Evert, >From my experience, hadoop loathes swap and you mention that all reduces and mappers are running (8 total) and from the ganglia screenshot I see that you have a thick crest of that purple swap.
If we do the math that means [ map.tasks.max * mapred.child.java.opts ] + [ reduce.tasks.max * mapred.child.java.opts ] => or [ 4 * 2.5G ] + [ 4 * 2.5G ] is greater than the amount of physical RAM in the machine. This doesn't account for the base tasktracker and datanode process + OS overhead and whatever else may be hoarding resources on the systems. I would play with this ratio, either less maps / reduces max - or lower your child.java.opts so that when you are fully subscribed you are not using more resource than the machine can offer. Also, setting mapred.reduce.slowstart.completed.maps to 1.00 or some other value close to 1 would be one way to guarantee only 4 either maps or reduces to be running at once and address (albeit in a duct tape like way) the oversubscription problem you are seeing (this represents the fractions of maps that should complete before initiating the reduce phase). hth EF On Wed, May 11, 2011 at 3:23 AM, Evert Lammerts <evert.lamme...@sara.nl>wrote: > Hi list, > > I notice that whenever our Hadoop installation is put under a heavy load we > lose one or two (on a total of five) datanodes. This results in > IOExceptions, and affects the overall performance of the job being run. Can > anybody give me advise or best practices on a different configuration to > increase the stability? Below I've included the specs of the cluster, the > hadoop related config and an example of when which things go wrong. Any help > is very much appreciated, and if I can provide any other info please let me > know. > > Cheers, > Evert > > == What goes wrong, and when == > > See attached a screenshot of Ganglia when the cluster is under load of a > single job. This job: > * reads ~1TB from HDFS > * writes ~200GB to HDFS > * runs 288 Mappers and 35 Reducers > > When the job runs it takes all available Map and Reduce slots. The system > starts swapping and there is a short time interval during which most cores > are in WAIT. After that the job really starts running. At around half way, > one or two datanodes become unreachable and are marked as dead nodes. The > amount of under-replicated blocks becomes huge. Then some > "java.io.IOException: Could not obtain block" are thrown in Mappers. The job > does manage to finish successfully after around 3.5 hours, but my fear is > that when we make the input much larger - which we want - the system becomes > too unstable to finish the job. > > Maybe worth mentioning - never know what might help diagnostics. We notice > that memory usage becomes less when we switch our keys from Text to > LongWritable. Also, the Mappers are done in a fraction of the time. However, > this for some reason results in much more network traffic and makes Reducers > extremely slow. We're working on figuring out what causes this. > > > == The cluster == > > We have a cluster that consists of 6 Sun Thumpers running Hadoop 0.20.2 on > CentOS 5.5. One of them acts as NN and JT, the other 5 run DN's and TT's. > Each node has: > * 16GB RAM > * 32GB swapspace > * 4 cores > * 11 LVM's of 4 x 500GB disks (2TB in total) for HDFS > * non-HDFS stuff on separate disks > * a 2x1GE bonded network interface for interconnects > * a 2x1GE bonded network interface for external access > > I realize that this is not a well balanced system, but it's what we had > available for a prototype environment. We're working on putting together a > specification for a much larger production environment. > > > == Hadoop config == > > Here some properties that I think might be relevant: > > __CORE-SITE.XML__ > > fs.inmemory.size.mb: 200 > mapreduce.task.io.sort.factor: 100 > mapreduce.task.io.sort.mb: 200 > # 1024*1024*4 MB, blocksize of the LVM's > io.file.buffer.size: 4194304 > > __HDFS-SITE.XML__ > > # 1024*1024*4*32 MB, 32 times the blocksize of the LVM's > dfs.block.size: 134217728 > # Only 5 DN's, but this shouldn't hurt > dfs.namenode.handler.count: 40 > # This got rid of the occasional "Could not obtain block"'s > dfs.datanode.max.xcievers: 4096 > > __MAPRED-SITE.XML__ > > mapred.tasktracker.map.tasks.maximum: 4 > mapred.tasktracker.reduce.tasks.maximum: 4 > mapred.child.java.opts: -Xmx2560m > mapreduce.reduce.shuffle.parallelcopies: 20 > mapreduce.map.java.opts: -Xmx512m > mapreduce.reduce.java.opts: -Xmx512m > # Compression codecs are configured and seem to work fine > mapred.compress.map.output: true > mapred.map.output.compression.codec: com.hadoop.compression.lzo.LzoCodec > >
