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From a.@apache.org
Subject [1/7] hadoop git commit: HADOOP-11495. Convert site documentation from apt to markdown (Masatake Iwasaki via aw)
Date Tue, 10 Feb 2015 21:40:05 GMT
Repository: hadoop
Updated Branches:
  refs/heads/trunk 6338ce3ae -> e9d26fe9e


http://git-wip-us.apache.org/repos/asf/hadoop/blob/e9d26fe9/hadoop-common-project/hadoop-common/src/site/markdown/SecureMode.md
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+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+* [Hadoop in Secure Mode](#Hadoop_in_Secure_Mode)
+    * [Introduction](#Introduction)
+    * [Authentication](#Authentication)
+        * [End User Accounts](#End_User_Accounts)
+        * [User Accounts for Hadoop Daemons](#User_Accounts_for_Hadoop_Daemons)
+        * [Kerberos principals for Hadoop Daemons and Users](#Kerberos_principals_for_Hadoop_Daemons_and_Users)
+        * [Mapping from Kerberos principal to OS user account](#Mapping_from_Kerberos_principal_to_OS_user_account)
+        * [Mapping from user to group](#Mapping_from_user_to_group)
+        * [Proxy user](#Proxy_user)
+        * [Secure DataNode](#Secure_DataNode)
+    * [Data confidentiality](#Data_confidentiality)
+        * [Data Encryption on RPC](#Data_Encryption_on_RPC)
+        * [Data Encryption on Block data transfer.](#Data_Encryption_on_Block_data_transfer.)
+        * [Data Encryption on HTTP](#Data_Encryption_on_HTTP)
+    * [Configuration](#Configuration)
+        * [Permissions for both HDFS and local fileSystem paths](#Permissions_for_both_HDFS_and_local_fileSystem_paths)
+        * [Common Configurations](#Common_Configurations)
+        * [NameNode](#NameNode)
+        * [Secondary NameNode](#Secondary_NameNode)
+        * [DataNode](#DataNode)
+        * [WebHDFS](#WebHDFS)
+        * [ResourceManager](#ResourceManager)
+        * [NodeManager](#NodeManager)
+        * [Configuration for WebAppProxy](#Configuration_for_WebAppProxy)
+        * [LinuxContainerExecutor](#LinuxContainerExecutor)
+        * [MapReduce JobHistory Server](#MapReduce_JobHistory_Server)
+
+Hadoop in Secure Mode
+=====================
+
+Introduction
+------------
+
+This document describes how to configure authentication for Hadoop in secure mode.
+
+By default Hadoop runs in non-secure mode in which no actual authentication is required. By configuring Hadoop runs in secure mode, each user and service needs to be authenticated by Kerberos in order to use Hadoop services.
+
+Security features of Hadoop consist of [authentication](#Authentication), [service level authorization](./ServiceLevelAuth.html), [authentication for Web consoles](./HttpAuthentication.html) and [data confidenciality](#Data_confidentiality).
+
+Authentication
+--------------
+
+### End User Accounts
+
+When service level authentication is turned on, end users using Hadoop in secure mode needs to be authenticated by Kerberos. The simplest way to do authentication is using `kinit` command of Kerberos.
+
+### User Accounts for Hadoop Daemons
+
+Ensure that HDFS and YARN daemons run as different Unix users, e.g. `hdfs` and `yarn`. Also, ensure that the MapReduce JobHistory server runs as different user such as `mapred`.
+
+It's recommended to have them share a Unix group, for e.g. `hadoop`. See also "[Mapping from user to group](#Mapping_from_user_to_group)" for group management.
+
+| User:Group | Daemons |
+|:---- |:---- |
+| hdfs:hadoop | NameNode, Secondary NameNode, JournalNode, DataNode |
+| yarn:hadoop | ResourceManager, NodeManager |
+| mapred:hadoop | MapReduce JobHistory Server |
+
+### Kerberos principals for Hadoop Daemons and Users
+
+For running hadoop service daemons in Hadoop in secure mode, Kerberos principals are required. Each service reads auhenticate information saved in keytab file with appropriate permission.
+
+HTTP web-consoles should be served by principal different from RPC's one.
+
+Subsections below shows the examples of credentials for Hadoop services.
+
+#### HDFS
+
+The NameNode keytab file, on the NameNode host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/nn.service.keytab
+    Keytab name: FILE:/etc/security/keytab/nn.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 nn/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 nn/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 nn/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+The Secondary NameNode keytab file, on that host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/sn.service.keytab
+    Keytab name: FILE:/etc/security/keytab/sn.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 sn/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 sn/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 sn/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+The DataNode keytab file, on each host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/dn.service.keytab
+    Keytab name: FILE:/etc/security/keytab/dn.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 dn/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 dn/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 dn/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+#### YARN
+
+The ResourceManager keytab file, on the ResourceManager host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/rm.service.keytab
+    Keytab name: FILE:/etc/security/keytab/rm.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 rm/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 rm/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 rm/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+The NodeManager keytab file, on each host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/nm.service.keytab
+    Keytab name: FILE:/etc/security/keytab/nm.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 nm/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 nm/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 nm/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+#### MapReduce JobHistory Server
+
+The MapReduce JobHistory Server keytab file, on that host, should look like the following:
+
+    $ klist -e -k -t /etc/security/keytab/jhs.service.keytab
+    Keytab name: FILE:/etc/security/keytab/jhs.service.keytab
+    KVNO Timestamp         Principal
+       4 07/18/11 21:08:09 jhs/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 jhs/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 jhs/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-256 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (AES-128 CTS mode with 96-bit SHA-1 HMAC)
+       4 07/18/11 21:08:09 host/full.qualified.domain.name@REALM.TLD (ArcFour with HMAC/md5)
+
+### Mapping from Kerberos principal to OS user account
+
+Hadoop maps Kerberos principal to OS user account using the rule specified by `hadoop.security.auth_to_local` which works in the same way as the `auth_to_local` in [Kerberos configuration file (krb5.conf)](http://web.mit.edu/Kerberos/krb5-latest/doc/admin/conf_files/krb5_conf.html). In addition, Hadoop `auth_to_local` mapping supports the **/L** flag that lowercases the returned name.
+
+By default, it picks the first component of principal name as a user name if the realms matches to the `default_realm` (usually defined in /etc/krb5.conf). For example, `host/full.qualified.domain.name@REALM.TLD` is mapped to `host` by default rule.
+
+### Mapping from user to group
+
+Though files on HDFS are associated to owner and group, Hadoop does not have the definition of group by itself. Mapping from user to group is done by OS or LDAP.
