hbase lzo table scan cause regionserver shutdown

Question

i have a problem , some info follow:

nodes : 3 node , but only configurtion 2 regionserver
os : Centos6.3
Apache Hadoop2.7.1
Apache Hbase0.98.12

my hadoop and hbase support lzo compression and at the same time support snappy comression successs , i have a hbase table using lzo compression and have other hbase table useing snappy compression, i insert 50 recoder data into this table , ok ,insert is no problem ,but when i use java api to scan this table , one of regionserver is deaded.

i check hbase log ,but no error or Exception , but i check hadoop log , i found some Exception :

java.io.IOException: Premature EOF from inputStream
    at org.apache.hadoop.io.IOUtils.readFully(IOUtils.java:201)
    at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.doReadFully(PacketReceiver.java:213)
    at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.doRead(PacketReceiver.java:134)
    at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.receiveNextPacket(PacketReceiver.java:109)
    at org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receivePacket(BlockReceiver.java:472)
    at org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receiveBlock(BlockReceiver.java:849)
    at org.apache.hadoop.hdfs.server.datanode.DataXceiver.writeBlock(DataXceiver.java:804)
    at org.apache.hadoop.hdfs.protocol.datatransfer.Receiver.opWriteBlock(Receiver.java:137)
    at org.apache.hadoop.hdfs.protocol.datatransfer.Receiver.processOp(Receiver.java:74)
    at org.apache.hadoop.hdfs.server.datanode.DataXceiver.run(DataXceiver.java:251)

i don't know why throws Exception in only scan hbase table , because i execute MR job read lzo file is Normal , thanks for your answer !

Answer 1

You are missing a return for the last line of your content. You have to use such condition to control the EOF:

while (line = mycontent.readLine()) != null)
{
...
...
}

Answer 2

好的，我终于找到了答案，这真是令人难以置信，通过Hbase gc日志，我看到很长的完整gc建议，我的hbase的堆大小默认为1 gb，所以当我将其增加到4 GB堆时，可能出现了问题，我使用大量压缩是正常的，所以请记住这一点！