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https://issues.apache.org/jira/browse/PIG-807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12714227#action_12714227
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David Ciemiewicz commented on PIG-807:
--------------------------------------

@Yiping

I see what you mean.  Maybe we should have FOREACH and FORALL as in B = FORALL 
A GENERATE SUM(m);

Another version of this my be B = OVER A GENERATE SUM(m); or B = OVERALL A 
GENERATE SUM(m);


There was a hallway conversation about the situation of:

{code}
B = GROUP A BY key;
C = FOREACH B {
        SORTED = ORDER A BY value;
        GENERATE
                COUNT(SORTED) as count,
                QUANTILES(SORTED.value, 0.0, 0.5, 0.75, 0.9, 1.0) as quantiles: 
(p00, p50, p75, p90, p100);
        };
{code}

I was told that a ReadOnce bag would not solve this problem because we'd need 
to pass through SORTED twice because there were two UDFs.

I disagree.  It is possible to pass over this data once and only once if we 
create a class of Accumulating or Running functions that differs from the 
current DataBag and AlgebraicDataBag functions.

First, functions like SUM, COUNT, AVG, VAR, MIN, MAX, STDEV, ResevoirSampling, 
statistics.SUMMARY, can all computed on a ReadOnce / Streaming DataBag of 
unknown length or size.  For each of these functions, we simply "add" or 
"accumulate"  the values on row at a time, we can invoke a combiner for 
intermediate results across partitions, and produce a final result, all without 
materializing a DataBag as implemented today.

QUANTILES is a different beast.  To compute quantiles, the data must be sorted, 
which I prefer to do outside the UDF at this time.  Also, the COUNT of the data 
is needed a prior.  Fortunately sorting COULD produce a ReadOnce / Streaming 
DataBag of KNOWN as opposed to unknown length or size so only two scans through 
the data (sorting and quantiles) are needed without needing three scans (sort, 
count, quantiles).

So, if Pig could understand two additional data types

ReadOnceSizeUnknown -- COUNT() counts all individual rows
ReadOnceSizeKnown -- COUNT() just returns size attribute of ReadOnce data 
reference

And if Pig had RunningEval and RunningAlgebraicEval classes of functions which 
accumulate values a row at a time, many computations in Pig could be much much 
more efficient.

In case anyone doesn't "get" what I mean by having running functions, here's 
some Perl code that implements what I'm suggesting. I'll leave it as an 
exercise for the Pig development team to figure out the RunningAlgebraicEval 
versions of these functions/classes. :^)

runningsums.pl
{code}
#! /usr/bin/perl

use RunningSum;
use RunningCount;

$a_count = RunningCount->new();
$a_sum = RunningSum->new();
$b_sum = RunningSum->new();
$c_sum = RunningSum->new();

while (<>)
{
        s/\r*\n*//g;

        ($a, $b, $c) = split(/\t/);

        $a_count->accumulate($a);
        $a_sum->accumulate($a);
        $b_sum->accumulate($b);
        $c_sum->accumulate($c);
}

print join("\t",
        $a_count->final(),
        $a_sum->final(),
        $b_sum->final(),
        $c_sum->final()
        ), "\n";
{code}

RunningCount.pm
{code}
package RunningCount;

sub new
{
        my $class = shift;
        my $self = {};
        bless $self, $class;
        return $self;
}

sub accumulate
{
        my $self = shift;
        my $value = shift;

        $self->{'count'} ++;
}

sub final
{
        my $self = shift;
        return $self->{'count'};
}

1;
{code}

RunningSum.pl
{code}
package RunningSum;

sub new
{
        my $class = shift;
        my $self = {};
        bless $self, $class;
        return $self;
}

sub accumulate
{
        my $self = shift;
        my $value = shift;

        $self->{'sum'} += $value;
}

sub final
{
        my $self = shift;
        return $self->{'sum'};
}

1;
{code}








> PERFORMANCE: Provide a way for UDFs to use read-once bags (backed by the 
> Hadoop values iterator)
> ------------------------------------------------------------------------------------------------
>
>                 Key: PIG-807
>                 URL: https://issues.apache.org/jira/browse/PIG-807
>             Project: Pig
>          Issue Type: Improvement
>    Affects Versions: 0.2.1
>            Reporter: Pradeep Kamath
>             Fix For: 0.3.0
>
>
> Currently all bags resulting from a group or cogroup are materialized as bags 
> containing all of the contents. The issue with this is that if a particular 
> key has many corresponding values, all these values get stuffed in a bag 
> which may run out of memory and hence spill causing slow down in performance 
> and sometime memory exceptions. In many cases, the udfs which use these bags 
> coming out a group and cogroup only need to iterate over the bag in a 
> unidirectional read-once manner. This can be implemented by having the bag 
> implement its iterator by simply iterating over the underlying hadoop 
> iterator provided in the reduce. This kind of a bag is also needed in 
> http://issues.apache.org/jira/browse/PIG-802. So the code can be reused for 
> this issue too. The other part of this issue is to have some way for the udfs 
> to communicate to Pig that any input bags that they need are "read once" bags 
> . This can be achieved by having an Interface - say "UsesReadOnceBags " which 
> is serves as a tag to indicate the intent to Pig. Pig can then rewire its 
> execution plan to use ReadOnceBags is feasible.

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