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EMR User Guide#

  1. Create bucket ${SECURE_BUCKET_NAME} which you need to protect.

  2. Download sample data from below link and put into your bucket at location (s3://${SECURE_BUCKET_NAME}/sample_data/customer_data)

    wget https://privacera-demo.s3.amazonaws.com/data/uploads/customer_data_clear/customer_data_without_header.csv

  3. Make sure cluster should not have direct access on ${SECURE_BUCKET_NAME} bucket.

  4. To verify, run the following commands:

    ssh -i ${KEY_FILE} hadoop@${EMR_PUBLIC_DNS}
aws s3 ls  s3://${SECURE_BUCKET_NAME}

    Result: Fatal error: An error occurred (403) when calling the HeadObject operation: Forbidden

Hive#

  1. Run the below in beeline, using an admin user who has permission on url in Ranger and also has permission to create tables and databases:

    beeline  -u "jdbc:hive2://`hostname -f`:10000/default;principal=hive/`hostname -f`@${REALM}"

  2. Create the table using this user, by running the below command in Hive.

    create database if not exists customer;
use customer;
CREATE EXTERNAL TABLE if not exists `customer_data_s3`(
`id` string,
`global_id` string,
`name` string,
`ssn` string,
`email_address` string,
`address` string)

ROW FORMAT DELIMITED

    FIELDS TERMINATED BY ','

STORED AS INPUTFORMAT

    'org.apache.hadoop.mapred.TextInputFormat'

OUTPUTFORMAT

    'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

LOCATION

    's3a://${SECURE_BUCKET_NAME}/sample_data/customer_data';

  3. Exit from beeline.

  4. Switch to ${TEST_USER} and kinit and try sample policy.

    beeline  -u "jdbc:hive2://`hostname -f`:10000/default;principal=hive/`hostname -f`@${REALM}"
#Check ranger audit for hive service
Select * from customer.customer_data_s3 limit 10;

Data_Admin Access#

To create a view using the Hive Plug-In, you need the DATA_ADMIN permission in Ranger.

The source table on which you are going to create a view requires the DATA_ADMIN Ranger policy.

Use Case

Let’s take a use case where we have 'employee_db' database and two tables inside it with below data:

#Requires create privilege on the database enabled by default;
create database if not exists employee_db;

  1. Create two tables.

    #Requires create privilege on the table level;

create table if not exists employee_db.employee_data(id int,userid string,country string);
create table if not exists employee_db.country_region(country string,region string);

  2. Insert test data.

    #Requires update privilege on the table level;

insert into employee_db.country_region values ('US','NA'), ('CA','NA'), ('UK','UK'), ('DE','EU'), ('FR','EU'); 

insert into employee_db.employee_data values (1,'james','US'),(2,'john','US'), (3,'mark','UK'), (4,'sally-sales','UK'),(5,'sally','DE'), (6,'emily','DE');


    select * from employee_db.country_region; 
#Requires select privilege on the column level;

    select * from employee_db.employee_data; 
#Requires select privilege on the column level;

  3. Create a view on top of above two tables created; we will get ERROR as below:

    create view employee_db.employee_region(userid, region) as select e.userid, cr.region from employee_db.employee_data e, employee_db.country_region cr where e.country = cr.country;

    Error: Error while compiling statement: 
FAILED: HiveAccessControlException 
Permission denied: user [emily] does not have [DATA_ADMIN] privilege on [employee_db/employee_data] (state=42000,code=40000)

  4. Create a view policy for table on employee_db.employee_region as shown in the above image.

    Now create a policy as shown in the above image and try to execute the same query, it will pass through.

    Note

    Granting Data_admin privileges on the resource implicitly grants Select privilege on the same resource as well.

Alter View#

create view if not exists employee_db.employee_region(userid, region) as select e.userid, cr.region from employee_db.employee_data e, employee_db.country_region cr where e.country = cr.country;

#Requires Create permission on the view;
ALTER VIEW employee_db.employee_region AS  select e.userid, cr.region from employee_db.employee_data e, employee_db.country_region cr where e.country = cr.country;

Rename View#

#Requires Alter permission on the view;
ALTER VIEW  employee_db.employee_region RENAME to employee_db.employee_region_renamed;

Drop View#

#Requires Drop permission on the view;
DROP VIEW employee_db.employee_region_renamed;

Row Level Filter#

select * from employee_db.employee_region;

Column Masking#

select * from employee_db.employee_region;

PrestoDB#

  1. SSH to EMR on master node.

  2. Start Presto shell (presto, spark-thrift, hive all three using same metastore)

    presto-cli --catalog hive

OR

/usr/lib/presto/bin/presto-cli-0.210-executable --server localhost:8889 --catalog hive --schema default