+
+You can change a way of mapping by specifying the name of mapping provider as a value of `hadoop.security.group.mapping` See [HDFS Permissions Guide](../hadoop-hdfs/HdfsPermissionsGuide.html) for details.
+
+Practically you need to manage SSO environment using Kerberos with LDAP for Hadoop in secure mode.
+
+### Proxy user
+
+Some products such as Apache Oozie which access the services of Hadoop on behalf of end users need to be able to impersonate end users. See [the doc of proxy user](./Superusers.html) for details.
+
+### Secure DataNode
+
+Because the data transfer protocol of DataNode does not use the RPC framework of Hadoop, DataNode must authenticate itself by using privileged ports which are specified by `dfs.datanode.address` and `dfs.datanode.http.address`. This authentication is based on the assumption that the attacker won't be able to get root privileges.
+
+When you execute `hdfs datanode` command as root, server process binds privileged port at first, then drops privilege and runs as the user account specified by `HADOOP_SECURE_DN_USER`. This startup process uses jsvc installed to `JSVC_HOME`. You must specify `HADOOP_SECURE_DN_USER` and `JSVC_HOME` as environment variables on start up (in hadoop-env.sh).
+
+As of version 2.6.0, SASL can be used to authenticate the data transfer protocol. In this configuration, it is no longer required for secured clusters to start the DataNode as root using jsvc and bind to privileged ports. To enable SASL on data transfer protocol, set `dfs.data.transfer.protection` in hdfs-site.xml, set a non-privileged port for `dfs.datanode.address`, set `dfs.http.policy` to *HTTPS\_ONLY* and make sure the `HADOOP_SECURE_DN_USER` environment variable is not defined. Note that it is not possible to use SASL on data transfer protocol if `dfs.datanode.address` is set to a privileged port. This is required for backwards-compatibility reasons.
+
+In order to migrate an existing cluster that used root authentication to start using SASL instead, first ensure that version 2.6.0 or later has been deployed to all cluster nodes as well as any external applications that need to connect to the cluster. Only versions 2.6.0 and later of the HDFS client can connect to a DataNode that uses SASL for authentication of data transfer protocol, so it is vital that all callers have the correct version before migrating. After version 2.6.0 or later has been deployed everywhere, update configuration of any external applications to enable SASL. If an HDFS client is enabled for SASL, then it can connect successfully to a DataNode running with either root authentication or SASL authentication. Changing configuration for all clients guarantees that subsequent configuration changes on DataNodes will not disrupt the applications. Finally, each individual DataNode can be migrated by changing its configuration and restarting. It is acceptable to have a
  mix of some DataNodes running with root authentication and some DataNodes running with SASL authentication temporarily during this migration period, because an HDFS client enabled for SASL can connect to both.
+
+Data confidentiality
+--------------------
+
+### Data Encryption on RPC
+
+The data transfered between hadoop services and clients. Setting `hadoop.rpc.protection` to `"privacy"` in the core-site.xml activate data encryption.
+
+### Data Encryption on Block data transfer.
+
+You need to set `dfs.encrypt.data.transfer` to `"true"` in the hdfs-site.xml in order to activate data encryption for data transfer protocol of DataNode.
+
+Optionally, you may set `dfs.encrypt.data.transfer.algorithm` to either "3des" or "rc4" to choose the specific encryption algorithm. If unspecified, then the configured JCE default on the system is used, which is usually 3DES.
+
+Setting `dfs.encrypt.data.transfer.cipher.suites` to `AES/CTR/NoPadding` activates AES encryption. By default, this is unspecified, so AES is not used. When AES is used, the algorithm specified in `dfs.encrypt.data.transfer.algorithm` is still used during an initial key exchange. The AES key bit length can be configured by setting `dfs.encrypt.data.transfer.cipher.key.bitlength` to 128, 192 or 256. The default is 128.
+
+AES offers the greatest cryptographic strength and the best performance. At this time, 3DES and RC4 have been used more often in Hadoop clusters.
+
+### Data Encryption on HTTP
+
+Data transfer between Web-console and clients are protected by using SSL(HTTPS).
+
+Configuration
+-------------
+
+### Permissions for both HDFS and local fileSystem paths
+
+The following table lists various paths on HDFS and local filesystems (on all nodes) and recommended permissions:
+
+| Filesystem | Path | User:Group | Permissions |
+|:---- |:---- |:---- |:---- |
+| local | `dfs.namenode.name.dir` | hdfs:hadoop | drwx------ |
+| local | `dfs.datanode.data.dir` | hdfs:hadoop | drwx------ |
+| local | $HADOOP\_LOG\_DIR | hdfs:hadoop | drwxrwxr-x |
+| local | $YARN\_LOG\_DIR | yarn:hadoop | drwxrwxr-x |
+| local | `yarn.nodemanager.local-dirs` | yarn:hadoop | drwxr-xr-x |
+| local | `yarn.nodemanager.log-dirs` | yarn:hadoop | drwxr-xr-x |
+| local | container-executor | root:hadoop | --Sr-s--* |
+| local | `conf/container-executor.cfg` | root:hadoop | r-------* |
+| hdfs | / | hdfs:hadoop | drwxr-xr-x |
+| hdfs | /tmp | hdfs:hadoop | drwxrwxrwxt |
+| hdfs | /user | hdfs:hadoop | drwxr-xr-x |
+| hdfs | `yarn.nodemanager.remote-app-log-dir` | yarn:hadoop | drwxrwxrwxt |
+| hdfs | `mapreduce.jobhistory.intermediate-done-dir` | mapred:hadoop | drwxrwxrwxt |
+| hdfs | `mapreduce.jobhistory.done-dir` | mapred:hadoop | drwxr-x--- |
+
+### Common Configurations
+
+In order to turn on RPC authentication in hadoop, set the value of `hadoop.security.authentication` property to `"kerberos"`, and set security related settings listed below appropriately.
+
+The following properties should be in the `core-site.xml` of all the nodes in the cluster.
