Authorization is one of the fundamental security requirements of any computing environment. Its goal is to ensure that only the appropriate people or processes can access, view, use, control, or change specific resources, services, or data. In any cluster deployed to meet specific workloads using various CDH components (Hive, HDFS, Impala, and so on), different authorization mechanisms can ensure that only authorized users or processes can access data, systems, and other resources as needed. Ideally, authorization mechanisms can leverage the authentication mechanisms, so that when users login to a system—a cluster, for example—they are transparently authorized based on their identity across the system for the applications, data, and other resources they are authorized to use.
For example, Cloudera CDH clusters can be configured to leverage the user and group accounts that exist in the organization's Active Directory (or other LDAP-accessible directory) instance.
The various possible configurations and integrations are discussed later in this guide.
Authorization Mechanisms in Hadoop
Hadoop supports several authorization mechanisms, including:
- Traditional POSIX-style permissions on files and directories. Each directory and file has a single owner and group with basic permissions that can be set to read, write, execute (at the file level). Directories have an additional permission that enables access to child directories.
- Access Control Lists (ACL) for management of services and resources. For example, Apache HBase uses ACLs to authorize various operations (READ, WRITE, CREATE, ADMIN) by column, column family, and column family qualifier. HBase ACLs are granted and revoked to users and groups. Fine-grained permissions can be applied to HDFS files using Apache HDFS ACLs to set permissions for specific named users and named groups.
- Apache Ranger manages access control through and ensures consistent policy administration across cluster services. See Using Ranger to Provide Authorization in CDP for more information.
- Apache Ranger also provides a centralized framework for collecting access audit history and reporting data, including filtering on various parameters. See Managing Auditing with Ranger for more information.
Most services running on Hadoop clusters, such as the command-line interface (CLI) or client applications that use Hadoop API, directly access data stored within HDFS. HDFS uses POSIX-style permissions for directories and files; each directory and file is assigned a single owner and group. Each assignment has a basic set of permissions available; file permissions are read, write, and execute, and directories have an additional permission to determine access to child directories.
Ownership and group membership for a given HDFS asset determines a user’s privileges. If a given user fails either of these criteria, they are denied access. For services that may attempt to access more than one file, such as MapReduce, Cloudera Search, and others, data access is determined separately for each file access attempt. File permissions in HDFS are managed by the NameNode.
Access Control Lists
Hadoop also maintains general access controls for the services themselves in addition to the data within each service and in HDFS. Service access control lists (ACL) are typically defined within the global hadoop-policy.xml file and range from NameNode access to client-to-DataNode communication. In the context of MapReduce and YARN, user and group identifiers form the basis for determining permission for job submission or modification.
In addition, with MapReduce and YARN, jobs can be submitted using queues controlled by a scheduler, which is one of the components comprising the resource management capabilities within the cluster. Administrators define permissions to individual queues using ACLs. ACLs can also be defined on a job-by-job basis. Like HDFS permissions, local user accounts and groups must exist on each executing server, otherwise the queues will be unusable except by superuser accounts.
Apache HBase also uses ACLs for data-level authorization. HBase ACLs authorize various operations (READ, WRITE, CREATE, ADMIN) by column, column family, and column family qualifier. HBase ACLs are granted and revoked to both users and groups. Local user accounts are required for proper authorization, similar to HDFS permissions.
Apache ZooKeeper also maintains ACLs to the information stored within the DataNodes of a ZooKeeper data tree.
Integration with Authentication Mechanisms for Identity Management
Like many distributed systems, Hadoop projects and workloads often consist of a collection of processes working in concert. In some instances, the initial user process conducts authorization throughout the entirety of the workload or job’s lifecycle. But for processes that spawn additional processes, authorization can pose challenges. In this case, the spawned processes are set to execute as if they were the authenticated user, that is, setuid, and thus only have the privileges of that user. The overarching system requires a mapping to the authenticated principal and the user account must exist on the local host system for the setuid to succeed.
System and Service Authorization - Several Hadoop services are limited to inter-service interactions and are not intended for end-user access. These services do support authentication to protect against unauthorized or malicious users. However, any user or, more typically, another service that has login credentials and can authenticate to the service is authorized to perform all actions allowed by the target service. Examples include ZooKeeper, which is used by internal systems such as YARN, Cloudera Search, and HBase, and Flume, which is configured directly by Hadoop administrators and thus offers no user controls.
The authenticated Kerberos principals for these “system” services are checked each time they access other services such as HDFS, HBase, and MapReduce, and therefore must be authorized to use those resources. Thus, the fact that Flume does not have an explicit authorization model does not imply that Flume has unrestricted access to HDFS and other services; the Flume service principals still must be authorized for specific locations of the HDFS file system. Hadoop administrators can establish separate system users for a services such as Flume to segment and impose access rights to only the parts of the file system for a specific Flume application.
Authorization within Hadoop Projects
|HDFS||File Permissions, Ranger|
|MapReduce||File Permissions, Ranger|
|YARN||File Permissions, Ranger|
|HBase||HBase ACLs, Ranger|
|HiveServer2||File Permissions, Ranger|
|Hue||Hue authorization mechanisms (assigning permissions to Hue apps)|
|Spark||File Permissions, Ranger|
|Cloudera Manager||Cloudera Manager roles|
|Backup and Disaster Recovery||N/A|