Page Comparison

There are plenty of folks who will tell you "Hadoop isn't secure!", but that's not the whole story.  Yes, as Merv Adrian points out, Hadoop in its most bare-bone mode does have a number of concerns.  Fortunately, like with the rest of Hadoop, software layers upon software layers come together in concert to provide various levels of security shields to address specific requirements.

The Infrastructure Architecture section described how the physical isolation of the cluster allows for network-enforce “perimeter security” from the edge nodes.  This section will walk through the multiple layers of Hadoop security that is available with HDP.  It will cover the full AAA (Authentication, Authorization and Auditing) functionality that is typical Hadoop started out with no heavy thought to security.  This is because there was a problem to be solved and the "users" of the earliest incarnations of Hadoop all worked together – and all trusted each other.  Fortunately for all of us, especially those of us who made career bets on this technology, Hadoop acceptance and adoption has been growing by leaps and bounds which only makes security that much more important.  Some of the earliest thoughts around Hadoop security was to simply "wall it off".  Yep, wrap it up with network security and only let a few, trusted, folks in.  Of course, then you needed to keep letting a few more folks in and from that approach of network isolation came the ever present edge node (aka gateway server, ingestion node, etc) that almost every cluster employs today.  But wait... I'm getting ahead of myself.

My goal for this posting is to cover how Hadoop addresses the AAA (Authentication, Authorization, and Auditing) spectrum that is typically used to describe system security. 

Image AddedThe following diagram presents the categorization that will be used to describe the components that are availablePICTURE..diagram on the right represents my view of Hadoop security; well, as it is in mid-2014 as there is rapid & continuous innovation in this domain. The sections of this blog posting are oriented around the italicized characteristics in the white boxes.

While the diagram above presents layers, it should be clearly stated that each of these components could be utilized without dependencies on any others.  The only exception to that is the based base HDFS authentication that is always present and is foundational as compared to the other components.  The overall security posture is enhanced by employing more (appropriate) components, but additional system complexity could be introduced into the overall system when using a component that has no direct alignment with security requirements.

...

• Host-Based: This is the simplest to understand, but surely the toughest to maintain and each user and group need to be created individually on every machine.  There will almost surely be security concerns around password strength & expiration and auditing as well.
• Directory-Based: In this approach a centralized resource, such as Active Directory (AD) or LDAP, is us to create users/groups and either through a commercial product or internally-development framework an integration with the hosts ensures the seamless interaction to users.  This is often addressed with Pluggable Authentication Modules (PAM) integration.
• Hybrid Solution: As Hadoop is unaware of the “hows” of user/group provisioning, a custom solution could also be employed.  An example could be PAM integration with AD user objects to handle authentication onto the hosts, but utilizing local groups for the authorization aspects of HDFS.

Mercy’s solution will be to utilize a hybrid approach like that described in the example.What are people doing in this space?  In my experience, it seems most Hadoop systems are leveraging a hybrid approach such as the example provided for their underlying linux user & group strategy.

Where do I go for more info?  http://hadoop.apache.org/docs/r2.4.1/hadoop-project-dist/hadoop-hdfs/HdfsPermissionsGuide.html

User Integrity

With the pervasive reliance on identities as they are presented to the OS, Kerberos allows for even stronger authentication.  Users can more reliability identify themselves and then have that identity propagated throughout the Hadoop cluster.  Kerberos also secures the accounts that run the various elements in the Hadoop ecosystem thereby preventing malicious systems “posing as” part of the cluster to gain access to the data.

Kerberos can be integrated with AD and this is Mercy’s preferred mechanism for implementationcorporate directory servers such as Microsoft's Active Directory which helps tons when there are significant number of direct Hadoop users.

What are people doing in this space?  In my experience, I'd estimate that about 50% of Hadoop clusters are utilizing Kerberos.  This seems to be because either the use case does not require this additional level of security and/or there is a believe that the integration & maintenance activities are too high. 

Where do I go for more info?  http://hortonworks.com/wp-content/uploads/2011/10/security-design_withCover-1.pdf

Additional Authentication

The right combination of secure users & groups, along with appropriate physical access points described later, provide a solid authentication model for users taking advantage of Command-Line Interface (CLI) tools from the Hadoop ecosystem (i.e. Hive, Pig, MapReduce, Streaming, etc).  Additional access points will need to be secured as well.  These access points could include the following.

