Create Cloudera Hadoop Cluster Using Cloudera Director on Google Cloud

I have a blog discussing how to install Cloudera Hadoop Cluster several years ago. It basically took about at least half day to complete the installation in my VM cluster. In my last post, I discussed an approach to deploy Hadoop cluster using DataProc on Google Cloud Platform. It literally took less than two minutes to create a Hadoop Cluster. Although it is a good to have a cluster launched in a very short time, it does not have the nice UI like Cloudera Manager as the Hadoop distribution used by Dataproc is not CDH. I could repeat my blogs to build a Hadoop Cluster using VM instances on Google Cloud Platform. But it will take some time and involve a lot of work. Actually there is another way to create Hadoop cluster on the cloud. Cloudera has a product, called Cloudera Director. It currently supports not only Google Cloud, but also AWS and Azure as well. It is designed to deploy CDH cluster faster and easier to scale the cluster on the cloud. Another important feature is that Cloud Director allows you to move your deployment scripts or steps easily from one cloud provider to another provider and you don’t have to be locked in one cloud vendor. In this blog, I will show you the way to create a CDH cluster using Cloudera Director.

The first step is to start my Cloudera Director instance. In my case, I have already installed Cloudera Director based on the instruction from Cloudera. It is pretty straight forward process and I am not going to repeat it here. The Cloudera Director instance is where you can launch your CDH cluster deployment.

Both Cloudera Director and Cloudera Manager UI are browser-based and you have to setup secure connection between your local machine and VM instances on the cloud. To achieve this, you need to configure SOCKS proxy on your local machine that is used to connect to the Cloudera Director VM. It provides a secure way to connect to your VM on the cloud and can use VM’s internal IP and hostname in the web browser. Google has a nice note about the steps, Securely Connecting to VM Instances. Following this note will help you to setup SOCKS proxy.

Ok, here are the steps.
Logon to Cloudera Director
Open a terminal session locally, and run the following code:

gcloud compute ssh cdh-director-1 \
    --project cdh-director-173715 \
    --zone us-central1-c \
    --ssh-flag="-D" \
    --ssh-flag="1080" \
    --ssh-flag="-N"    

cdh-director-1 is the name of my Cloudera Director instance on Google cloud and cdh-director-173715 is my Google Cloud project id. After executing the above command, it looks hang and never complete. This is CORRECT behavior. Do not kill or exit this session. Open a browser and type in the internal IP of Cloudera Director instance with port number 7189. For my cdh-director-1 instance, the internal IP is 10.128.0.2.

After input the URL http://10.128.0.2:7189 for Cloudera Director. The login screen shows up. Login as admin user.

Deployment
After login, the initial setup wizard shows up. Click Let’s get started.

In the Add Environment screen, input the information as follows. The Client ID JSON Key is the file you can create during the initial setup of you Google project with SSH key stuff.

In the next Add Cloudera Manager screen, I usually create the Instance Template first. Click the drop down of Instance Template, then select Create a new instance template. I need at least three template, one for Cloudera Manager, one for Master nodes, and one for Worker nodes. In my case here, I did not create a template for Edge nodes. To save resource on my Google cloud environment, I did not create the template for Edge node. Here are the configuration for all three templates.

Cloudera Manager Template

Master Node Template

Worker Node Template

Input the following for Cloudera Manager. For my test, I use Embedded Database. If it is used for production, you need to setup external database first and register the external database here.

After click Continue, Add Cluster screen shows up. There is a gateway instance group and I removed it by clicking Delete Group because I don’t have edge node here. Input the corresponding template and number of instances for masters and workders.

After click Continue, the deployment starts.

After about 20 minutes, it completes. Click Continue.

Review Cluster
The nice Cloudera Director dashboard shows up.

You can also login to Cloudera Manager from the link on Cloudera Director.

Nice and easy. Excellent product from Cloudera. For more information about deploying CDH cluster on Google Cloud, you can also check out Cloudera’s document, Getting Started on Google Cloud Platform.

Resolve Sparklyr not Respond Issue on Port 8880

Recently I was approached by one of my clients to help them to investigate a weird Sparklyr issue. sparklyr is an interface between R and Spark introduced by RStudio about a years ago. The following is the the sparklyr architecture.

