histograms that follow it:
require 'rubygems'
require 'aggregate'
# Create an Aggregate instance
binary_aggregate = Aggregate.new
linear_aggregate = Aggregate.new(0, 65536, 8192)
65536.times do
x = rand(65536)
binary_aggregate << x
linear_aggregate << x
end
puts binary_aggregate.to_s
puts linear_aggregate.to_s
h4. Binary Histogram
value |------------------------------------------------------------------| count
1 | | 3
2 | | 1
4 | | 5
8 | | 9
16 | | 15
32 | | 29
64 | | 62
128 | | 115
256 | | 267
512 |@ | 523
1024 |@ | 970
2048 |@@@ | 1987
4096 |@@@@@@@@ | 4075
8192 |@@@@@@@@@@@@@@@@ | 8108
16384 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 16405
32768 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@| 32961
~
Total |------------------------------------------------------------------| 65535
h4. Linear (0, 65536, 4096) Histogram
value |------------------------------------------------------------------| count
0 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4094
4096 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@| 4202
8192 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4118
12288 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4059
16384 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 3999
20480 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4083
24576 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4134
28672 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4143
32768 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4152
36864 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4033
40960 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4064
45056 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4012
49152 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4070
53248 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4090
57344 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4135
61440 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | 4144
Total |------------------------------------------------------------------| 65532
We can see from these histograms that Ruby's rand function does a relatively good
job of distributing returned values in the requested range.
h2. Examples
Here's an example of a "handy timing benchmark":http://gist.github.com/187669
implemented with aggregate.
h2. NOTES
Ruby doesn't have a log2 function built into Math, so we approximate with
log(x)/log(2). Theoretically log( 2^n - 1 )/ log(2) == n-1. Unfortunately due
to precision limitations, once n reaches a certain size (somewhere > 32)
this starts to return n. The larger the value of n, the more numbers i.e.
(2^n - 2), (2^n - 3), etc fall trap to this errors. Could probably look into
using something like BigDecimal, but for the current purposes of the binary
histogram i.e. a simple coarse-grained view the current implementation is
sufficient.
ruby-aggregate-0.2.2/Rakefile 0000664 0000000 0000000 00000001301 11717730045 0016065 0 ustar 00root root 0000000 0000000 require 'rake'
begin
require 'jeweler'
Jeweler::Tasks.new do |gemspec|
gemspec.name = "aggregate"
gemspec.summary = "Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support"
gemspec.description = "Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support. For a detailed README see: http://github.com/josephruscio/aggregate"
gemspec.email = "joe@ruscio.org"
gemspec.homepage = "http://github.com/josephruscio/aggregate"
gemspec.authors = ["Joseph Ruscio"]
end
rescue LoadError
puts "Jeweler not available. Install it with: sudo gem install technicalpickles-jeweler -s http://gems.github.com"
end
ruby-aggregate-0.2.2/VERSION 0000664 0000000 0000000 00000000006 11717730045 0015471 0 ustar 00root root 0000000 0000000 0.2.2
ruby-aggregate-0.2.2/aggregate.gemspec 0000664 0000000 0000000 00000002542 11717730045 0017723 0 ustar 00root root 0000000 0000000 # Generated by jeweler
# DO NOT EDIT THIS FILE DIRECTLY
# Instead, edit Jeweler::Tasks in Rakefile, and run the gemspec command
# -*- encoding: utf-8 -*-
Gem::Specification.new do |s|
s.name = %q{aggregate}
s.version = "0.2.2"
s.required_rubygems_version = Gem::Requirement.new(">= 0") if s.respond_to? :required_rubygems_version=
s.authors = ["Joseph Ruscio"]
s.date = %q{2011-03-05}
s.description = %q{Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support. For a detailed README see: http://github.com/josephruscio/aggregate}
s.email = %q{joe@ruscio.org}
s.extra_rdoc_files = [
"LICENSE",
"README.textile"
]
s.files = [
".gitignore",
"LICENSE",
"README.textile",
"Rakefile",
"VERSION",
"aggregate.gemspec",
"lib/aggregate.rb",
"test/ts_aggregate.rb"
]
s.homepage = %q{http://github.com/josephruscio/aggregate}
s.rdoc_options = ["--charset=UTF-8"]
s.require_paths = ["lib"]
s.rubygems_version = %q{1.4.2}
s.summary = %q{Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support}
s.test_files = [
"test/ts_aggregate.rb"
]
if s.respond_to? :specification_version then
s.specification_version = 3
if Gem::Version.new(Gem::VERSION) >= Gem::Version.new('1.2.0') then
else
end
else
end
end
ruby-aggregate-0.2.2/lib/ 0000775 0000000 0000000 00000000000 11717730045 0015173 5 ustar 00root root 0000000 0000000 ruby-aggregate-0.2.2/lib/aggregate.rb 0000664 0000000 0000000 00000016130 11717730045 0017447 0 ustar 00root root 0000000 0000000 # Implements aggregate statistics and maintains