• HDP Access Components
  • HiveServer2 (HS2): Provides JDBC/ODBC connectivity to Hive
  • Hue: Provide Provides a user-friendly web application to the majority of the Hadoop ecosystem tools to complement the CLI approach.
• 3^rd Party Components: Tools that sit “in front” of the Hadoop cluster may use a traditional model where they connect to the Hadoop cluster via a single “system” account (ex: via JDBC) and then present their own AAA implementations. 

Hue and HS2 will be configured to utilize AD to be aligned with the same authentication that the CLI tools are usingeach have multiple authentication configuration options, to include various approaches to impersonation, that can augment, or even replace, whatever approach is being leveraged by the CLI tooling environment.  Additionally, 3^rd party tools such as Business Objects will provide their own AAA functionality to manage existing users and utilize a system account to connect to Hive/HS2.

What are people doing in this space?  Generally, it seems to me that teams are configuring authentication in these access points to be aligned with the same authentication that the CLI tools are using.  Some organizations with a more narrowly-focused plan for utilization of their cluster are "locking down" access to the data from first-class Hadoop clients (FS Shell, Pig, Hive/HS2, MapReduce, etc) to only the true exploratory data science and software development communities and taking an approach from the application server playbook – having 3rd party BI/reporting tools use a "system account" to interact with relational-oriented data via HS2 and leveraging the rich AAA abilities of these user-facing tools.

Where do I go for more info?  https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2 and https://github.com/cloudera/hue/blob/master/desktop/conf.dist/hue.ini

Additional Authorization

The POSIX-based permission model described earlier will accommodate most security needs, but there is an additional option should the security authorization rules become more complex than this model can handle.  HDFS ACLs (Access Control Lists) have surfaced to accommodate this need.  This feature became available in HDP 2.1 and is inherently available thus can be taken advantage of when the authorization use case demands it.

The long-time “data services” members of the HDP stack, HBase and Hive, also allow additional ACLs to be set on the “objects” that are present in their frameworks.  For Hive, this is presented as the ability to grant/revoke access controls on tables.  HBase provides authorization on tables and column families.  Accumulo (another “data services” offering from HDP that Mercy is currently not planning on leveraging) extends the HBase model down to the ability to control cell-level authorization.The additional ACL abilities can become useful as Mercy’s applications and expertise mature, but the recommendation is to first address data access controls via the base POSIX permission model as this will the first line of defense for CLI access to the file system and with ecosystem tools like Pig.  Attention needs to be paid to this level of authorization especially considering the approach of limiting access on data, not the tools.

As a result of Hortonworks’ recent acquisition of XA Secure, the addition of a unified console for authorization across the ecosystem offerings has become available. Additional research can be done to determine if this software that Hortonworks is working to introduce as an incubating Apache project is applicable for Mercy’s needs  The maturity of this product, which is actively being spun into the new incubating project called Apache Argus, should greatly help its eventual promotion to a top-level Apache project.

What are people doing in this space?  Those already using HBase are leveraging its features as appropriate to their projects, but HDFS ACLs and Hive's ATZ-NG are still gaining adoption due to their relatively recent introductions.

Where do I go for more info?  http://hbase.apache.org/book/hbase.accesscontrol.configuration.html, https://cwiki.apache.org/confluence/display/Hive/SQL+Standard+Based+Hive+Authorization, http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.1.5/bk_system-admin-guide/content/ch_acls-on-hdfs.html and http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.1.3/bk_HDPSecure_Admin/content/ch_XA-overview.html

Activity Logging

HDFS has a built in ability to log access requests to the filesystem.  This provides a low-level snapshot of events that occur and who did them.  These are human-readable, but not necessarily reader-friendly.  They are detailed logs that can themselves be used as a basis for reporting and/or ad-hoc querying with Hadoop frameworks and/or other 3^rd party tools.

Hive also has an ability to log metastore API invocations.  Additionally, HBase offers its own audit log.  To pull this all together into a single pane of glass , Hortonworks offers you can leverage XA Secure / Apache Argus.  This software layer pulls this dispersed audit information into a cohesive user experience.  The audit data can be further broken down into access audit, policy audit and agent audit data, giving granular visibility to auditors on users’ access as well as administrative actions within this security portal.