When trying to do sc <- spark_connect in RStudio, we got two errors as follows:

Failed while connecting to sparklyr to port (8880) for sessionid (3859): Gateway in port (8880) did not respond.

Exception in thread “main” java.lang.NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream

Here is the detail message.

> library(sparklyr)
> library(dplyr) 
> sc <- spark_connect(master = "yarn-client", config=spark_config(), version="1.6.0", spark_home = '/opt/cloudera/parcels/CDH/lib/spark/')
 
Error in force(code) :
Failed while connecting to sparklyr to port (8880) for sessionid (3859): Gateway in port (8880) did not respond.
Path: /opt/cloudera/parcels/CDH-5.10.1-1.cdh5.10.1.p0.10/lib/spark/bin/spark-submit
Parameters: --class, sparklyr.Shell, --jars, '/usr/lib64/R/library/sparklyr/java/spark-csv_2.11-1.3.0.jar','/usr/lib64/R/library/sparklyr/java/commons-csv-1.1.jar','/usr/lib64/R/library/sparklyr/java/univocity-parsers-1.5.1.jar', '/usr/lib64/R/library/sparklyr/java/sparklyr-1.6-2.10.jar', 8880, 3859
Log: /tmp/RtmzpSIMln/file9e23246605df7_spark.log
 
....
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream
               at org.apache.spark.deploy.SparkSubmitArguments.handle(SparkSubmitArguments.scala:394)
               at org.apache.spark.launcher.SparkSubmitOptionParser.parse(SparkSubmitOptionParser.java:163)
               at org.apache.spark.deploy.SparkSubmitArguments.<init>(SparkSubmitArguments.scala:97)
               at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:114)
               at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.fs.FSDataInputStream
               at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
               at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
               at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
               at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
               ... 5 more

Did some research and found many people having the similar issue. Ok, try their recommendations one by one as follows.

Set SPARK_HOME environment

Try run Sys.setEnv(SPARK_HOME = “/opt/cloudera/parcels/CDH/lib/spark/”). No, not working.

Install latest version sparklyr

My client installed sparklyr less than one month ago. I don’t see why this option makes sense. Don’t even pursue this path.

Check Java Installation

The R on the same server uses the same version of Java without any issue. I don’t see why Java installation become a major concern here. Ignore this one.

No Hadoop Installation

Someone said just Spark installation is not enough, not to have Hadoop Installation as well. Clearly it does not fit our situation. The server is an edge node and has hadoop installation.

Do not have a valid kerberos ticket

Running system2(‘klist’) does show no kerberos ticket. Ok, I then open up a shell within RStudio Server by clicking tools -> shell, then issuing the kinit command.
Rerun system2(‘klist’) shows I have a valid kerberos ticket. Try again. still not working.
Note: even it is not working, this step is necessary for further action when the issue is fixed. So still need to run this one no matter what the result is.

Create a different configure and pass to spark_connect

Someone recommended to create a new configure and pass it in. It looks like a good idea. Unfortunately, just doesn’t work.

wzconfig <- spark_config()
wzconfig$`sparklyr.shell.deploy-mode` <- "client"
wzconfig$spark.driver.cores <- 1
wzconfig$spark.executor.cores <- 2
wzconfig$spark.executor.memory <- "4G"
sc <- spark_connect(master = "yarn-client", config=wzconfig, version="1.6.0", spark_home = '/opt/cloudera/parcels/CDH/lib/spark/')

Actually this recommendation is missing another key parameter. By default the total number of executors launched is 2. I would usually bump up this number a little to get a better performance. You can use the following way to set up the
total number of executors.

wzconfig$spark.executor.instances <- 3

Although this approach looks promising, still not working. But this approach is definitely a way to use for other purpose to better control the Spark resource usage.

Add remote address

Someone mentioned to set remote address. I thought this could another potential option as I resolved issues in Spark related to local IP issue in the past. So I add the following code in the configuration from the previous example, note parameter sparklyr.gateway.address is the hostname of active Resource Manager.

wzconfig$sparklyr.gateway.remote <- TRUE
wzconfig$sparklyr.gateway.address <- "cdhcluster01n03.mycompany.com" 

Not working for this case.

