Parquet File Can not Be Read in Sparkling Water H2O

For the past few months, I wrote several blogs related to H2O topic:
Use Python for H2O
H2O vs Sparkling Water
Sparking Water Shell: Cloud size under 12 Exception
Access Sparkling Water via R Studio
Running H2O Cluster in Background and at Specific Port Number
Weird Ref-count mismatch Message from H2O

Sparkling Water and H2O are very good in terms of performance for data science projects. But if something works beautifully in one environment, not working in another environment could be the most annoyed thing in the world. This is exactly what had happened at one of my clients.

They used to use Sparkling Water and H2O in Oracle BDA environment and worked great. However, even with a full rack BDA (18 nodes), it is still not enough to run big dataset on H2O. Recently they moved to a much bigger CDH cluster (non-BDA environment) with CDH 5.13 installed. Sparkling Water is still working, however there was one major issue: parquet files can not be read correctly. There are no issue in reading the same parquet files from Spark shell and pyspark. This is really an annoying issue as parquet format is one of data formats that are heavily used by the client. After many failed tries and investigation, I was finally able to figure out the issue and implement a workaround solution. This blog discussed this parquet reading issue and workaround solution in Sparkling Water.

Create Test Data Set
I did the test in my CDH cluster (CDH 5.13). I first created a small test data set, stock.csv, and uploaded to /user/root directory on HDFS.


Create a Parquet File
Run the following in spark2-shell to create a parquet file and make sure that I can read it back.

scala> val"csv").load("/user/root/stock.csv")
myTest: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 4 more fields]
|    _c0|  _c1|   _c2|  _c3|  _c4|  _c5|
|   date|close|volume| open| high|  low|
|9/23/16|24.05| 56837|24.13|24.22|23.88|
|9/22/16| 24.1| 56675|23.49|24.18|23.49|
only showing top 3 rows
scala> myTest.write.format("parquet").save("/user/root/mytest.parquet")
scala> val readTest ="parquet").load("/user/root/mytest.parquet")
readTest: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 4 more fields]
|    _c0|  _c1|   _c2|  _c3|  _c4|  _c5|
|   date|close|volume| open| high|  low|
|9/23/16|24.05| 56837|24.13|24.22|23.88|
|9/22/16| 24.1| 56675|23.49|24.18|23.49|
only showing top 3 rows

Start a Sparkling Water H2O Cluster
I started a Sparking Water Cluster with 2 nodes.

[root@a84-master--2df67700-f9d1-46f3-afcf-ba27a523e143 sparkling-water-2.2.7]# . /etc/spark2/conf.cloudera.spark2_on_yarn/
[root@a84-master--2df67700-f9d1-46f3-afcf-ba27a523e143 sparkling-water-2.2.7]# bin/sparkling-shell \
> --master yarn \
> --conf spark.executor.instances=2 \
> --conf spark.executor.memory=1g \
> --conf spark.driver.memory=1g \
> --conf spark.scheduler.maxRegisteredResourcesWaitingTime=1000000 \
> --conf \
> --conf spark.dynamicAllocation.enabled=false \
> --conf spark.sql.autoBroadcastJoinThreshold=-1 \
> --conf spark.locality.wait=30000 \
> --conf spark.scheduler.minRegisteredResourcesRatio=1

  Spark master (MASTER)     : yarn
  Spark home   (SPARK_HOME) : /opt/cloudera/parcels/SPARK2-2.2.0.cloudera1-1.cdh5.12.0.p0.142354/lib/spark2
  H2O build version         : (wheeler)
  Spark build version       : 2.2.1
  Scala version             : 2.11

Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at
Spark context available as 'sc' (master = yarn, app id = application_1518097883047_0001).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.2.0.cloudera1
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_131)
Type in expressions to have them evaluated.
Type :help for more information.

scala> import org.apache.spark.h2o._
import org.apache.spark.h2o._

scala> val h2oContext = H2OContext.getOrCreate(spark) 
h2oContext: org.apache.spark.h2o.H2OContext =                                   

Sparkling Water Context:
 * H2O name: sparkling-water-root_application_1518097883047_0001
 * cluster size: 2
 * list of used nodes:
  (executorId, host, port)

  Open H2O Flow in browser: (CMD + click in Mac OSX)

scala> import h2oContext._
import h2oContext._

Read Parquet File from H2O Flow UI
Open H2O Flow UI and read the same parquet file.

After click Parse these files, got corrupted file.

