Hadoop Encryption at Rest

At Rest, as in not motion, not REST as in web services. For a long time the only real answer Hadoop had for encryption at rest was to leverage a third-party tool or consider the use of LUKS for whole disk encryption. What I see customers asking for these days is really encryption in motion (aka wire encryption), encryption at rest (at a HDFS layer) plus policies that will eliminate data from specific directories in HDFS based upon some business rule. The good news is that as of Hadoop 2.6 we now have HDFS-6134 in play so there is light at the end of the security tunnel.

The implementation of this new transparent encryption is supported via the normal Hadoop Filesystem Java API, the libhdfs C API and WebHDFS (REST) API. The great news is that once it is set up normal HDFS ACL control access to reading and writing so while there is some administration upfront from a user perspective there is not a terribly large new burden. This essentially means that third-party integration work should be largely left intact.

There is now a Key Management Server (KMS) used to create keys for the encryption process of “encryption zones” also know as directories in HDFS.

So how does all this happen? The design doc describes both the read and write action processes. Illustrated here is the read process:


So how does one functionally use encryption zones. Cloudera has a great docs page talking about how to create encryption zones based upon the technology used (over hdfs).

Like most newly invented technology the design doc also calls out some potential issues with the design. While there are some potential vulnerabilities called out in the spec I would still say this is a massive step in the right direction. I also noted this first version really only uses AES-CTR.

It might seem like this is small matter but in the larger context of a security discussion native encryption at rest is an important part of the Hadoop puzzle.


Hadoop and the mystery of the version number

When I’m working with people on Hadoop I ask what you would think is a simple question. What version of Hadoop are you using? The answer normally is one of several attempts to explain what’s installed including –


Answer Translation
Hortonworks/Cloudera This is my Hadoop Distribution.
Hortonworks 2 I know we aren’t using version 1.
Hadoop 2 I dont know my distro but I’m using Hadoop 2.
Apache someone else is working this. I have no idea.

In reality though it’s not as straight forward as you might think. I think the easiest way to get the most bang for your buck is to simply take a look at the version number of the package installed. So on yum based systems you could simply do

and get back of list of whats installed and whats available. You could also simply query the rpm database:

If you run SLES you will need to do zypper and on windows look at your add/remove programs dialog on most major newer versions of windows. In the end you are still left with this cryptic string to decode. If you look closely there is a method to the madness and it helps to know this level of detail when working in an area like Hadoop where minor version numbers or a build number could make all the difference.

For example:
package nameversionarchitecture

The version number in this case is from a Hortonworks distribution so  we have a seven digit (8 places) version number.

package versionHDP Versionbuild number 385

It’s important to know both the version of Hadoop and the version of the package you are working on. For example if someone says “I’m working on Hive”. You really need to know what hive version AND what Hadoop version because the two are intimately linked. If someone gives you the hive package string:

It’s really not enough information for you to tell what version of Hadoop someone is using. You know they are using HDP so one either asks for the same information on the Hadoop package installed OR goes to the release notes for the distro to decode the distribution version number into the Apache Hadoop version. Each distribution uses a different combination of packages and it pays to know EXACTLY what you are getting when you download a distro. Cloudera has exactly the same issues and their packaging may in fact be even more forthcoming in that they tell you how many patches were applied. Hortonworks does this in the context of their release notes.

package namepackage version+CDH version+patches


Hopefully now you have a better understanding of Hadoop package versions.


Hive with JSON data

I stumbled across this and thought it would be helpful to write this up to save everyone else some time. So I went to use JSON with Hive 13 for what I thought was a pretty simple use case of creating a table with JSON data. I was looking for the right SerDe and stumbled across this blog entry stating that we should use the code from this github repo to make a jar that works with Hive 0.13. So here we go…

Sigh…so after some searching I stumbled across another few blog posts and finally a github repo fork that I cloned and built to create a jar that works with Hive 13 and Hadoop 2.4.

Ahhhh. So much better. I am using the latest HDP 2.1 sandbox for writing code so my packages are:

I will create another blog post (and link it here) to explain the version numbers of the packages in HDP.

Many Thanks to KunBetter who saved the day for us in our work at a recent customer.


This saved us many hours of aggravation. Open Source works. Give it a try. Someday someone on the other side of the planet may have the answer you need.