Change deployment mode to yarn-cluster

This is probably the most unrealistic one. If connect as with master = “yarn-cluster”, the spark driver will be somewhere inside the Spark cluster. For our current case, I don’t believe this is the right solution. Don’t even try it.

Run Spark example

Someone recommended to run a spark-submit to verify SparkPi can be run from the environment. This looks reasonable. The good thing I figured out the issue before executing this one. But this definitely a valid and good test to verify spark-submit.

/opt/cloudera/parcels/SPARK2/lib/spark2/bin/spark-submit --class org.apache.spark.examples.SparkPi --deploy-mode client --master yarn /opt/cloudera/parcels/SPARK2/lib/spark2/examples/jars/spark-examples_2.11-2.1.0.jar 10

HA for yarn-cluster

There is an interesting post Add support for `yarn-cluster` with high availability #905 discussing about the issue might relate to multiple resource managers. We use HA and this post is an interesting one. But might not fit into our case because I feel we have not reached to the HA part yet with Class Not Found message.

Need to set JAVA_HOME

Verified it and we have it. So this is not the issue.

My Solution

After reviewing or trying out some of above solutions, I like to go back my way of thinking. I must say I am not an expert in R or RStudio with very limited knowledge about how it works. But I did have extensive background in Spark tuning and trouble shooting.

I know the error message Gateway in port (8880) did not respond is always the first message shows up and looks like the cause of the issue. But I thought differently. I believe the 2nd error NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream looks more suspicious than the first one. Early this year I helped one of another clients on a weird Spark job issue, which is in the end, was caused by the incorrect path. It seems to me the path might not be right and cause Spark issue, then caused the first error of port not respond.

With this idea in mind, I focused more the path verification. Run the command Sys.getenv() to get the environment as follows.

> Sys.getenv()
DISPLAY                 :0
EDITOR                  vi
GIT_ASKPASS             rpostback-askpass
HADOOP_CONF_DIR         /etc/hadoop/conf.cloudera.hdfs
HADOOP_HOME             /opt/cloudera/parcels/CDH
HOME                    /home/wzhou
JAVA_HOME               /usr/java/jdk1.8.0_144/jre
LANG                    en_US.UTF-8
LD_LIBRARY_PATH         /usr/lib64/R/lib::/lib:/usr/java/jdk1.8.0_92/jre/lib/amd64/server
LN_S                    ln -s
LOGNAME                 wzhou
MAKE                    make
PAGER                   /usr/bin/less
PATH                    /usr/local/sbin:/usr/local/bin:/usr/bin:/usr/sbin:/sbin:/bin
R_BROWSER               /usr/bin/xdg-open
R_BZIPCMD               /usr/bin/bzip2
R_DOC_DIR               /usr/share/doc/R-3.3.2
R_GZIPCMD               /usr/bin/gzip
R_HOME                  /usr/lib64/R
R_INCLUDE_DIR           /usr/include/R
R_LIBS_SITE             /usr/local/lib/R/site-library:/usr/local/lib/R/library:/usr/lib64/R/library:/usr/share/R/library
R_LIBS_USER             ~/R/x86_64-redhat-linux-gnu-library/3.3
R_PAPERSIZE             a4
R_PDFVIEWER             /usr/bin/xdg-open
R_PLATFORM              x86_64-redhat-linux-gnu
R_PRINTCMD              lpr
R_RD4PDF                times,hyper
R_SESSION_TMPDIR        /tmp/RtmpZf9YMN
R_SHARE_DIR             /usr/share/R
R_SYSTEM_ABI            linux,gcc,gxx,gfortran,?
R_TEXI2DVICMD           /usr/bin/texi2dvi
R_UNZIPCMD              /usr/bin/unzip
R_ZIPCMD                
RMARKDOWN_MATHJAX_PATH
						/usr/lib/rstudio-server/resources/mathjax-26
RS_RPOSTBACK_PATH       /usr/lib/rstudio-server/bin/rpostback
RSTUDIO                 1
RSTUDIO_HTTP_REFERER    http://hadoop-edge06.mycompany.com:8787/
RSTUDIO_PANDOC          /usr/lib/rstudio-server/bin/pandoc
RSTUDIO_SESSION_STREAM
						wzhou-d
RSTUDIO_USER_IDENTITY   wzhou
RSTUDIO_WINUTILS        bin/winutils
SED                     /bin/sed
SPARK_HOME              /opt/cloudera/parcels/SPARK2/lib/spark2
SSH_ASKPASS             rpostback-askpass
TAR                     /bin/gtar
USER                    wzhou
YARN_CONF_DIR           /etc/hadoop/conf.cloudera.yarn

Ahhh, I noticed the environment missed SPARK_DIST_CLASSPATH environment variable. Then I set it using the command below just before sc <- spark_connect.

