Re: Hadoop and Hibernate
This is a tardy response. I'm spread pretty thinly right now. DistributedCachehttp://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCacheis apparently deprecated. Is there a replacement? I didn't see anything about this in the documentation, but then I am still using 0.21.0. I have to for performance reasons. 1.0.1 is too slow and the client won't have it. Also, the DistributedCachehttp://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCacheapproach seems only to work from within a hadoop job. i.e. From within a Mapper or a Reducer, but not from within a Driver. I have libraries that I must access both from both places. I take it that I am stuck keeping two copies of these libraries in synch--Correct? It's either that, or copy them into hdfs, replacing them all at the beginning of each job run. Looking for best practices. Thanks On 28 February 2012 10:17, Owen O'Malley omal...@apache.org wrote: On Tue, Feb 28, 2012 at 5:15 PM, Geoffry Roberts geoffry.robe...@gmail.com wrote: If I create an executable jar file that contains all dependencies required by the MR job do all said dependencies get distributed to all nodes? You can make a single jar and that will be distributed to all of the machines that run the task, but it is better in most cases to use the distributed cache. See http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache If I specify but one reducer, which node in the cluster will the reducer run on? The scheduling is done by the JobTracker and it isn't possible to control the location of the reducers. -- Owen -- Geoffry Roberts
Re: Hadoop and Hibernate
No, I am using 0.21.0 for better performance. I am interested in DistributedCache so certain libraries can be found during MR processing. As it is now, I'm getting ClassNotFoundException being thrown by the Reducers. The Driver throws no error, the Reducer(s) does. It would seem something is not being distributed across the cluster as I assumed it would. After all, the whole business is in a single, executable jar file. On 2 March 2012 09:46, Kunaal kunaalbha...@gmail.com wrote: Are you looking to use DistributedCache for better performance? On Fri, Mar 2, 2012 at 9:42 AM, Geoffry Roberts geoffry.robe...@gmail.comwrote: This is a tardy response. I'm spread pretty thinly right now. DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache is apparently deprecated. Is there a replacement? I didn't see anything about this in the documentation, but then I am still using 0.21.0. I have to for performance reasons. 1.0.1 is too slow and the client won't have it. Also, the DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache approach seems only to work from within a hadoop job. i.e. From within a Mapper or a Reducer, but not from within a Driver. I have libraries that I must access both from both places. I take it that I am stuck keeping two copies of these libraries in synch--Correct? It's either that, or copy them into hdfs, replacing them all at the beginning of each job run. Looking for best practices. Thanks On 28 February 2012 10:17, Owen O'Malley omal...@apache.org wrote: On Tue, Feb 28, 2012 at 5:15 PM, Geoffry Roberts geoffry.robe...@gmail.com wrote: If I create an executable jar file that contains all dependencies required by the MR job do all said dependencies get distributed to all nodes? You can make a single jar and that will be distributed to all of the machines that run the task, but it is better in most cases to use the distributed cache. See http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache If I specify but one reducer, which node in the cluster will the reducer run on? The scheduling is done by the JobTracker and it isn't possible to control the location of the reducers. -- Owen -- Geoffry Roberts -- What we are is the universe's gift to us. What we become is our gift to the universe. -- Geoffry Roberts
Re: Hadoop and Hibernate
