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NAME
Stream::Aggregate - generate aggregate information from a stream of data
SYNOPSIS
use Stream::Aggregate;
my $af = generate_aggregation_func(
$agg_config,
$extra_parameters,
$user_extra_parameters);
while ($log = ???) {
@stats = $af->($log);
}
@stats = $af->(undef);
DESCRIPTION
Stream::Aggregate is a general-purpose aggregation module that will
aggregate from a stream of perl objects. While it was written
specifically for log processing, it can be used for other things too.
Aggregation has two key elements: how you group things and what you
aggregate. This module understands two different ways to group things:
nested and cross-product.
Nested groupings come from processing sorted input: if you have three
fields you are considering your context, the order in which the data is
sorted must match the order in which these fields make up your context.
If you want to count things by URL, then you must sort your input by
URL.
Cross-product groupings come from processing unsorted input. Each
combination of values of the fields that make up your context is another
context. This can lead to memory exhaustion so you must specify the
maximum number of values for each of the fields.
Nested groupings
Nested groups are most easily illustrated with a simple example:
aggregating by year, month, and day. The input data must be sorted by
year, month, and day. The current context is defined by the triplet:
(year, month, day). That triplet must be returned by the "context" code.
It is stored in the @current_context array. When a context is finished,
it must be converted into a hash by "context2columns".
Doing it this way, you can, for example, get the average of some data
item per day, per month, and per year in one pass though your data.
Cross-Product grouping
Cross Product grouping does not depend on the sort order of the input
and can have many contexts active at the same time.
For example, if you're aggregating sales figures for shoes and want
statistics for the combinations of size, width, and color there isn't a
sort or nesting order that will answer your questions.
Use "crossproduct" to limit yourself to a certain number of values for
each variable (say 10 sizes, 3 widths, and 5 colors).
API
The configuration for Stream::Aggregate is compiled into a perl function
which is then called once for each input object. Each time it is called,
it may produce one or more aggregate objects as output. When there is no
more input data, call the aggregation function with "undef" as its
input.
The generate-the-function routine, "generate_aggregation_func" takes
three parameters. The first is the configuration object (defined below).
The configuration object is expected (but not required) to come from a
YAML file. All examples are in YAML format. The second and third
arguments provide extra information. Currently they are only used to get
a description of what this aggregation is trying to do using the "name"
field. Eg:
generate_aggregation_func($agg_config, $extra, $user_extra);
my $code = qq{#line 1 "FAKE-all-code-for-$extra->{name}"\n};
The configuration object for Stream::Aggregate is expected to be read
from a YAML file but it does not have to be created that way.
For some of the code fields (below), marked as Closure/Config, you can
provide a closure instead of code. To do that, have a "BEGIN" block
assign a value (the closure) to the variable $coderef. If you do this,
code outside the "BEGIN" block will only be compiled but will never be
run. When evalutating the BEGIN block, the variable $agg_config will be
set to the value of *key_config* (assuming the field was *key*).
The behavior of "generate_aggregation_func" in array context may change
in the future to provide additional return values.
CONTEXT-RELATED CONFIGURATION PARAMETERS
As the aggregator runs over the input, it needs to know the boundries of
the contexts so that it knows when to generate an aggregation result
record.
For example, if you were aggretgating information about URL with nested
groupings, you need to sort your input by URL and you need to define a
"context" that returns the URL (in YAML format, with $log as your input
variable):
context: |
return ($log->{url})
If you want to aggregate over both the URL and the web host, the
"context" must return an array: host & URL (in YAML format):
context: |
$log->{url} =~ m{(?:https?|ftp)://([^/:]+)}
my $host = $1;
return ($host, $log->{url})
When the context is has multiple levels like that, there will be a
resultant aggregation record for each value at each level.
context Code, Optional. Given a $log entry, return an
array that describes the aggregation context. For
example, for a URL, this array might be: domain
name; host name (if different from domain name);
each component of the path of the URL except the
final filename. As Aggregate runs, it will
generate an aggregation record for each element
of the array.