  3. Attempt the below use case as a test.

    CREATE SCHEMA customer WITH (location = 's3a://${SECURE_BUCKETNAME}/presto_data/customer/');
use customer;
CREATE TABLE  cust_data (

    EMP_SSN varchar,

CC varchar,

    FIRST_NAME varchar ,

    LAST_NAME varchar ,

ADDRESS varchar ,

ZIPCODE varchar ,

EMAIL varchar ,

    US_PHONE_FORMATTED varchar

    );
    insert into  cust_data values ('12345', '6789', 'Will','Smith', 'US', '400098','ws@gmail.com', '010-564-333');
    select * from  cust_data;

  4. Full Table Access.

    #Add policy in ranger to access everything in the table
SELECT * FROM cust_data;

  5. Restricted Column Access.

    #Column level permission in table. If User doesn't have permission to “first_name” column
#Will be denied in audit
select first_name from cust_data;
#Will be allowed in audit
select last_name, address, zipcode, email from cust_data;

  6. Enable additional operations on Hive catalog by updating hive.properties. By default, PrestoDB blocks the operations. For more information, see Hive Security Configuration.

    1. Edit hive.properties.

      sudo vi /etc/presto/conf/catalog/hive.properties

    2. Add the following properties as required.

      connector.name=hive-hadoop2
hive.allow-drop-table=true
hive.allow-rename-table=true
hive.allow-add-column=true
hive.allow-rename-column=true
hive.allow-drop-column=true
hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml
hive.s3-file-system-type=EMRFS
hive.hdfs.impersonation.enabled=false

      Note

      The hive.properties file needs to be updated on all the EMR nodes.

    3. Restart Presto.

      sudo systemctl restart presto-server

    Alternatively, you can include the properties while creating the EMR itself in the CloudFormation Template. Below is the sample JSON:

    {
"Classification":"presto-connector-hive",
"ConfigurationProperties":{
    "hive.metastore":"glue",
    "hive.allow-drop-table":"true",
    "hive.allow-add-column":"true",
    "hive.allow-rename-column":"true",
    "connector.name":"hive-hadoop2",
    "hive.config.resources":"/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml",
    "hive.s3-file-system-type":"EMRFS",
    "hive.hdfs.impersonation.enabled":"false",
    "hive.allow-drop-column":"true",
    "hive.allow-rename-table":"true"
}
}

Presto SQL - Use Case#

  1. Start PrestoSQL shell.

    presto-cli --catalog hive

  2. Create the schema using admin/superuser.

    CREATE SCHEMA customer WITH (location = 's3a://${SECURE_BUCKETNAME}/presto_data/schema/customer’);
use customer;

  3. Create the table using admin/superuser

    use customer;

CREATE TABLE customer_data(
id varchar,
name varchar,
ssn varchar,
email_address varchar,
address varchar)
WITH (
    format = 'textfile',
    external_location = 's3a://${SECURE_BUCKETNAME}/presto_data/table/customer_data'
    );

  4. Exit from Presto-CLI and switch to {TEST_USER} and kinit and try sample policy.

    presto-cli --catalog hive
use customer;
select * from customer_data limit 10;

Data_Admin Access#

To create a view using the Presto SQL Plug-In, you need the DATA_ADMIN permission in Ranger,

The source table on which you are going to create a view requires the DATA_ADMIN Ranger policy.

Use Case

Let’s take a use case where you have 'employee_db' database and two tables inside it with below data:

#Requires create privilege on the database enabled by default;
create schema if not exists employee_db;

  1. Create two tables.

    #Requires create privilege on the table level;

CREATE TABLE IF NOT EXISTS employee_db.employee_data(id int, userid string, country string);

CREATE TABLE IF NOT EXISTS employee_db.country_region(country string, region string);

  2. Insert test data.

    #Requires update privilege on the table level;

insert into employee_db.country_region values ('US','NA'), ('CA','NA'), ('UK','UK'), ('DE','EU'), ('FR','EU');

insert into employee_db.employee_data values (1,'james','US'),(2,'john','US'), (3,'mark','UK'), (4,'sally-sales','UK'),(5,'sally','DE'), (6,'emily','DE');

  3. Create a view on top of above two tables created; you will get ERROR as below:

    Query 20210223_051227_00005_nyxtw failed: Access Denied: Cannot create view tbl_view_5

    You need to ‘Create View’ permission.