Obviously, parquet file was not read correctly. At this moment, there are no error messages in the H2O console. If continue to import the file, the H2O Flow UI throw the following error

The H2O console would show the following error:

scala> 18/02/08 09:23:59 WARN servlet.ServletHandler: Error for /3/Parse
java.lang.NoClassDefFoundError: org/apache/parquet/hadoop/api/ReadSupport
	at water.parser.parquet.ParquetParser.correctTypeConversions(
	at water.parser.parquet.ParquetParserProvider.createParserSetup(
	at water.parser.ParseSetup.getFinalSetup(
	at water.parser.ParseDataset.forkParseDataset(
	at water.parser.ParseDataset.parse(
	at water.api.ParseHandler.parse(
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(
	at java.lang.reflect.Method.invoke(
	at water.api.Handler.handle(
	at water.api.RequestServer.serve(
	at water.api.RequestServer.doGeneric(
	at water.api.RequestServer.doPost(
	at javax.servlet.http.HttpServlet.service(
	at javax.servlet.http.HttpServlet.service(
	at water.JettyHTTPD$LoginHandler.handle(
Caused by: java.lang.ClassNotFoundException: org.apache.parquet.hadoop.api.ReadSupport
	at java.lang.ClassLoader.loadClass(
	at java.lang.ClassLoader.loadClass(
	... 39 more

As BDA has no issue in the same Sparkling Water H2O deployment and BDA used CDH 5.10, I initially focused more on CDH version difference. I built three CDH clusters using three different CDH versions: 5.13, 5.12 and 5.10. All of them show the exact same error. This made me rule out the possibility from CDH version difference and shifted focus on the environment difference, especially class path and jar files. Tried setting JAVA_HOME, SPARK_HOME, SPARK_DIST_CLASSPATH and unfortunately none of them worked.

I noticed /etc/spark2/conf.cloudera.spark2_on_yarn/classpath.txt seem have much less entries than classpath.txt under spark 1.6. Tried adding back the missing entries. Still no luck.

Added two more parameters to get more information about H2O log.

--conf spark.ext.h2o.node.log.level=INFO \
--conf spark.ext.h2o.client.log.level=INFO \

It gave a little more useful information. It complained about class ParquetFileWriter not found.

$ cat h2o_10.54.225.9_54000-5-error.log
01-17 04:55:47.406     18567  #6115-112 ERRR: DistributedException from / 'org/apache/parquet/hadoop/ParquetFileWriter', caused by java.lang.NoClassDefFoundError: org/apache/parquet/hadoop/ParquetFileWriter
01-17 04:55:47.406     18567  #6115-112 ERRR:         at water.MRTask.getResult(
01-17 04:55:47.406     18567  #6115-112 ERRR:         at water.MRTask.getResult(
01-17 04:55:47.406     18567  #6115-112 ERRR:         at water.MRTask.doAll(
01-17 04:55:47.406     18567  #6115-112 ERRR:         at water.parser.ParseSetup.guessSetup(
01-17 04:55:47.406     18567  #6115-112 ERRR:         at water.api.ParseSetupHandler.guessSetup(
01-17 04:55:47.406     18567  #6115-112 ERRR:         at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
01-17 04:55:47.406     18567  #6115-112 ERRR:         at sun.reflect.NativeMethodAccessorImpl.invoke(

My client found a temporary solution by using h2odriver.jar following the instruction from Using H2O on Hadoop. The command used is shown below:

cd /opt/h2o-3
hadoop jar h2odriver.jar -nodes 70 -mapperXmx 40g -output hdfs://PROD01ns/user/mytestuser/log24

Although this solution provides similar functionalities in Sparkling Water, it has some critical performance issues:
1. The above command would create 70 nodes H2O cluster. If using Sparkling Water, it would be evenly distribute to all available nodes. But the above h2odriver.jarapproach would heavily use a few hadoop nodes. For big dataset, majority of activities happened only to 3~4 nodes, which made those nodes’ cpu utilization close to 100%. For one test big dataset, it has never completed the parsing file. It failed after 20 minutes run.
2. Unlike Sparkling Water, it actually read files during the parsing phase, not in the importing phase.
3. The performance is pretty bad compared with Sparkling Water. I guess Sparkling Water is using underlined Spark to distribute the load evenly.

Anyway this hadoop jar h2odriver.jar solution is not an ideal workaround for this issue.

