Use Ranger with Kerberos

The following examples create and use a Kerberos enabled Dataproc cluster with Ranger and Solr components to control access by users to Hadoop, YARN, and HIVE resources.

Notes:

The Ranger Web UI can be accessed through the Component Gateway .
In a Ranger with Kerberos cluster, Dataproc maps a Kerberos user to the system user by stripping the Kerberos user's realm and instance. For example, Kerberos principal user1/cluster-m@MY.REALM is mapped to system user1 , and Ranger policies are defined to allow or deny permissions for user1 .

Set up the Ranger admin password .
Set up the Kerberos root principal password .

Create the cluster.

The following gcloud command can be run in a local terminal window or from a project's Cloud Shell .

gcloud dataproc clusters create cluster-name 
\
    --region= region 
\
    --optional-components=SOLR,RANGER \
    --enable-component-gateway \
    --properties="dataproc:ranger.kms.key.uri=projects/ project-id 
/locations/global/keyRings/ keyring 
/cryptoKeys/ key 
,dataproc:ranger.admin.password.uri=gs:// bucket 
/admin-password.encrypted" \
    --kerberos-root-principal-password-uri=gs:// bucket 
/kerberos-root-principal-password.encrypted \
    --kerberos-kms-key=projects/ project-id 
/locations/global/keyRings/ keyring 
/cryptoKeys/ key

After the cluster is running, navigate to the Dataproc Clusters page on Google Cloud console, then select the cluster's name to open the Cluster detailspage. Click the Web Interfacestab to display a list of Component Gateway links to the web interfaces of default and optional components installed on the cluster. Click the Ranger link.
Sign in to Ranger by entering the "admin" username and the Ranger admin password.
The Ranger admin UI opens in a local browser.

The following examples create Ranger policies to allow or deny access to two OS users and Kerberos principals: userone and usertwo .

YARN access policy

This example creates a Ranger policy to allow and deny user access to the YARN root.default queue .

Select yarn-dataproc from the Ranger Admin UI.
On the yarn-dataproc Policiespage, click Add New Policy. On the Create Policypage, the following fields are entered or selected:
- Policy Name : "yarn-policy-1"
- Queue : "root.default"
- Audit Logging : "Yes"
- Allow Conditions :
  - Select User : "userone"
  - Permissions : "Select All" to grant all permissions
- Deny Conditions :
  - Select User : "usertwo"
  - Permissions : "Select All" to deny all permissions
  Click Addto save the policy. The policy is listed on the yarn-dataproc Policiespage:
Run a Hadoop mapreduce job in the master SSH session window as userone:
```
userone@example-cluster-m:~$ hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduced-examples.
jar pi 5 10
```
1. The Ranger UI shows that userone was allowed to submit the job.
Run the Hadoop mapreduce job from the VM master SSH session window as usertwo :
```
usertwo@example-cluster-m:~$ hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduced-examples.
jar pi 5 10
```
1. The Ranger UI shows that usertwo was denied access to submit the job.

HDFS access policy

This example creates a Ranger policy to allow and deny user access to the HDFS /tmp directory.

Select hadoop-dataproc from the Ranger Admin UI.
On the hadoop-dataproc Policiespage, click Add New Policy. On the Create Policypage, the following fields are entered or selected:
- Policy Name : "hadoop-policy-1"
- Resource Path : "/tmp"
- Audit Logging : "Yes"
- Allow Conditions :
  - Select User : "userone"
  - Permissions : "Select All" to grant all permissions
- Deny Conditions :
  - Select User : "usertwo"
  - Permissions : "Select All" to deny all permissions
  Click Addto save the policy. The policy is listed on the hadoop-dataproc Policiespage:
Access the HDFS /tmp directory as userone:
```
userone@example-cluster-m:~$ hadoop fs -ls /tmp
```
1. The Ranger UI shows that userone was allowed access to the HDFS /tmp directory.
Access the HDFS /tmp directory as usertwo :
```
usertwo@example-cluster-m:~$ hadoop fs -ls /tmp
```
1. The Ranger UI shows that usertwo was denied access to the HDFS /tmp directory.