# configurable histogram for a set of given samples. Convenient for tracking
# high throughput data.
class Aggregate
#The current number of samples
attr_reader :count
#The maximum sample value
attr_reader :max
#The minimum samples value
attr_reader :min
#The sum of all samples
attr_reader :sum
#The number of samples falling below the lowest valued histogram bucket
attr_reader :outliers_low
#The number of samples falling above the highest valued histogram bucket
attr_reader :outliers_high
# The number of buckets in the binary logarithmic histogram (low => 2**0, high => 2**@@LOG_BUCKETS)
@@LOG_BUCKETS = 128
# Create a new Aggregate that maintains a binary logarithmic histogram
# by default. Specifying values for low, high, and width configures
# the aggregate to maintain a linear histogram with (high - low)/width buckets
def initialize (low=nil, high=nil, width=nil)
@count = 0
@sum = 0.0
@sum2 = 0.0
@outliers_low = 0
@outliers_high = 0
# If the user asks we maintain a linear histogram where
# values in the range [low, high) are bucketed in multiples
# of width
if (nil != low && nil != high && nil != width)
#Validate linear specification
if high <= low
raise ArgumentError, "High bucket must be > Low bucket"
end
if high - low < width
raise ArgumentError, "Histogram width must be <= histogram range"
end
if 0 != (high - low).modulo(width)
raise ArgumentError, "Histogram range (high - low) must be a multiple of width"
end
@low = low
@high = high
@width = width
else
@low = 1
@width = nil
@high = to_bucket(@@LOG_BUCKETS - 1)
end
#Initialize all buckets to 0
@buckets = Array.new(bucket_count, 0)
end
# Include a sample in the aggregate
def << data
# Update min/max
if 0 == @count
@min = data
@max = data
else
@max = data if data > @max
@min = data if data < @min
end
# Update the running info
@count += 1
@sum += data
@sum2 += (data * data)
# Update the bucket
@buckets[to_index(data)] += 1 unless outlier?(data)
end
#The current average of all samples
def mean
@sum / @count
end
#Calculate the standard deviation
def std_dev
Math.sqrt((@sum2.to_f - ((@sum.to_f * @sum.to_f)/@count.to_f)) / (@count.to_f - 1))
end
# Combine two aggregates
#def +(b)
# a = self
# c = Aggregate.new
# c.count = a.count + b.count
#end
#Generate a pretty-printed ASCII representation of the histogram
def to_s(columns=nil)
#default to an 80 column terminal, don't support < 80 for now
if nil == columns
columns = 80
else
raise ArgumentError if columns < 80
end
#Find the largest bucket and create an array of the rows we intend to print
disp_buckets = Array.new
max_count = 0
total = 0
@buckets.each_with_index do |count, idx|
next if 0 == count
max_count = [max_count, count].max
disp_buckets << [idx, to_bucket(idx), count]
total += count
end
#XXX: Better to print just header --> footer
return "Empty histogram" if 0 == disp_buckets.length
#Figure out how wide the value and count columns need to be based on their
#largest respective numbers
value_str = "value"
count_str = "count"
total_str = "Total"
value_width = [disp_buckets.last[1].to_s.length, value_str.length].max
value_width = [value_width, total_str.length].max
count_width = [total.to_s.length, count_str.length].max
max_bar_width = columns - (value_width + " |".length + "| ".length + count_width)
#Determine the value of a '@'
weight = [max_count.to_f/max_bar_width.to_f, 1.0].max
#format the header
histogram = sprintf("%#{value_width}s |", value_str)
max_bar_width.times { histogram << "-"}
histogram << sprintf("| %#{count_width}s\n", count_str)
# We denote empty buckets with a '~'
def skip_row(value_width)
sprintf("%#{value_width}s ~\n", " ")
end
#Loop through each bucket to be displayed and output the correct number
prev_index = disp_buckets[0][0] - 1
disp_buckets.each do |x|
#Denote skipped empty buckets with a ~
histogram << skip_row(value_width) unless prev_index == x[0] - 1
prev_index = x[0]
#Add the value
row = sprintf("%#{value_width}d |", x[1])
#Add the bar
bar_size = (x[2]/weight).to_i
bar_size.times { row += "@"}
(max_bar_width - bar_size).times { row += " " }
#Add the count
row << sprintf("| %#{count_width}d\n", x[2])
#Append the finished row onto the histogram
histogram << row
end
#End the table
histogram << skip_row(value_width) if disp_buckets.last[0] != bucket_count-1
histogram << sprintf("%#{value_width}s", "Total")
histogram << " |"
max_bar_width.times {histogram << "-"}
histogram << "| "
histogram << sprintf("%#{count_width}d\n", total)
end
#Iterate through each bucket in the histogram regardless of
#its contents
def each
@buckets.each_with_index do |count, index|
yield(to_bucket(index), count)
end
end
#Iterate through only the buckets in the histogram that contain
#samples
def each_nonzero
@buckets.each_with_index do |count, index|
yield(to_bucket(index), count) if count != 0
end
end
private
def linear?
