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[SPARK-23572][DOCS] Bring "security.md" up to date.

Browse source 
This change basically rewrites the security documentation so that it's
up to date with new features, more correct, and more complete.

Because security is such an important feature, I chose to move all the
relevant configuration documentation to the security page, instead of
having them peppered all over the place in the configuration page. This
allows an almost one-stop shop for security configuration in Spark. The
only exceptions are some YARN-specific minor features which I left in
the YARN page.

I also re-organized the page's topics, since they didn't make a lot of
sense. You had kerberos features described inside paragraphs talking
about UI access control, and other oddities. It should be easier now
to find information about specific Spark security features. I also
enabled TOCs for both the Security and YARN pages, since that makes it
easier to see what is covered.

I removed most of the comments from the SecurityManager javadoc since
they just replicated information in the security doc, with different
levels of out-of-dateness.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #20742 from vanzin/SPARK-23572.
This commit is contained in:
Marcelo Vanzin 2018-03-26 12:45:45 -07:00
parent eb48edf9ca
commit b30a7d28b3
6 changed files with 675 additions and 705 deletions
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1
.gitignore vendored
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@ -76,6 +76,7 @@ streaming-tests.log
target/
unit-tests.log
work/
docs/.jekyll-metadata
# For Hive
TempStatsStore/

