wiki:GENIExperimenter/Tutorials/jacks/HadoopInASlice/ExecuteExperiment

Version 1 (modified by sedwards@bbn.com, 5 years ago) (diff)

--

Hadoop in a Slice

Part II: Execute Experiment: Login to the nodes and execute the Hadoop experiment

Image Map

Instructions

Now that you have reserved your resources, you are ready to login to the slice and run some Hadoop examples.

1. Login to Hadoop Master

  1. Login (ssh) to the hadoop-master using the credentials associated with the GENI Portal and the IP address displayed by Flack. The ssh application you use will depend on the configuration of your laptop/desktop.

2. Check the status/properties of the VMs.

A. Observe the properties of the network interfaces

# /sbin/ifconfig 
eth0      Link encap:Ethernet  HWaddr fa:16:3e:72:ad:a6  
          inet addr:10.103.0.20  Bcast:10.103.0.255  Mask:255.255.255.0
          inet6 addr: fe80::f816:3eff:fe72:ada6/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:1982 errors:0 dropped:0 overruns:0 frame:0
          TX packets:1246 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1000 
          RX bytes:301066 (294.0 KiB)  TX bytes:140433 (137.1 KiB)
          Interrupt:11 Base address:0x2000 

eth1      Link encap:Ethernet  HWaddr fe:16:3e:00:6d:af  
          inet addr:172.16.1.1  Bcast:172.16.1.255  Mask:255.255.255.0
          inet6 addr: fe80::fc16:3eff:fe00:6daf/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:21704 errors:0 dropped:0 overruns:0 frame:0
          TX packets:4562 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1000 
          RX bytes:3100262 (2.9 MiB)  TX bytes:824572 (805.2 KiB)

lo        Link encap:Local Loopback  
          inet addr:127.0.0.1  Mask:255.0.0.0
          inet6 addr: ::1/128 Scope:Host
          UP LOOPBACK RUNNING  MTU:16436  Metric:1
          RX packets:19394 errors:0 dropped:0 overruns:0 frame:0
          TX packets:19394 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0 
          RX bytes:4010954 (3.8 MiB)  TX bytes:4010954 (3.8 MiB)

B. Observe the contents of the NEuca user data file. This file includes a script that will install and execute the script that you configured for the VM.

# neuca-user-data 
[global]
actor_id=67C4EFB4-7CBF-48C9-8195-934FF81434DC
slice_id=39672f6e-610a-4d86-8810-30e02d20cc99
reservation_id=55676541-5221-483d-bb60-429de025f275
unit_id=902709a4-32f2-41fc-b85c-b4791c779580
;router= Not Specified
;iscsi_initiator_iqn= Not Specified
slice_name=urn:publicid:IDN+ch.geni.net:ADAMANT+slice+pruth-winter-camp
unit_url=http://geni-orca.renci.org/owl/8210b4d7-4afc-4838-801f-c20a8f1f75ae#hadoop-master
host_name=hadoop-master
[interfaces]
fe163e006daf=up:ipv4:172.16.1.1/24
[storage]
[routes]
[scripts]
bootscript=#!/bin/bash
	# Automatically generated boot script
	# wget or curl must be installed on the image
	mkdir -p /tmp
	cd /tmp
	if [ -x `which wget 2>/dev/null` ]; then
	  wget -q -O `basename http://geni-images.renci.org/images/GENIWinterCamp/master.sh` http://geni-images.renci.org/images/GENIWinterCamp/master.sh
	else if [ -x `which curl 2>/dev/null` ]; then
	  curl http://geni-images.renci.org/images/GENIWinterCamp/master.sh > `basename http://geni-images.renci.org/images/GENIWinterCamp/master.sh`
	fi
	fi
	eval "/bin/sh -c \"chmod +x /tmp/master.sh; /tmp/master.sh\""