  4. After granting ‘Create View’ permission, the query will result in the below error message:

    Query 20210223_050930_00004_nyxtw failed: Access Denied: User [emily] does not have [DATA_ADMIN] privilege on [hive/employee_db/employee_data]

    You need to grant ‘Data_Admin’ permission for both the tables as mentioned in below image and execute the create view query again, it will pass through.

    Note

    Granting Data_admin privileges on the resource implicitly grants Select privilege on the same resource as well.

Alter View#

Create View

presto:customer> create view tbl_view_1 as SELECT * FROM tbl_1;
CREATE VIEW
presto:customer> select * from tbl_view_1;
c0 |   c1   |    c2     |          c3           |           c4
----+--------+-----------+-----------------------+------------------------
2  | James  | 892821225 | james@walt.com        | 4578 Extension xxx
1  | Dennis | 619821225 | thomasashley@walt.com | 9478 Anthony Extension
3  | Sally  | 092341225 | sally@walt.com        | 5678 Extension xyxx
(3 rows)


Query 20210303_142252_00006_g76nu, FINISHED, 1 node
Splits: 19 total, 19 done (100.00%)
1.86 [3 rows, 169B] [1 rows/s, 91B/s]

Alter View

presto:customer> CREATE OR REPLACE VIEW tbl_view_1 as select * from tbl_3;
CREATE VIEW
presto:customer> select * from tbl_view_1;
slno | name | mobile |  email  | address
------+------+--------+---------+---------
1    | emily |   1234 | s@s.com | in
(1 row)

Query 20210303_142341_00009_g76nu, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
0.91 [1 rows, 0B] [1 rows/s, 0B/s]

Rename View#

presto:customer> alter view tbl_view_1 rename to tbl_view_2;
RENAME VIEW
presto:customer>

Drop View#

presto:customer> drop view tbl_view_1;
DROP VIEW
presto:customer>

Row Level Filter#

presto:employee_db> select * from tbl_1;

id |   userid    | country
----+-------------+---------
1 | james       | US
2 | john        | US
3 | mark        | UK
4 | sally-sales | UK
5 | sally       | DE
6 | emily       | DE
(6 rows)

Query 20210309_060602_00022_5amn7, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
4.11 [6 rows, 0B] [1 rows/s, 0B/s]


presto:employee_db>
presto:employee_db> select * from tbl_1;
id | userid | country
----+--------+---------
1 | james  | US
2 | john   | US
(2 rows)

Query 20210309_061202_00024_5amn7, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
0.45 [6 rows, 0B] [13 rows/s, 0B/s]

Column Masking#

presto:employee_db> select * from tbl_1;
id |   userid    | country
----+-------------+---------
1 | james       | US
2 | john        | US
3 | mark        | UK
4 | sally-sales | UK
5 | sally       | DE
6 | emily       | DE
(6 rows)

Query 20210309_062000_00027_5amn7, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
0.30 [6 rows, 0B] [20 rows/s, 0B/s]


presto:employee_db>
presto:employee_db> select * from tbl_1;
id |   userid    | country
----+-------------+---------
1 | james       | NULL
2 | john        | NULL
3 | mark        | NULL
4 | sally-sales | NULL
5 | sally       | NULL
6 | emily       | NULL
(6 rows)

Query 20210309_061856_00026_5amn7, FINISHED, 1 node
Splits: 17 total, 17 done (100.00%)
0.32 [6 rows, 0B] [18 rows/s, 0B/s]

Access Views in AWS Athena#

Use the following steps to provide access for views created in AWS Athena. As a result, you will be able to query the views.

  1. Copy the Hive catalog properties (or create a symlink) as awsdatacatalog.properties in /etc/presto/conf/catalog folder.

    ln -s /etc/presto/conf/catalog/hive.properties /etc/presto/conf/catalog/awsdatacatalog.properties

  2. Restart the Presto server. 

    sudo systemctl restart presto-server

  3. In Access Management > Resource Policies, update the privacera_hive default policy.

    1. Edit all - database, table policy.

    2. In Select User, add 'Presto' from the dropdown as the default view owner, and save.

  4. (Optional) To change the default view owner from 'Presto' to any other owner such as 'Hadoop', do the following:

    1. In the access-control.properties file, add the owner to the ranger.policy.authorization.viewowner.default variable.

      vi /usr/lib/presto/etc/access-control.properties
ranger.policy.authorization.viewowner.default=<view-owner>

    2. Restart the Presto server.

      sudo systemctl restart presto-server
      Accordingly, update the owner in the all - database, table policy of the privacera_hive service.