144
core/src/main/scala/org/apache/spark/SecurityManager.scala
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@ -42,148 +42,10 @@ import org.apache.spark.util.Utils
* should access it from that. There are some cases where the SparkEnv hasn't been
* initialized yet and this class must be instantiated directly.
*
* Spark currently supports authentication via a shared secret.
* Authentication can be configured to be on via the 'spark.authenticate' configuration
* parameter. This parameter controls whether the Spark communication protocols do
* authentication using the shared secret. This authentication is a basic handshake to
* make sure both sides have the same shared secret and are allowed to communicate.
* If the shared secret is not identical they will not be allowed to communicate.
*
* The Spark UI can also be secured by using javax servlet filters. A user may want to
* secure the UI if it has data that other users should not be allowed to see. The javax
* servlet filter specified by the user can authenticate the user and then once the user
* is logged in, Spark can compare that user versus the view acls to make sure they are
* authorized to view the UI. The configs 'spark.acls.enable', 'spark.ui.view.acls' and
* 'spark.ui.view.acls.groups' control the behavior of the acls. Note that the person who
* started the application always has view access to the UI.
*
* Spark has a set of individual and group modify acls (`spark.modify.acls`) and
* (`spark.modify.acls.groups`) that controls which users and groups have permission to
* modify a single application. This would include things like killing the application.
* By default the person who started the application has modify access. For modify access
* through the UI, you must have a filter that does authentication in place for the modify
* acls to work properly.
*
* Spark also has a set of individual and group admin acls (`spark.admin.acls`) and
* (`spark.admin.acls.groups`) which is a set of users/administrators and admin groups
* who always have permission to view or modify the Spark application.
*
* Starting from version 1.3, Spark has partial support for encrypted connections with SSL.
*
* At this point spark has multiple communication protocols that need to be secured and
* different underlying mechanisms are used depending on the protocol:
*
* - HTTP for broadcast and file server (via HttpServer) -> Spark currently uses Jetty
* for the HttpServer. Jetty supports multiple authentication mechanisms -
* Basic, Digest, Form, Spnego, etc. It also supports multiple different login
* services - Hash, JAAS, Spnego, JDBC, etc. Spark currently uses the HashLoginService
* to authenticate using DIGEST-MD5 via a single user and the shared secret.
* Since we are using DIGEST-MD5, the shared secret is not passed on the wire
* in plaintext.
*
* We currently support SSL (https) for this communication protocol (see the details
* below).
*
* The Spark HttpServer installs the HashLoginServer and configures it to DIGEST-MD5.
* Any clients must specify the user and password. There is a default
* Authenticator installed in the SecurityManager to how it does the authentication
* and in this case gets the user name and password from the request.
*
* - BlockTransferService -> The Spark BlockTransferServices uses java nio to asynchronously
* exchange messages. For this we use the Java SASL
* (Simple Authentication and Security Layer) API and again use DIGEST-MD5
* as the authentication mechanism. This means the shared secret is not passed
* over the wire in plaintext.
* Note that SASL is pluggable as to what mechanism it uses. We currently use
* DIGEST-MD5 but this could be changed to use Kerberos or other in the future.
* Spark currently supports "auth" for the quality of protection, which means
* the connection does not support integrity or privacy protection (encryption)
* after authentication. SASL also supports "auth-int" and "auth-conf" which
* SPARK could support in the future to allow the user to specify the quality
* of protection they want. If we support those, the messages will also have to
* be wrapped and unwrapped via the SaslServer/SaslClient.wrap/unwrap API's.
*
* Since the NioBlockTransferService does asynchronous messages passing, the SASL
* authentication is a bit more complex. A ConnectionManager can be both a client
* and a Server, so for a particular connection it has to determine what to do.
* A ConnectionId was added to be able to track connections and is used to
* match up incoming messages with connections waiting for authentication.
* The ConnectionManager tracks all the sendingConnections using the ConnectionId,
* waits for the response from the server, and does the handshake before sending
* the real message.
*
* The NettyBlockTransferService ensures that SASL authentication is performed
* synchronously prior to any other communication on a connection. This is done in
* SaslClientBootstrap on the client side and SaslRpcHandler on the server side.
*
* - HTTP for the Spark UI -> the UI was changed to use servlets so that javax servlet filters
* can be used. Yarn requires a specific AmIpFilter be installed for security to work
* properly. For non-Yarn deployments, users can write a filter to go through their
* organization's normal login service. If an authentication filter is in place then the
* SparkUI can be configured to check the logged in user against the list of users who
* have view acls to see if that user is authorized.
* The filters can also be used for many different purposes. For instance filters
* could be used for logging, encryption, or compression.
*
* The exact mechanisms used to generate/distribute the shared secret are deployment-specific.
*
* For YARN deployments, the secret is automatically generated. The secret is placed in the Hadoop
* UGI which gets passed around via the Hadoop RPC mechanism. Hadoop RPC can be configured to
* support different levels of protection. See the Hadoop documentation for more details. Each
* Spark application on YARN gets a different shared secret.
*
* On YARN, the Spark UI gets configured to use the Hadoop YARN AmIpFilter which requires the user
* to go through the ResourceManager Proxy. That proxy is there to reduce the possibility of web
* based attacks through YARN. Hadoop can be configured to use filters to do authentication. That
* authentication then happens via the ResourceManager Proxy and Spark will use that to do
* authorization against the view acls.
*
* For other Spark deployments, the shared secret must be specified via the
* spark.authenticate.secret config.
* All the nodes (Master and Workers) and the applications need to have the same shared secret.
* This again is not ideal as one user could potentially affect another users application.
* This should be enhanced in the future to provide better protection.
* If the UI needs to be secure, the user needs to install a javax servlet filter to do the
* authentication. Spark will then use that user to compare against the view acls to do
* authorization. If not filter is in place the user is generally null and no authorization
* can take place.
*
* When authentication is being used, encryption can also be enabled by setting the option
* spark.authenticate.enableSaslEncryption to true. This is only supported by communication
* channels that use the network-common library, and can be used as an alternative to SSL in those
* cases.
*
* SSL can be used for encryption for certain communication channels. The user can configure the
* default SSL settings which will be used for all the supported communication protocols unless
* they are overwritten by protocol specific settings. This way the user can easily provide the
* common settings for all the protocols without disabling the ability to configure each one
* individually.
*
* All the SSL settings like `spark.ssl.xxx` where `xxx` is a particular configuration property,
* denote the global configuration for all the supported protocols. In order to override the global
* configuration for the particular protocol, the properties must be overwritten in the
* protocol-specific namespace. Use `spark.ssl.yyy.xxx` settings to overwrite the global
* configuration for particular protocol denoted by `yyy`. Currently `yyy` can be only`fs` for
* broadcast and file server.
*
* Refer to [[org.apache.spark.SSLOptions]] documentation for the list of
* options that can be specified.
*
* SecurityManager initializes SSLOptions objects for different protocols separately. SSLOptions
* object parses Spark configuration at a given namespace and builds the common representation
* of SSL settings. SSLOptions is then used to provide protocol-specific SSLContextFactory for
* Jetty.
*
* SSL must be configured on each node and configured for each component involved in
* communication using the particular protocol. In YARN clusters, the key-store can be prepared on
* the client side then distributed and used by the executors as the part of the application
* (YARN allows the user to deploy files before the application is started).
* In standalone deployment, the user needs to provide key-stores and configuration
* options for master and workers. In this mode, the user may allow the executors to use the SSL
* settings inherited from the worker which spawned that executor. It can be accomplished by
* setting `spark.ssl.useNodeLocalConf` to `true`.
* This class implements all of the configuration related to security features described
* in the "Security" document. Please refer to that document for specific features implemented
* here.
*/
private[spark] class SecurityManager(
sparkConf: SparkConf,
val ioEncryptionKey: Option[Array[Byte]] = None)

