securing spark applications by kostas sakellis and marcelo vanzin

Securing Spark Applications

Kostas SakellisMarcelo Vanzin

What is Security?• Security has many facets• This talk will focus on three areas:

– Encryption– Authentication– Authorization

Why do I need security?• Multi-tenancy• Application isolation• User identification• Access control enforcement• Compliance with government regulations

Before we go further...• Set up Kerberos• Use HDFS (or another secure filesystem)• Use YARN!• Configure them for security (enable auth, encryption).

Kerberos, HDFS, and YARN provide the security backbone for Spark.

Encryption• In a secure cluster, data should not be visible in the clear• Very important to financial / government institutions

What a Spark app looks like

RM NM NM

AM / Driver Executor

Executor

SparkSubmit

Control RPC

File Download

Shuffle / Cached Blocks

Shuffle Service

Shuffle Service

Shuffle Blocks

UI

Shuffle Blocks / Metadata

Data Flow in SparkEvery connection in the previous slide can transmit sensitive data!

• Input data transmitted via broadcast variables• Computed data during shuffles• Data in serialized tasks, files uploaded with the job

How to prevent other users from seeing this data?

Encryption in Spark• Almost all channels support encryption.

– Exception 1: UI (SPARK-2750) – Exception 2: local shuffle / cache files (SPARK-5682)

For local files, set up YARN local dirs to point at local encrypted disk(s) if desired. (SPARK-5682)

Encryption: Current StateDifferent channel, different method.

• Shuffle protocol uses SASL• RPC / File download use SSL

SSL can be hard to set up.• Need certificates readable on every node• Sharing certificates not as secure• Hard to have per-user certificate

Encryption: The GoalSASL everywhere for wire encryption (except UI).

• Minimum configuration (one boolean config)• Uses built-in JVM libraries• SPARK-6017

For UI:• Support for SSL• Or audit UI to remove sensitive info (e.g. information on

environment page).

AuthenticationWho is reading my data?

• Spark uses Kerberos – the necessary evil

• Ubiquitous among other services– YARN, HDFS, Hive, HBase etc.

Who’s reading my data?Kerberos provides secure authentication.

KDC

Application

Hi I’m Bob.

Hello Bob. Here’s your TGT.

Here’s my TGT. I want to talk to HDFS.

Here’s your HDFS ticket.

User

Now with a distributed app...

KDC

Executor

Executor

Executor

Executor

Executor

Executor

Executor

Executor

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Hi I’m Bob.

Something is wrong.

Kerberos in Hadoop / SparkKDCs do not allow multiple concurrent logins at the scale distributed applications need. Hadoop services use delegation tokens instead.

Driver

NameNode

Executor

DataNode

Delegation TokensLike Kerberos tickets, they have a TTL.

• OK for most batch applications.• Not OK for long running applications

– Streaming– Spark SQL Thrift Server

Delegation TokensSince 1.4, Spark can manage delegation tokens!

• Restricted to HDFS currently• Requires user’s keytab to be deployed with application• Still some remaining issues in client deploy mode

AuthorizationHow can I share my data?

Simplest form of authorization: file permissions.• Use Unix-style permissions or ACLs to let others read

from and / or write to files and directories• Simple, but high maintenance. Set permissions /

ownership for new files, mess with umask, etc.

More than just FS semantics...Authorization becomes more complicated as abstractions are created.

• Tables, columns, partitions instead of files and directories

• Semantic gap• Need a trusted entity to enforce access control

Trusted Service: HiveHive has a trusted service (“HiveServer2”) for enforcing authorization.

• HS2 parses queries and makes sure users have access to the data they’re requesting / modifying.

HS2 runs as a trusted user with access to the whole warehouse. Users don’t run code directly in HS2*, so there’s no danger of code escaping access checks.

Untrusted Apps: SparkEach Spark app runs as the requesting user, and needs access to the underlying files.

• Spark itself cannot enforce access control, since it’s running as the user and is thus untrusted.

• Restricted to file system permission semantics.

How to bridge the two worlds?

Apache Sentry• Role-based access control to resources• Integrates with Hive / HS2 to control access to data• Fine-grained (up to column level) controls

Hive data and HDFS data have different semantics. How to bridge that?

The Sentry HDFS PluginSynchronize HDFS file permissions with higher-level abstractions.

• Permission to read table = permission to read table’s files

• Permission to create table = permission to write to database’s directory

Uses HDFS ACLs for fine-grained user permissions.

Still restricted to FS view of the world!• Files, directories, etc…• Cannot provide column-level and row-level access

control.• Whole table or nothing.

Still, it goes a long way in allowing Spark applications to work well with Hive data in a shared, secure environment.

But...

Future: RecordServiceA distributed, scalable, data access service for unified authorization in Hadoop.

RecordService

RecordService• Drop in replacement for InputFormats• SparkSQL: Integration with Data Sources API

– Predicate pushdown, projection

RecordService• Assume we had a table tpch.nation

column_name column_type

n_nationkey smallint

n_name string

n_regionkey smallint

n_comment string

import com.cloudera.recordservice.spark._

val context = new org.apache.spark.sql.SQLContext(sc)

val df = context.load("tpch.nation",

"com.cloudera.recordservice.spark")

val results = df.groupBy("n_regionkey")

.count()

.collect()

RecordService

RecordService• Users can enforce Sentry permissions using views• Allows column and row level security

> CREATE ROLE restrictedrole;

> GRANT ROLE restrictedrole to GROUP restrictedgroup;

> USE tpch;

> CREATE VIEW nation_names AS

SELECT n_nationkey, n_name

FROM tpch.nation;

> GRANT SELECT ON TABLE tpch.nation_names TO ROLE restrictedrole;

...

val df = context.load("tpch.nation",

"com.cloudera.recordservice.spark")

val results = df.collect()

>> TRecordServiceException(code:INVALID_REQUEST, message:Could not plan

request., detail:AuthorizationException: User 'kostas' does not have

privileges to execute 'SELECT' on: tpch.nation)

RecordService

...

val df = context.load("tpch.nation_names",

"com.cloudera.recordservice.spark")

val results = df.collect()

RecordService

RecordService• Documentation: http://cloudera.github.io/RecordServiceClient/

• Beta Download: http://www.cloudera.com/content/cloudera/en/downloads/betas/recordservice/0-1-0.html

Takeaways• Spark can be made secure today!• Benefits from a lot of existing Hadoop platform work• Still work to be done

– Ease of use– Better integration with Sentry / RecordService

References• Encryption: SPARK-6017, SPARK-5682• Delegation tokens: SPARK-5342• Sentry: http://sentry.apache.org/

– HDFS synchronization: SENTRY-432• RecordService:

http://cloudera.github.io/RecordServiceClient/

Thanks!

Questions?