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `hadoop.security.authentication` | *kerberos* | `simple` : No authentication. (default)  `kerberos` : Enable authentication by Kerberos. |
+| `hadoop.security.authorization` | *true* | Enable [RPC service-level authorization](./ServiceLevelAuth.html). |
+| `hadoop.rpc.protection` | *authentication* | *authentication* : authentication only (default)  *integrity* : integrity check in addition to authentication  *privacy* : data encryption in addition to integrity |
+| `hadoop.security.auth_to_local` | `RULE:`*exp1* `RULE:`*exp2* *...* DEFAULT | The value is string containing new line characters. See [Kerberos documentation](http://web.mit.edu/Kerberos/krb5-latest/doc/admin/conf_files/krb5_conf.html) for format for *exp*. |
+| `hadoop.proxyuser.`*superuser*`.hosts` | | comma separated hosts from which *superuser* access are allowd to impersonation. `*` means wildcard. |
+| `hadoop.proxyuser.`*superuser*`.groups` | | comma separated groups to which users impersonated by *superuser* belongs. `*` means wildcard. |
+
+### NameNode
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `dfs.block.access.token.enable` | *true* | Enable HDFS block access tokens for secure operations. |
+| `dfs.https.enable` | *true* | This value is deprecated. Use dfs.http.policy |
+| `dfs.http.policy` | *HTTP\_ONLY* or *HTTPS\_ONLY* or *HTTP\_AND\_HTTPS* | HTTPS\_ONLY turns off http access. This option takes precedence over the deprecated configuration dfs.https.enable and hadoop.ssl.enabled. If using SASL to authenticate data transfer protocol instead of running DataNode as root and using privileged ports, then this property must be set to *HTTPS\_ONLY* to guarantee authentication of HTTP servers. (See `dfs.data.transfer.protection`.) |
+| `dfs.namenode.https-address` | *nn\_host\_fqdn:50470* | |
+| `dfs.https.port` | *50470* | |
+| `dfs.namenode.keytab.file` | */etc/security/keytab/nn.service.keytab* | Kerberos keytab file for the NameNode. |
+| `dfs.namenode.kerberos.principal` | nn/\_HOST@REALM.TLD | Kerberos principal name for the NameNode. |
+| `dfs.namenode.kerberos.internal.spnego.principal` | HTTP/\_HOST@REALM.TLD | HTTP Kerberos principal name for the NameNode. |
+
+### Secondary NameNode
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `dfs.namenode.secondary.http-address` | *c\_nn\_host\_fqdn:50090* | |
+| `dfs.namenode.secondary.https-port` | *50470* | |
+| `dfs.secondary.namenode.keytab.file` | */etc/security/keytab/sn.service.keytab* | Kerberos keytab file for the Secondary NameNode. |
+| `dfs.secondary.namenode.kerberos.principal` | sn/\_HOST@REALM.TLD | Kerberos principal name for the Secondary NameNode. |
+| `dfs.secondary.namenode.kerberos.internal.spnego.principal` | HTTP/\_HOST@REALM.TLD | HTTP Kerberos principal name for the Secondary NameNode. |
+
+### DataNode
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `dfs.datanode.data.dir.perm` | 700 | |
+| `dfs.datanode.address` | *0.0.0.0:1004* | Secure DataNode must use privileged port in order to assure that the server was started securely. This means that the server must be started via jsvc. Alternatively, this must be set to a non-privileged port if using SASL to authenticate data transfer protocol. (See `dfs.data.transfer.protection`.) |
+| `dfs.datanode.http.address` | *0.0.0.0:1006* | Secure DataNode must use privileged port in order to assure that the server was started securely. This means that the server must be started via jsvc. |
+| `dfs.datanode.https.address` | *0.0.0.0:50470* | |
+| `dfs.datanode.keytab.file` | */etc/security/keytab/dn.service.keytab* | Kerberos keytab file for the DataNode. |
+| `dfs.datanode.kerberos.principal` | dn/\_HOST@REALM.TLD | Kerberos principal name for the DataNode. |
+| `dfs.encrypt.data.transfer` | *false* | set to `true` when using data encryption |
+| `dfs.encrypt.data.transfer.algorithm` | | optionally set to `3des` or `rc4` when using data encryption to control encryption algorithm |
+| `dfs.encrypt.data.transfer.cipher.suites` | | optionally set to `AES/CTR/NoPadding` to activate AES encryption when using data encryption |
+| `dfs.encrypt.data.transfer.cipher.key.bitlength` | | optionally set to `128`, `192` or `256` to control key bit length when using AES with data encryption |
+| `dfs.data.transfer.protection` | | *authentication* : authentication only  *integrity* : integrity check in addition to authentication  *privacy* : data encryption in addition to integrity This property is unspecified by default. Setting this property enables SASL for authentication of data transfer protocol. If this is enabled, then `dfs.datanode.address` must use a non-privileged port, `dfs.http.policy` must be set to *HTTPS\_ONLY* and the `HADOOP_SECURE_DN_USER` environment variable must be undefined when starting the DataNode process. |
+
+### WebHDFS
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `dfs.web.authentication.kerberos.principal` | http/\_HOST@REALM.TLD | Kerberos keytab file for the WebHDFS. |
+| `dfs.web.authentication.kerberos.keytab` | */etc/security/keytab/http.service.keytab* | Kerberos principal name for WebHDFS. |
+
+### ResourceManager
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `yarn.resourcemanager.keytab` | */etc/security/keytab/rm.service.keytab* | Kerberos keytab file for the ResourceManager. |
+| `yarn.resourcemanager.principal` | rm/\_HOST@REALM.TLD | Kerberos principal name for the ResourceManager. |
+
+### NodeManager
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `yarn.nodemanager.keytab` | */etc/security/keytab/nm.service.keytab* | Kerberos keytab file for the NodeManager. |
+| `yarn.nodemanager.principal` | nm/\_HOST@REALM.TLD | Kerberos principal name for the NodeManager. |
+| `yarn.nodemanager.container-executor.class` | `org.apache.hadoop.yarn.server.nodemanager.LinuxContainerExecutor` | Use LinuxContainerExecutor. |
+| `yarn.nodemanager.linux-container-executor.group` | *hadoop* | Unix group of the NodeManager. |
+| `yarn.nodemanager.linux-container-executor.path` | */path/to/bin/container-executor* | The path to the executable of Linux container executor. |
+
+### Configuration for WebAppProxy
+
+The `WebAppProxy` provides a proxy between the web applications exported by an application and an end user. If security is enabled it will warn users before accessing a potentially unsafe web application. Authentication and authorization using the proxy is handled just like any other privileged web application.
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `yarn.web-proxy.address` | `WebAppProxy` host:port for proxy to AM web apps. | *host:port* if this is the same as `yarn.resourcemanager.webapp.address` or it is not defined then the `ResourceManager` will run the proxy otherwise a standalone proxy server will need to be launched. |
+| `yarn.web-proxy.keytab` | */etc/security/keytab/web-app.service.keytab* | Kerberos keytab file for the WebAppProxy. |
+| `yarn.web-proxy.principal` | wap/\_HOST@REALM.TLD | Kerberos principal name for the WebAppProxy. |
+
+### LinuxContainerExecutor
+
+A `ContainerExecutor` used by YARN framework which define how any *container* launched and controlled.
