Today I met a strange issue, the perl script run out of memory.

The running result:

 $ perl count.pl
 Out of memory!
 Killed
 

This is the script content:

 use strict;
 my %hash;
 my %stat;
 # dataset: userId, itemId, rate, time
 # AV056ETQ5RXLN,0000031887,1.0,1397692800
 open HD,"rate.csv" or die $!;
 while(<HD>) {
     my ($item,$rate) = (split /\,/)[1,2];
     $hash{$item}{total} += $rate;
     $hash{$item}{count} +=1;
 }
 close HD; 
 for my $key (keys %hash) {
     $stat{$key} = $hash{$key}{total} / $hash{$key}{count};
 }
 my $i = 0;
 for (sort { $stat{$b} <=> $stat{$a}} keys %stat) {
     print "$_: $stat{$_}\n";
     last if $i == 99;
     $i ++;
 }
 

The purpose is to aggregate and average the itemId's scores, and print the result after sorting.

As you see what in the script, the dataset is a csv file, each item includes the four parts:

 userId, itemId, rate, time
 

The size of dataset:

 $ wc -l rate.csv 
 82677131 rate.csv
 

The memory is somewhat limited:

 $ free -m
               total        used        free      shared  buff/cache   available
 Mem:           1992        1385          76           0         530         466
 Swap:          1023         779         244
 

What confused me is that Apache Spark can make this job done with this limited memory. It got the statistics done within 2 minutes. But I want to give perl a try since it's not that convenient to run a spark job always.

The spark implementation:

 scala> val schema="uid STRING,item STRING,rate FLOAT,time INT"
 val schema: String = uid STRING,item STRING,rate FLOAT,time INT
   
 scala> val df = spark.read.format("csv").schema(schema).load("skydrive/rate.csv")
 val df: org.apache.spark.sql.DataFrame = [uid: string, item: string ... 2 more fields]
   
 scala> df.groupBy("item").agg(avg("rate").alias("avg_rate")).orderBy(desc("avg_rate")).show()
 +----------+--------+                                                           
 |      item|avg_rate|
 +----------+--------+
 |0001061100|     5.0|
 |0001543849|     5.0|
 |0001061127|     5.0|
 |0001019880|     5.0|
 |0001062395|     5.0|
 |0000143502|     5.0|
 |000014357X|     5.0|
 |0001527665|     5.0|
 |000107461X|     5.0|
 |0000191639|     5.0|
 |0001127748|     5.0|
 |0000791156|     5.0|
 |0001203088|     5.0|
 |0001053744|     5.0|
 |0001360183|     5.0|
 |0001042335|     5.0|
 |0001374400|     5.0|
 |0001046810|     5.0|
 |0001380877|     5.0|
 |0001050230|     5.0|
 +----------+--------+
 only showing top 20 rows
 

I will continue to research this situation.

Return to home | Generated on 09/29/22