nil != @width
end
def outlier? (data)
if data < @low
@outliers_low += 1
elsif data >= @high
@outliers_high += 1
else
return false
end
end
def bucket_count
if linear?
return (@high-@low)/@width
else
return @@LOG_BUCKETS
end
end
def to_bucket(index)
if linear?
return @low + (index * @width)
else
return 2**(index)
end
end
def right_bucket? index, data
# check invariant
raise unless linear?
bucket = to_bucket(index)
#It's the right bucket if data falls between bucket and next bucket
bucket <= data && data < bucket + @width
end
=begin
def find_bucket(lower, upper, target)
#Classic binary search
return upper if right_bucket?(upper, target)
# Cut the search range in half
middle = (upper/2).to_i
# Determine which half contains our value and recurse
if (to_bucket(middle) >= target)
return find_bucket(lower, middle, target)
else
return find_bucket(middle, upper, target)
end
end
=end
# A data point is added to the bucket[n] where the data point
# is less than the value represented by bucket[n], but greater
# than the value represented by bucket[n+1]
def to_index (data)
# basic case is simple
return log2(data).to_i if !linear?
# Search for the right bucket in the linear case
@buckets.each_with_index do |count, idx|
return idx if right_bucket?(idx, data)
end
#find_bucket(0, bucket_count-1, data)
#Should not get here
raise "#{data}"
end
# log2(x) returns j, | i = j-1 and 2**i <= data < 2**j
@@LOG2_DIVEDEND = Math.log(2)
def log2( x )
Math.log(x) / @@LOG2_DIVEDEND
end
end
ruby-aggregate-0.2.2/metadata.yml 0000664 0000000 0000000 00000002712 11717730045 0016732 0 ustar 00root root 0000000 0000000 --- !ruby/object:Gem::Specification
name: aggregate
version: !ruby/object:Gem::Version
hash: 19
prerelease:
segments:
- 0
- 2
- 2
version: 0.2.2
platform: ruby
authors:
- Joseph Ruscio
autorequire:
bindir: bin
cert_chain: []
date: 2011-03-05 00:00:00 -08:00
default_executable:
dependencies: []
description: "Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support. For a detailed README see: http://github.com/josephruscio/aggregate"
email: joe@ruscio.org
executables: []
extensions: []
extra_rdoc_files:
- LICENSE
- README.textile
files:
- LICENSE
- README.textile
- Rakefile
- VERSION
- aggregate.gemspec
- lib/aggregate.rb
- test/ts_aggregate.rb
has_rdoc: true
homepage: http://github.com/josephruscio/aggregate
licenses: []
post_install_message:
rdoc_options: []
require_paths:
- lib
required_ruby_version: !ruby/object:Gem::Requirement
none: false
requirements:
- - ">="
- !ruby/object:Gem::Version
hash: 3
segments:
- 0
version: "0"
required_rubygems_version: !ruby/object:Gem::Requirement
none: false
requirements:
- - ">="
- !ruby/object:Gem::Version
hash: 3
segments:
- 0
version: "0"
requirements: []
rubyforge_project:
rubygems_version: 1.4.2
signing_key:
specification_version: 3
summary: Aggregate is a Ruby class for accumulating aggregate statistics and includes histogram support
test_files:
- test/ts_aggregate.rb
ruby-aggregate-0.2.2/test/ 0000775 0000000 0000000 00000000000 11717730045 0015404 5 ustar 00root root 0000000 0000000 ruby-aggregate-0.2.2/test/ts_aggregate.rb 0000664 0000000 0000000 00000007316 11717730045 0020374 0 ustar 00root root 0000000 0000000 require 'test/unit'
require 'lib/aggregate'