+
+The following are the available in Hadoop YARN:
+
+| ContainerExecutor | Description |
+|:---- |:---- |
+| `DefaultContainerExecutor` | The default executor which YARN uses to manage container execution. The container process has the same Unix user as the NodeManager. |
+| `LinuxContainerExecutor` | Supported only on GNU/Linux, this executor runs the containers as either the YARN user who submitted the application (when full security is enabled) or as a dedicated user (defaults to nobody) when full security is not enabled. When full security is enabled, this executor requires all user accounts to be created on the cluster nodes where the containers are launched. It uses a *setuid* executable that is included in the Hadoop distribution. The NodeManager uses this executable to launch and kill containers. The setuid executable switches to the user who has submitted the application and launches or kills the containers. For maximum security, this executor sets up restricted permissions and user/group ownership of local files and directories used by the containers such as the shared objects, jars, intermediate files, log files etc. Particularly note that, because of this, except the application owner and NodeManager, no other user can access any of the lo
 cal files/directories including those localized as part of the distributed cache. |
+
+To build the LinuxContainerExecutor executable run:
+
+     $ mvn package -Dcontainer-executor.conf.dir=/etc/hadoop/
+
+The path passed in `-Dcontainer-executor.conf.dir` should be the path on the cluster nodes where a configuration file for the setuid executable should be located. The executable should be installed in $HADOOP\_YARN\_HOME/bin.
+
+The executable must have specific permissions: 6050 or --Sr-s--- permissions user-owned by *root* (super-user) and group-owned by a special group (e.g. `hadoop`) of which the NodeManager Unix user is the group member and no ordinary application user is. If any application user belongs to this special group, security will be compromised. This special group name should be specified for the configuration property `yarn.nodemanager.linux-container-executor.group` in both `conf/yarn-site.xml` and `conf/container-executor.cfg`.
+
+For example, let's say that the NodeManager is run as user *yarn* who is part of the groups users and *hadoop*, any of them being the primary group. Let also be that *users* has both *yarn* and another user (application submitter) *alice* as its members, and *alice* does not belong to *hadoop*. Going by the above description, the setuid/setgid executable should be set 6050 or --Sr-s--- with user-owner as *yarn* and group-owner as *hadoop* which has *yarn* as its member (and not *users* which has *alice* also as its member besides *yarn*).
+
+The LinuxTaskController requires that paths including and leading up to the directories specified in `yarn.nodemanager.local-dirs` and `yarn.nodemanager.log-dirs` to be set 755 permissions as described above in the table on permissions on directories.
+
+* `conf/container-executor.cfg`
+
+The executable requires a configuration file called `container-executor.cfg` to be present in the configuration directory passed to the mvn target mentioned above.
+
+The configuration file must be owned by the user running NodeManager (user `yarn` in the above example), group-owned by anyone and should have the permissions 0400 or r--------.
+
+The executable requires following configuration items to be present in the `conf/container-executor.cfg` file. The items should be mentioned as simple key=value pairs, one per-line:
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `yarn.nodemanager.linux-container-executor.group` | *hadoop* | Unix group of the NodeManager. The group owner of the *container-executor* binary should be this group. Should be same as the value with which the NodeManager is configured. This configuration is required for validating the secure access of the *container-executor* binary. |
+| `banned.users` | hdfs,yarn,mapred,bin | Banned users. |
+| `allowed.system.users` | foo,bar | Allowed system users. |
+| `min.user.id` | 1000 | Prevent other super-users. |
+
+To re-cap, here are the local file-sysytem permissions required for the various paths related to the `LinuxContainerExecutor`:
+
+| Filesystem | Path | User:Group | Permissions |
+|:---- |:---- |:---- |:---- |
+| local | container-executor | root:hadoop | --Sr-s--* |
+| local | `conf/container-executor.cfg` | root:hadoop | r-------* |
+| local | `yarn.nodemanager.local-dirs` | yarn:hadoop | drwxr-xr-x |
+| local | `yarn.nodemanager.log-dirs` | yarn:hadoop | drwxr-xr-x |
+
+### MapReduce JobHistory Server
+
+| Parameter | Value | Notes |
+|:---- |:---- |:---- |
+| `mapreduce.jobhistory.address` | MapReduce JobHistory Server *host:port* | Default port is 10020. |
+| `mapreduce.jobhistory.keytab` | */etc/security/keytab/jhs.service.keytab* | Kerberos keytab file for the MapReduce JobHistory Server. |
+| `mapreduce.jobhistory.principal` | jhs/\_HOST@REALM.TLD | Kerberos principal name for the MapReduce JobHistory Server. |
+
+

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+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+Service Level Authorization Guide
+=================================
+
+* [Service Level Authorization Guide](#Service_Level_Authorization_Guide)
+    * [Purpose](#Purpose)
+    * [Prerequisites](#Prerequisites)
+    * [Overview](#Overview)
+    * [Configuration](#Configuration)
+        * [Enable Service Level Authorization](#Enable_Service_Level_Authorization)
+        * [Hadoop Services and Configuration Properties](#Hadoop_Services_and_Configuration_Properties)
+        * [Access Control Lists](#Access_Control_Lists)
+        * [Refreshing Service Level Authorization Configuration](#Refreshing_Service_Level_Authorization_Configuration)
+        * [Examples](#Examples)
+
+Purpose
+-------
+
+This document describes how to configure and manage Service Level Authorization for Hadoop.
+
+Prerequisites
+-------------
+
+Make sure Hadoop is installed, configured and setup correctly. For more information see:
+
+* [Single Node Setup](./SingleCluster.html) for first-time users.
+* [Cluster Setup](./ClusterSetup.html) for large, distributed clusters.
+
+Overview
+--------
+
+Service Level Authorization is the initial authorization mechanism to ensure clients connecting to a particular Hadoop service have the necessary, pre-configured, permissions and are authorized to access the given service. For example, a MapReduce cluster can use this mechanism to allow a configured list of users/groups to submit jobs.
+
+The `$HADOOP_CONF_DIR/hadoop-policy.xml` configuration file is used to define the access control lists for various Hadoop services.
+
+Service Level Authorization is performed much before to other access control checks such as file-permission checks, access control on job queues etc.
+
+Configuration
+-------------
+
+This section describes how to configure service-level authorization via the configuration file `$HADOOP_CONF_DIR/hadoop-policy.xml`.
+
+### Enable Service Level Authorization
+
+By default, service-level authorization is disabled for Hadoop. To enable it set the configuration property hadoop.security.authorization to true in `$HADOOP_CONF_DIR/core-site.xml`.
