Apache Ranger for authorization in Hadoop - Step-by-Step Execution

Concept Flow - Apache Ranger for authorization

User Request Access

↓

Apache Ranger Receives Request

↓

Check Policies in Ranger Admin

↓

Match User and Resource

↓

Allow or Deny Access

↓

Return Decision to Hadoop Service

↓

Grant or Block User Action

Apache Ranger intercepts user requests, checks policies, and decides if access is allowed or denied.

Execution Sample

Hadoop

# Pseudo-code for Ranger authorization check
user = 'alice'
resource = '/data/sales'
policy = ranger.get_policy(user, resource)
if policy.allows('read'):
    print('Access granted')
else:
    print('Access denied')

This code simulates how Ranger checks if user 'alice' can read the sales data.

Execution Table

Step	Action	Input	Policy Check Result	Decision
1	Receive user request	User=alice, Resource=/data/sales	N/A	Pending
2	Fetch policy for user and resource	User=alice, Resource=/data/sales	Policy found: read allowed	Pending
3	Check if 'read' permission allowed	Permission=read	Allowed	Access granted
4	Return decision to Hadoop service	Decision=Access granted	N/A	User allowed to read data

💡 Access granted because policy allows 'read' permission for user 'alice' on resource '/data/sales'.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
user	None	alice	alice	alice	alice
resource	None	/data/sales	/data/sales	/data/sales	/data/sales
policy	None	None	read allowed	read allowed	read allowed
decision	None	Pending	Pending	Access granted	Access granted

Key Moments - 2 Insights

Why does Ranger check policies before allowing access?

Ranger must verify user permissions against policies to ensure only authorized actions are allowed, as shown in execution_table step 2 and 3.

What happens if no policy matches the user and resource?

If no policy matches, Ranger denies access by default to keep data safe, which would be reflected in a 'Policy not found' and 'Access denied' decision.

Visual Quiz - 3 Questions

Look at the execution table, what is the policy check result at step 2?

A Policy not found

B Policy found: read allowed

C Access denied

D Permission denied

💡 Hint

Check the 'Policy Check Result' column in row for step 2.

At which step does Ranger decide to grant access?

A Step 1

B Step 4

C Step 3

D Step 2

💡 Hint

Look at the 'Decision' column to find when 'Access granted' appears.

If the policy did not allow 'read', what would be the decision at step 3?

A Access denied

B Access pending

C Access granted

D Policy found

💡 Hint

Refer to the logic in execution_table step 3 where permission check determines decision.

Concept Snapshot

Apache Ranger controls access by checking user requests against policies.
It intercepts requests, matches user and resource to policies,
and allows or denies actions based on permissions.
Default is deny if no matching policy.
Used to secure Hadoop data access centrally.

Full Transcript

Apache Ranger is a tool that helps control who can do what with data in Hadoop. When a user tries to access data, Ranger checks its policies to see if the user has permission. If the policy allows the action, Ranger grants access; otherwise, it denies it. This process protects data by making sure only authorized users can read or modify it. The example shows a user named alice trying to read sales data. Ranger finds a policy that allows this, so access is granted. If no policy matched, access would be denied by default.

Practice

1. What is the main purpose of Apache Ranger in a Hadoop ecosystem?

easy

A. To speed up data processing tasks

B. To store large amounts of data efficiently

C. To control who can access data and what actions they can perform

D. To backup data automatically

Solution

1. Step 1: Understand Apache Ranger's role

   Apache Ranger is designed to manage security by controlling access to data.

2. Step 2: Compare options with Ranger's function

   Only To control who can access data and what actions they can perform describes access control, which matches Ranger's purpose.

3. Final Answer:

   To control who can access data and what actions they can perform -> Option C

4. Quick Check:

   Authorization = Access control [OK]

Hint: Remember: Ranger = data access control tool [OK]

Common Mistakes:

• Confusing Ranger with data storage tools
• Thinking Ranger speeds up processing
• Assuming Ranger handles backups

2. Which of the following is the correct way to define a policy in Apache Ranger?

easy

A. Specify users/groups, resources, and allowed actions

B. Write SQL queries to filter data

C. Create MapReduce jobs for data processing

D. Configure Hadoop cluster nodes

Solution

1. Step 1: Identify what a Ranger policy includes

   A policy defines who (users/groups) can do what (actions) on which data (resources).

2. Step 2: Match options to policy components

   Only Specify users/groups, resources, and allowed actions correctly lists these elements.

3. Final Answer:

   Specify users/groups, resources, and allowed actions -> Option A

4. Quick Check:

   Policy = Users + Resources + Actions [OK]

Hint: Policies = users/groups + resources + permissions [OK]

Common Mistakes:

• Confusing policies with SQL queries
• Mixing policy with data processing tasks
• Thinking policies configure cluster hardware

3. Given the following Ranger policy snippet:
{"users": ["alice"], "resources": {"database": "sales"}, "permissions": ["select", "update"]}
What actions can user 'alice' perform on the 'sales' database?

medium

A. Only select data

B. Select and update data

C. Delete data

D. No actions allowed

Solution

1. Step 1: Read the policy details

   User 'alice' is allowed 'select' and 'update' permissions on the 'sales' database.

2. Step 2: Determine allowed actions

   Since both 'select' and 'update' are listed, alice can perform both actions.

3. Final Answer:

   Select and update data -> Option B

4. Quick Check:

   Permissions = select + update [OK]

Hint: Check permissions list carefully for allowed actions [OK]

Common Mistakes:

• Assuming delete permission without it listed
• Ignoring update permission
• Thinking no actions allowed if unsure

4. You created a Ranger policy but users report they cannot access the resource. Which of the following is the most likely error?

medium

A. The policy does not include the correct user or group

B. The Hadoop cluster is down

C. The data is corrupted

D. The policy has too many permissions

Solution

1. Step 1: Analyze why access is denied

   If users cannot access, often the policy lacks their user or group in its definition.

2. Step 2: Evaluate other options

   Cluster down or data corruption would cause broader issues, not just access denial. Too many permissions does not block access.

3. Final Answer:

   The policy does not include the correct user or group -> Option A

4. Quick Check:

   User/group missing = access denied [OK]

Hint: Check user/group inclusion in policy first [OK]

Common Mistakes:

• Blaming cluster or data issues prematurely
• Assuming too many permissions cause denial
• Not verifying policy user/group settings

5. You want to create a Ranger policy that allows group 'data_scientists' to read any table in the 'analytics' database but not modify data. Which permission set should you assign?

hard

A. no permissions

B. select and update permissions

C. all permissions including delete

D. select permission only

Solution

1. Step 1: Understand the requirement

   The group should only read data, which means 'select' permission is needed without any modify permissions.

2. Step 2: Match permissions to requirement

   Only 'select' permission allows reading without modification.

3. Final Answer:

   select permission only -> Option D

4. Quick Check:

   Read only = select permission [OK]

Hint: Read-only access = select permission only [OK]

Common Mistakes:

• Giving update permission when only read is needed
• Assigning all permissions unnecessarily
• Leaving permissions empty causing no access