Hue#

  1. SSH to master node.

  2. Edit /etc/hue/conf/hue.ini and modify the JDBC Presto parameters:

    In hue.ini:

    options = '{"url": "jdbc:presto://ip-10-230-0-234.ca-central-1.compute.internal:8889/hive/default", "driver": "com.facebook.presto.jdbc.PrestoDriver", "user":"","password":""}'

    Change the user to “” so that it takes the credentials of a logged in user of Hue.

  3. Restart Hue.

    sudo stop hue
sudo start hue

  4. Login to Hue console through /<master-node>:8888

  5. Set the Admin username and password.

  6. Add more Hue users through the Admin console.

  7. Logout and login using the newly created user in Hue console.

  8. Access the tables through Hive/Presto.

  9. Check in Privacera Ranger, if the username is the same as the user logged in to Hue.

Livy#

  1. Setup Livy and Zeppelin.

    SSH with port forwarding or open 8890 port to access Zeppelin from the web browser.

    ssh -i ${KEY_FILE}  -L 8890:localhost:8890
hadoop@${EMR_PUBLIC_DNS}

  2. Go to Zeppelin web UI (http://localhost:8890).

  3. Enable the user based login (https://zeppelin.apache.org/docs/0.6.2/security/shiroauthentication.html).

    sudo su
cp /etc/zeppelin/conf/zeppelin-site.xml.template /etc/zeppelin/conf/zeppelin-site.xml
chown zeppelin:zeppelin /etc/zeppelin/conf/zeppelin-site.xml

vi /etc/zeppelin/conf/zeppelin-site.xml

#Change the property, if exists
#This property removed from zeppelin 0.9.0 (https://issues.apache.org/jira/browse/ZEPPELIN-4489)
zeppelin.anonymous.allowed=false

cp /etc/zeppelin/conf/shiro.ini.template /etc/zeppelin/conf/shiro.ini

vi /etc/zeppelin/conf/shiro.ini

#Add required users in [users] as below  --
[users]
hadoop = hadoop123, admin

chown zeppelin:zeppelin /etc/zeppelin/conf/shiro.ini

  4. Check Livy port using the below command.

    vi /etc/livy/conf/livy.conf

livy.server.port=8998

  5. Stop and restart the Zeppelin.

    sudo stop zeppelin

sudo start zeppelin

  6. Go to /<master-node>:8890. Login with the required username/password which you have created in step 3.

  7. Go to Settings > Interpreter > Livy > Edit and perform the following steps:

    • Keep only Scope with per user.

    • Set the properties below.

      • livy.spark.driver.cores=1

      • livy.spark.driver.memory=1g

      • livy.spark.executor.cores=1

      • livy.spark.executor.instances=2

      • livy.spark.executor.memory=1g

      • livy.spark.driver.extraClassPath=/opt/privacera/plugin/privacera-spark-plugin/spark-plugin/*:{copy spark.driver.extraClassPath from /etc/spark/conf/spark-defaults.conf}

  8. Save and restart.

  9. Run the sample Livy Spark code.

    • Go to Zeppelin web UI (http://localhost:8890).

    • Create a new notebook using the below command.

      %livy.spark

val df =spark.read.csv("s3://${SECURE_BUCKET_NAME}/sample_data/customer_data/customer_data_without_header.csv");
df.show()

    • Check audit for the above executed command in Privacera Access Manager using the below steps:

      • On the Privacera Portal home page, expand Access Management and click the Audit from the left menu.

      • The Audit page will be displayed with Ranger Audit details.

Spark Object-Level Access Control (OLAC)#

Submit Spark Applications#

You can submit an application consisting of compiled and packaged Java or Spark JAR. You can deploy the JAR locally (client) or cluster. Use the tabs according to your deployment mode.

  1. SSH to the master node.

  2. Run the following command:

spark-submit \
--master yarn \
--driver-memory 512m \
--executor-memory 512m \
--class <clas-to-run> <your-jar> <arg1> <arg2>

  1. SSH to the master node.

  2. Run the following command:

spark-submit \
--master yarn \
--deploy-mode cluster \
--driver-memory 512m \
--executor-memory 512m \
--driver-class-path "/opt/privacera/plugin/privacera-spark-plugin/spark-plugin/*:<copy spark.driver.extraClassPath from /etc/spark/conf/spark-defaults.conf>" \
--class <clas-to-run> <your-jar> <arg1> <arg2>

Spark Fine-grained Access Control (FGAC)#

View-level Access#

To enable the view-level access control, do the following:

  1. SSH to the master node of EMR cluster.

  2. Edit the spark-defaults.conf file.

    sudo vim /etc/spark/conf/spark-defaults.conf

  3. Add the following property.

    spark.hadoop.privacera.spark.view.levelmaskingrowfilter.extension.enable true

To learn how to use view-level access control in Spark, click here.