Then I happened to read this blog: Incorrect results with corrupt binary statistics with the new Parquet reader. This article has nothing to do my issue, but it mentioned about parquet v1.8. I did remember seeing one note from one Sparkling Water developer discussing should integrate with parquet v1.8 in the future for certain parquet issue in H2O. Unfortunately I could not find the link to this discussion any more. But it inspired me to think that maybe the issue is that Sparkling Water depends certain parquet library and the current environment don’t have it. The standard CDH distribution and Spark2 seem using parquet v1.5. Oracle BDA has many more software installed and maybe it happened to have the correct library installed somewhere. It seems H2O related jar file may contain this library, what’s happened if I include the H2O jar somewhere in Sparkling Water.

With this idea in mind, I download H2O from Unzip the file and h2o.jar file is the one I need. I then modified sparkling-shell and change the last line of code as follows by add h2o.jar file to jars parameter.

#spark-shell --jars "$FAT_JAR_FILE" --driver-memory "$DRIVER_MEMORY" --conf spark.driver.extraJavaOptions="$EXTRA_DRIVER_PROPS" "$@"
spark-shell --jars "$FAT_JAR_FILE,$H2O_JAR_FILE" --driver-memory "$DRIVER_MEMORY" --conf spark.driver.extraJavaOptions="$EXTRA_DRIVER_PROPS" "$@"

Restart my H2O cluster. It worked!

Finally after many days work, Sparkling Water can work again in the new cluster. Reloading the big testing dataset, it took less than 1 minute to load the same dataset with only 24 H2O nodes. The load was also evenly distributed to the cluster. Problem solved!

Finding out Keystore and Truststore Passwords on BDA

I am working in a project involving configuring SSL with Cloudera Manager on BDA. There are several ways to do it: go with Oracle’s bdacli approach or use Cloudera’s approach. For BDA related work, I usually prefer Oracle’s BDA approach because it needs to write some information to Oracle BDA’s configuration files, which are usually outside the control of Cloudera Manager. Cloudera’s approach is definitely working as well. But during the time when doing BDA upgrade or patching, if mammoth couldn’t find the correct value in BDA’s configuration files, it might cause unnecessary trouble. For example, if mammoth think certain features are not enabled, then it could skip certain steps to disable the features before upgrade. Anyway, it is another unrelated topic.

To enable TLS on Cloudera Manager is pretty easy on BDA, instead of doing so many steps stated in Cloudera Manager’s document. On BDA, just run the following command:
bdacli enable https_cm_hue_oozie

The command will automatically enable TLS for all major services on CDH, such Cloudera Manager, Hue and Oozie. Please note: TLS on Cloudera Manager agent is automatically enabled during BDA installation. Usually running this command is enough for many clients as client just need to encrypt the content when communicating
with Cloudera Manager. There is a downside for this approach: BDA uses self-signed certificates during the execution of bdacli enable https_cm_hue_oozie. This kind of self-signed certificate is good for security, but sometime can be annoying with browsing alerts. Therefore some users might prefer to use their own signed SSL certificates.

After working with Eric from Oracle Support, he recommended a way actually pretty good documented in Doc ID 2187903.1: How to Use Certificates Signed by a User’s Certificate Authority for Web Consoles and Hadoop Network Encryption Use on the BDA. The key of this approach is to get keystore’s and truststore’s paths and passwords, creating new keystore and truststore, and then importing customer’s certificates. Unfortunately, this approach works for BDA version 4.5 and above. It is not going to work in my current client environment, which is using BDA v4.3. One of major issue is that BDA v4.5 and above has the following bdacli commands while BDA v4.3 doesn’t have the following commands:
bdacli getinfo cluster_https_keystore_password
bdacli getinfo cluster_https_truststore_password

Eric then recommended a potential workaround by querying MySQL database directly by using the commands below:

use scm;
select * from CONFIGS where ATTR = 'truststore_password' or ATTR = 'keystore_password'; 

I then used two BDAs in our lab for the verification.
First, I tested on our X4 Starter rack.

[root@enkx4bda1node01 ~]# bdacli getinfo cluster_https_keystore_password
Enter the admin user for CM (press enter for admin): 
Enter the admin password for CM: 

[root@enkx4bda1node01 ~]# bdacli getinfo cluster_https_truststore_password
Enter the admin user for CM (press enter for admin): 
Enter the admin password for CM: 

Interestingly, the keystore password is still showing ****** while truststore password is empty. I can understand empty password for truststore as nothing is configured for truststore. But keystore password shouldn’t show hidden value as ******.