Hive access policy

This example creates a Ranger policy to allow and deny user access to a Hive table.

Create a small employee table using the hive CLI on the master instance.

hive> CREATE TABLE IF NOT EXISTS employee (eid int, name String); INSERT INTO employee VALUES (1 , 'bob') , (2 , 'alice'), (3 , 'john');

Select hive-dataproc from the Ranger Admin UI.
On the hive-dataproc Policiespage, click Add New Policy. On the Create Policypage, the following fields are entered or selected:
- Policy Name : "hive-policy-1"
- database : "default"
- table : "employee"
- Hive Column : "*"
- Audit Logging : "Yes"
- Allow Conditions :
  - Select User : "userone"
  - Permissions : "Select All" to grant all permissions
- Deny Conditions :
  - Select User : "usertwo"
  - Permissions : "Select All" to deny all permissions
  Click Addto save the policy. The policy is listed on the hive-dataproc Policiespage:

Run a query from the VM master SSH session against Hive employee table as userone:

userone@example-cluster-m:~$ beeline -u "jdbc:hive2://$(hostname -f):10000/default;principal=hive/$(hostname -f)@ REALM 
" -e "select * from employee;"

The userone query succeeds:

Connected to: Apache Hive (version 2.3.6)
Driver: Hive JDBC (version 2.3.6)
Transaction isolation: TRANSACTION_REPEATABLE_READ
+---------------+----------------+
| employee.eid  | employee.name  |
+---------------+----------------+
| 1             | bob            |
| 2             | alice          |
| 3             | john           |
+---------------+----------------+
3 rows selected (2.033 seconds)

Run a query from the VM master SSH session against Hive employee table as usertwo:

usertwo@example-cluster-m:~$ beeline -u "jdbc:hive2://$(hostname -f):10000/default;principal=hive/$(hostname -f)@ REALM 
" -e "select * from employee;"

usertwo is denied access to the table:

Error: Could not open client transport with JDBC Uri:
...
Permission denied: user=usertwo, access=EXECUTE, inode="/tmp/hive"

Fine-Grained Hive Access

Ranger supports Masking and Row Level Filters on Hive. This example builds on the previous hive-policy-1 by adding masking and filter policies.

Select hive-dataproc from the Ranger Admin UI, then select the Maskingtab and click Add New Policy.
1. On the Create Policypage, the following fields are entered or selected to create a policy to mask (nullify) the employee name column.:
  - Policy Name : "hive-masking policy"
  - database : "default"
  - table : "employee"
  - Hive Column : "name"
  - Audit Logging : "Yes"
  - Mask Conditions :
    - Select User : "userone"
    - Access Types : "select" add/edit permissions
    - Select Masking Option : "nullify"
      
      Click Addto save the policy.
Select hive-dataproc from the Ranger Admin UI, then select the Row Level Filtertab and click Add New Policy.
1. On the Create Policypage, the following fields are entered or selected to create a policy to filter (return) rows where eid is not equal to 1 :
  - Policy Name : "hive-filter policy"
  - Hive Database : "default"
  - Hive Table : "employee"
  - Audit Logging : "Yes"
  - Mask Conditions :
    - Select User : "userone"
    - Access Types : "select" add/edit permissions
    - Row Level Filter : "eid != 1" filter expression
      
      Click Addto save the policy.
2. Repeat the previous query from the VM master SSH session against Hive employee table as userone:
```
userone@example-cluster-m:~$ beeline -u "jdbc:hive2://$(hostname -f):10000/default;principal=hive/$(hostname -f)@ REALM 
" -e "select * from employee;"
```
  1. The query returns with the name column masked out and bob (eid=1) filtered from the results.:
```
Transaction isolation: TRANSACTION_REPEATABLE_READ
+---------------+----------------+
| employee.eid  | employee.name  |
+---------------+----------------+
| 2             | NULL           |
| 3             | NULL           |
+---------------+----------------+
2 rows selected (0.47 seconds)
```