+
+### Hadoop Services and Configuration Properties
+
+This section lists the various Hadoop services and their configuration knobs:
+
+| Property | Service |
+|:---- |:---- |
+| security.client.protocol.acl | ACL for ClientProtocol, which is used by user code via the DistributedFileSystem. |
+| security.client.datanode.protocol.acl | ACL for ClientDatanodeProtocol, the client-to-datanode protocol for block recovery. |
+| security.datanode.protocol.acl | ACL for DatanodeProtocol, which is used by datanodes to communicate with the namenode. |
+| security.inter.datanode.protocol.acl | ACL for InterDatanodeProtocol, the inter-datanode protocol for updating generation timestamp. |
+| security.namenode.protocol.acl | ACL for NamenodeProtocol, the protocol used by the secondary namenode to communicate with the namenode. |
+| security.inter.tracker.protocol.acl | ACL for InterTrackerProtocol, used by the tasktrackers to communicate with the jobtracker. |
+| security.job.submission.protocol.acl | ACL for JobSubmissionProtocol, used by job clients to communciate with the jobtracker for job submission, querying job status etc. |
+| security.task.umbilical.protocol.acl | ACL for TaskUmbilicalProtocol, used by the map and reduce tasks to communicate with the parent tasktracker. |
+| security.refresh.policy.protocol.acl | ACL for RefreshAuthorizationPolicyProtocol, used by the dfsadmin and mradmin commands to refresh the security policy in-effect. |
+| security.ha.service.protocol.acl | ACL for HAService protocol used by HAAdmin to manage the active and stand-by states of namenode. |
+
+### Access Control Lists
+
+`$HADOOP_CONF_DIR/hadoop-policy.xml` defines an access control list for each Hadoop service. Every access control list has a simple format:
+
+The list of users and groups are both comma separated list of names. The two lists are separated by a space.
+
+Example: `user1,user2 group1,group2`.
+
+Add a blank at the beginning of the line if only a list of groups is to be provided, equivalently a comma-separated list of users followed by a space or nothing implies only a set of given users.
+
+A special value of `*` implies that all users are allowed to access the service.
+
+If access control list is not defined for a service, the value of `security.service.authorization.default.acl` is applied. If `security.service.authorization.default.acl` is not defined, `*` is applied.
+
+* Blocked Access Control ListsIn some cases, it is required to specify blocked access control list for a service. This specifies the list of users and groups who are not authorized to access the service. The format of the blocked access control list is same as that of access control list. The blocked access control list can be specified via `$HADOOP_CONF_DIR/hadoop-policy.xml`. The property name is derived by suffixing with ".blocked".
+
+    Example: The property name of blocked access control list for `security.client.protocol.acl>> will be <<<security.client.protocol.acl.blocked`
+
+    For a service, it is possible to specify both an access control list and a blocked control list. A user is authorized to access the service if the user is in the access control and not in the blocked access control list.
+
+    If blocked access control list is not defined for a service, the value of `security.service.authorization.default.acl.blocked` is applied. If `security.service.authorization.default.acl.blocked` is not defined, empty blocked access control list is applied.
+
+### Refreshing Service Level Authorization Configuration
+
+The service-level authorization configuration for the NameNode and JobTracker can be changed without restarting either of the Hadoop master daemons. The cluster administrator can change `$HADOOP_CONF_DIR/hadoop-policy.xml` on the master nodes and instruct the NameNode and JobTracker to reload their respective configurations via the `-refreshServiceAcl` switch to `dfsadmin` and `mradmin` commands respectively.
+
+Refresh the service-level authorization configuration for the NameNode:
+
+       $ bin/hadoop dfsadmin -refreshServiceAcl
+
+Refresh the service-level authorization configuration for the JobTracker:
+
+       $ bin/hadoop mradmin -refreshServiceAcl
+
+Of course, one can use the `security.refresh.policy.protocol.acl` property in `$HADOOP_CONF_DIR/hadoop-policy.xml` to restrict access to the ability to refresh the service-level authorization configuration to certain users/groups.
+
+* Access Control using list of ip addresses, host names and ip rangesAccess to a service can be controlled based on the ip address of the client accessing the service. It is possible to restrict access to a service from a set of machines by specifying a list of ip addresses, host names and ip ranges. The property name for each service is derived from the corresponding acl's property name. If the property name of acl is security.client.protocol.acl, property name for the hosts list will be security.client.protocol.hosts.
+
+    If hosts list is not defined for a service, the value of `security.service.authorization.default.hosts` is applied. If `security.service.authorization.default.hosts` is not defined, `*` is applied.
+
+    It is possible to specify a blocked list of hosts. Only those machines which are in the hosts list, but not in the blocked hosts list will be granted access to the service. The property name is derived by suffixing with ".blocked".
+
+    Example: The property name of blocked hosts list for `security.client.protocol.hosts>> will be <<<security.client.protocol.hosts.blocked`
+
+    If blocked hosts list is not defined for a service, the value of `security.service.authorization.default.hosts.blocked` is applied. If `security.service.authorization.default.hosts.blocked` is not defined, empty blocked hosts list is applied.
+
+### Examples
+
+Allow only users `alice`, `bob` and users in the `mapreduce` group to submit jobs to the MapReduce cluster:
+
+    <property>
+         <name>security.job.submission.protocol.acl</name>
+         <value>alice,bob mapreduce</value>
+    </property>
+
+Allow only DataNodes running as the users who belong to the group datanodes to communicate with the NameNode:
+
+    <property>
+         <name>security.datanode.protocol.acl</name>
+         <value>datanodes</value>
+    </property>
+
+Allow any user to talk to the HDFS cluster as a DFSClient:
+
+    <property>
+         <name>security.client.protocol.acl</name>
+         <value>*</value>
+    </property>

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+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+#set ( $H3 = '###' )
+#set ( $H4 = '####' )
+#set ( $H5 = '#####' )
+
+Hadoop: Setting up a Single Node Cluster.
+=========================================
+
+* [Hadoop: Setting up a Single Node Cluster.](#Hadoop:_Setting_up_a_Single_Node_Cluster.)