This code will be invoked on every input record.
This is not required for cross-product
aggregations but is required for nested
aggregations.
context2columns Code, Optional. Given a context, in
@current_context, return additional key/value
pairs for the resulting aggregation record. This
is how the context gets described in the
aggregation results records.
This code will be invoked to generate resultant
values just before a context is closed.
If this code sets the variable $suppress_result,
then this aggregation result will be discarded.
stringify_context Code, Optional.
If the new context array returned by the
"context" code (soon to become @current_context)
is not an array of strings but rather an array of
references, it will be turned into strings using
YAML. These strings may not matter because you
control how the context manifests in the result
records with "context2columns".
If this isn't what you want, use
"stringify_context" to do something different.
Unlike most of the other functions,
"stringify_context" operates on @_.
This will be invoked for every input record.
crossproduct Hash, Name->Number, Optional.
For crossproduct groupings, this defines the
dimensions. The keys are the variables. The
values are the maximum number of values for each
variable to track.
The keys must be "ephemeral0", "ephemeral", or
"ephemeral2" column names.
combinations Hash, Name->Code, Optional.
If you have crossproduct groupings, do you also
want to synthesize contexts that exclude some or
all of the crossproduct dimensions?
For each dimension, provide code that that
answers the question: if you remove this
dimension from the crossproduct, should this new
context be considered? This code can look at $row
to see what other dimensions are active for this
potential context. To keep this context, the code
must evaluate to a true value.
Combinations are evaluated in alphabetical order.
If there is more than one path to a combination,
only the first path will be considered. For
example, if you have three crossproduct
dimensions, "a", "b" and "c" then the
combinations are (in the order the'll be
considered):
"b" and "c", excluding "a"
"a" and "c", excluding "b"
"a" and "b", excluding "c"
These have no dependency and will be produced
if the combinations code returns a true
value.
"c", excluding "a" and "b"
This possibility will only be explored coming
from "b" and "c". If the combination rule for
"a" rejected the combination, then the
"c"-only permuation will never be reached.
"b", excluding "a" and "c"
This possibility will only be explored coming
from "b" and "c". If the combination rule for
"a" rejected the combination, then the
"b"-only permuation will never be reached.
"a", excluding "b" and "c"
This possibility will only be explored coming
from "a" and "c". If the combination rule for
"b" rejected the combination, then the
"a"-only permuation will never be reached.
excluding "a", "b" and "c"
This possibility will only be explored coming
from "c". If the combination rule for "a" or
"c" rejected the combination, then the empty
permuation will never be reached.
Using combinations can greatly expand your memory
requirements. More than four dimensions of
combinations is probably a bad idea.
If you want skip a dimension entirely, leave it
out of the combinations key rather than adding
key that evaluates to false. A key the evaluates
to false will still have to be tested many times
whereas a missing key won't have any code
generated for it.
simplify Hash, Name->Code, Optional.
When a cross-product key is exceeding its quota
of values, the default replacement value is "*".
This hash allows you to override the code that
chooses the new value.
finalize_result Code, Optional, Closure/Config.
This code will be called after the resultant
values for a context have been calculated. It is
a last-chance to modify them or to suppress the
results. The values can be found as a reference
to a hash: $row. To suppress the results, set
$suppress_results.
new_context Code, Optional, Closure/Config.
This code will be called each time there is a new
context. At the time it is called, $ps is a
reference to the new context, but
@current_context will not yet have been updated
to the new value.
merge Code, Optional, Closure/Config.
When using multiple levels of contexts, data is
counted for the top-most context layer only. When
that top-most layer finishes, the counts are
merged into the next more-general layer.
During the merge there is both $ps and $oldps
available to for code to reference. The default
merge handles all of the pre-defined column
types. If you are using "$ps->{heap}" storage for
context data, you need to merge that data from
$oldps to $ps yourself.
CONTROL FLOW CONFIGURATION
filter Code, Optional. Before any of the columns are
calculated or any of the values saved, run this
filter code. If it returns a true value then
proceed as normal. If it returns a false value,
then do not count any of the values. The filter
code can remember things in "$ps-"{heap}> that
might effect how other things are counted.
