Hortonworks Improves Hadoop Security

© Hortonworks Inc. 2011
Improvements in Hadoop Security
Sanjay Radia
sanjay@hortonworks.com
@srr
Chris Nauroth
cnauroth@hortonworks.com
@cnauroth
Page 1

Hello
Sanjay Radia
• Founder, Hortonworks
• Part of the Hadoop team at Yahoo! since 2007
– Chief Architect of Hadoop Core at Yahoo!
– Long time Apache Hadoop PMC and Committer
– Designed and developed several key Hadoop features
• Prior
– Data center automation, virtualization, Java, HA, OSs, File Systems (Startup, Sun Microsystems, …)
– Ph.D., University of Waterloo
Chris Nauroth
• Member of Technical Staff, Hortonworks
– Apache Hadoop Committer
– Major contributor to HDFS ACLs
• Hadoop user since 2010
– Prior employment experience deploying, maintaining and using Hadoop clusters
Page 2
Architecting the Future of Big Data

Overview
• Models of Deployment
– Secure and insecure
• Hadoop Authentication
– The how and why
– Knox – perimeter security
• Authorization – existing and what is new
– HDFS
– Tables and Hive
– HBase and Accumulo
• Data protection and encryption
– Wire
– Data at rest
Page 3

Two Reasons for Security in Hadoop
Hadoop Contains Sensitive Data
– As Hadoop adoption grows so too has the types of data organizations look to store. Often the
data is proprietary or personal and it must be protected.
– In this context, Hadoop is governed by the same security requirements as any data center
platform.
Hadoop is subject to Compliance adherence
– Organizations are often subject to comply with regulations such as HIPPA, PCI DSS, FISAM that
require protection of personal information.
– Adherence to other Corporate security policies.
1
2

Three Models of Hadoop Deployment
• Insecure cluster
– You have protected it via the perimeter
– You trust the code that runs in the system
– Note In Hadoop cluster, user submitted code runs inside the cluster
– (Not true in typical client-server applications)
– The client-side libraries pass the client’s login credential
– There is no end-end-authentication here
– Authorization is done against this credential
• Secure cluster
– Full authentication
– Can run arbitrary code in jobs
• Perimeter security using Knox
– Internal cluster can be secure or insecure depending on your needs

Pillars of Hadoop Security
Authorization
Restrict access to
explicit data
Audit
Understand who did
what
Data Protection
Encrypt data at rest &
motion
AD/Kerberos in native
Apache Hadoop
Perimeter Security
with Apache Knox
Gateway
Authentication
Who am I/prove it?
Control access to cluster.
Every service has
audit logs
Knox and XASecure
provide central audit
logs

Hadoop Authentication Overview
• Kerberos/Active Directory based security
– SSO – users do not have to re-login into Hadoop
– Hadoop accounts do not have to be created
– Caveat – MR Task isolation require Unix accounts for each user current but this is going away with Linux containers
– Hadoop tokens – supplement the Kerberos authentication
– Delegation tokens – deal with the delayed job execution
– Block tokens – capabilities to deal with the distributed nature of HDFS
– Trusted Proxies – support for third party services to act as proxy
– Oozie
– Gateways – HDFS proxy, Knox, etc.
• Knox – Perimeter Security and Rest Gateway
Page 7

Why the tokens
• Why does Hadoop have its own tokens?
– Standard client-server security model is not sufficient for Hadoop
– Works when logged-in client is directly accessing a Hadoop service
– But for a job, the execution happens much later
– The job submitter has long logged off
• Hence we needed to add delegation tokens
• HDFS is a distributed service and needed to add capability-like tokens for datanode
authentication
The permissions/ ACLs are in the Namenode

Apache Knox
Perimeter Security with Hadoop REST APIs
Page 10

The Gateway or Edge Node
• Hadoop APIs can be used from any desktop after SSO login
– FileSystem and MapReduce Java APIs
– Pig, Hive and Oozie clients (that wrap the Java APIs)
• However it is typical to Use “Edge Node” or “Gateway Node” that is “inside” cluster
– The libraries for the APIs are generally only installed on the gateway
– Users SSH to Edge Node and execute API commands from shell
Page 11
HadoopUser Edge Node
SSH

