Your understanding of the proposal is correct. The goal would be to produce Java code rather than a pipeline configuration. But the reasoning is not so that users can then take that and modify themselves. There's nothing preventing them from doing it, but it has a couple of major drawbacks.

1) Code generators generally generate horrific looking code, because they are going for speed and compactness not human maintainability. Trying to work in that code would be very difficult.


2) If you start adding code to generated code, you can no longer use the original Pig Latin. You are from that point forward stuck in Java, since you can't backport your Java into the Pig Latin.

The proposal is designed to test the performance of Pig based on generated Java (or for that matter any other language, it need not be Java). For the idea you suggest, the NATIVE keyword (proposed here https://issues.apache.org/jira/browse/PIG-506) is a better solution.

Alan.

On Apr 16, 2009, at 12:54 AM, nitesh bhatia wrote:

Hi
Can you briefly explain what is required in the first project? After reading the description my impression is, currently when we are executing commands on Pig Shell, Pig is first converting to map-reduce jobs and then feeding it to hadoop. In this project are we proposing that, the execution plan made by Pig will be first converted to a java file for map-reduce procedure and then
feed onto hadoop network ?

If this is the case then I am sure it will be great help to users as this functionality can be used to write complicated map-reduce jobs very easily. Initially user can write the Pig scripts / commands required for his job and get the map-reduce java files. Then he can edit map-reduce files to extend the functionality and add extra procedures that are not provided by Pig but
can be executed over hadoop.

--nitesh

On Wed, Apr 15, 2009 at 9:57 PM, Apache Wiki <wikidi...@apache.org> wrote:

Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Pig Wiki" for
change notification.

The following page has been changed by AlanGates:
http://wiki.apache.org/pig/ProposedProjects

New page:
= Proposed Pig Projects =
This page describes projects what we (the committers) would like to see
added
to Pig. The scale of these projects vary, but they are larger projects,
usually on the weeks or months scale.  We have not yet filed
[https://issues.apache.org/jira/browse/PIG JIRAs] for some of these
because they are still in the vague idea stage.  As they become more
concrete,
[https://issues.apache.org/jira/browse/PIG JIRAs] will be filed for them.

We welcome contributers to take on one of these projects. If you would
like
to do so, please file a JIRA (if one does not already exist for the
project)
with a proposed solution. Pig's committers will work with you from there
to
help refine your solution. Once a solution is agreed upon, you can begin
implementation.

If you see a project here that you would like to see Pig implement but you
are
not in a position to implement the solution right now, feel free to vote
for
the project. Add your name to the list of supporters. This will help contributers looking for a project to select one that will benefit many
users.

If you would like to propose a project for Pig, feel free to add to this
list.
If it is a smaller project, or something you plan to begin work on
immediately, filing a [https://issues.apache.org/jira/browse/PIG JIRA] is
a better route.

|| Catagory || Project || JIRA || Proposed By || Votes For ||
|| Execution || Pig currently executes scripts by building a pipeline of pre-built operators and running data through those operators in map reduce jobs. We need to investigate instead have Pig generate java code specific to a job, and then compiling that code and using it to run the map reduce
jobs. || || Many conference attendees || gates ||
|| Language || Currently only DISTINCT, ORDER BY, and FILTER are allowed inside FOREACH. All operators should be allowed in FOREACH. (Limit is being worked on [https://issues.apache.org/jira/browse/PIG-741 741] || || gates
|| ||
|| Optimization || Speed up comparison of tuples during shuffle for ORDER BY || [https://issues.apache.org/jira/browse/PIG-659 659] || olgan || || || Optimization || Order by should be changed to not use POPackage to put
all of the tuples in a bag on the reduce side, as the bag is just
immediately flattened. It can instead work like join does for the last
input in the join. || || gates || ||
|| Optimization || Often in a Pig script that produces a chain of MR jobs, the map phases of 2nd and subsequent jobs very little. What little they do should be pushed into the proceeding reduce and the map replaced by the identity mapper. Initial tests showed that the identity mapper was 50% faster than using a Pig mapper (because Pig uses the loader to parse out
tuples even if the map itself is empty). || [
https://issues.apache.org/jira/browse/PIG-480 480] || olgan || gates || || Optimization || Use hand crafted calls to do string to integer or float conversions. Initial tests showed these could be done about 8x faster than
String.toIntger() and String.toFloat(). || [
https://issues.apache.org/jira/browse/PIG-482 482] || olgan || gates ||
|| Optimization || Currently Pig always samples for and ORDER BY to
determine how to partition, and then runs another job to do the sort. For
small enough inputs, it should just sort with a single reducer. || [
https://issues.apache.org/jira/browse/PIG-483 483] || olgan || ||
|| Optimization || In many cases data to be joined is already sorted and partitioned on the same key. Pig needs to be able to take advantage of this and do these joins in the map. The join could be done by sampling one input to determine the value of the join key at the beginning of every HDFS block. This would form an index. Then in a second MR job can be run with the other input. Based on the key seen in the second input, the appropriate blocks of the first input can also be loaded into the map and the join done.
|| || gates || ||
|| Optimization || The combiner is not currently used if FILTER is in the
FOREACH.  In some cases it could still be used.  || [
https://issues.apache.org/jira/browse/PIG-479 479] || olgan || ||
|| Optimization || Currently when types of data are declared Pig inserts a
FOREACH immediately after the LOAD that does the conversions.  These
conversions should be delayed until the field is actually used. || [
https://issues.apache.org/jira/browse/PIG-410 410] || olgan || gates || || Optimization || When an order by is not the only operation in a pig script, it is done in two additional MR jobs. The first job samples using a sampling loader, the second does the sort. The sample is used to construct a partitioner that equally balances the data in the sort. The sampler needs to be changed to be a !EvalFunc instead of a loader. This way a split can be but in the proceeding MR job, with the main data being written out and the other part flowing to the sampler func, which can then write out the
sample.  The final MR job can then be the sort. || || gates || ||
|| Optimization || When an order by is the only operation in a pig script it is currently done in 3 MR jobs. The first converts it to BinStorage format (because the sample loader reads that format), the second samples, and the third sorts. Once the changes mentioned above to make the sampler an !EvalFunc are done it should be changed to be done in 2 MR jobs instead of 3. || [https://issues.apache.org/jira/browse/PIG-460 460] || gates ||
||
|| Optimization || The Pig optimizer should be used to determine when
fields in a record are no longer needed and put in FOREACH statements to
project out the unecessary data as early as possible. || [
https://issues.apache.org/jira/browse/PIG-466 466] || olgan || ||
|| Optimization || The Pig optimizers needs to call fieldsToRead so that
Load functions that can do column skipping do it. || || gates || ||
|| Scalability || Pig's default join (symmetric hash) currently depends on being able to fit all of the values for a given join key for one of the inputs into memory. (It does try to spill to disk in the case where it cannot fit them all into memory. In practice this often fails as it is not good at understanding when memory is low enough that it should spill. Even in the case where it does not fail, spilling to disk and rereading from disk is very slow.) If instances of keys with a large number of values were
broken up so that the row set could fit in memory and then shipped to
multiple reducers. A sampling pass would need to be done first to determine
which keys to break up. || || chris olston || gates ||




--
Nitesh Bhatia
Dhirubhai Ambani Institute of Information & Communication Technology
Gandhinagar
Gujarat

"Life is never perfect. It just depends where you draw the line."

visit:
http://www.awaaaz.com - connecting through music
http://www.volstreet.com - lets volunteer for better tomorrow
http://www.instibuzz.com - Voice opinions, Transact easily, Have fun

Reply via email to