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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Update of "Hbase/Troubleshooting" by DougMeil
Date Wed, 04 May 2011 20:58:37 GMT
Dear Wiki user,

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

The "Hbase/Troubleshooting" page has been changed by DougMeil.
The comment on this change is: Per stack, the Amazon EC2 info is stale.  Just refer people
to the EC2 threads on the hbase dist-list.


  == 7. Problem: Instability on Amazon EC2 ==
+  * Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list.  Search
for old threads using SearchHadoop: http://www.search-hadoop.com
-  * Various problems suggesting overloading on Amazon EC2 deployments: Scanner timeouts,
problems locating HDFS blocks, missed heartbeats, "We slept xxx ms, ten times longer than
scheduled" messages, and so on.
-  * These problems continue after following the other relevant advice on this page.
-  * Or, you are trying to use Small or Medium instance types. (Do not.)
- === Causes ===
-  * Hadoop and HBase daemons require 1GB heap, therefore RAM, per daemon. For load intensive
environments, HBase regionservers may require more heap than this. There must be enough available
RAM to comfortably hold the working sets of all Java processes running on the instance. This
includes any mapper or reducer tasks which may run co-located with system daemons. Small and
Medium instances do not have enough available RAM to contain typical Hadoop+HBase deployments.
-  * Hadoop and HBase daemons are latency sensitive. There should be enough free RAM so no
swapping occurs. Swapping during garbage collection may cause JVM threads to be suspended
for a critically long time. Also, there should be sufficient virtual cores to service the
JVM threads whenever they become runnable. Large instances have two virtual cores, so they
can run HDFS and HBase daemons concurrently, but nothing more. X-Large instances have four
virtual cores, so they can run in addition to HDFS and HBase daemons two mappers or reducers
concurrently. Configure TaskTracker concurrency limits accordingly, or separate mapreduce
computation from storage functions.
- === Resolution ===
-  * Use X-Large (c1.xlarge) instances
-  * Consider splitting storage and computational function over disjoint instance sets.

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