[jira] [Created] (HADOOP-9334) Update netty version
nkeywal created HADOOP-9334: --- Summary: Update netty version Key: HADOOP-9334 URL: https://issues.apache.org/jira/browse/HADOOP-9334 Project: Hadoop Common Issue Type: Improvement Components: build Affects Versions: 3.0.0, 2.0.4-beta Reporter: nkeywal Priority: Minor There are newer version available. HBase for example depends on the 3.5.9. Latest 3.5 is 3.5.11, there is the 3.6.3 as well. While there is no point in trying to have exactly the same version, things are more comfortable if the gap in version is minimal, as the dependency is client side as well (i.e. HBase has to choose a version anyway). Attached a patch for the branch 2. I haven't executed the unit tests, but HBase works ok with Hadoop on Netty 3.5.9. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
ANNOUNCEMENT: Project Rhino: Enhanced Data Protection for the Apache Hadoop Ecosystem
Project Rhino As the Apache Hadoop ecosystem extends into new markets and sees new use cases with security and compliance challenges, the benefits of processing sensitive and legally protected data with Hadoop must be coupled with protection for private information that limits performance impact. Project Rhinohttps://github.com/intel-hadoop/project-rhino/ is our open source effort to enhance the existing data protection capabilities of the Hadoop ecosystem to address these challenges, and contribute the code back to Apache. The core of the Apache Hadoop ecosystem as it is commonly understood is: - Core: A set of shared libraries - HDFS: The Hadoop filesystem - MapReduce: Parallel computation framework - ZooKeeper: Configuration management and coordination - HBase: Column-oriented database on HDFS - Hive: Data warehouse on HDFS with SQL-like access - Pig: Higher-level programming language for Hadoop computations - Oozie: Orchestration and workflow management - Mahout: A library of machine learning and data mining algorithms - Flume: Collection and import of log and event data - Sqoop: Imports data from relational databases These components are all separate projects and therefore cross cutting concerns like authN, authZ, a consistent security policy framework, consistent authorization model and audit coverage are loosely coordinated. Some security features expected by our customers, such as encryption, are simply missing. Our aim is to take a full stack view and work with the individual projects toward consistent concepts and capabilities, filling gaps as we go. Our initial goals are: 1) Framework support for encryption and key management There is currently no framework support for encryption or key management. We will add this support into Hadoop Core and integrate it across the ecosystem. 2) A common authorization framework for the Hadoop ecosystem Each component currently has its own authorization engine. We will abstract the common functions into a reusable authorization framework with a consistent interface. Where appropriate we will either modify an existing engine to work within this framework, or we will plug in a common default engine. Therefore we also must normalize how security policy is expressed and applied by each component. Core, HDFS, ZooKeeper, and HBase currently support simple access control lists (ACLs) composed of users and groups. We see this as a good starting point. Where necessary we will modify components so they each offer equivalent functionality, and build support into others. 3) Token based authentication and single sign on Core, HDFS, ZooKeeper, and HBase currently support Kerberos authentication at the RPC layer, via SASL. However this does not provide valuable attributes such as group membership, classification level, organizational identity, or support for user defined attributes. Hadoop components must interrogate external resources for discovering these attributes and at scale this is problematic. There is also no consistent delegation model. HDFS has a simple delegation capability, and only Oozie can take limited advantage of it. We will implement a common token based authentication framework to decouple internal user and service authentication from external mechanisms used to support it (like Kerberos). 4) Extend HBase support for ACLs to the cell level Currently HBase supports setting access controls at the table or column family level. However, many use cases would benefit from the additional capability to do this on a per cell basis. In fact for many users dealing with sensitive information the ability to do this is crucial. 5) Improve audit logging Audit messages from various Hadoop components do not use a unified or even consistently formatted format. This makes analysis of logs for verifying compliance or taking corrective action difficult. We will build a common audit logging facility as part of the common authorization framework work. We will also build a set of common audit log processing tools for transforming them to different industry standard formats, for supporting compliance verification, and for triggering responses to policy violations. Current JIRAs: As part of this ongoing effort we are contributing our work to-date against the JIRAs listed below. As you may appreciate, the goals for Project Rhino covers a number of different Apache projects, the scope of work is significant and likely to only increase as we get additional community input. We also appreciate that there may be others in the Apache community that may be working on some of this or are interested in contributing to it. If so, we look forward to partnering with you in Apache to accelerate this effort so the Apache community can see the benefits from our collective efforts sooner. You can also find a more detailed version of this announcement at Project Rhinohttps://github.com/intel-hadoop/project-rhino/. Please feel free to
