Skip to main content

NFS exported HDFS (CDH3)

By Anonymous
Listen:

For some reasons it could be a good idea to make a hdfs filesystem available across networks as a exported share. Here I describe a working scenario with linux and hadoop with tools both have on board.
I used fuse and libhdfs to mount a hdfs filesystem. Change namenode.local and <PORT> to fit your environment.

Install:
 yum install hadoop-0.20-fuse.x86_64 hadoop-0.20-libhdfs.x86_64

Create a mountpoint:
 mkdir /hdfs-mount

Mount your hdfs (testing):
 hadoop-fuse-dfs dfs://namenode.local:<PORT> /hdfs-mount -d

You will show like that:
 INFO fuse_options.c:162 Adding FUSE arg /hdfs-mount
 INFO fuse_options.c:110 Ignoring option -d
 unique: 1, opcode: INIT (26), nodeid: 0, insize: 56
 INIT: 7.10
 flags=0x0000000b
 max_readahead=0x00020000
 INFO fuse_init.c:101 Mounting namenode.local:<PORT>
 INIT: 7.8
 flags=0x00000001
 max_readahead=0x00020000
 max_write=0x00020000
 unique: 1, error: 0 (Success), outsize: 40

Hit crtl-C after you see "Success".

Make the mount available at boot time:
 echo "hadoop-fuse-dfs#dfs://namenode.local:<PORT> /hdfs-mount fuse usetrash,rw 0 0" >> /etc/fstab

Test:
#> mount -a
#> mount
 [..]
 sunrpc on /var/lib/nfs/rpc_pipefs type rpc_pipefs (rw)
 fuse on /hdfs-mount type fuse (rw,nosuid,nodev,allow_other,default_permissions)

To tune the memory for each JVM process take a look into /etc/default/hadoop-0.20-fuse and adjust the settings there.

Export via NFS (unsecure):
First we have to decide which user we use, I suppose the user hdfs. Use "id hdfs":
 uid=104(hdfs) gid=105(hdfs) groups=105(hdfs),104(hadoop) context=root:staff_r:staff_t:SystemLow-SystemHigh

Create an exports-file:
 cat /etc/exports
 /hdfs-mount/user    (fsid=111,rw,wdelay,anonuid=104,anongid=105,sync,insecure,no_subtree_check,no_root_squash)

Expl.: read-write, fsid=unused ID (man 5 exports), write-delay, hdfs user, sync

To export only the user-directory from HDFS prevents you from unwanted changes in system relevant directories (mapred as example).
Restart your NFS Server (service nfs restart).

Now you can use your hdfs as a "local" filesystem, which makes some tasks easier. Note that the "use user" are mapped to the local user, to using root is a bad idea.
Mount the exported NFS on your machine and create / copy your jobdefinitions or files simply.

PS: works only from kernel 2.6.27 upwards
Labels:

Comments

  1. Ted Dunning ... apparently Bayesian30 November, 2011

    What kind of throughput do you observe with this setup?

    Does rsync work?

    Does NFS reorder writes?

    ReplyDelete
  2. Anonymous07 December, 2011

    Upps, did see your post now, sry Ted. Of course, I'm agree with you, performance looks other. Its only a PoC and my private playground.

    I tested with a scp:
    scp /tmp/10GB hdfs@dn-node:/123/hdfs/user/
    10GB 100% 10GB 45.7MB/s 03:44

    - alex

    ReplyDelete
  3. Unknown15 August, 2012

    I have followed your steps and it works fine on centos(although there are still some issues). But when I export the mount point to windows7 via NFS, here comes some problems. I can mount NFS on win7, but I can't see anything in the directory. Can win7 access HDFS via NFS? Or should I use samba3?

    ReplyDelete
  4. Anonymous15 August, 2012

    Did you have installed the Unix Support Tools? For my experience, I use smb3/4 for, since we have here also xattr as well as kerberos support.

    ReplyDelete
    Replies
    1. Unknown16 August, 2012

      Thanks for reply! Do you mean SFU or cygwin or something else? I have no idea about Unix Support Tools. Have you ever access HDFS via NFS on windows?

      Delete

Post a Comment

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

By
Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this inform
Post a Comment
Read more

Hive query shows ERROR "too many counters"

By Anonymous
A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s
2 comments
Read more

AI's False Reality: Understanding Hallucination

By
Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese
Post a Comment
Read more