oh, really?
ulimit -n is 2048, I'd assumed that would be sufficient for just
testing on my machine. I was going to use 4096 in production.
my hdfs-site.xml has "dfs.datanode.max.xcievers" set to 4096.
As for my logs... there's a lot of "INFO" entries, I haven't gotten
around to configuring it down yet - I'm not quite sure why it's so
extensive at INFO level. My log files is 4.4gb (is this a sign I've
configured or done something wrong?)
I grep -v "INFO" in the log to get the actual error entry (assuming
the stack trace is actually is on the same line or else those stack
lines maybe misleading)
------------
2013-03-23 15:11:43,653 ERROR
org.apache.hadoop.hdfs.server.datanode.DataNode:
DatanodeRegistration(127.0.0.1:50010,
storageID=DS-1419421989-192.168.1.5-50010-1363780956652,
infoPort=50075, ipcPort=50020):DataXceiveServer: Exiting due
to:java.lang.OutOfMemoryError: unable to create new native thread
at java.lang.Thread.start0(Native Method)
at java.lang.Thread.start(Thread.java:691)
at
org.apache.hadoop.hdfs.server.datanode.DataXceiverServer.run(DataXceiverServer.java:133)
at java.lang.Thread.run(Thread.java:722)
2013-03-23 15:11:44,177 ERROR
org.apache.hadoop.hdfs.server.datanode.DataNode:
DatanodeRegistration(127.0.0.1:50010,
storageID=DS-1419421989-192.168.1.5-50010-1363780956652,
infoPort=50075, ipcPort=50020):DataXceiver
java.io.InterruptedIOException: Interruped while waiting for IO on
channel java.nio.channels.SocketChannel[closed]. 0 millis timeout
left.
at
org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:349)
at
org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
at
org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
at
org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:273)
at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
at java.io.DataInputStream.read(DataInputStream.java:149)
at
org.apache.hadoop.hdfs.server.datanode.BlockReceiver.readToBuf(BlockReceiver.java:292)
at
org.apache.hadoop.hdfs.server.datanode.BlockReceiver.readNextPacket(BlockReceiver.java:339)
at
org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receivePacket(BlockReceiver.java:403)
at
org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receiveBlock(BlockReceiver.java:581)
at
org.apache.hadoop.hdfs.server.datanode.DataXceiver.writeBlock(DataXceiver.java:406)
at
org.apache.hadoop.hdfs.server.datanode.DataXceiver.run(DataXceiver.java:112)
at java.lang.Thread.run(Thread.java:722)
On 3/23/13, Harsh J <ha...@cloudera.com> wrote:
> I'm guessing your OutOfMemory then is due to "Unable to create native
> thread" message? Do you mind sharing your error logs with us? Cause if
> its that, then its a ulimit/system limits issue and not a real memory
> issue.
>
> On Sat, Mar 23, 2013 at 2:30 PM, Ted <r6squee...@gmail.com> wrote:
>> I just checked and after running my tests, I generate only 670mb of
>> data, on 89 blocks.
>>
>> What's more, when I ran the test this time, I had increased my memory
>> to 2048mb so it completed fine - but I decided to run jconsole through
>> the test so I could see what's happenning. The data node never
>> exceeded 200mb of memory usage. It mostly stayed under 100mb.
>>
>> I'm not sure why it would complain about out of memory and shut itself
>> down when it was only 1024. It was fairly consistently doing that the
>> last few days including this morning right before I switched it to
>> 2048.
>>
>> I'm going to run the test again with 1024mb and jconsole running, none
>> of this makes any sense to me.
>>
>> On 3/23/13, Harsh J <ha...@cloudera.com> wrote:
>>> I run a 128 MB heap size DN for my simple purposes on my Mac and it
>>> runs well for what load I apply on it.
>>>
>>> A DN's primary, growing memory consumption comes from the # of blocks
>>> it carries. All of these blocks' file paths are mapped and kept in the
>>> RAM during its lifetime. If your DN has acquired a lot of blocks by
>>> now, like say close to a million or more, then 1 GB may not suffice
>>> anymore to hold them in and you'd need to scale up (add more RAM or
>>> increase heap size if you have more RAM)/scale out (add another node
>>> and run the balancer).
>>>
>>> On Sat, Mar 23, 2013 at 10:03 AM, Ted <r6squee...@gmail.com> wrote:
>>>> Hi I'm new to hadoop/hdfs and I'm just running some tests on my local
>>>> machines in a single node setup. I'm encountering out of memory errors
>>>> on the jvm running my data node.
>>>>
>>>> I'm pretty sure I can just increase the heap size to fix the errors,
>>>> but my question is about how memory is actually used.
>>>>
>>>> As an example, with other things like an OS's disk-cache or say
>>>> databases, if you have or let it use as an example 1gb of ram, it will
>>>> "work" with what it has available, if the data is more than 1gb of ram
>>>> it just means it'll swap in and out of memory/disk more often, i.e.
>>>> the cached data is smaller. If you give it 8gb of ram it still
>>>> functions the same, just performance increases.
>>>>
>>>> With my hdfs setup, this does not appear to be true, if I allocate it
>>>> 1gb of heap, it doesn't just perform worst / swap data to disk more.
>>>> It out right fails with out of memory and shuts the data node down.
>>>>
>>>> So my question is... how do I really tune the memory / decide how much
>>>> memory I need to prevent shutdowns? Is 1gb just too small even on a
>>>> single machine test environment with almost no data at all, or is it
>>>> suppose to work like OS-disk caches were it always works but just
>>>> performs better or worst and I just have something configured wrong?.
>>>> Basically my objective isn't performance, it's that the server must
>>>> not shut itself down, it can slow down but not shut off.
>>>>
>>>> --
>>>> Ted.
>>>
>>>
>>>
>>> --
>>> Harsh J
>>>
>>
>>
>> --
>> Ted.
>
>
>
> --
> Harsh J
>
--
Ted.
