More general info: I'm doing the insert from a node on the same rack as the cluster but it is not part of it. Data is being read from a local disk and the datanodes store to the local partitions as well. The filesystem is ext3, but if it were an inode issue the 4-node cluster would perform much worse than the 11-node. No MR jobs or any other activity is present - these are test clusters that I create and remove with HOD. I'm using revision 897952 (the hdfs ui reports 897347 for some reason) , checked out the branch-0.20 a few days ago. Todd: I will repeat the test, waiting several hours after the first round of inserts. Unless the balancer daemon starts by default, I have not started it. The datablocks seemed uniformly spread amongst the datanodes. I've added two additional metrics to be recorded by the datanode - DataNode.xmitsInProgress and DataNode.getXCeiverCount(). These are polled every 10 seconds. If anyone wants me to add additional metrics at any component let me know. > The test case is making files with a ton of blocks. Appending a block to an end of a file might be O(n) - > usually this isn't a problem since even large files are almost always <100 blocks, and the majority <10. > In the test, there are files with 50,000+ blocks, so O(n) runtime anywhere in the blocklist for a file is pretty bad.
The files in the first test are 16k blocks each. I am inserting the same file under different filenames in consecutive runs. If that were the reason, the first insert should take the same amount of time as the last. Nevertheless, I will run the next test with 4k blocks per file and increase the number of consecutive insertions. Dhruba: Unless there is a very high number of collisions, the hashmap should perform in constant time. Even if there were collisions, I would be seeing much higher CPU usage on the NameNode. According to the metrics I've already sent, towards the end of the test the capacity of the BlockMap was 512k and the load approaching 0.66. Best Regards, Zlatin Balevsky P.S. I could not find contact info for the HOD developers. I'd like to ask them to document the "walltime" and "idleness-limit" parameters! ________________________________ From: Dhruba Borthakur [mailto:dhr...@gmail.com] Sent: Thursday, January 14, 2010 9:04 AM To: hdfs-user@hadoop.apache.org Subject: Re: Exponential performance decay when inserting large number of blocks Here is another thing that came to my mind. The Namenode has a hash map in memory where it inserts all blocks. when a new block needs to be allocated, the namenode first generates a random number and checks to see if ti exists in the hashmap. If it does not exist in the hash map, then that number is the block id of the to-be-allocated block. The namenode then inserts this number into the hash map and sends it to te client. The client receives it as the blockid and uses it to write data to the datanode(s). One possibility is that that the time to do a hash-lookup varies depending on the number of blocks in the hash. dhruba On Wed, Jan 13, 2010 at 8:57 PM, alex kamil <alex.ka...@gmail.com> wrote: >launched 8 instances of the bin/hadoop fs -put utility Zlatin, may be a silly question, are you running dfs -put locally on each datanode, or from a single box Also where are you copying the data from, do you have local copies on each node before the insert or all your files reside on a single server, or may be on NFS? i would also chk the network stats on datanodes and namenode and see if the nics are not saturated, i guess you have enough bandwidth but may be there is some issue with NIC on the namenode or something, i saw strange things happening. you can probably monitor the number of conections/sockets, bandwidth, IO waits, # of threads if you are writing to dfs from a single location may be there is a problem on a single node to handle all this outbound traffic, if you are distributing files in parallel from multiple nodes, than mat be there is an inbound congestion on namenode or something like that if its not the case, i'd explore using distcp utility for copying data in parallel (it comes with the distro) also if you really hit a wall, and have some time, i'd take look at alternatives to Filesystem API, may be simething like Fuse-DFS and other packages supported by libhdfs (http://wiki.apache.org/hadoop/LibHDFS) On Wed, Jan 13, 2010 at 11:00 PM, Todd Lipcon <t...@cloudera.com> wrote: Err, ignore that attachment - attached the wrong graph with the right labels! Here's the right graph. -Todd On Wed, Jan 13, 2010 at 7:53 PM, Todd Lipcon <t...@cloudera.com> wrote: On Wed, Jan 13, 2010 at 6:59 PM, Eric Sammer <e...@lifeless.net> wrote: On 1/13/10 8:12 PM, zlatin.balev...@barclayscapital.com wrote: > Alex, Dhruba > > I repeated the experiment increasing the block size to 32k. Still doing > 8 inserts in parallel, file size now is 512 MB; 11 datanodes. I was > also running iostat on one of the datanodes. Did not notice anything > that would explain an exponential slowdown. There was more activity > while the inserts were active but far from the limits of the disk system. While creating many blocks, could it be that the replication pipe lining is eating up the available handler threads on the data nodes? By increasing the block size you would see better performance because the system spends more time writing data to local disk and less time dealing with things like replication "overhead." At a small block size, I could imagine you're artificially creating a situation where you saturate the default size configured thread pools or something weird like that. If you're doing 8 inserts in parallel from one machine with 11 nodes this seems unlikely, but it might be worth looking into. The question is if testing with an artificially small block size like this is even a viable test. At some point the overhead of talking to the name node, selecting data nodes for a block, and setting up replication pipe lines could become some abnormally high percentage of the run time. The concern isn't why the insertion is slow, but rather why the scaling curve looks the way it does. Looking at the data, it looks like the insertion rate (blocks per second) is actually related as 1/n where N is the number of blocks. Attaching another graph of the same data which I think is a little clearer to read. Also, I wonder if the cluster is trying to rebalance blocks toward the end of your runtime (if the balancer daemon is running) and this is causing additional shuffling of data. That's certainly one possibility. Zlatin: here's a test to try: after the FS is full with 400,000 blocks, let the cluster sit for a few hours, then come back and start another insertion. Is the rate slow, or does it return to the fast starting speed? -Todd -- Connect to me at http://www.facebook.com/dhruba _______________________________________________ This e-mail may contain information that is confidential, privileged or otherwise protected from disclosure. If you are not an intended recipient of this e-mail, do not duplicate or redistribute it by any means. Please delete it and any attachments and notify the sender that you have received it in error. 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