Re: ANNOUNCEMENT: Project Rhino: Enhanced Data Protection for the Apache Hadoop Ecosystem
[yanking away most of the cross-posts...] An interesting cross component project Avik. Any plans to incubate it in Apache? Cos On Mon, Feb 25, 2013 at 11:46PM, Dey, Avik wrote: Project Rhino As the Apache Hadoop ecosystem extends into new markets and sees new use cases with security and compliance challenges, the benefits of processing sensitive and legally protected data with Hadoop must be coupled with protection for private information that limits performance impact. Project Rhinohttps://github.com/intel-hadoop/project-rhino/ is our open source effort to enhance the existing data protection capabilities of the Hadoop ecosystem to address these challenges, and contribute the code back to Apache. The core of the Apache Hadoop ecosystem as it is commonly understood is: - Core: A set of shared libraries - HDFS: The Hadoop filesystem - MapReduce: Parallel computation framework - ZooKeeper: Configuration management and coordination - HBase: Column-oriented database on HDFS - Hive: Data warehouse on HDFS with SQL-like access - Pig: Higher-level programming language for Hadoop computations - Oozie: Orchestration and workflow management - Mahout: A library of machine learning and data mining algorithms - Flume: Collection and import of log and event data - Sqoop: Imports data from relational databases These components are all separate projects and therefore cross cutting concerns like authN, authZ, a consistent security policy framework, consistent authorization model and audit coverage are loosely coordinated. Some security features expected by our customers, such as encryption, are simply missing. Our aim is to take a full stack view and work with the individual projects toward consistent concepts and capabilities, filling gaps as we go. Our initial goals are: 1) Framework support for encryption and key management There is currently no framework support for encryption or key management. We will add this support into Hadoop Core and integrate it across the ecosystem. 2) A common authorization framework for the Hadoop ecosystem Each component currently has its own authorization engine. We will abstract the common functions into a reusable authorization framework with a consistent interface. Where appropriate we will either modify an existing engine to work within this framework, or we will plug in a common default engine. Therefore we also must normalize how security policy is expressed and applied by each component. Core, HDFS, ZooKeeper, and HBase currently support simple access control lists (ACLs) composed of users and groups. We see this as a good starting point. Where necessary we will modify components so they each offer equivalent functionality, and build support into others. 3) Token based authentication and single sign on Core, HDFS, ZooKeeper, and HBase currently support Kerberos authentication at the RPC layer, via SASL. However this does not provide valuable attributes such as group membership, classification level, organizational identity, or support for user defined attributes. Hadoop components must interrogate external resources for discovering these attributes and at scale this is problematic. There is also no consistent delegation model. HDFS has a simple delegation capability, and only Oozie can take limited advantage of it. We will implement a common token based authentication framework to decouple internal user and service authentication from external mechanisms used to support it (like Kerberos). 4) Extend HBase support for ACLs to the cell level Currently HBase supports setting access controls at the table or column family level. However, many use cases would benefit from the additional capability to do this on a per cell basis. In fact for many users dealing with sensitive information the ability to do this is crucial. 5) Improve audit logging Audit messages from various Hadoop components do not use a unified or even consistently formatted format. This makes analysis of logs for verifying compliance or taking corrective action difficult. We will build a common audit logging facility as part of the common authorization framework work. We will also build a set of common audit log processing tools for transforming them to different industry standard formats, for supporting compliance verification, and for triggering responses to policy violations. Current JIRAs: As part of this ongoing effort we are contributing our work to-date against the JIRAs listed below. As you may appreciate, the goals for Project Rhino covers a number of different Apache projects, the scope of work is significant and likely to only increase as we get additional community input. We also appreciate that there may be others in the Apache community that may be working on some of this or are interested in contributing to it. If so, we look forward to partnering with you in
Re: ANNOUNCEMENT: Project Rhino: Enhanced Data Protection for the Apache Hadoop Ecosystem
