I don't think you're subscribed to pig-dev (your emails have been bouncing to the moderator). So I've cc'd you explicitly on this.

I don't think we need a Pig JIRA, it's probably easier if we all work on the hive one. I'll post my comments on the various scripts to that bug. I've also attached them here since pig-dev won't see the updates to that bug.



Adding types in the LOAD statement will force Pig to cast the key field, even though it doesn't need to (it only reads and writes the key field). So I'd change the query to be:

rmf output/PIG_bench/grep_select;
a = load '/data/grep/*' using PigStorage as (key,field);
b = filter a by field matches '.*XYZ.*';
store b into 'output/PIG_bench/grep_select';

field will still be cast to a chararray for the matches, but we won't waste time casting key and then turning it back into bytes for the store.


Same comment, remove the casts. pagerank will be properly cast to an integer.

rmf output/PIG_bench/rankings_select;
a = load '/data/rankings/*' using PigStorage('|') as (pagerank,pageurl,aveduration);
b = filter a by pagerank > 10;
store b into 'output/PIG_bench/rankings_select';


Here you want to keep the casts of pagerank so that it is handled as the right type. adRevenue will default to double in SUM when you don't specify a type. You also want to project out all unneeded columns as soon as possible. You should set PARALLEL on the join to use the number of reducers appropriate for your cluster. Given that you have 10 machines and 5 reduce slots per machine, and speculative execution is off you probably want 50 reducers. I notice you set parallel to 60 on the group by. That will give you 10 trailing reducers. Unless you have a need for the result to be split 60 ways you should reduce that to 50 as well. (I'm assuming here when you say you have a 10 node cluster you mean 10 data nodes, not counting your name node and task tracker. The reduce formula should be 5 * number of data nodes.)

A last question is how large are the uservisits and rankings data sets? If either is < 80M or so you can use the fragment/replicate join, which is much faster than the general join. The following script assumes that isn't the case; but if it is let me know and I can show you the syntax for it.

So the end query looks like:

rmf output/PIG_bench/html_join;
a = load '/data/uservisits/*' using PigStorage('|') as
(sourceIP ,destURL ,visitDate ,adRevenue,userAgent,countryCode,languageCode:,searchWord,duration); b = load '/data/rankings/*' using PigStorage('|') as (pagerank:int,pageurl,aveduration);
c = filter a by visitDate > '1999-01-01' AND visitDate < '2000-01-01';
c1 = fjjkkoreach c generate sourceIP, destURL, addRevenue;
b1 = foreach b generate pagerank, pageurl;
d = JOIN c1 by destURL, b1 by pageurl parallel 50;
d1 = foreach d generate sourceIP, pagerank, adRevenue;
e = group d1 by sourceIP parallel 50;
f = FOREACH e GENERATE group, AVG(d1.pagerank), SUM(d1.adRevenue);
store f into 'output/PIG_bench/html_join';


Same comments as above on projecting out as early as possible and on setting parallel appropriately for your cluster.

rmf output/PIG_bench/uservisits_aggre;
a = load '/data/uservisits/*' using PigStorage('|') as
(sourceIP ,destURL ,visitDate ,adRevenue,userAgent,countryCode,languageCode,searchWord,duration);
a1 = foreach a generate sourceIP, adRevenue;
b = group a by sourceIP parallel 50;
c = FOREACH b GENERATE group, SUM(a. adRevenue);
store c into 'output/PIG_bench/uservisits_aggre';

On Jun 22, 2009, at 10:36 PM, Zheng Shao wrote:

Hi Pig team,

We’d like to get your feedback on a set of queries we implemented on Pig.

We’ve attached the hadoop configuration and pig queries in the email. We start the queries by issuing “pig xxx.pig”. The queries are from SIGMOD’2009 paper. More details are athttps:// issues.apache.org/jira/browse/HIVE-396 (Shall we open a JIRA on PIG for this?)

One improvement is that we are going to change hadoop to use LZO as intermediate compression algorithm very soon. Previously we used gzip for all performance tests including hadoop, hive and pig.

The reason that we specify the number of reducers in the query is to try to match the same number of reducer as Hive automatically suggested. Please let us know what is the best way to set the number of reducers in Pig.

Are there any other improvements we can make to the Pig query and the hadoop configuration?



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