• Single Hadoop access point
• REST API hierarchy
• Consolidated API calls
• Multi-cluster support
• Eliminates SSH “edge
node”
• Central API management
• Central audit control
• Simple Service level
Authorization
• SSO Integration –
Siteminder, API Key*,
OAuth* & SAML*
• LDAP & AD integration
Perimeter Security with Apache Knox
Integrated with existing
systems to simplify identity
maintenance
Incubated and led by Hortonworks,
Apache Knox provides a simple and open framework for
Hadoop perimeter security.
Single, simple point of
access for a cluster
Central controls ensure
consistency across one or
more clusters

Hadoop REST APIs
• Useful for connecting to Hadoop from the outside the cluster
• When more client language flexibility is required
– i.e. Java binding not an option
• Challenges (Knox addresses these challenges)
– Client must have knowledge of cluster topology
– Required to open ports (and in some cases, on every host) outside the cluster
Page 13
Service API
WebHDFS Supports HDFS user operations including reading files, writing to files,
making directories, changing permissions and renaming. Learn more about
WebHDFS.
WebHCat Job control for MapReduce, Pig and Hive jobs, and HCatalog DDL
commands. Learn more about WebHCat.
Hive Hive REST API operations
HBase HBase REST API operations
Oozie Job submission and management, and Oozie administration. Learn more
about Oozie.

What can be done today?
Authorization
Restrict access to
explicit data
Audit
Understand who did
what
Data Protection
motion
Previously
• All Services: Service level ACLs
• HDFS: Permissions
• Yarn: Queue ACLs
• Hive/Pig Tables: Table level via
HDFS
• Apache Accumulo: Cell level
• HBase: Namespace, Table,
Column Family and Column level
ACLs
Authentication
Who am I/prove it?
Hadoop 2.x
• HDFS: ACLs
• Hive: Column level ACLs
• HBase: Cell level ACLs
• Knox:
• Rest Service level Authorization
• Access Audit with Knox

HDFS ACLs
• Existing HDFS POSIX permissions good, but not flexible enough
– Permission requirements may differ from the natural organizational hierarchy of users and groups.
• HDFS ACLs augment the existing HDFS POSIX permissions model by implementing the POSIX
ACL model.
– An ACL (Access Control List) provides a way to set different permissions for specific named users or named
groups, not only the file’s owner and file’s group.
Page 15

HDFS File Permissions Example
• Authorization requirements:
–In a sales department, they would like a single user Maya (Department Manager) to
control all modifications to sales data
–Other members of sales department need to view the data, but can’t modify it.
–Everyone else in the company must not be allowed to view the data.
• Can be implemented via the following:
Read/Write perm for user
maya
User
Group
Read perm for group sales
File with sales data

HDFS ACLs
• Problem
–No longer feasible for Maya to control all modifications to the file
– New Requirement: Maya, Diane and Clark are allowed to make modifications
– New Requirement: New group called executives should be able to read the sales data
–Current permissions model only allows permissions at 1 group and 1 user
• Solution: HDFS ACLs
–Now assign different permissions to different users and groups
Owner
Group
Others
HDFS
Directory
… rwx
… rwx
… rwx
Group D … rwx
Group F … rwx
User Y … rwx

HDFS ACLs
New Tools for ACL Management (setfacl, getfacl)
– hdfs dfs -setfacl -m group:execs:r-- /sales-data
– hdfs dfs -getfacl /sales-data
# file: /sales-data
# owner: maya
# group: sales
user::rw-
group::r—
group:execs:r—
mask::r—
other::--
– How do you know if a directory has ACLs set?
– hdfs dfs -ls /sales-data
Found 1 items
-rw-r-----+ 3 maya sales 0 2014-03-04 16:31 /sales-data

HDFS ACLs
Default ACLs
–hdfs dfs -setfacl -m default:group:execs:r-x /monthly-sales-data
–hdfs dfs -mkdir /monthly-sales-data/JAN
–hdfs dfs –getfacl /monthly-sales-data/JAN
– # file: /monthly-sales-data/JAN
# owner: maya
# group: sales
user::rwx
group::r-x
group:execs:r-x
mask::r-x
other::---
default:user::rwx
default:group::r-x
default:group:execs:r-x
default:mask::r-x
default:other::---