[thanks appreciate your doing that, the announcement itself was cross-posted as outreach] Thanks Cos. As I see the work currently, I believe most, if not all of these, will be work against JIRAs in individual projects similar to the JIRAs posted here https://github.com/intel-hadoop/project-rhino. If we get to a point where some of the future work needs a home outside of the individual projects, happy to incubate that work in Apache. ~avik On Mon, Feb 25, 2013 at 4:18 PM, Konstantin Boudnik c...@apache.org wrote: [yanking away most of the cross-posts...] An interesting cross component project Avik. Any plans to incubate it in Apache? Cos On Mon, Feb 25, 2013 at 11:46PM, Dey, Avik wrote: Project Rhino As the Apache Hadoop ecosystem extends into new markets and sees new use cases with security and compliance challenges, the benefits of processing sensitive and legally protected data with Hadoop must be coupled with protection for private information that limits performance impact. Project Rhinohttps://github.com/intel-hadoop/project-rhino/ is our open source effort to enhance the existing data protection capabilities of the Hadoop ecosystem to address these challenges, and contribute the code back to Apache. The core of the Apache Hadoop ecosystem as it is commonly understood is: - Core: A set of shared libraries - HDFS: The Hadoop filesystem - MapReduce: Parallel computation framework - ZooKeeper: Configuration management and coordination - HBase: Column-oriented database on HDFS - Hive: Data warehouse on HDFS with SQL-like access - Pig: Higher-level programming language for Hadoop computations - Oozie: Orchestration and workflow management - Mahout: A library of machine learning and data mining algorithms - Flume: Collection and import of log and event data - Sqoop: Imports data from relational databases These components are all separate projects and therefore cross cutting concerns like authN, authZ, a consistent security policy framework, consistent authorization model and audit coverage are loosely coordinated. Some security features expected by our customers, such as encryption, are simply missing. Our aim is to take a full stack view and work with the individual projects toward consistent concepts and capabilities, filling gaps as we go. Our initial goals are: 1) Framework support for encryption and key management There is currently no framework support for encryption or key management. We will add this support into Hadoop Core and integrate it across the ecosystem. 2) A common authorization framework for the Hadoop ecosystem Each component currently has its own authorization engine. We will abstract the common functions into a reusable authorization framework with a consistent interface. Where appropriate we will either modify an existing engine to work within this framework, or we will plug in a common default engine. Therefore we also must normalize how security policy is expressed and applied by each component. Core, HDFS, ZooKeeper, and HBase currently support simple access control lists (ACLs) composed of users and groups. We see this as a good starting point. Where necessary we will modify components so they each offer equivalent functionality, and build support into others. 3) Token based authentication and single sign on Core, HDFS, ZooKeeper, and HBase currently support Kerberos authentication at the RPC layer, via SASL. However this does not provide valuable attributes such as group membership, classification level, organizational identity, or support for user defined attributes. Hadoop components must interrogate external resources for discovering these attributes and at scale this is problematic. There is also no consistent delegation model. HDFS has a simple delegation capability, and only Oozie can take limited advantage of it. We will implement a common token based authentication framework to decouple internal user and service authentication from external mechanisms used to support it (like Kerberos). 4) Extend HBase support for ACLs to the cell level Currently HBase supports setting access controls at the table or column family level. However, many use cases would benefit from the additional capability to do this on a per cell basis. In fact for many users dealing with sensitive information the ability to do this is crucial. 5) Improve audit logging Audit messages from various Hadoop components do not use a unified or even consistently formatted format. This makes analysis of logs for verifying compliance or taking corrective action difficult. We will build a common audit logging facility as part of the common authorization framework work. We will also build a set of common audit log processing tools for transforming them to different industry standard formats, for supporting compliance verification, and for triggering responses to policy
[jira] [Created] (HADOOP-9335) Including UNIX like sort options for ls shell command
Arjun K R created HADOOP-9335: - Summary: Including UNIX like sort options for ls shell command Key: HADOOP-9335 URL: https://issues.apache.org/jira/browse/HADOOP-9335 Project: Hadoop Common Issue Type: Improvement Components: fs Affects Versions: 0.20.2 Reporter: Arjun K R Priority: Minor Currently ls shell command does not support sort optiions.The ls shell command should include following unix like sort options : -t : sort by modification time -S : sort by file size -r : reverse the sort order -u : sort by acess time -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