Thanks Leo. I appreciate your response. Let me explain my situation more precisely. I am running a series of MR sub-jobs all harnessed together so they run as a single job. The last MR sub-job does nothing more than aggregate the output of the previous sub-job into a single file(s). It does this, by having but a single reducer. I could eliminate this aggregation sub-job if I could have the aforementioned previous sub-job insert its output into a database instead of hdfs. Doing this, would also eliminate my current dependance on MultipleOutputs. The trouble comes when the Reducer(s) cannot find the persistent objects hence the dreaded CNFE. I find this odd because they are in the same package as the Reducer. Your comment about the back end crying is duly noted. btw, MPI = Message Passing Interface? On 2 March 2012 10:30, Leo Leung lle...@ddn.com wrote: Geoffry, Hadoop distributedCache (as of now) is used to cache M/R application specific files. These files are used by M/R app only and not the framework. (Normally as side-lookup) You can certainly try to use Hibernate to query your SQL based back-end within the M/R code. But think of what happens when a few hundred or thousands of M/R task do that concurrently. Your back-end is going to cry. (if it can - before it dies) So IMO, prep your M/R job with distributedCache files (pull it down first) is a better approach. Also, MPI is pretty much out of question (not baked into the framework). You'll likely have to roll your own. (And try to trick the JobTracker in not starting the same task) Anyone has a better solution for Geoffry? -Original Message- From: Geoffry Roberts [mailto:geoffry.robe...@gmail.com] Sent: Friday, March 02, 2012 9:42 AM To: common-user@hadoop.apache.org Subject: Re: Hadoop and Hibernate This is a tardy response. I'm spread pretty thinly right now. DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache is apparently deprecated. Is there a replacement? I didn't see anything about this in the documentation, but then I am still using 0.21.0. I have to for performance reasons. 1.0.1 is too slow and the client won't have it. Also, the DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache approach seems only to work from within a hadoop job. i.e. From within a Mapper or a Reducer, but not from within a Driver. I have libraries that I must access both from both places. I take it that I am stuck keeping two copies of these libraries in synch--Correct? It's either that, or copy them into hdfs, replacing them all at the beginning of each job run. Looking for best practices. Thanks On 28 February 2012 10:17, Owen O'Malley omal...@apache.org wrote: On Tue, Feb 28, 2012 at 5:15 PM, Geoffry Roberts geoffry.robe...@gmail.com wrote: If I create an executable jar file that contains all dependencies required by the MR job do all said dependencies get distributed to all nodes? You can make a single jar and that will be distributed to all of the machines that run the task, but it is better in most cases to use the distributed cache. See http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#Distr ibutedCache If I specify but one reducer, which node in the cluster will the reducer run on? The scheduling is done by the JobTracker and it isn't possible to control the location of the reducers. -- Owen -- Geoffry Roberts -- Geoffry Roberts
Re: Hadoop and Hibernate
Queries are nothing but inserts. Create an object, populated it, persist it. If it worked, life would be good right now. I've considered JDBC and may yet take that approach. re: Hibernate outside of Spring -- I'm getting tired already. Interesting thing: I use EMF (Eclipse Modelling Framework). The supporting jar files for emf and ecore are built into the job. They are being found by the Driver(s) and the MR(s) no problemo. If these work, why not the hibernate stuff? Mystery! On 2 March 2012 10:50, Tarjei Huse tar...@scanmine.com wrote: On 03/02/2012 07:31 PM, Geoffry Roberts wrote: No, I am using 0.21.0 for better performance. I am interested in DistributedCache so certain libraries can be found during MR processing. As it is now, I'm getting ClassNotFoundException being thrown by the Reducers. The Driver throws no error, the Reducer(s) does. It would seem something is not being distributed across the cluster as I assumed it would. After all, the whole business is in a single, executable jar file. How complex are the queries you are doing? Have you considered one of the following: 1) Use plain jdbc instead of integrating Hibernate into Hadoop. 2) Create a local version of the db that can be in the Distributed Cache. I tried using Hibernate with hadoop (the queries were not an important part of the size of the jobs) but I ran up against so many issues trying to get Hibernate to start up within the MR job that i ended up just exporting the tables, loading them into memory and doing queries against them with basic HashMap lookups. My best advice is that if you can, you should consider a way to abstract away Hibernate from the job and use something closer to the metal like either JDBC or just dump the data to files. Getting Hibernate to run outside of Spring and friends can quickly grow tiresome. T On 2 March 2012 09:46, Kunaal kunaalbha...