HDFS ACLs Best Practices
• Start with traditional HDFS permissions to implement most permission requirements.
• Define a smaller number of ACLs to handle exceptional cases.
• A file with an ACL incurs an additional cost in memory in the NameNode compared to a file that
has only traditional permissions.
Page 20

Tables and Hive
Page 21

Table ACLs – The Challenge and Solution
• Hive and Pig have traditionally offer full table access control via HDFS access control
• The challenge in column-level access control
– Hive and Pig queries are executed as Tez-based tasks that access the HDFS files directly
– HDFS does not have knowledge of columns (there are several file/table formats)
• Solution for Column level ACLs
– Let Hive server check and submit the query execution
– Let the table be accessible only by special user (“HiveServer”)
– But one has to restrict the UDFs and file formats
– Good news: Hive provides an authorization plugin to do this cleanly
• Use standard SQL permission constructs
– GRANT/REVOKE
• Store the ACLs in Hive Metastore instead of some external DB
• But what about Pig, there is no Pig server …
Page 22

Hive ATZ-NG – Architecture
HDFS
Metastore
HiveServer2
O/JDBC Beeline CLI
• ATZ-NG is called for O/JDBC & Beeline CLI
• Standard SQL GRANT / REVOKE for management
• Privilege to register UDF restricted to Admin user
• Policy integrated with Table/View life cycle
Storage Based Authorization Provider
Hive
CLI
OozieHue
PIG HCat
Ambari
0. Enable HiveATZ-NG
1. Authentication
UDFs
Protected – column level
Protected – table level
Restrict direct access to Metastore
Protect HDFS with Kerberos & HDFS ACL
ATZ-NG
2. Authorization

What about MR/Pig
• Note there is no Pig/MR server to submit and check column ACLs
• Hence in the same cluster running Hive
–You cannot give Pig similar column level access control
–If Pig/MR is important,
–Use coarse grained table level
–Or run Pig/MR as privileged uses with full table level access
Page 26

Hive ATZ-NG Example
Page 28

Scenario
• Objective: Share Product Management Roadmap securely
• Actors:
–Admin Role – Specified in hive-site
– Admin role controls role memberships
–Product Management Role
– Should be able to create, read all road map details.
– Members: Vinay Shukla, Tim Hall
–Engineering Role
– Should be able to read (see) all roadmap details
– Members: Kevin Minder, Larry McCay
Page 29

Step 1: Admin role Creates Roles, Adds Users
1. CREATE ROLE PM;
1. CREATE ROLE ENG;
1. GRANT ROLE PM to user timhall with admin option;
1. GRANT ROLE PM to user vinayshukla;
1. GRANT ROLE ENG to user kevinminder with admin option;
1. GRANT ROLE ENG to user larrymccay;

Step 2: Super-user Creates Tables/Views
create table hdp_hadoop_plans (
id int,
hadoop_roadmap string,
hdp_roadmap string
);

Step 3: Users or Roles Assigned To Tables
1. GRANT ALL ON hdp_hadoop_plans TO ROLE PM;
1. GRANT SELECT ON hdp_hadoop_plans TO ROLE ENG;

HBase Cell Level Authorization
• The HBase permissions model already supports ACLs defined at the
namespace, table, column family and column level.
–This is sufficient to meet many requirements
–This can be insufficient if a data model requires protection on individual rows/cells.
–Example: Medical data, each row representing a patient, may require customizing who
can see an individual patient’s data, and the social security number of each row may
need further restriction.
Page 33

• Cell level authorization augments the permissions model by allowing
ACLs specified on individual cells.
–ACLs are now supported at the individual cell level.
–Individual operations may choose order of evaluation. Cell level ACLs may be evaluated
last or first.
–Evaluating last is useful if the common case is access granted through table or column
family ACLs, and cell level ACLs define exceptions for denial.
–Evaluating first is useful if many users are granted access through cell level ACLs.
Page 34