Sys.setenv(SPARK_DIST_CLASSPATH = '/etc/hadoop/con:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop/lib/*:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop/.//*:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop-hdfs/./:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop-hdfs/lib/*:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop-hdfs/.//*:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop-yarn/lib/*:/opt/cloudera/parcels/CDH/lib/hadoop/libexec/../../hadoop-yarn/.//*:/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/lib/*:/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/.//*')

Ok, try it again. Fantastic, it works!

Cause
Ok, here is the real cause of the issue. It’s unnecessary to specify java path for sparklyr as it does not require a java path. However, it does have dependency on spark-submit. When spark-submit is executed, it can read java path and then submit the jar files to Spark accordingly. The cause of the issue if SPARK_DIST_CLASSPATH is not set, spark-submit is not working and Spark executors can not be launched.

Other Note
The following are some of useful commands:

ls()
spark_installed_versions()
sessionInfo()
spark_home_dir() or spark_home
path.expand(“~”)
Sys.getenv(“SPARK_HOME”)
spark_home_dir()
character(0)
config <- spark_config()
spark_install_dir()
sc
backend
monitor
output_file
spark_context
java_context
hive_context

master
method
app_name
config
config$sparklyr.cores.local
config$spark.sql.shuffle.partitions.local
config$spark.env.SPARK_LOCAL_IP.local
config$sparklyr.csv.embedded
config$`sparklyr.shell.driver-class-path`

Also there are a few useful articles about sparklyr and Rstudio:
RStudio’s R Interface to Spark on Amazon EMR
How to Install RStudio Server on CentOS 7
Using R with Apache Spark
sparklyr: a test drive on YARN
Analyzing a billion NYC taxi trips in Spark

Create Hadoop Cluster on Google Cloud Platform

There are many ways to create Hadoop clusters and I am going to show a few ways on Google Cloud Platform (GCP). The first approach is the standard way to build a Hadoop cluster, no matter whether you do it on cloud or on-premise. Basically create a group of VM instances and manually install Hadoop cluster on these VM instances. Many people have blogs or articles about this approach and I am not going to repeat the steps here.

In this blog, I am going to discuss the approach using Google Cloud Dataproc and you can actually have a Hadoop cluster up and running
within 2 minutes. Google Cloud Dataproc is a fully-managed cloud service for running Apache Hadoop cluster in a simple and fast way. The followings show the steps to create a Hadoop Cluster and submit a spark job to the cluster.

Create a Hadoop Cluster
Click Dataproc -> Clusters

Then click Enable API

Cloud Dataproc screen shows up. Click Create cluster

Input the following parameters:
Name : cluster-test1
Region : Choose use-central1
Zone : Choose us-central1-c

1. Master Node
Machine Type : The default is n1-standard-4, but I choose n1-standard-1 just for simple testing purpose.
Cluster Mode : There are 3 modes here. Single Mode (1 master, 0 worker), Standard Mode (1 master, N worker), and High Mode (3 masters, N workers). Choose Standard Mode.
Primary disk size : For my testing, 10GB 1s enough.

2. Worker Nodes
Similar configuration like Worker node. I use 3 worker nodes and disk size is 15 GB. You might notice that there is option to use local SSD storage. You can attach up to 8 local SSD devices to the VM instance. Each disk is 375 GB in size and you can not specify 10GB disk size here. The local SSDs are physically attached to the host server and offer higher performance and lower latency storage than Google’s persistent disk storage. The local SSDs is used for temporary data like shuffling data in MapReduce. The data on the local SSD storage is not persistent. For more information, please visit https://cloud.google.com/compute/docs/disks/local-ssd.