@gmail.com wrote: Are you looking to use DistributedCache for better performance? On Fri, Mar 2, 2012 at 9:42 AM, Geoffry Roberts geoffry.robe...@gmail.comwrote: This is a tardy response. I'm spread pretty thinly right now. DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache is apparently deprecated. Is there a replacement? I didn't see anything about this in the documentation, but then I am still using 0.21.0. I have to for performance reasons. 1.0.1 is too slow and the client won't have it. Also, the DistributedCache http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache approach seems only to work from within a hadoop job. i.e. From within a Mapper or a Reducer, but not from within a Driver. I have libraries that I must access both from both places. I take it that I am stuck keeping two copies of these libraries in synch--Correct? It's either that, or copy them into hdfs, replacing them all at the beginning of each job run. Looking for best practices. Thanks On 28 February 2012 10:17, Owen O'Malley omal...@apache.org wrote: On Tue, Feb 28, 2012 at 5:15 PM, Geoffry Roberts geoffry.robe...@gmail.com wrote: If I create an executable jar file that contains all dependencies required by the MR job do all said dependencies get distributed to all nodes? You can make a single jar and that will be distributed to all of the machines that run the task, but it is better in most cases to use the distributed cache. See http://hadoop.apache.org/common/docs/r1.0.0/mapred_tutorial.html#DistributedCache If I specify but one reducer, which node in the cluster will the reducer run on? The scheduling is done by the JobTracker and it isn't possible to control the location of the reducers. -- Owen -- Geoffry Roberts -- What we are is the universe's gift to us. What we become is our gift to the universe. -- Regards / Med vennlig hilsen Tarjei Huse Mobil: 920 63 413 -- Geoffry Roberts
Hadoop and Hibernate
All,
I am trying to use Hibernate within my reducer and it goeth not well. Has
anybody ever successfully done this?
I have a java package that contains my Hadoop driver, mapper, and reducer
along with a persistence class. I call Hibernate from the cleanup() method
in my reducer class. It complains that it cannot find the persistence
class. The class is in the same package as the reducer and this all would
work outside of Hadoop. The error is thrown when I attempt to begin a
transaction.
The error:
org.hibernate.MappingException: Unknown entity: qq.mob.depart.EpiState
The code:
protected void cleanup(Context ctx) throws IOException,
InterruptedException {
...
org.hibernate.cfg.Configuration cfg = new org.hibernate.cfg.Configuration();
SessionFactory sessionFactory =
cfg.configure(hibernate.cfg.xml).buildSessionFactory();
cfg.addAnnotatedClass(EpiState.class); // This class is in the same
package as the reducer.
Session session = sessionFactory.openSession();
Transaction tx = session.getTransaction();
tx.begin(); //Error is thrown here.
...
}
If I create an executable jar file that contains all dependencies required
by the MR job do all said dependencies get distributed to all nodes?
If I specify but one reducer, which node in the cluster will the reducer
run on?
Thanks
--
Geoffry Roberts
Runtime Comparison of Hadoop 0.21.0 and 1.0.1
All, I saw the announcement that hadoop 1.0.1 micro release was available. I have been waiting for this because I need the MutipleOutputs capability, which 1.0.0 didn't support. I grabbed a copy of the release candidate. I was happy to see that the directory structure once again conforms (mostly) to the older releases as opposed to what was in the 1.0.0 release. I did a comparison of run times between 1.0.1 and 0.21.0, which is my production version. It seems that 1.0.1 runs about four times slower than 0.21.0. With the same code, same hardware, same configuration, and the same data set; end to end times are: 0.21.0 = 8.83 minutes. 1.0.1 = 30.26 minutes. Is this a known condition? Thanks -- Geoffry Roberts
Re: Same Hadoop, Same MR Job, Different cluster