Query MySQL db on the same rack.

[root@enkx4bda1node03 ~]# mysql -u root -p
Enter password: 
mysql> show databases;
| Database           |
| information_schema |
| activity_monitor   |
| hive               |
| host_monitor       |
| hue                |
| mysql              |
| navigator          |
| navigator_metadata |
| oozie              |
| performance_schema |
| reports_manager    |
| resource_manager   |
| scm                |
| sentry_db          |
| service_monitor    |
| studio             |
16 rows in set (0.00 sec)

mysql> use scm;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A
Database changed

mysql> select * from CONFIGS where ATTR = 'truststore_password' or ATTR = 'keystore_password'; 
|         8 |    NULL | keystore_password | ****** |       NULL |    NULL |                   2 |                       2 |                 NULL | NONE    |
1 row in set (0.00 sec)

MySQL database also store the password as *****. I remember my colleague mentioned this BDA has some issue. This could be one of them.

Ok, this rack doesn’t really tell me anything and I move to the 2nd full rack BDA. Perform the same commands there.

[root@enkbda1node03 ~]# bdacli getinfo cluster_https_keystore_password 
Enter the admin user for CM (press enter for admin): 
Enter the admin password for CM: 

[root@enkbda1node03 ~]# bdacli getinfo cluster_https_truststore_password
Enter the admin user for CM (press enter for admin): 
Enter the admin password for CM: 

[root@enkbda1node03 ~]# mysql -u root -p
Enter password: 
mysql> use scm;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Database changed
mysql> select * from CONFIGS where ATTR = 'truststore_password' or ATTR = 'keystore_password'; 
| CONFIG_ID | ROLE_ID | ATTR                | VALUE                                                            | SERVICE_ID | HOST_ID | CONFIG_CONTAINER_ID | OPTIMISTIC_LOCK_VERSION | ROLE_CONFIG_GROUP_ID | CONTEXT |
|         7 |    NULL | keystore_password   | KUSld8yni8PMQcJbltvCnZEr2XG4BgKohAfnW6O02jB3tCP8v1DYlbMO5PqhJCVR |       NULL |    NULL |                   2 |                       0 |                 NULL | NULL    |
|       991 |    NULL | truststore_password | NULL                                                             |       NULL |    NULL |                   2 |                       1 |                 NULL | NONE    |
2 rows in set (0.00 sec)

MySQL database show same value as the value as the result from command bdacli getinfo cluster_https_keystore_password. This is exactly what I want to know. It looks like I can use MySQL query to get the necessary passwords for my work.

One side note: In case you want to check out those self-signed certificates on BDA, run the following command. When prompting for password, just press ENTER.

[root@enkx4bda1node03 ~]# bdacli getinfo cluster_https_keystore_path
Enter the admin user for CM (press enter for admin): 
Enter the admin password for CM: 

[root@enkx4bda1node03 ~]# keytool -list -v -keystore /opt/cloudera/security/jks/node.jks
Enter keystore password:  

*****************  WARNING WARNING WARNING  *****************
* The integrity of the information stored in your keystore  *
* has NOT been verified!  In order to verify its integrity, *
* you must provide your keystore password.                  *
*****************  WARNING WARNING WARNING  *****************

Keystore type: JKS
Keystore provider: SUN

Your keystore contains 1 entry

Alias name: enkx4bda1node03.enkitec.local
Creation date: Mar 5, 2016
Entry type: PrivateKeyEntry
Certificate chain length: 1
Owner: CN=enkx4bda1node03.enkitec.local, OU=, O=, L=, ST=, C=
Issuer: CN=enkx4bda1node03.enkitec.local, OU=, O=, L=, ST=, C=
Serial number: 427dc79f
Valid from: Sat Mar 05 02:17:45 CST 2016 until: Fri Feb 23 02:17:45 CST 2018
Certificate fingerprints:
	 MD5:  A1:F9:78:EE:D4:C7:C0:D0:65:25:4C:30:09:D8:18:6E
	 SHA1: 8B:E3:7B:5F:76:B1:81:33:35:03:B9:00:97:D0:F7:F9:03:F9:74:C2
	 SHA256: EC:B5:F3:EB:E5:DC:D9:19:DB:2A:D6:3E:71:9C:62:55:10:0A:59:59:E6:98:2C:AD:23:AC:24:48:E4:68:6A:AF
	 Signature algorithm name: SHA256withRSA
	 Version: 3


#1: ObjectId: Criticality=false
SubjectKeyIdentifier [
KeyIdentifier [
0000: 36 D2 3D 49 AF E2 C6 7A   3C C6 14 D5 4D 64 81 F2  6.=I...z<...Md..
0010: 6E F2 2C B6                                        n.,.