DZone – The Evolution of MapReduce and Hadoop

cover Recently I authored a section of the DZone Guide for Big Data 2014. I wrote about MapReduce and the evolution of Hadoop. There is a ton of buzz in the market about speed to insight and the push toward alternative DAG engines like Spark. I see these new techs as exciting and awesome. I welcome innovation and creativity in the Hadoop market. I also temper this excitement with a bit of reality. The reality is that technology has a maturity curve. It comes out from the huge brain incubators like Google to the rest of us. Much like MapReduce, HDFS and Hbase these technologies have a long road until they grow into a vibrant open source community with dedicated developers. Then comes a stage where the project may become its own incubator project within Apache or other open source framework. Finally, you may see the tech evolve to the point that its included in a supported Hadoop distribution. Now in their efforts to compete you will see vendors clamor over one another to be the first to include hot tech brand X in their offering. I see this as competition and healthy but I will say this could lead to the inclusion of technology that we may not see removed from a distro immediately but it may just fade away. Much like RedHat many years ago with a Linux distribution, Hadoop distributions are still relatively new. Their choices are still small. Eventually, hopefully, we will see the number of projects grow to the point where vendors are actually refusing or removing projects that are not longer relevant. I’m not sure we are there yet. All that said do I think MapReduce is going to dry up and blow away? No I don’t. Do I think Spark and alternative engines will gain steam as they prove their efficiencies? Yes I do. I think most folks need to hMRandBigDataear more than one group validate scalability. I’m sure Google has tooling that works for them. Are they sharing it or better yet has it matured to the point that we mere mortals can use it? I think that remains to be seen. MapReduce and further more Hive which uses MapReduce has a HUGE ecosystem of tools that depend on Hive.Also take a look at Stinger Next to see some exciting new developments of Hive. Take a walk down the rows of tables of vendors at the next Hadoop Summit or Strata and ask them in detail which Hadoop tool their tool depends. Most likely you will eventually get to an answer that basically is Hive (or Hcatalog). Hive and MapReduce will be here for years to come if for no other reason than market penetration. Anyway, take a look at the new DZone Guide. Its has many good topics that may interest you.

Implementing Tools Interface in MapReduce

I was banging around with MapReduce the other day and web surfing. I came across this post on implementing the Tools interface in your MapReduce driver program. Most of the first level examples show a static main method which as the author describes doesn’t allow you to use the configuration dynamically (i.e., you cannot use -D at the command line to pass options to the configuration object). For fun I took Word Count and refactored it using this suggestion. I thought it might be good to share this with folks. I have posted the full code to github and display it below as well.

Using this method you can now pass options to the configuration option via the command line using -D. This is a handy addition to any MapReduce program.

Creating a Multinode Hadoop Sandbox

One of the great things about all the Hadoop vendors is that they have made it very easy for people to obtain and start using their technology rapidly. I will say that I think Cloudera has done the best job by providing cloud based access to their distribution via Cloudera Live. All vendors seems to have a VMware and VirtualBox based sandbox/trial image. Having worked with Hortonworks I have the most experience with and thought a quick initial blog post would be helpful.

While one could simply do this installation from scratch following the package instructions, it’s also possible to short-circuit much of the setup as well as take advantage of the scaled down configuration work already put into the virtual machine provided by Hortonworks. In short the idea would be to use the VM as a single master node and simply add data nodes to this master. Running this way provides and easy way to install and expand an initial Hadoop system up to about 10 nodes. As the system grows you will need to add RAM to not only the virtual host but to Hadoop Daemons as it scales. A full script is available here. Below is a description of the process.

The general steps include:

1. The Sandbox 

Download and install the Hortonworks Sandbox as your head node in your virtualization system of choice. The sandbox tends to be produced prior to the latest major release (compare yum list hadoop *\ output). Make sure you have first enabled Ambari by running the script in root’s home directory and reboot.

In order to make sure you are using the very latest stable release and that the Ambari server and agent daemons have matching versions upgrading is easiest. This includes following:

2. The Nodes  

Install 1-N Centos 6.5 nodes as slaves and prep them as worker nodes. These can be default installs of the OS but need to be on the same network as the Ambari server. This can also be facilitated via pdsh (but this requires passwordless ssh) OR better yet simply creating one “data node” image via a PXE boot environment or snapshot of the Virtual machine to quickly replicate 1-N nodes with these changes.

If you want to use SSH you can do this from the head node to quickly enable passwordless SSH:

You then want to make sure you make the following changes to your slave nodes. Again this could easily be done via pdsh by pcdp the a script to each node and executing with the following content.

Push this file to slave nodes and run it. This does NOT need to be done on the sandbox/headnode.

3. Configure Services Run the Ambari “add nodes” GUI installer to add data nodes. Be sure to select “manual registration” and follow the on-screen prompts to install components. I recommend installing everything on all nodes and simply turning the services off and on as needed. Also installing the client binaries on all nodes helps to make sure you can do debugging from any node in the cluster.

