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https://issues.apache.org/jira/browse/HIVE-396?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12723199#action_12723199
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Alan Gates commented on HIVE-396:
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Comments on how to speed up the Pig Latin scripts used in this benchmark.
grep_select.pig:
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:
{code}
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';
{code}
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.
rankings_select.pig:
Same comment, remove the casts. pagerank will be properly cast to an integer.
{code}
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';
{code}
rankings_uservisits_join.pig:
Here you want to keep the cast of pagerank so that it is handled as the right
type, since AVG can take either double or int and would default to double.
adRevenue will default to double in SUM when you don't specify a type.
You 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'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.)
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.
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:
{code}
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';
{code}
uservisists_agrre.pig:
Same comments as above on projecting out as early as possible and on setting
parallel appropriately for your cluster.
{code}
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';
{code}
> Hive performance benchmarks
> ---------------------------
>
> Key: HIVE-396
> URL: https://issues.apache.org/jira/browse/HIVE-396
> Project: Hadoop Hive
> Issue Type: New Feature
> Reporter: Zheng Shao
> Attachments: hive_benchmark_2009-06-18.pdf,
> hive_benchmark_2009-06-18.tar.gz
>
>
> We need some performance benchmark to measure and track the performance
> improvements of Hive.
> Some references:
> PIG performance benchmarks PIG-200
> PigMix: http://wiki.apache.org/pig/PigMix
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