class SimpleStatsTest < Test::Unit::TestCase
def setup
@stats = Aggregate.new
@@DATA.each do |x|
@stats << x
end
end
def test_stats_count
assert_equal @@DATA.length, @stats.count
end
def test_stats_min_max
sorted_data = @@DATA.sort
assert_equal sorted_data[0], @stats.min
assert_equal sorted_data.last, @stats.max
end
def test_stats_mean
sum = 0
@@DATA.each do |x|
sum += x
end
assert_equal sum.to_f/@@DATA.length.to_f, @stats.mean
end
def test_bucket_counts
#Test each iterator
total_bucket_sum = 0
i = 0
@stats.each do |bucket, count|
assert_equal 2**i, bucket
total_bucket_sum += count
i += 1
end
assert_equal total_bucket_sum, @@DATA.length
#Test each_nonzero iterator
prev_bucket = 0
total_bucket_sum = 0
@stats.each_nonzero do |bucket, count|
assert bucket > prev_bucket
assert_not_equal count, 0
total_bucket_sum += count
end
assert_equal total_bucket_sum, @@DATA.length
end
=begin
def test_addition
stats1 = Aggregate.new
stats2 = Aggregate.new
stats1 << 1
stats2 << 3
stats_sum = stats1 + stats2
assert_equal stats_sum.count, stats1.count + stats2.count
end
=end
#XXX: Update test_bucket_contents() if you muck with @@DATA
@@DATA = [ 1, 5, 4, 6, 1028, 1972, 16384, 16385, 16383]
def test_bucket_contents
#XXX: This is the only test so far that cares about the actual contents
# of @@DATA, so if you update that array ... update this method too
expected_buckets = [1, 4, 1024, 8192, 16384]
expected_counts = [1, 3, 2, 1, 2]
i = 0
@stats.each_nonzero do |bucket, count|
assert_equal expected_buckets[i], bucket
assert_equal expected_counts[i], count
# Increment for the next test
i += 1
end
end
def test_histogram
puts @stats.to_s
end
def test_outlier
assert_equal 0, @stats.outliers_low
assert_equal 0, @stats.outliers_high
@stats << -1
@stats << -2
@stats << 0
@stats << 2**128
# This should be the last value in the last bucket, but Ruby's native
# floats are not precise enough. Somewhere past 2^32 the log(x)/log(2)
# breaks down. So it shows up as 128 (outlier) instead of 127
#@stats << (2**128) - 1
assert_equal 3, @stats.outliers_low
assert_equal 1, @stats.outliers_high
end
def test_std_dev
@stats.std_dev
end
end
class LinearHistogramTest < Test::Unit::TestCase
def setup
@stats = Aggregate.new(0, 32768, 1024)
@@DATA.each do |x|
@stats << x
end
end
def test_validation
# Range cannot be 0
assert_raise(ArgumentError) {bad_stats = Aggregate.new(32,32,4)}
# Range cannot be negative
assert_raise(ArgumentError) {bad_stats = Aggregate.new(32,16,4)}
# Range cannot be < single bucket
assert_raise(ArgumentError) {bad_stats = Aggregate.new(16,32,17)}
# Range % width must equal 0 (for now)
assert_raise(ArgumentError) {bad_stats = Aggregate.new(1,16384,1024)}
end
#XXX: Update test_bucket_contents() if you muck with @@DATA
# 32768 is an outlier
@@DATA = [ 0, 1, 5, 4, 6, 1028, 1972, 16384, 16385, 16383, 32768]
def test_bucket_contents
#XXX: This is the only test so far that cares about the actual contents
# of @@DATA, so if you update that array ... update this method too
expected_buckets = [0, 1024, 15360, 16384]
expected_counts = [5, 2, 1, 2]
i = 0
@stats.each_nonzero do |bucket, count|
assert_equal expected_buckets[i], bucket
assert_equal expected_counts[i], count
# Increment for the next test
i += 1
end
end
end