In some situations, you many want to throw away
most data and count things in the filter. When
doing that, it may be that all of the columns
come from "output".
This may be redesigned in a future release in a
way that is not backwards compatible.
filter_early Boolean, Optional, default "false". Check the
filter early before figuring out contexts? If so,
and the result is filtered, don't check to see if
the context changed.
passthrough Code, Optional. Add results to the output of the
aggregation. A value of $log (assuming that's
your input record variable) adds the input data
stream to the output stream. The passthrough code
is run before the input line is counted.
PARAMETERS CONFIGURATION
max_stats_to_keep Number, Optional, default: 4000.
When aggregating large amounts of data, limit
memory use by throwing away some of the data.
When data is thrown away, keep this number of
samples for statistics functions that need bulk
data like standard_deviation.
reduce Code, Optional, Closure/Config.
When "max_stats_to_keep" is exceeded, data will
be thrown away. This function will be called when
that has happened.
preprocess Code, Optional. Code to preprocess the input $log
objects.
item_name String, Optional, default: $log. The default name
for the input values to aggregate over is $log.
If this name is not appropriate, you can use
"item_name" to change the variable name of the
input values to something else. Include the
dollar sign ("$") in the name.
debug Boolean, Optional. Print out some debugging
information, including the code that is generated
for building the columns.
strict Boolean, Optional, default: false. Enforce strict
and warnings for user code.
AGGREGATE OUTPUT COLUMNS CONFIGURATION
Each of these (except "ephemeral" & "keep") defines additional columns
of output that will be included in each aggregation record. These are
all optional and all are defined as key/value pairs where the keys are
column names and the values are perl code. You can refer to other
columns using the variable "$column_*column_name*" where *column_name*
is the name of one of the other columns. When refering to other columns,
the order in which columns are processed matters: "ephemeral" and "keep"
are processed first and second respecively. Idential code fragments will
be evaluated only once. Within a group, columns are evaluated
alphabetically.
Some of the columns will have their code evaluated per-item and some are
evaluated per-aggregation.
The input data is in $log unless overriden by "item_name".
Per item callbacks
ephemeral These columns will not be included in the
aggregation data. Refer to them as
"$column_*column_name*". If you are using
ephemeral to declare the column but do not want
to assign it a value, set the value for the
ephemeral code to be undef. In YAML, thats "~":
ephemeral:
var1: ~
var2: ~
ephemeral0 Same as "ephemeral", will be evaluated before
"ephemeral".
ephemeral2 Same as "ephemeral", will be evaluated after
"ephemeral".
counter Keep a counter. Add one if the code returns true.
percentage Keep a counter. Include the percentage of items
for which the code returned true as an output
column as opposed to the number of items where
the code returned 0. A return value of "undef"
does not get counted at all.
sum Keep an accumulator. Add the return values.
mean Keep an accumulator. Add the return values.
Divide by the number of items before inserting
into the results. Items whose value is "undef" do
not count towards the number of items or the sum.
standard_deviation Remeber the return values. Compute the standard
deviation of the accumulated return values and
insert that into the results. Items whose value
is "undef" are removed before calculating the
standard_deviation.
median Remeber the return values. Compute the median of
the accumulated return values and insert that
into the results. Items whose value is "undef"
are removed before calculating the median.
dominant Remeber the return values. Compute the mode (most
frequent) of the accumulated return values and
insert that into the results. Items whose value
is "undef" are removed before calculating the
mode.
min Keep a minimum numeric value. Replace it with the
return value if the return value is less than the
current value. Items whose value is "undef" are
removed before calculating the min.
max Keep a maximum numeric value. Replace it with the
return value if the return value is greater than
the current value. Items whose value is "undef"
are removed before calculating the max.
minstr Keep a minimum string value. Replace it with the
return value if the return value is less than the
current value. Items whose value is "undef" are
removed before calculating the minstr.
maxstr Keep a maximum string value. Replace it with the
return value if the return value is greater than
the current value. Items whose value is "undef"
are removed before calculating the maxstr.
keep Remember the return values. The return values are
available at aggregation time as
"@{$ps->{keep}{column_name}}". Items whose value
is "undef" are kept but they're ignored by
Stream::Aggregate::Stats functions.