+    * [Purpose](#Purpose)
+    * [Prerequisites](#Prerequisites)
+        * [Supported Platforms](#Supported_Platforms)
+        * [Required Software](#Required_Software)
+        * [Installing Software](#Installing_Software)
+    * [Download](#Download)
+    * [Prepare to Start the Hadoop Cluster](#Prepare_to_Start_the_Hadoop_Cluster)
+    * [Standalone Operation](#Standalone_Operation)
+    * [Pseudo-Distributed Operation](#Pseudo-Distributed_Operation)
+        * [Configuration](#Configuration)
+        * [Setup passphraseless ssh](#Setup_passphraseless_ssh)
+        * [Execution](#Execution)
+        * [YARN on a Single Node](#YARN_on_a_Single_Node)
+    * [Fully-Distributed Operation](#Fully-Distributed_Operation)
+
+Purpose
+-------
+
+This document describes how to set up and configure a single-node Hadoop installation so that you can quickly perform simple operations using Hadoop MapReduce and the Hadoop Distributed File System (HDFS).
+
+Prerequisites
+-------------
+
+$H3 Supported Platforms
+
+* GNU/Linux is supported as a development and production platform. Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
+
+* Windows is also a supported platform but the followings steps are for Linux only. To set up Hadoop on Windows, see [wiki page](http://wiki.apache.org/hadoop/Hadoop2OnWindows).
+
+$H3 Required Software
+
+Required software for Linux include:
+
+1.  Java™ must be installed. Recommended Java versions are described at [HadoopJavaVersions](http://wiki.apache.org/hadoop/HadoopJavaVersions).
+
+2.  ssh must be installed and sshd must be running to use the Hadoop scripts that manage remote Hadoop daemons if the optional start and stop scripts are to be used. Additionally, it is recommmended that pdsh also be installed for better ssh resource management.
+
+$H3 Installing Software
+
+If your cluster doesn't have the requisite software you will need to install it.
+
+For example on Ubuntu Linux:
+
+      $ sudo apt-get install ssh
+      $ sudo apt-get install pdsh
+
+Download
+--------
+
+To get a Hadoop distribution, download a recent stable release from one of the [Apache Download Mirrors](http://www.apache.org/dyn/closer.cgi/hadoop/common/).
+
+Prepare to Start the Hadoop Cluster
+-----------------------------------
+
+Unpack the downloaded Hadoop distribution. In the distribution, edit the file `etc/hadoop/hadoop-env.sh` to define some parameters as follows:
+
+      # set to the root of your Java installation
+      export JAVA_HOME=/usr/java/latest
+
+Try the following command:
+
+      $ bin/hadoop
+
+This will display the usage documentation for the hadoop script.
+
+Now you are ready to start your Hadoop cluster in one of the three supported modes:
+
+* [Local (Standalone) Mode](#Standalone_Operation)
+* [Pseudo-Distributed Mode](#Pseudo-Distributed_Operation)
+* [Fully-Distributed Mode](#Fully-Distributed_Operation)
+
+Standalone Operation
+--------------------
+
+By default, Hadoop is configured to run in a non-distributed mode, as a single Java process. This is useful for debugging.
+
+The following example copies the unpacked conf directory to use as input and then finds and displays every match of the given regular expression. Output is written to the given output directory.
+
+      $ mkdir input
+      $ cp etc/hadoop/*.xml input
+      $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
+      $ cat output/*
+
+Pseudo-Distributed Operation
+----------------------------
+
+Hadoop can also be run on a single-node in a pseudo-distributed mode where each Hadoop daemon runs in a separate Java process.
+
+$H3 Configuration
+
+Use the following:
+
+etc/hadoop/core-site.xml:
+
+    <configuration>
+        <property>
+            <name>fs.defaultFS</name>
+            <value>hdfs://localhost:9000</value>
+        </property>
+    </configuration>
+
+etc/hadoop/hdfs-site.xml:
+
+    <configuration>
+        <property>
+            <name>dfs.replication</name>
+            <value>1</value>
+        </property>
+    </configuration>
+
+$H3 Setup passphraseless ssh
+
+Now check that you can ssh to the localhost without a passphrase:
+
+      $ ssh localhost
+
+If you cannot ssh to localhost without a passphrase, execute the following commands:
+
+      $ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
+      $ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
+      $ chmod 0700 ~/.ssh/authorized_keys
+
+$H3 Execution
+
+The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see [YARN on Single Node](#YARN_on_Single_Node).
+
+1.  Format the filesystem:
+
+          $ bin/hdfs namenode -format
+
+2.  Start NameNode daemon and DataNode daemon:
+
+          $ sbin/start-dfs.sh
+
+    The hadoop daemon log output is written to the `$HADOOP_LOG_DIR` directory (defaults to `$HADOOP_HOME/logs`).
+
+3.  Browse the web interface for the NameNode; by default it is available at:
+
+    * NameNode - `http://localhost:50070/`
+
+4.  Make the HDFS directories required to execute MapReduce jobs:
+
+          $ bin/hdfs dfs -mkdir /user
+          $ bin/hdfs dfs -mkdir /user/<username>
+
+5.  Copy the input files into the distributed filesystem:
+
+          $ bin/hdfs dfs -put etc/hadoop input
+
+6.  Run some of the examples provided:
+
+          $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
+
+7.  Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:
+
+          $ bin/hdfs dfs -get output output
+          $ cat output/*
+
+    or
+
+    View the output files on the distributed filesystem:
+
+          $ bin/hdfs dfs -cat output/*
+
+8.  When you're done, stop the daemons with:
+
+          $ sbin/stop-dfs.sh
+
+$H3 YARN on a Single Node
+
+You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.
+
+The following instructions assume that 1. ~ 4. steps of [the above instructions](#Execution) are already executed.
+
+1.  Configure parameters as follows:`etc/hadoop/mapred-site.xml`:
+
+        <configuration>
+            <property>
+                <name>mapreduce.framework.name</name>
+                <value>yarn</value>
+            </property>
+        </configuration>
+
+    `etc/hadoop/yarn-site.xml`:
+
+        <configuration>
+            <property>
+                <name>yarn.nodemanager.aux-services</name>
+                <value>mapreduce_shuffle</value>
+            </property>
+        </configuration>
+
+2.  Start ResourceManager daemon and NodeManager daemon:
+
+          $ sbin/start-yarn.sh
+
+3.  Browse the web interface for the ResourceManager; by default it is available at:
+
+    * ResourceManager - `http://localhost:8088/`
+
+4.  Run a MapReduce job.
+
+5.  When you're done, stop the daemons with:
+
+          $ sbin/stop-yarn.sh
+
+Fully-Distributed Operation
+---------------------------
+
+For information on setting up fully-distributed, non-trivial clusters see [Cluster Setup](./ClusterSetup.html).

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+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+Single Node Setup
+=================
+
+This page will be removed in the next major release.
+
+See [Single Cluster Setup](./SingleCluster.html) to set up and configure a single-node Hadoop installation.

