Removing the filter (B) in the example will still result in similar
plan, just that the filter operator will be absent.
For example, the following query uses only 1 MR job. If your explain
plan showed more MR jobs, your query probably has something that does
not allow the work to be done in single MR job.
cat mutli.pig
l = load 'x' as (a,b,c);
g1 = group l by a;
f1 = foreach g1 generate group as a, MAX(l.b);
store f1 into 'out1';
g2 = group l by b;
f2 = foreach g2 generate group as b, MAX(l.c);
store f2 into 'out2';
g3 = group l by c;
f3 = foreach g3 generate group as c, MAX(l.a);
store f3 into 'out3';
I used following explain command. Disabled combiner so that the plan is
smaller (to make it easy to read/understand) -
java -Dpig.exec.nocombiner=true -cp pig.jar org.apache.pig.Main -x
local -e explain -script mutli.pig
Pasted the explain plan output here so that formatting is fine -
http://pastebin.com/yGJs8Q4t
-Thejas
On 10/4/11 4:13 PM, Something Something wrote:
Thanks, Thejas! Great presentation. But the slides 16& 17 are a bit
different from my example. In your example on slide 16, the output from
filter (B) is used twice. Question is what happens when 'A' (output from
LOAD) is used multiple times.
I ran my pig script with command similar to the following:
pig -e 'explain -dot -out ./my.dot -script myscript.pig'>> explain.txt
I noticed in the Graph that 'LOAD' splits the input into 3 SplitOutput[log]
and then runs 3 MR jobs - which is what I expected. Now I am just trying to
understand how I can create 3 SplitOutput like this outside Pig& feed them
in 3 different MR jobs in Java. Knowing this will make me appreciate Pig
more -:)
On Tue, Oct 4, 2011 at 10:15 AM, Thejas Nair<[email protected]> wrote:
See slides 16,17 in http://www.slideshare.net/**thejasmn/apache-pig-**
performance-optimizations-**talk-at-apachecon-2010<http://www.slideshare.net/thejasmn/apache-pig-performance-optimizations-talk-at-apachecon-2010>.
For the query in example, pig includes an index in the map output key, and
the reduce has a PODemux operator that sends the records to appropriate
reduce plans. There are no distinct reduce tasks for each group operation.
-Thejas
On 10/3/11 9:35 PM, Something Something wrote:
Let me ask the question differently. Let's say I was not using Pig. I
wanted to do this just using Java MapReduce. The input file is HUGE. One
obvious way to do this would be to write 3 different MR jobs. But that
means this huge file be read 3 times which is what I am trying to avoid.
Is there a way to write a Mapper that will read this file only once, and
then write to 3 different Reducers with different keys?
Going back to Pig, when I LOAD this file& then later 'group by' 3
different
keys, how does Pig do this? Does it "LOAD" this input file into some
interim file& call 3 different Map Reduce jobs?
If this makes no sense, please ignore it. I will try to use 'Explain',
'Describe' to learn the internals. Thanks.
On Mon, Oct 3, 2011 at 6:04 PM, Jonathan Coveney<[email protected]>
wrote:
If you want to know more about the internals, I'd check out the paper
Yahoo
put out on the topic (or, of course, buy the book Programming Pig).
The answer to this is pretty simple: if you load a file multiple times
into
different relations, then it will be scanned multiple times. So...
a = load 'thing';
b = load 'thing;
{..stuff using a..}
{..stuff using b..}
would load 'thing' twice. This is done for joins and whatnot -- there are
cases when you need to load the same file separately, twice. What happens
is
essentially that you're going to load and scan the data twice.
However, as in your case, if you instead combine the load, then you'd
have
a = load 'thing';
{..stuff using a..}
{..stuff using a (which previously used b)..}
Now it will just scan a once, and then go into each of the pipelines you
defined.
Obviously it's more complex than that, but that's the general gist.
2011/10/3 Something
Something<mailinglists19@**gmail.com<[email protected]>
I have 3 Pig scripts that load data from the same log file, but filter&
group this data differently. If I combine these 3 into one& LOAD only
once, performance seems to have improved, but now I am curious exactly
what
does LOAD do?
How does LOAD work internally? Does Pig save results of the LOAD into
some
separate location in HDFS? Someone please explain how LOAD relates to
MapReduce? Thanks.