Per aggregation result record callbacks
For code that is per-aggregation, the saved aggregation state can be
found in $ps. One item that is probably needed is "$ps->{item_count}".
output Extra columns to include in the output. This is
where to save "$ps->{item_count}".
stat Use arbitrary perl code to compute statistics on
remembered return values kept with "keep". Write
your own function or use any of the functions in
Stream::Aggregate::Stats (the global variable is
pre-loaded). No, there isn't any difference
between this and "output".
VARIALBES AVAILABLE FOR CODE SNIPPETS TO USE
The following variables are available for the code that generates
per-item and per-aggregation statistics:
$log The current input item (unless overridden by
"item_name")
$ps->{keep}{column_name}
An array of return values kept by "keep".
$ps->{numeric}{column_name}
If Stream::Aggregate::Stats functions are called,
they will grab the numeric values from
"$ps->{keep}{column_name}" and store them in
"$ps->{numeric}{column_name}".
$ps->{random} For each kept item in "$ps->{keep}{column_name}",
there is a corrosponding item in $ps->{random}
that is a random number. These random numbers are
used to determine which values to keep and which
values to toss if there are too many values to
keep them all.
$ps->{$column_type}{column_name}
For each type of column (output, counter,
percentage, sum, min, standard_deviation, median,
stat) the values that will be part of the final
aggregation record.
$ps->{$tempoary_type}{column_name}
Some columns need temporary storage for their
values: percentage_counter (the counter used by
percentage); percentage_total (the number of
total items); mean_sum (the sum used to compute
the mean); mean_count (the number of items for
the mean).
$ps->{heap} A hash that can be used by the configured perl
code for whatever it wants.
$ps->{item_counter} The count of items.
$agg_config The configuration object for Stream::Aggregate
$itemref A reference to $log. It's always $itemref even if
$log is something else.
@current_context The current context as returned by "context".
@context_strings The string-ified version @current_context as
returned by "stringify_context" or YAML.
@contexts The array of context objects. $ps is always
$context[-1].
@items_seen An array that counts the number of rows of output
from this aggregation. When the context is
multi-level, the counter is multi-level. For
example, if the context is *domain*, *host*, and
*URL*; then $items_seen[0] is the number of
*domains* (so far), and $items_seen[1] is the
number of *hosts* for this *domain* (so far), and
$items_seen[2] is the number of *URLs* for this
*host* (so far).
Passthrough rows do not count.
XXX what about cross-product aggregations?
$row When gathering results, the variable that holds
them is a reference to a hash: $row.
$suppress_result After gathering results, the $suppress_result
variable is examined. If it's set the results (in
$row) are discards.
To skip results that aren't crossproduct results,
in "finalize_result", set $suppress_result if
$cross_count isn't true.
$cross_count The number of currently active crossproduct
accumulator contexts.
$extra, $user_extra The additional paramerts (beyond $agg_config)
that were passed to
"generate_aggregation_func()".
%persist This hash is not used by Stream::Aggregate. It's
available for any supplied code to use however it
wants.
$last_item A refernece to the previous $log object. This is
valid during "finalize_result" and
"context2columns".
There are more. Read the code.
HELPER FUNCTIONS
The following helper functions are available: everything in
Stream::Aggregate::Stats and:
nonblank($value)
Returns $value if $value is defined and not the empty string.
Returns undef otherwise.
EXAMPLE1
This example will look at a set of records regarding health risks. Each
record represents a person:
name<TAB>birthday<TAB>sex<TAB>number of hospital visits in the last year
We will generate the following aggregation records:
* Average age of entire sample
* Median number of hospital visits in the last year for the entire
sample.
* Median number of hospital visits in the last year by sex.