C. Observe the contents of the of the script that was installed and executed on the VM.

# cat /tmp/master.sh 
#!/bin/bash

 echo "Hello from neuca script" > /home/hadoop/log
 MY_HOSTNAME=hadoop-master
 hostname $MY_HOSTNAME
 echo 172.16.1.1  hadoop-master  >> /etc/hosts
  echo 172.16.1.10 hadoop-worker-0 >> /etc/hosts
  echo 172.16.1.11 hadoop-worker-1 >> /etc/hosts
  echo 172.16.1.12 hadoop-worker-2 >> /etc/hosts
  echo 172.16.1.13 hadoop-worker-3 >> /etc/hosts
  echo 172.16.1.14 hadoop-worker-4 >> /etc/hosts
  echo 172.16.1.15 hadoop-worker-5 >> /etc/hosts
  echo 172.16.1.16 hadoop-worker-6 >> /etc/hosts
  echo 172.16.1.17 hadoop-worker-7 >> /etc/hosts
  echo 172.16.1.18 hadoop-worker-8 >> /etc/hosts
  echo 172.16.1.19 hadoop-worker-9 >> /etc/hosts
  echo 172.16.1.20 hadoop-worker-10 >> /etc/hosts
  echo 172.16.1.21 hadoop-worker-11 >> /etc/hosts
  echo 172.16.1.22 hadoop-worker-12 >> /etc/hosts
  echo 172.16.1.23 hadoop-worker-13 >> /etc/hosts
  echo 172.16.1.24 hadoop-worker-14 >> /etc/hosts
  echo 172.16.1.25 hadoop-worker-15 >> /etc/hosts
  while true; do
      PING=`ping -c 1 172.16.1.1 > /dev/null 2>&1`
      if [ "$?" = "0" ]; then 
          break
      fi
      sleep 5
  done
  echo '/home/hadoop/hadoop-euca-init.sh 172.16.1.1 -master' >> /home/hadoop/log
  /home/hadoop/hadoop-euca-init.sh 172.16.1.1 -master
  echo "Done starting daemons" >> /home/hadoop/log

D. Test for connectivity between the VMs.

# ping hadoop-worker-0
PING hadoop-worker-0 (172.16.1.10) 56(84) bytes of data.
64 bytes from hadoop-worker-0 (172.16.1.10): icmp_req=1 ttl=64 time=0.747 ms
64 bytes from hadoop-worker-0 (172.16.1.10): icmp_req=2 ttl=64 time=0.459 ms
64 bytes from hadoop-worker-0 (172.16.1.10): icmp_req=3 ttl=64 time=0.411 ms
^C
--- hadoop-worker-0 ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 1998ms
rtt min/avg/max/mdev = 0.411/0.539/0.747/0.148 ms
# ping hadoop-worker-1
PING hadoop-worker-1 (172.16.1.11) 56(84) bytes of data.
64 bytes from hadoop-worker-1 (172.16.1.11): icmp_req=1 ttl=64 time=0.852 ms
64 bytes from hadoop-worker-1 (172.16.1.11): icmp_req=2 ttl=64 time=0.468 ms
64 bytes from hadoop-worker-1 (172.16.1.11): icmp_req=3 ttl=64 time=0.502 ms
^C
--- hadoop-worker-1 ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 1999ms
rtt min/avg/max/mdev = 0.468/0.607/0.852/0.174 ms

3. Check the status of the Hadoop filesystem.

A. Query for the status of the filesystem and its associated workers.

# hadoop dfsadmin -report
Configured Capacity: 54958481408 (51.18 GB)
Present Capacity: 48681934878 (45.34 GB)
DFS Remaining: 48681885696 (45.34 GB)
DFS Used: 49182 (48.03 KB)
DFS Used%: 0%
Under replicated blocks: 1
Blocks with corrupt replicas: 0
Missing blocks: 0

-------------------------------------------------
Datanodes available: 2 (2 total, 0 dead)

Name: 172.16.1.11:50010
Rack: /default/rack0
Decommission Status : Normal
Configured Capacity: 27479240704 (25.59 GB)
DFS Used: 24591 (24.01 KB)
Non DFS Used: 3137957873 (2.92 GB)
DFS Remaining: 24341258240(22.67 GB)
DFS Used%: 0%
DFS Remaining%: 88.58%
Last contact: Sat Jan 04 21:49:32 UTC 2014


Name: 172.16.1.10:50010
Rack: /default/rack0
Decommission Status : Normal
Configured Capacity: 27479240704 (25.59 GB)
DFS Used: 24591 (24.01 KB)
Non DFS Used: 3138588657 (2.92 GB)
DFS Remaining: 24340627456(22.67 GB)
DFS Used%: 0%
DFS Remaining%: 88.58%
Last contact: Sat Jan 04 21:49:33 UTC 2014