Ambari Add Nodes Dialog

4. Turn off select services as required. 

There should now be 1-N data nodes/slaves attached to your Ambari/Sandbox head node. Here are some suggested changes.  
1. Turn off large services you aren’t using like HBase, Storm, Falcon. This will help save RAM.  
Ambari Services
2. Decommission the Data node on this machine! No! a head node is not a datanode. If you run jobs here you will have problems.  
3. HDFS Replication factor – This is set to 1 in the sandbox because there is only one datanode. If you only have 1-3 datanodes then triple replication doesn’t make sense. I suggest you use 1 until you get over 3 data nodes at a bare minimum. If you have the resources just start with 10 data nodes (that’s why it’s called Big Data). If not stick with replication factor of 1 but be aware this will function as a prototype system and wont provide the natural safeguards or parallelism of normal HDFS.  
4. Increase RAM to Head node – At a bare minimum Ambari requires 4096MB. If you plan to run the sandbox as a head node consider increasing from this minimum. Also consider giving running services room to breath by increasing the RAM allocated in Ambari for each service. Here is a great review and script for guestimating how to scale services for MapReduce and Yarn.  
5. NFS – to make your life easier you might want to enable NFS on a data node or two.

Creating a Virtualized Hadoop Lab

Over the past few years I have let my home lab dwindle a little. I have been very busy at work and for the most part I was able to prototype what I needed for work on my laptop given the generous amount of RAM on the MacBook Pro. That said I was still not able to have the type of permanent setup I really wanted. I know lots of guys who go to the trouble of setting up racks to create their own clusters at home. Given that I really only need a functional lab environment and don’t want to waste the power, cooling or space in my home I turned to virtualization. While I would be the first one in the room to start babbling on about how Hadoop is not supposed to be virtualized in production it is appropriate for development. I wanted a place to test and use a variety of Hadoop virtual machines:

Vendor Distro URL
Hortonworks Hortonworks Data Platform http://hortonworks.com/products/hortonworks-sandbox/
Cloudera Quick Start VMs http://go.cloudera.com/vm-download
MapR MapR Distribution for Apache™ Hadoop® https://www.mapr.com/products/mapr-sandbox-hadoop
Oracle Big Data Lite link
IBM Big Insights http://www-01.ibm.com/software/data/infosphere/biginsights/quick-start/downloads.html


* If you are feeling froggy here is a full list of Hadoop Vendors.

So I dusted off an old workstation I had in my attic from a couple of years ago. This is a Dell Precision T3400 workstation that I used a few moons ago for the same reason. A couple of years ago to run a handful of minimal Linux instances this system was fine. To make is useful today it needed some work. I obviously had to upgrade the Ubuntu to 14.04 as it was still some version in the 12 range. I wont bother with the details of these gymnastics as I believe the Ubuntu community has this covered.

While I did take a look at VirtualBox and VMware Player I think I wanted to use something open source but also sans GUI. I realize there are command line options for both VirtualBox and VMware but in the end using QEMU/ KVM with libvirt fit the bill as the most open source and command line way to go. For those new to virtualization and in need of a GUI one of the other solutions might be a better fit for you. Its left as an exercise for the reader to get QEMU and libvirt installed on your OS. An important point I worked through was creating a bridged adapter on the host machine. I only have one installed network card and wanted my hosted machines on my internal network. In short you are creating a network adapter that the virtualization system can use on top of a single physical adapter. The server can still use the regular IP of the original adapter but now virtual host can act as if they are fully on the local network. Since this system wont be leaving my lab this a perfect solution. If you want something mobile on your laptop such as you should consider an internal or host only network setup. Make sure you reboot after changing the following.

cat /etc/network/interfaces
auto lo
iface lo inet loopback

auto br0
iface br0 inet static
bridge_ports eth0
bridge_fd 9
bridge_hello 2
bridge_maxage 12
bridge_stp off

Although QEMU supports a variety of disk formats natively I decided to convert the images I collected for my Hadoop play ground into qcow2 the native format for QEMU. I collected a variety of “sandbox” images from a number of Hadoop and Big Data vendors. Most come in OVA format which is really just a tarball of the vmdk file and ovf file describing the disk image. To convert you simply extract the vmdk file: 

tar -xvf /path/to/file/Hortonworks_Sandbox_2.1_vmware.ova

and convert the resulting vmdk file:

qemu-img convert -O qcow2 Hortonworks_Sandbox_2.1-disk1.vmdk /path/to/Hortonworks_Sandbox_2.1-disk1.qcow2

Have more than 1 vmdk like the MapR sandbox? No problem: 

qemu-img convert -O qcow2 ./MapR-Sandbox-For-Hadoop-3.1.1_VM-disk1.vmdk ./MapR-Sandbox-For-Hadoop-3.1.1_VM-disk2.vmdk ../MapR-Sandbox-For-Hadoop-3.1.1_VM.qcow2