If you dont’ like this kind of default password, you can use command keytool -storepasswd -keystore /opt/cloudera/security/jks/node.jks to change the password.

Install Cloudera Hadoop Cluster using Cloudera Manager

Three years ago I tried to build up a Hadoop Cluster using Cloudera Manager. The GUI looked nice, but the installation was pain and full of issues. I gave up after many failed tries, and then went with the manual installation. It worked fine and I have built several clusters since then. After several years working on Oracle Exadata, I go back and retry the hadoop installation using Cloudera Manager. This time I installed CDH 5 cluster. The installation experience was much better than three years ago. But not surprised, the installation still has some issues and I can easily identify some bugs during the installation. But at least I can successfully install a 3 node hadoop cluster after several tries. The followings are my steps during the installation.

First, let me give a little detail about my VM environment. I am using Virtualbox and build three VMs.
vmhost1: This is where name node, clouder manager and many other roles are located.
vmhost2: Data Node
vmhost3: Data Node

Note: the default replication factor is 3 for hadoop. In my environment, it is under replicated. So I have to adjust replication factor from 3 to 2 after installation, just to get rid of some annoying alerts.

  • OS: Oracle Linux 6.7, 64-bit
  • CPU: 1 CPU initially for all 3 VMs. Then I realize vmhost1 needs a lot of processing power as majority of the installation and configuration happen on node 1. I gave vmhost1 2 CPUs. It proved still not enough and vmhost1 tended to freeze after installation. After I bump it up to 4 CPUs, vmhost1 looks fine. 1 CPU for Data Node host is enough.
  • Memory: Initially I gave 3G to all of 3 VMs. Then bump up node 1 to 5G before installation. It proved still not enough. After bumping up to 7G on vmhost1, the VM is not freezing anymore. I can see the memory usage is around 6.2G. So 7G configuration is good one. After installation, I reduced Data Node’s memory to 2G to free some memory. If not much job running, the memory usage is less than 1G on Data Node. If just testing out hadoop configuration, I can further reduce the memory to 1.5G per Data Node.
  • Network: Although I have 3 network adpaters built in the VM, I actually use only two of them. One is configured as Internal Network and this is where my cluster VMs are using to communicate with each other. Another one is configured as NAT, just to get internet connection to download packages from Cloudera site.
  • Storage: 30G. The actual size after installation is about 10~12G and really depended on how many times you fail and retry for the installation. The clean installation uses about 10G of space.

Pre-Steps Before the Installation

Before doing the installation, make sure configure the following in the VM:
1. Set SELinux policy to diasabled. Modify the following parameter in /etc/selinux/config file.

2. Disable firewall.
chkconfig iptables off

3. Set swappiness to 0 in /etc/sysctl.conf file. In the latest Cloudera CDH releases, it actually recommends changing to non-zero value, like 10. But for my little test, I set it to 0 like many people did.

4. Disable IPV6 in /etc/sysctl.conf file.
net.ipv6.conf.default.disable_ipv6 = 1
net.ipv6.conf.all.disable_ipv6 = 1

5. Configure passwordless SSH for root user. This is common step for Oracle RAC installation and I do not repeat the steps here.

Ok, ready for the installation. Here are the steps.
1. Download and Run the Cloudera Manager Server Installer
Logon as root user on vmhost1. All of the installations are under root user.
Run the following commands.

chmod u+x cloudera-manager-installer.bin

It popups the following screen, just click Next or Yes for the rest of screens.

If successful, you will see the following screen.

After click Close, it will pop up a browser window and point to http://localhost:7180/. At this moment, you can click Finish button on the previous installation GUI and close the installation GUI. Then move to browser and patiently wait for your Cloudera Manager starts up. Note. It usually takes several minutes. So be patient.

2. Logon Screen
After the following screen shows up, logon as admin user and use the same admin as password.

3. Choose Version
The next screen is to choose which version to use. The default option is Cloudera Enterprise Data Hub Edition Trial, but with 60 days limit. Although Cloudera Express has no time limit, the Express version misses a lot of features I would like to test out. So I go with the Enterprise 60 days trial version.