* Median number of hospital visits in the last year by age, with a
minimum sample size of five.
* Median number of hospital vists in the last year by age and sex with
a minimum sample size of five.
The code:
#!/usr/bin/perl
#
# Our input data is the raw strings from the input file. Most of the
# work is parsing them and reformatting the data.
#
use strict;
use warnings;
use Stream::Aggregate;
use YAML;
my $aconfig = Load(<<'END_ACONFIG');
strict: 1
debug: 0
item_name: $record
max_stats_to_keep: 500
filter_early: 1
filter: |
# ignore black lines and comments
return 0 if $record =~ /^#/;
return 0 if $record =~ /^$/;
return 1;
crossproduct:
sex: 3
age: 150
combinations:
sex: 1
age: 1
ephemeral0:
#
# We are using ephemeral0 to declare the column variables
#
name: ~
birthday: ~
gender: ~
number_of_visits: ~
ephemeral:
#
# We are using a fake column ($column_step1) in ephemeral to initialize
# the raw column variables we declared in ephemeral0
#
step1: |
chomp($record);
($column_name, $column_birthday, $column_gender, $column_number_of_visits) = split(/\t/, $record);
ephemeral2:
#
# We are using ephemeral2 to generate the computed input data
#
age: |
use Time::ParseDate qw(parsedate);
my $t = parsedate($column_birthday, NO_RELATIVE => 1, DATE_REQUIRED => 1, WHOLE => 1, GMT => 1);
return undef unless $t;
return int ((parsedate('2011-05-01', GMT => 1) - $t) / (365.24 * 86400))
sex: |
return 'M' if $column_gender =~ /^m/i;
return 'F' if $column_gender =~ /^f/i;
return undef;
hospital_visits: |
$column_number_of_visits =~ /^(\d+)$/;
$1
output:
sample_size: $ps->{item_counter}
median:
avg_hospital_visits: $column_hospital_visits
mean:
avg_age: $column_age
finalize_result: |
#
# Don't generate result records unless there are at
# least five items being aggregated.
#
$suppress_result = 1 if $ps->{item_counter} < 5;
END_ACONFIG
my $ag = generate_aggregation_func($aconfig, {
name => 'Aggregate Hospital Visits',
});
my @results;
while (<>) {
for my $result ($ag->($_)) {
# do something with the result records
}
}
for my $result ($ag->(undef)) {
# do something with the result records
}
EXAMPLE2
Our example will count the following things: number of unique URLs per
domain, average length of the URL.
#!/usr/bin/perl
use strict;
use warnings;
use Stream::Aggregate;
use YAML;
my $aconfig = Load(<<'END_ACONFIG');
strict: 1
debug: 0
item_name: $item
context: $item->{domain}
context2columns: return (domain => $current_context[0])
ephemeral:
#
# The only persistent unstructured place to store data from
# one row to the next is $ps->{heap}. $ps->{heap}
# is per-context, but that's okay for our usage.
#
is_different: |
my $old = $ps->{heap}{last_item};
$ps->{heap}{last_item} = $item;
return 1 unless $old;
return 0 if $old->{url} eq $item->{url};
return 1;
sum:
unique_urls: $column_is_different
mean:
avg_url_length: length($item->{url})
finalize_result: |
# we don't want the roll-up context of all domains
$suppress_result = 1 unless $row->{domain};
END_ACONFIG
my $ag = generate_aggregation_func($aconfig, {
name => 'Aggregate URL data'
});
while(<>) {
# we'll parse the input here
my $item;
chomp;
next if /^$/;
next if /^#/;
die "'$_'" unless m{^\w+:\/\/([^/]+)(?:/.*)?};
$item = {
domain => $1,
url => $_,
};
for my $result ($ag->($item)) {
# do something with the result records
}
}
for my $result ($ag->(undef)) {
# do something with the results records
}
LICENSE
Copyright (C) 2008-2010 David Sharnoff; Copyright (C) 2011 Google, Inc.
This package may be used and redistributed under the terms of either the
Artistic 2.0 or LGPL 2.1 license.
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