• Visibility labels
–Visibility expressions can be stored as metadata in a cell’s tag.
–A visibility expression consists of labels combined with boolean operators.
–E.g. (financial | strategy | research) & !newhire
–This means that a user must be labeled financial or strategy or research and not be a
newhire in order to see the column.
–The mapping of users to their labels is pluggable. By default, a user’s labels are specified
as authorizations in the individual operation.
–HBase visibility labels were inspired by similar features in Apache Accumulo, and the
model will look very familiar to Accumulo users.
Page 35

What can be done today?
Authorization
Restrict access to
explicit data
Audit
Understand who did
what
Data Protection
motion
Wire encryption
• In native Hadoop
• With Knox
• SSL for Rest (2.x)
File encryption
• Via MR file format
• 3rd Party encryption
tools for col level
encryption
• Native HDFS support
coming
Authentication
Who am I/prove it?

Wire Encryption – for data in motion
Page 37
• Hadoop client to DataNode is via Data Transfer Protocol
– HDFS client reads/writes to HDFS service over encrypted channel
– Configurable encryption strength
• ODBC/JDBC Client to HiveServer 2
– Encryption is via SASL Quality Of Protection
• Map to Reduce via shuffle
– Shuffle is over HTTP(S)
– Supports mutual authentication via SSL
– Host name verification enabled
• Rest Protocols
– SSL support

Data at Rest
• Coming: HDFS encrypted file system currently under development in Apache
– https://issues.apache.org/jira/browse/HADOOP-10150
– https://issues.apache.org/jira/browse/HDFS-6134
Page 38

XA Secure
A Major step forward in Hadoop security
See Shaun Connolly’s Key note on Wednesday June 4
Page 39

Security in Hadoop with HDP + XA Secure
Authorization
Restrict access to
explicit data
Audit
Understand who
did what
Data Protection
Encrypt data at
rest & in motion
• Kerberos in native
Apache Hadoop
• HTTP/REST API
Secured with
Apache Knox
Gateway
• MapReduce Access Control Lists
• HDFS Permissions, HDFS ACL,
• Audit logs in with HDFS & MR
• Hive ATZ-NG
• Cell level access control in
Apache Accumulo
Authentication
Who am I/prove it?
• Wire encryption
in Hadoop
• Orchestrated
encryption with
3rd party tools
• HDFS, Hive and
Hbase
• Fine grain
access control
• RBAC
• Centralized
audit reporting
• Policy and
access history
• Future roadmap
• Strategy to be
finalized
HDP2.1XASecure
Centralized Security Administration
• As-Is, works with
current
authentication
methods

Open Source?
•Yes XASecure technology will be open sourced
–Not just a Apache license where you are forced to get
the latest from Hortonworks
–But a full-fledged Apache Project that is truly open to the
community of developers and users
•See Shaun Connolly’s Keynote on Wednesday for
details
Page 41

Summary
• Very strong Authentication via Kerberos and Active directory
– Uses your organization's user DB and integrates to its group and role membership
– Supplemented by Hadoop tokens
– Note these are necessary due to delayed job execution after user logs-off
• Strong fine grained authentication with some recent improvements
– HDFS ACLs
– Hive – integrated via SQL model and Hive Metastore
– Note a hacked side addon
– HBase Cell Level Authorization
• Strong encryption support
– Wire
– Data
– Some improvements coming soon
• Every product has audit logs
• XASecure adds a major step forward
– Yes it will be open sourced as a Apache Project
Page 42

Thank you, Q&A
Page 43
Resource Location
Hortonworks Security Labs http://hortonworks.com/labs/security/
Apache Knox Project Page http://knox.incubator.apache.org/
HDFS ACLs Blog Post http://hortonworks.com/blog/hdfs-acls-fine-grained-permissions-hdfs-files-hadoop/
Encrypted File System
Development
https://issues.apache.org/jira/browse/HADOOP-10150
https://issues.apache.org/jira/browse/HDFS-6134
HBase Cell Level
Authorization
https://blogs.apache.org/hbase/entry/hbase_cell_security
Learn more

Hortonworks Improves Hadoop Security

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