The use of the images is quick and easy:

virt-install --connect qemu:///system --ram 1024 -n HWXSandbox21 -r 2048 --os-type=linux --os-variant=rhel6 --disk path=/home/user/virtual_machines/Hortonworks_Sandbox_2.1-disk1-VMware.qcow2,device=disk,bus=virtio,format=qcow2 --vcpus=2 --vnc --noautoconsole --import

If you were to go the GUI route one could use virt-manager at this point to get a console and manage the machine. Of course in the interest of saving RAM and pure stubbornness I use the command line. First find the list of installed systems and then open a console to that instance. 

virsh list --all
virsh console guestvmname


While this will get you a console, you might not see all the console output you want to when using a monitor. For CentOS you need to create a serial interface in the OS (ttyS0) and instruct the OS to use that new interface. From this point you one should able to log in, find the IP address and be off to the races. With the use of the new serial interface you will see the normal boot up action if you reboot.

The real saving here is memory. Turning of Xserver and all the unnecessary OS services saves memory for running the various sandboxes. This should allow you to use Linux and a machine with 8 to 16GB of RAM effectively for development.

The next step will be to automate the installation base operating systems via PXE boot environment followed by installation of a true multinode virtualized Hadoop cluster. That I will leave for another post.

Updating Ambari Stack Repos via REST API

If you use Ambari to deploy Hadoop you may have had occasion to need to change your the repo used after you installed. At the time of this article version 1.5.1 of Ambari requires you do this via API as it is not exposed in Ambari web. The HDP repo file placed in /etc/yum.repos.d is generated by Ambari under certain conditions. In any case this is a good review of how to use the REST interface to manipulate Ambari. The basic call includes the use of the curl command to GET and PUT items to the Ambari API layer. Want some basic information about your cluster try this:

curl -H “X-Requested-By: ambari” -X GET -u admin:XXXXX http://AMBARIHOSTNAME:8080/api/v1/clusters

Screen Shot 2014-05-20 at 1.41.59 PM

You can check your existing reponame via this command:

curl -H “X-Requested-By: ambari” -X GET -u admin:XXXXX http://AMBARIHOSTNAME:8080/api/v1/stacks2/HDP/versions/2.0.6/ operatingSystems/centos6/repositories/HDP-2.0.6 

Screen Shot 2014-05-20 at 1.42.28 PM

Then you can set this repo name to whatever name you need via this PUT command. This again is help in situations where perhaps the internal repo name has changed post install. After this call you can double check your work by rerunning the above command OR via Ambari web interface under Admin>Clusters>Repositories.

curl -H “X-Requested-By: ambari” -X PUT -u admin:XXXXX http://AMBARIHOSTNAME:8080/api/v1/stacks2/HDP/versions/2.0.6
/operatingSystems/centos6/repositories/HDP-2.0.6 -d ‘{“Repositories”: {“base_url”: “http://REPOHOSTNAMEHERE/HDP/centos6/2.x/updates/”, “verify_base_url”: false}}’

This works for all the different repos for each stack. You can play with the GET command above to explore the different options available for each installed stack.

Look Ma…Im Famous, Im in Linux Journal.

But seriously it is kind of cool that I finally made it into Linux Journal. It has been one of my long time reads and subscriptions. I have learned lots of cool tech from what I consider the original magazine in Linux. Anywho take a look at my article on Yarn in the latest edition of Linux Journal (the HPC edition too!). It’s about YARN and understanding the difference in HPC scheduling systems compared to YARN.

Screen Shot 2014-04-02 at 7.52.49 PM

Hadoop Audit Logging

A recent interesting list of customer question included a query about audit logging in Hadoop. Specifically the logging of actions in Hive such as create table actions and queries. Audit was a feature added to Hadoop some time ago. Several JIRAs addressed it including HIVE-3505 and HIVE-1948 but wasnt really addressed in terms of documentation until recently via HIVE-5988.

The enablement of detail logging is done via log4j settings in /etc/hadoop/conf/log4j.properties. Changing from WARN to INFO enables the logging.

Screen Shot 2014-01-27 at 10.06.32 AM

In Hortonwork Data Platform this occurs around line 106 in the provided log4j file. The output of which places notes around actions that happen in HDFS. Since Hive also uses HDFS actions are logged in the /var/log/hadoop/hdfs/hdfs-audit.log

Screen Shot 2014-01-27 at 10.06.53 AM

Actions such as create table, show database etc. are listed for audit logging. Parsing of this log can yield detailed reports on how data was access in Hadoop for technologies that rely on HDFS.