All, This a follow up to my last post. Turns out there was yet another hadoop cluster available running Ubuntu 10.10 on identical hardware to what i was using in may last post. The difference is between the two versions of Ubuntu: 10.10 vs 11.10. All else is equal. Things work on 10.10. Things do not work on 11.10. I can't explain. I'm not going to try. I'm rolling back to 10.10 for my project. On 8 February 2012 13:45, Geoffry Roberts geoffry.robe...@gmail.com wrote: All, I am setting up a new cluster on some new machines and am experiencing errors that did not happen before. I am using the same Hadoop-1.0 with the same configuration, and the same jvm on both machines. The thing that is different is the OS. The old cluster was Ubuntu 10.10 and the new is Ubuntu 11.10. Since the OS seems to be the difference, are there any issues with Hadoop-1.0 and Ubuntu 11.10? Also, I remember at one time Hadoop having troubles with ipv6. Do I still need to disable it? The same job runs on the old but not on the new. Error message is below. Thanks 12/02/08 13:29:39 INFO mapred.JobClient: map 0% reduce 0% 12/02/08 13:29:46 INFO mapred.JobClient: Task Id : attempt_201202081216_0003_m_ 99_0, Status : FAILED java.lang.Throwable: Child Error at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:271) Caused by: java.io.IOException: Task process exit with nonzero status of 1. at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:258) 12/02/08 13:29:46 WARN mapred.JobClient: Error reading task outputhttp://q001q:50060/tasklog?plaintext=trueattemptid=attempt_201202081216_0003_m_99_0filter=stdout 12/02/08 13:29:46 WARN mapred.JobClient: Error reading task outputhttp://q001q:50060/tasklog?plaintext=trueattemptid=attempt_201202081216_0003_m_99_0filter=stderr 12/02/08 13:29:52 INFO mapred.JobClient: Task Id : attempt_201202081216_0003_r_11_0, Status : FAILED java.lang.Throwable: Child Error at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:271) Caused by: java.io.IOException: Task process exit with nonzero status of 1. at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:258) -- Geoffry Roberts -- Geoffry Roberts
Same Hadoop, Same MR Job, Different cluster
All, I am setting up a new cluster on some new machines and am experiencing errors that did not happen before. I am using the same Hadoop-1.0 with the same configuration, and the same jvm on both machines. The thing that is different is the OS. The old cluster was Ubuntu 10.10 and the new is Ubuntu 11.10. Since the OS seems to be the difference, are there any issues with Hadoop-1.0 and Ubuntu 11.10? Also, I remember at one time Hadoop having troubles with ipv6. Do I still need to disable it? The same job runs on the old but not on the new. Error message is below. Thanks 12/02/08 13:29:39 INFO mapred.JobClient: map 0% reduce 0% 12/02/08 13:29:46 INFO mapred.JobClient: Task Id : attempt_201202081216_0003_m_ 99_0, Status : FAILED java.lang.Throwable: Child Error at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:271) Caused by: java.io.IOException: Task process exit with nonzero status of 1. at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:258) 12/02/08 13:29:46 WARN mapred.JobClient: Error reading task outputhttp://q001q:50060/tasklog?plaintext=trueattemptid=attempt_201202081216_0003_m_99_0filter=stdout 12/02/08 13:29:46 WARN mapred.JobClient: Error reading task outputhttp://q001q:50060/tasklog?plaintext=trueattemptid=attempt_201202081216_0003_m_99_0filter=stderr 12/02/08 13:29:52 INFO mapred.JobClient: Task Id : attempt_201202081216_0003_r_11_0, Status : FAILED java.lang.Throwable: Child Error at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:271) Caused by: java.io.IOException: Task process exit with nonzero status of 1. at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:258) -- Geoffry Roberts
Seeking Advice on Upgrading a Cluster
All, I am a developer, not a super networking guy or hardware guy, and new to Hadoop. I'm working a research project. Funds are limited. I have a compute problem where I need to get the performance up on the processing of large text files and no doubt Hadoop can help if I do things well. I am cobbling my cluster together, to the greatest extent possible, out of spare parts. I can spend some money, but must do so with deliberation and prudence. I have at my disposal twelve, one time desk top computers: - Pentium 4 3.80GHz - 2-4G of memory - 1 Gigabit NIC - 1 Disk, Serial ATA/150 7,200 RPM I have installed: - Ubuntu 10.10 /64 server - JDK /64 - Hadoop 0.21.0 Processing is still slow. I am tuning Hadoop, but I'm guessing I should also upgrade my hardware. What will give me the most bang for my buck? - Should I bring all machines up to 8G of memory? or is 4G good enough? (8 is the max.) - Should I double up the NICs and use LACP? - Should I double up the disks and attempt to flow my I/O from one disk to the another on the theory that this will minimizing contention? - Should I get another switch? (I have a 10/100, 24 port Dlink and it's about 5 years old.) Thanks in advance -- Geoffry Roberts