4. Test the filesystem with a small file

A. Create a small test file

# echo Hello GENI World > hello.txt

B. Push the file into the Hadoop filesystem

# hadoop fs -put hello.txt hello.txt

C. Check for the file's existence

# hadoop fs -ls
Found 1 items
-rw-r--r--   3 root supergroup         12 2014-01-04 21:59 /user/root/hello.txt

D. Check the contents of the file

# hadoop fs -cat hello.txt
Hello GENI World

4. Run the Hadoop Sort Testcase

Test the true power of the Hadoop filesystem by creating and sorting a large random dataset. It may be useful/interesting to login to the master and/or worker VMs and use tools like top, iotop, and iftop to observe the resource utilization on each of the VMs during the sort test. Note: on these VMs iotop and iftop must be run as root.

A. Create a 1 GB random data set.

After the data is created, use the ls functionally to confirm the data exists. Note that the data is composed of several files in a directory.

#  hadoop jar /usr/local/hadoop-0.20.2/hadoop-0.20.2-examples.jar teragen 10000000 random.data.1G
Generating 10000000 using 2 maps with step of 5000000
14/01/05 18:47:58 INFO mapred.JobClient: Running job: job_201401051828_0003
14/01/05 18:47:59 INFO mapred.JobClient:  map 0% reduce 0%
14/01/05 18:48:14 INFO mapred.JobClient:  map 35% reduce 0%
14/01/05 18:48:17 INFO mapred.JobClient:  map 57% reduce 0%
14/01/05 18:48:20 INFO mapred.JobClient:  map 80% reduce 0%
14/01/05 18:48:26 INFO mapred.JobClient:  map 100% reduce 0%
14/01/05 18:48:28 INFO mapred.JobClient: Job complete: job_201401051828_0003
14/01/05 18:48:28 INFO mapred.JobClient: Counters: 6
14/01/05 18:48:28 INFO mapred.JobClient:   Job Counters 
14/01/05 18:48:28 INFO mapred.JobClient:     Launched map tasks=2
14/01/05 18:48:28 INFO mapred.JobClient:   FileSystemCounters
14/01/05 18:48:28 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=1000000000
14/01/05 18:48:28 INFO mapred.JobClient:   Map-Reduce Framework
14/01/05 18:48:28 INFO mapred.JobClient:     Map input records=10000000
14/01/05 18:48:28 INFO mapred.JobClient:     Spilled Records=0
14/01/05 18:48:28 INFO mapred.JobClient:     Map input bytes=10000000
14/01/05 18:48:28 INFO mapred.JobClient:     Map output records=10000000