4. Thank You Screen
Click Continue for the next Thank You screen.

5. Host Screen
Input vmhost[1-3].local, then click New Search. Note, make sure to use FQDN. I used to have bad experience not using FQDN in the old version of CDH installation. I am not going to waste my time in trying out what happens if not using FQDN.

After the following screen shows up, Click New Search, then the 3 hosts shows up. Then click Continue.

6. Select Repository
For Select Repository screen, the default option is using Parcels. Unfortunately I had issue using Parcel during the installation. It passed the step of installation on all of 3 hosts, but was stuck in download the latest Parcel file. After looking around, it seems the issue was that the default release was for September version, but the latest Parcel is pointing to the old August release. It seems version mismatch to me. Anyway, I am going to try out the Parcels option in the future again. But for this installation I changed to use Packages version. I intentionally did not choose the latest CDH 5.4.5 version. I would like to go with the version has long lag in time. For example there is about one month lag between CDH 5.4.3 and CDH 5.4.4. If 5.4.3 is not stable, Cloudera would put a new release a few days later and can not wait for one month to release new version. So I went with CDH 5.4.3.
Make sure to choose 5.4.3 for Navigator Key Trustee as well.

7. Java Installation
For Java installation, leave it uncheck in default and click Continue.

8. Single User
For Enable Single User Mode, I did NOT check Single User Mode as I want cluster installation.

9. SSH Login Credentials
For SSH Login Credentials, input root password. For Number of Simultaneous Installations, the default value is 10. It created a lot of headache during my installation. Each host downloads its own copy from cloudera website. As three of VMs were fighting each other for the internet bandwidth on my host machine, certain VM could wait there for several minutes for downloading the next package. If wait for more than 30 seconds, Cloudera Manager would time out the installation for this host and marked as failed installation. I am fine with the time out, but not happy with the next action. The the next step after clicking Retry Failed Hosts, it rolls back the installed packages on this VM and restart from scratch for the next try. It could take hours before I could reach to that point. The more elegant way to do the installation should be download once on host and distribute to other hosts for installation. If failed, retry from the failing point. Although the total download files is about a few GB per host, the failed retries can easily make it 10GB per host. So I have to set Number of Simultaneous Installation to 1 to limit to one VM for installation to reduce my failure rate.

10. Installation
The majority of installation time spends here if going with Package option. For Parcel option, this step is very fast because the majority of downloads are in the different screen. The time in this step really depends on the following factors:
1. How fast your internet bandwidth. The faster, the better.
2. The download speed from Cloudera site. Although my internet download speed can easily reach to 12M per second, my actual download time from Cloudera could vary depend on the time of day. Majority of the time is around 1~2M per second. Not great, but manageable. But sometimes it could drop down to 100K per second. This is the time I have higher chance to see the time out failure and fail the installation. At one point I could not tolerate this, I wake up at 2am and began my installation process. It was much faster. I can get 10M per second download speed with about 4~7 M on average. I only saw a few timeout failure on one host.
3. How many times the installation time out and have to retry.

If successful, the following screen shows.

11. Detect Version
After the success of installation, it shows the version screen.

12. Finish Screen
Finally, I can see this Finish screen. Life is good? Wrong! See my comment in the Cluster Setup step.

13. Cluster Setup
When I reached to this step, I knew I was almost done. Just a few more steps, less than 30 minutes work. After a long day, I went for dinner and resume my configuration later. It proved to be the most expensive mistake I have done during this installation. After the dinner, I went back the same screen, click Continue. It show Session Time Out error. Not a big deal as I thought the background process knew where I was for the installation. Open the browser and type in the url, http://localhost:7180. Guess what, not the Cluster Setup screen, but the screen at step 4. Tried many ways and could not find a workaround. Out of ideas, I had to reinstall from step 4. What’s a pain! Another 7~8 hours work. My next installation did not waste any time on this step and completed it as quickly as possible.

Ok, go back to this screen. I want to use both Impala and Spark and could not find the combination for these two except all services. So I chose Custom Services and chose the services mainly from Core with Impala + Spark. Make sure to check Include Cloudera Navigator.

14. Role Assignment
I chose the default, click Continue.

15. Database Setup
Choose the default. Make sure to click Test Connection before clicking Continue.

16. Review
Click Continue.

17. Completion
It shows the progress during the setup.

Finally it show the real completion screen.

After clicking Finish, you should screen similar as follows.
The life is good right now. The powerful Cloudera Manager has much more nice features than three years ago. Really worth my effort to go through the installation.