1. © Hortonworks Inc. 2013 Secure SQL Standard based Authorization for Apache Hive Thejas Nair @thejasn Page 1

2. © Hortonworks Inc. 2011 Current status Page 2 Architecting the Future of Big Data Unsecure fine grained authorization Supports authorization on views Incomplete model No authorization for access control statements Client side checks, but lacks features to secure the client Secure coarse grained storage Secure, storage based authorization Works on metastore server, provides authorization for hcat users (pig, MR,..) Does not work on views

3. © Hortonworks Inc. 2011 SQL Standard based authorization Page 3 Architecting the Future of Big Data Model based on SQL:2011 Fine grained authorization Hive client side checks – (in HiveServer2) Authorization checks for user submitting query Query run as HiveServer2 user

4. © Hortonworks Inc. 2011 Requirements Page 4 Architecting the Future of Big Data Data accessible to HiveServer2 (HS2) user Disable non sql commands (dfs, shell, transform), untrusted udfs.

5. © Hortonworks Inc. 2011 What about pig, MR users Page 5 Architecting the Future of Big Data Pig/MR do not use hive client, uses hcatalog ( => metastore api) Storage based authorization (SBA) can be enabled on metastore server New authorization model requires HS2 user to have access on storage => SBA pass Pig/MR users are often the ‘data curators’ and coarse grain authorization is what they usually need.

6. © Hortonworks Inc. 2011 SQL authorization model Page 6 Architecting the Future of Big Data Users Roles Privileges Objects

7. © Hortonworks Inc. 2011 Users and Roles Page 7 Architecting the Future of Big Data Users can belong to one or more roles Special roles (sql extn) PUBLIC – all users belong to this role SUPERUSER – priv to create/drop role, access management. Pluggable sources for user to role mapping. Eg hdfs groups. A namespace portion will help identify the source.

8. © Hortonworks Inc. 2011 Privileges Page 8 Architecting the Future of Big Data ● SELECT - read access to object ● INSERT - add data to object (table) ● UPDATE - update queries on object (table) ● DELETE - delete data in object (table) ● ALL PRIVILEGES all privileges

9. © Hortonworks Inc. 2011 Objects Page 9 Architecting the Future of Big Data Access control statements on tables and views will be supported first.

10. © Hortonworks Inc. 2011 Details in functional spec in HIVE-5837 Architecting the Future of Big Data Page 10