B. Sort the dataset.

# hadoop jar /usr/local/hadoop-0.20.2/hadoop-0.20.2-examples.jar terasort random.data.1G sorted.data.1G
14/01/05 18:50:49 INFO terasort.TeraSort: starting
14/01/05 18:50:49 INFO mapred.FileInputFormat: Total input paths to process : 2
14/01/05 18:50:50 INFO util.NativeCodeLoader: Loaded the native-hadoop library
14/01/05 18:50:50 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
14/01/05 18:50:50 INFO compress.CodecPool: Got brand-new compressor
Making 1 from 100000 records
Step size is 100000.0
14/01/05 18:50:50 INFO mapred.JobClient: Running job: job_201401051828_0004
14/01/05 18:50:51 INFO mapred.JobClient:  map 0% reduce 0%
14/01/05 18:51:05 INFO mapred.JobClient:  map 6% reduce 0%
14/01/05 18:51:08 INFO mapred.JobClient:  map 20% reduce 0%
14/01/05 18:51:11 INFO mapred.JobClient:  map 33% reduce 0%
14/01/05 18:51:14 INFO mapred.JobClient:  map 37% reduce 0%
14/01/05 18:51:29 INFO mapred.JobClient:  map 55% reduce 0%
14/01/05 18:51:32 INFO mapred.JobClient:  map 65% reduce 6%
14/01/05 18:51:35 INFO mapred.JobClient:  map 71% reduce 6%
14/01/05 18:51:38 INFO mapred.JobClient:  map 72% reduce 8%
14/01/05 18:51:44 INFO mapred.JobClient:  map 74% reduce 8%
14/01/05 18:51:47 INFO mapred.JobClient:  map 74% reduce 10%
14/01/05 18:51:50 INFO mapred.JobClient:  map 87% reduce 12%
14/01/05 18:51:53 INFO mapred.JobClient:  map 92% reduce 12%
14/01/05 18:51:56 INFO mapred.JobClient:  map 93% reduce 12%
14/01/05 18:52:02 INFO mapred.JobClient:  map 100% reduce 14%
14/01/05 18:52:05 INFO mapred.JobClient:  map 100% reduce 22%
14/01/05 18:52:08 INFO mapred.JobClient:  map 100% reduce 29%
14/01/05 18:52:14 INFO mapred.JobClient:  map 100% reduce 33%
14/01/05 18:52:23 INFO mapred.JobClient:  map 100% reduce 67%
14/01/05 18:52:26 INFO mapred.JobClient:  map 100% reduce 70%
14/01/05 18:52:29 INFO mapred.JobClient:  map 100% reduce 75%
14/01/05 18:52:32 INFO mapred.JobClient:  map 100% reduce 80%
14/01/05 18:52:35 INFO mapred.JobClient:  map 100% reduce 85%
14/01/05 18:52:38 INFO mapred.JobClient:  map 100% reduce 90%
14/01/05 18:52:46 INFO mapred.JobClient:  map 100% reduce 100%
14/01/05 18:52:48 INFO mapred.JobClient: Job complete: job_201401051828_0004
14/01/05 18:52:48 INFO mapred.JobClient: Counters: 18
14/01/05 18:52:48 INFO mapred.JobClient:   Job Counters 
14/01/05 18:52:48 INFO mapred.JobClient:     Launched reduce tasks=1
14/01/05 18:52:48 INFO mapred.JobClient:     Launched map tasks=16
14/01/05 18:52:48 INFO mapred.JobClient:     Data-local map tasks=16
14/01/05 18:52:48 INFO mapred.JobClient:   FileSystemCounters
14/01/05 18:52:48 INFO mapred.JobClient:     FILE_BYTES_READ=2382257412
14/01/05 18:52:48 INFO mapred.JobClient:     HDFS_BYTES_READ=1000057358
14/01/05 18:52:48 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=3402255956
14/01/05 18:52:48 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=1000000000
14/01/05 18:52:48 INFO mapred.JobClient:   Map-Reduce Framework
14/01/05 18:52:48 INFO mapred.JobClient:     Reduce input groups=10000000
14/01/05 18:52:48 INFO mapred.JobClient:     Combine output records=0
14/01/05 18:52:48 INFO mapred.JobClient:     Map input records=10000000
14/01/05 18:52:48 INFO mapred.JobClient:     Reduce shuffle bytes=951549012
14/01/05 18:52:48 INFO mapred.JobClient:     Reduce output records=10000000
14/01/05 18:52:48 INFO mapred.JobClient:     Spilled Records=33355441
14/01/05 18:52:48 INFO mapred.JobClient:     Map output bytes=1000000000
14/01/05 18:52:48 INFO mapred.JobClient:     Map input bytes=1000000000
14/01/05 18:52:48 INFO mapred.JobClient:     Combine input records=0
14/01/05 18:52:48 INFO mapred.JobClient:     Map output records=10000000
14/01/05 18:52:48 INFO mapred.JobClient:     Reduce input records=10000000
14/01/05 18:52:48 INFO terasort.TeraSort: done

C. Look at the output.

You can use Hadoop's cat and/or get functionally to look at the random and sorted files to confirm their size and that the sort actually worked.

Try some or all of these commands. Does the output make sense to you?

hadoop fs -ls random.data.1G
hadoop fs -ls sorted.data.1G
hadoop fs -cat random.data.1G/part-00000 | less
hadoop fs -cat sorted.data.1G/part-00000 | less

5. Advanced Example

Re-do the tutorial with a different number of workers, amount of bandwidth, and/or worker instance types. Warning: be courteous to other users and do not use too many of the resources.

A. Time the performance of runs with different resources.

B. Observe largest size file you can create with different resources.


Introduction

Next: Teardown Experiment