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File Permission Issue using Distributed Cache of Hadoop-2.2.0

+1 vote
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The original local file has execution permission, and then it was distributed to multiple nodemanager nodes with Distributed Cache feature of Hadoop-2.2.0, but the distributed file has lost the execution permission.

However I did not encounter such issue in Hadoop-1.1.1.

Why this happened? Some changes about dfs.umask option or related staffs?

posted May 27, 2014 by Majula Joshi

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+3 votes

I am trying to access a hadoop 1 installation via the hadoop 2.2.0 command line tools. I am wondering if this is possible at all?

From hadoop 1 I get:

$ hadoop fs -ls hdfs://127.0.0.1:9000/
Found 2 items
drwxr-xr-x - cs supergroup 0 2014-02-01 08:18 /tmp
drwxr-xr-x - cs supergroup 0 2014-02-01 08:19 /user

From hadoop 2.2.0 I get:

$ hadoop fs -ls hdfs://127.0.0.1:9000/
ls: Failed on local exception: java.io.EOFException; Host Details : 
local host is: "i7/127.0.1.1"; destination host is: "localhost":9000;

I am trying to find this information via a web-search, but up to now no success.

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We plan to migrate a 30 nodes hadoop 1.0.1 cluster to the version 2.3.0. We dont have extra machines to setup a separate new cluster, thus hope to do an in-place migration by replacing the components on the existing computers. So the questions are:

1) Is it possible to do an in-place migration, while keeping all data in HDFS safely?
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I currently have a hadoop 2.0 cluster in production, I want to upgrade to latest release.
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+1 vote

We are currently facing a frustrating hadoop streaming memory problem. our setup:

  • our compute nodes have about 7 GB OF RAM
  • hadoop streaming starts a bash script wich uses about 4 GB OF RAM
  • therefore it is only possible to start one and only ONE TASK PER NODE

out of the box each hadoop instance starts about 7 hadoop containers with default hadoop settings. each hadoop task forks a bash script that need about 4 GB of RAM, the first fork works, all following fail because THEY RUN OUT OF MEMORY. so what we are looking for is to LIMIT the number of containers TO ONLY ONE. so what we found on the internet:

  • yarn.scheduler.maximum-allocation-mb and mapreduce.map.memory.mb is set to values such that there is at most one container. this means, mapreduce.map.memory.mb must be MORE THAN HALF of the maximum memory (otherwise there will be multiple containers).

done right, this gives us one container per node. but it produces a new problem: since our java process is now using at least half of the max memory, our child (bash) process we fork will INHERIT THE PARENT MEMORY FOOTPRINT and since the memory used by our parent was more than half of total memory, WE RUN OUT OF MEMORY AGAIN. if we lower the map memory, hadoop will allocate 2 containers per node, which will run out of memory too.

since this problem is a blocker in our current project we are evaluating adapting the source code to solve this issue. as a last resort. any ideas on this are very much welcome.

+3 votes

Few questions about the new Hadoop release regarding YARN:

  1. Does YARN need to run on the same machines that are hosting the HDFS services or can HDFS be remote of a YARN cluster? If this done by placing the remote HDFS clusters configuration files (core-site.xml and hdfs-site.xml) on the YARN clusters machines?

  2. According to http://www.i-programmer.info/news/197-data-mining/6518-hadoop-2-introduces-yarn.html, Hadoop 2.2.0 supports Microsoft Windows. How do/Can you configure YARN for secure container isolation in Windows? It seems that the ContainerExecutor and DefaultContainerExecutor can detect and run on Windows, but the secure LinuxContainerExecutor are for *nix systems, so is there anything in place for maximum security like LCE is?

  3. If 1 is yes, then is it possible to have a cluster mixed with both Linux and Windows machines running YARN and working together?

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