This feature set, coupled with the single pane of glass for authorization rights, seems to be a solid fit for Mercy who, by nature of the data being stored, will require this level of administrative controls.  Additional investigations are warranted to ensure XA Secure will meet the end goals of the auditing requirements that are present and additional hardware will need to be secured to run this web tier service.

Mercy should ensure that appropriate levels of auditing information is being produced and maintained to support any requirements or desired reporting opportunities

Perimeter Security

Establishing What are people doing in this space?  XA Secure / Apache Argus is early in its adoption curve, but is a feature-rich application so I do expect quick adoption of it.  While the raw data is there, it seems that few organizations are currently rolling the disparate activity logs into a combined view for audit reporting.

Where do I go for more info?  http://books.google.com/books?id=drbI_aro20oC&pg=PA346&lpg=PA346&dq=hdfs+audit+log&source=bl&ots=t_wnyhn0i4&sig=PEbiD5LdLdkUP0jnjhtUoOoBDMM&hl=en&sa=X&ei=YwsJVKeGDZSBygT5x4D4Cg&sqi=2&ved=0CEIQ6AEwAg#v=onepage&q=hdfs%20audit%20log&f=false and http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.1.3/bk_HDPSecure_Admin/content/ch_XA-audit.html

Perimeter Security

As mentioned at the beginning of this post, establishing perimeter security has been a foundational Hadoop security approach.  This is simply viewed as having limited access into the HDP Hadoop cluster itself and utilizing an “edge” node (aka gateway) as a host users and systems can utilize directly and then have all other actions fired from this gateway into the cluster itself. 

The Knox Gateway is a recent addition to the HDP stack and Apache Knox gateway provides a software layer intended to perform this perimeter security function.  It has a pluggable provider based mechanism to integrate customer AAA mechanisms.  Not all operations are fully supported yet with Knox to have it completely replace the need for the traditional edge node (ex: HS2 access only supported for JDBC; ODBC functionality still lacking). 

Mercy could benefit from additional investigations into Knox at this time, but would need to provision additional hosts and wall off the Knox Gateway web server farm with firewalls similar to the network architecture diagram below.

KNOX PICTURE

The traditional edge node also often is satisfying the ingestion node requirements that are in place in many models that need a landing zone for data before being persisted into HDFS.  Mercy’s current investment in in traditional edge nodes is a meaningful one, but the project's roadmap addresses missing functionality.  It's REST API extends the reach to different types of clients and eliminates the need to SSH to a fully configured "Hadoop client" to interact with the Hadoop cluster (or several as Knox can front multiple clusters).

What are people doing in this space?  Early adopters have already deployed Knox, but the majority of clusters still rely heavily on traditional edge nodes.  The interest is clearly present in almost all customers that I work and I expect a rapid adoption of this technology.

Where do I go for more info?  http://www.dummies.com/how-to/content/edge-nodes-in-hadoop-clusters.html and http://knox.apache.org/

Data Encryption

Data encryption can be broken into two primary scenarios; encryption in-transit and encryption at-rest.  Wire encryption options exist in Hadoop to aid with the in-transit needs that might be present.  HDP has many There are multiple options available to protect data as it moves through Hadoop over RPC, HTTP, Data Transfer Protocol (DTP), and JDBC. 

For encryption at-rest, there are some open source activities underway, but HDP Hadoop does not inherently have a baseline encryption solution for the data that is persisted within HDFS.  There are several 3^rd party solutions available (including Hortonworks partners) that specifically target this requirement.  Custom development could also be undertaken, but the absolute easiest mechanism to obtain encryption at-rest is to tackle this at an OS or hardware level. Mercy will need to determine what, if any, of the available encryption options provide the right trade-off of simplicity and security.  This decision will surely be impacted by what other security components and practices are employed

What are people doing in this space?  My awareness is that few Hadoop administrators have enabled encryption, at-rest or in-transit, at this time.

Where do I go for more info?  http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.1.3/bk_reference/content/reference_chap-wire-encryption.html

Summary

As you can see, there are many avenues to explore to ensure you create the best security posture for your particular needs.  Remember, the vast majority of these options are mutually exclusive allowing for multiple approaches to security.  This is surely one area there still is work to be done and definitely in pulling together the disparate pieces into tools that are easy to adopt by enterprises.