Another thing to mention is that Dataproc uses Cloud Storage bucket instead of HDFS for the Hadoop cluster. Although the hadoop command is still working and you won’t feel anything different, the underline storage is different. In my opinion, it is actually better because Google Cloud Storage bucket definitely has much better reliability and scalability than HDFS.

Click Create when everything is done. After a few minutes, the cluster is created.

Click cluster-test1 and it should show the cluster information.

If click VM Instances tab, we can see there is one master and 3 worker instances.

Click Configuration tab. It shows all configuration information.

Submit a Spark Job
Click Cloud Dataproc -> Jobs.

Once Submit Job screen shows up, input the following information, then click Submit.

After the job completes, you should see the followings:

To verify the result, I need to ssh to the master node to find out which ports are listening for connections. Click the drop down on the right of SSH of master node, then click Open in browser window.

Then run the netstat command.

cluster-test1-m:~$ netstat -a |grep LISTEN |grep tcp
tcp        0      0 *:10033                 *:*                     LISTEN     
tcp        0      0 *:10002                 *:*                     LISTEN     
tcp        0      0 cluster-test1-m.c.:8020 *:*                     LISTEN     
tcp        0      0 *:33044                 *:*                     LISTEN     
tcp        0      0 *:ssh                   *:*                     LISTEN     
tcp        0      0 *:52888                 *:*                     LISTEN     
tcp        0      0 *:58266                 *:*                     LISTEN     
tcp        0      0 *:35738                 *:*                     LISTEN     
tcp        0      0 *:9083                  *:*                     LISTEN     
tcp        0      0 *:34238                 *:*                     LISTEN     
tcp        0      0 *:nfs                   *:*                     LISTEN     
tcp        0      0 cluster-test1-m.c:10020 *:*                     LISTEN     
tcp        0      0 localhost:mysql         *:*                     LISTEN     
tcp        0      0 *:9868                  *:*                     LISTEN     
tcp        0      0 *:9870                  *:*                     LISTEN     
tcp        0      0 *:sunrpc                *:*                     LISTEN     
tcp        0      0 *:webmin                *:*                     LISTEN     
tcp        0      0 cluster-test1-m.c:19888 *:*                     LISTEN     
tcp6       0      0 [::]:10001              [::]:*                  LISTEN     
tcp6       0      0 [::]:44884              [::]:*                  LISTEN     
tcp6       0      0 [::]:50965              [::]:*                  LISTEN     
tcp6       0      0 [::]:ssh                [::]:*                  LISTEN     
tcp6       0      0 cluster-test1-m:omniorb [::]:*                  LISTEN     
tcp6       0      0 [::]:46745              [::]:*                  LISTEN     
tcp6       0      0 cluster-test1-m.c.:8030 [::]:*                  LISTEN     
tcp6       0      0 cluster-test1-m.c.:8031 [::]:*                  LISTEN     
tcp6       0      0 [::]:18080              [::]:*                  LISTEN     
tcp6       0      0 cluster-test1-m.c.:8032 [::]:*                  LISTEN     
tcp6       0      0 cluster-test1-m.c.:8033 [::]:*                  LISTEN     
tcp6       0      0 [::]:nfs                [::]:*                  LISTEN     
tcp6       0      0 [::]:33615              [::]:*                  LISTEN     
tcp6       0      0 [::]:56911              [::]:*                  LISTEN     
tcp6       0      0 [::]:sunrpc             [::]:*                  LISTEN  

Check out directories.

cluster-test1-m:~$ hdfs dfs -ls /
17/09/12 12:12:24 INFO gcs.GoogleHadoopFileSystemBase: GHFS version: 1.6.1-hadoop2
Found 2 items
drwxrwxrwt   - mapred hadoop          0 2017-09-12 11:56 /tmp
drwxrwxrwt   - hdfs   hadoop          0 2017-09-12 11:55 /user

There are a few UI screens available to check out the Hadoop cluster and job status.
HDFS NameNode (port 9870)

YARN Resource Manager (port 8088)

Spark Job History (port 18080)

Dataproc approach is an easy deployment tool to create a Hadoop cluster. Although it is powerful, I miss the nice UI like Cloudera Manager. To install Cloudera CDH cluster, I need to use a different approach and I am going to discuss it in the future blog.