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+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+Proxy user - Superusers Acting On Behalf Of Other Users
+=======================================================
+
+* [Proxy user - Superusers Acting On Behalf Of Other Users](#Proxy_user_-_Superusers_Acting_On_Behalf_Of_Other_Users)
+    * [Introduction](#Introduction)
+    * [Use Case](#Use_Case)
+    * [Code example](#Code_example)
+    * [Configurations](#Configurations)
+    * [Caveats](#Caveats)
+
+Introduction
+------------
+
+This document describes how a superuser can submit jobs or access hdfs on behalf of another user.
+
+Use Case
+--------
+
+The code example described in the next section is applicable for the following use case.
+
+A superuser with username 'super' wants to submit job and access hdfs on behalf of a user joe. The superuser has kerberos credentials but user joe doesn't have any. The tasks are required to run as user joe and any file accesses on namenode are required to be done as user joe. It is required that user joe can connect to the namenode or job tracker on a connection authenticated with super's kerberos credentials. In other words super is impersonating the user joe.
+
+Some products such as Apache Oozie need this.
+
+Code example
+------------
+
+In this example super's credentials are used for login and a proxy user ugi object is created for joe. The operations are performed within the doAs method of this proxy user ugi object.
+
+        ...
+        //Create ugi for joe. The login user is 'super'.
+        UserGroupInformation ugi =
+                UserGroupInformation.createProxyUser("joe", UserGroupInformation.getLoginUser());
+        ugi.doAs(new PrivilegedExceptionAction<Void>() {
+          public Void run() throws Exception {
+            //Submit a job
+            JobClient jc = new JobClient(conf);
+            jc.submitJob(conf);
+            //OR access hdfs
+            FileSystem fs = FileSystem.get(conf);
+            fs.mkdir(someFilePath);
+          }
+        }
+
+Configurations
+--------------
+
+You can configure proxy user using properties `hadoop.proxyuser.$superuser.hosts` along with either or both of `hadoop.proxyuser.$superuser.groups` and `hadoop.proxyuser.$superuser.users`.
+
+By specifying as below in core-site.xml, the superuser named `super` can connect only from `host1` and `host2` to impersonate a user belonging to `group1` and `group2`.
+
+       <property>
+         <name>hadoop.proxyuser.super.hosts</name>
+         <value>host1,host2</value>
+       </property>
+       <property>
+         <name>hadoop.proxyuser.super.groups</name>
+         <value>group1,group2</value>
+       </property>
+
+If these configurations are not present, impersonation will not be allowed and connection will fail.
+
+If more lax security is preferred, the wildcard value \* may be used to allow impersonation from any host or of any user. For example, by specifying as below in core-site.xml, user named `oozie` accessing from any host can impersonate any user belonging to any group.
+
+      <property>
+        <name>hadoop.proxyuser.oozie.hosts</name>
+        <value>*</value>
+      </property>
+      <property>
+        <name>hadoop.proxyuser.oozie.groups</name>
+        <value>*</value>
+      </property>
+
+The `hadoop.proxyuser.$superuser.hosts` accepts list of ip addresses, ip address ranges in CIDR format and/or host names. For example, by specifying as below, user named `super` accessing from hosts in the range `10.222.0.0-15` and `10.113.221.221` can impersonate `user1` and `user2`.
+
+       <property>
+         <name>hadoop.proxyuser.super.hosts</name>
+         <value>10.222.0.0/16,10.113.221.221</value>
+       </property>
+       <property>
+         <name>hadoop.proxyuser.super.users</name>
+         <value>user1,user2</value>
+       </property>
+
+Caveats
+-------
+
+If the cluster is running in [Secure Mode](./SecureMode.html), the superuser must have kerberos credentials to be able to impersonate another user.
+
+It cannot use delegation tokens for this feature. It would be wrong if superuser adds its own delegation token to the proxy user ugi, as it will allow the proxy user to connect to the service with the privileges of the superuser.
+
+However, if the superuser does want to give a delegation token to joe, it must first impersonate joe and get a delegation token for joe, in the same way as the code example above, and add it to the ugi of joe. In this way the delegation token will have the owner as joe.

http://git-wip-us.apache.org/repos/asf/hadoop/blob/e9d26fe9/hadoop-common-project/hadoop-common/src/site/markdown/Tracing.md
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diff --git a/hadoop-common-project/hadoop-common/src/site/markdown/Tracing.md b/hadoop-common-project/hadoop-common/src/site/markdown/Tracing.md
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+++ b/hadoop-common-project/hadoop-common/src/site/markdown/Tracing.md
@@ -0,0 +1,182 @@
+<!---
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+Enabling Dapper-like Tracing in Hadoop
+======================================
+
+* [Enabling Dapper-like Tracing in Hadoop](#Enabling_Dapper-like_Tracing_in_Hadoop)
+    * [Dapper-like Tracing in Hadoop](#Dapper-like_Tracing_in_Hadoop)
+        * [HTrace](#HTrace)
+        * [Samplers Configure the samplers in core-site.xml property: hadoop.htrace.sampler. The value can be NeverSampler, AlwaysSampler or ProbabilitySampler. NeverSampler: HTrace is OFF for all spans; AlwaysSampler: HTrace is ON for all spans; ProbabilitySampler: HTrace is ON for some percentage% of top-level spans.](#Samplers_Configure_the_samplers_in_core-site.xml_property:_hadoop.htrace.sampler._The_value_can_be_NeverSampler_AlwaysSampler_or_ProbabilitySampler._NeverSampler:_HTrace_is_OFF_for_all_spans_AlwaysSampler:_HTrace_is_ON_for_all_spans_ProbabilitySampler:_HTrace_is_ON_for_some_percentage_of_top-level_spans.)
+        * [SpanReceivers](#SpanReceivers)
+        * [Setting up ZipkinSpanReceiver](#Setting_up_ZipkinSpanReceiver)
+        * [Dynamic update of tracing configuration](#Dynamic_update_of_tracing_configuration)
+        * [Starting tracing spans by HTrace API](#Starting_tracing_spans_by_HTrace_API)
+        * [Sample code for tracing](#Sample_code_for_tracing)
+  
+Dapper-like Tracing in Hadoop
+-----------------------------
+
+### HTrace
+
+[HDFS-5274](https://issues.apache.org/jira/browse/HDFS-5274) added support for tracing requests through HDFS, using the open source tracing library, [Apache HTrace](https://git-wip-us.apache.org/repos/asf/incubator-htrace.git). Setting up tracing is quite simple, however it requires some very minor changes to your client code.
+
+### Samplers Configure the samplers in `core-site.xml` property: `hadoop.htrace.sampler`. The value can be NeverSampler, AlwaysSampler or ProbabilitySampler. NeverSampler: HTrace is OFF for all spans; AlwaysSampler: HTrace is ON for all spans; ProbabilitySampler: HTrace is ON for some percentage% of top-level spans.
+
+      <property>
+        <name>hadoop.htrace.sampler</name>
+        <value>NeverSampler</value>
+      </property>
+
+### SpanReceivers
+
+The tracing system works by collecting information in structs called 'Spans'. It is up to you to choose how you want to receive this information by implementing the SpanReceiver interface, which defines one method:
+
+    public void receiveSpan(Span span);
+
+Configure what SpanReceivers you'd like to use by putting a comma separated list of the fully-qualified class name of classes implementing SpanReceiver in `core-site.xml` property: `hadoop.htrace.spanreceiver.classes`.
+
+      <property>
+        <name>hadoop.htrace.spanreceiver.classes</name>
+        <value>org.apache.htrace.impl.LocalFileSpanReceiver</value>
+      </property>
+      <property>
+        <name>hadoop.htrace.local-file-span-receiver.path</name>
+        <value>/var/log/hadoop/htrace.out</value>
+      </property>
+
+You can omit package name prefix if you use span receiver bundled with HTrace.
+
+      <property>
+        <name>hadoop.htrace.spanreceiver.classes</name>
+        <value>LocalFileSpanReceiver</value>
+      </property>
+
+### Setting up ZipkinSpanReceiver
+
+Instead of implementing SpanReceiver by yourself, you can use `ZipkinSpanReceiver` which uses [Zipkin](https://github.com/twitter/zipkin) for collecting and displaying tracing data.
+
+In order to use `ZipkinSpanReceiver`, you need to download and setup [Zipkin](https://github.com/twitter/zipkin) first.
+
+you also need to add the jar of `htrace-zipkin` to the classpath of Hadoop on each node. Here is example setup procedure.
+
+      $ git clone https://github.com/cloudera/htrace
+      $ cd htrace/htrace-zipkin
+      $ mvn compile assembly:single
+      $ cp target/htrace-zipkin-*-jar-with-dependencies.jar $HADOOP_HOME/share/hadoop/common/lib/
+
+The sample configuration for `ZipkinSpanReceiver` is shown below. By adding these to `core-site.xml` of NameNode and DataNodes, `ZipkinSpanReceiver` is initialized on the startup. You also need this configuration on the client node in addition to the servers.
+
+      <property>
+        <name>hadoop.htrace.spanreceiver.classes</name>
+        <value>ZipkinSpanReceiver</value>
+      </property>
+      <property>
+        <name>hadoop.htrace.zipkin.collector-hostname</name>
+        <value>192.168.1.2</value>
+      </property>
+      <property>
+        <name>hadoop.htrace.zipkin.collector-port</name>
+        <value>9410</value>
+      </property>
+
+### Dynamic update of tracing configuration
+
+You can use `hadoop trace` command to see and update the tracing configuration of each servers. You must specify IPC server address of namenode or datanode by `-host` option. You need to run the command against all servers if you want to update the configuration of all servers.
+
+`hadoop trace -list` shows list of loaded span receivers associated with the id.
+
+      $ hadoop trace -list -host 192.168.56.2:9000
+      ID  CLASS
+      1   org.apache.htrace.impl.LocalFileSpanReceiver
+
+      $ hadoop trace -list -host 192.168.56.2:50020
+      ID  CLASS
+      1   org.apache.htrace.impl.LocalFileSpanReceiver
+
+`hadoop trace -remove` removes span receiver from server. `-remove` options takes id of span receiver as argument.
+
+      $ hadoop trace -remove 1 -host 192.168.56.2:9000
+      Removed trace span receiver 1
+
+`hadoop trace -add` adds span receiver to server. You need to specify the class name of span receiver as argument of `-class` option. You can specify the configuration associated with span receiver by `-Ckey=value` options.
+
+      $ hadoop trace -add -class LocalFileSpanReceiver -Chadoop.htrace.local-file-span-receiver.path=/tmp/htrace.out -host 192.168.56.2:9000
+      Added trace span receiver 2 with configuration hadoop.htrace.local-file-span-receiver.path = /tmp/htrace.out
+
+      $ hadoop trace -list -host 192.168.56.2:9000
+      ID  CLASS
+      2   org.apache.htrace.impl.LocalFileSpanReceiver
+
+### Starting tracing spans by HTrace API
+
+In order to trace, you will need to wrap the traced logic with **tracing span** as shown below. When there is running tracing spans, the tracing information is propagated to servers along with RPC requests.
+
+In addition, you need to initialize `SpanReceiver` once per process.
+
+    import org.apache.hadoop.hdfs.HdfsConfiguration;
+    import org.apache.hadoop.tracing.SpanReceiverHost;
+    import org.apache.htrace.Sampler;
+    import org.apache.htrace.Trace;
+    import org.apache.htrace.TraceScope;
+
+    ...
+
+        SpanReceiverHost.getInstance(new HdfsConfiguration());
+
+    ...
+
+        TraceScope ts = Trace.startSpan("Gets", Sampler.ALWAYS);
+        try {
+          ... // traced logic
+        } finally {
+          if (ts != null) ts.close();
+        }
+
+### Sample code for tracing
+
+The `TracingFsShell.java` shown below is the wrapper of FsShell which start tracing span before invoking HDFS shell command.
+
+    import org.apache.hadoop.conf.Configuration;
+    import org.apache.hadoop.fs.FsShell;
+    import org.apache.hadoop.tracing.SpanReceiverHost;
+    import org.apache.hadoop.util.ToolRunner;
+    import org.apache.htrace.Sampler;
+    import org.apache.htrace.Trace;
+    import org.apache.htrace.TraceScope;
+
+    public class TracingFsShell {
+      public static void main(String argv[]) throws Exception {
+        Configuration conf = new Configuration();
+        FsShell shell = new FsShell();
+        conf.setQuietMode(false);
+        shell.setConf(conf);
+        SpanReceiverHost.getInstance(conf);
+        int res = 0;
+        TraceScope ts = null;
+        try {
+          ts = Trace.startSpan("FsShell", Sampler.ALWAYS);
+          res = ToolRunner.run(shell, argv);
+        } finally {
+          shell.close();
+          if (ts != null) ts.close();
+        }
+        System.exit(res);
+      }
+    }
+
+You can compile and execute this code as shown below.
+
+    $ javac -cp `hadoop classpath` TracingFsShell.java
+    $ java -cp .:`hadoop classpath` TracingFsShell -ls /


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