359
docs/configuration.md
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@ -712,30 +712,6 @@ Apart from these, the following properties are also available, and may be useful
When we fail to register to the external shuffle service, we will retry for maxAttempts times.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.enabled</code></td>
<td>false</td>
<td>
Enable IO encryption. Currently supported by all modes except Mesos. It's recommended that RPC encryption
be enabled when using this feature.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.keySizeBits</code></td>
<td>128</td>
<td>
IO encryption key size in bits. Supported values are 128, 192 and 256.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.keygen.algorithm</code></td>
<td>HmacSHA1</td>
<td>
The algorithm to use when generating the IO encryption key. The supported algorithms are
described in the KeyGenerator section of the Java Cryptography Architecture Standard Algorithm
Name Documentation.
</td>
</tr>
</table>
### Spark UI
@ -893,6 +869,23 @@ Apart from these, the following properties are also available, and may be useful
How many dead executors the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
<tr>
<td><code>spark.ui.filters</code></td>
<td>None</td>
<td>
Comma separated list of filter class names to apply to the Spark Web UI. The filter should be a
standard <a href="http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html">
javax servlet Filter</a>.
<br />Filter parameters can also be specified in the configuration, by setting config entries
of the form <code>spark.&lt;class name of filter&gt;.param.&lt;param name&gt;=&lt;value&gt;</code>
<br />For example:
<br /><code>spark.ui.filters=com.test.filter1</code>
<br /><code>spark.com.test.filter1.param.name1=foo</code>
<br /><code>spark.com.test.filter1.param.name2=bar</code>
</td>
</tr>
</table>
### Compression and Serialization
@ -1446,6 +1439,15 @@ Apart from these, the following properties are also available, and may be useful
Duration for an RPC remote endpoint lookup operation to wait before timing out.
</td>
</tr>
<tr>
<td><code>spark.core.connection.ack.wait.timeout</code></td>
<td><code>spark.network.timeout</code></td>
<td>
How long for the connection to wait for ack to occur before timing
out and giving up. To avoid unwilling timeout caused by long pause like GC,
you can set larger value.
</td>
</tr>
</table>
### Scheduling
@ -1817,313 +1819,8 @@ Apart from these, the following properties are also available, and may be useful
### Security
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.acls.enable</code></td>
<td>false</td>
<td>
Whether Spark acls should be enabled. If enabled, this checks to see if the user has
access permissions to view or modify the job. Note this requires the user to be known,
so if the user comes across as null no checks are done. Filters can be used with the UI
to authenticate and set the user.
</td>
</tr>
<tr>
<td><code>spark.admin.acls</code></td>
<td>Empty</td>
<td>
Comma separated list of users/administrators that have view and modify access to all Spark jobs.
This can be used if you run on a shared cluster and have a set of administrators or devs who
help debug when things do not work. Putting a "*" in the list means any user can have the
privilege of admin.
</td>
</tr>
<tr>
<td><code>spark.admin.acls.groups</code></td>
<td>Empty</td>
<td>
Comma separated list of groups that have view and modify access to all Spark jobs.
This can be used if you have a set of administrators or developers who help maintain and debug
the underlying infrastructure. Putting a "*" in the list means any user in any group can have
the privilege of admin. The user groups are obtained from the instance of the groups mapping
provider specified by <code>spark.user.groups.mapping</code>. Check the entry
<code>spark.user.groups.mapping</code> for more details.
</td>
</tr>
<tr>
<td><code>spark.user.groups.mapping</code></td>
<td><code>org.apache.spark.security.ShellBasedGroupsMappingProvider</code></td>
<td>
The list of groups for a user is determined by a group mapping service defined by the trait
org.apache.spark.security.GroupMappingServiceProvider which can be configured by this property.
A default unix shell based implementation is provided <code>org.apache.spark.security.ShellBasedGroupsMappingProvider</code>
which can be specified to resolve a list of groups for a user.
<em>Note:</em> This implementation supports only a Unix/Linux based environment. Windows environment is
currently <b>not</b> supported. However, a new platform/protocol can be supported by implementing
the trait <code>org.apache.spark.security.GroupMappingServiceProvider</code>.
</td>
</tr>
<tr>
<td><code>spark.authenticate</code></td>
<td>false</td>
<td>
Whether Spark authenticates its internal connections. See
<code>spark.authenticate.secret</code> if not running on YARN.
</td>
</tr>
<tr>
<td><code>spark.authenticate.secret</code></td>
<td>None</td>
<td>
Set the secret key used for Spark to authenticate between components. This needs to be set if
not running on YARN and authentication is enabled.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.enabled</code></td>
<td>false</td>
<td>
Enable encryption using the commons-crypto library for RPC and block transfer service.
Requires <code>spark.authenticate</code> to be enabled.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.keyLength</code></td>
<td>128</td>
<td>
The length in bits of the encryption key to generate. Valid values are 128, 192 and 256.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.keyFactoryAlgorithm</code></td>
<td>PBKDF2WithHmacSHA1</td>
<td>
The key factory algorithm to use when generating encryption keys. Should be one of the
algorithms supported by the javax.crypto.SecretKeyFactory class in the JRE being used.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.saslFallback</code></td>
<td>true</td>
<td>
Whether to fall back to SASL authentication if authentication fails using Spark's internal
mechanism. This is useful when the application is connecting to old shuffle services that
do not support the internal Spark authentication protocol. On the server side, this can be
used to block older clients from authenticating against a new shuffle service.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.config.*</code></td>
<td>None</td>
<td>
Configuration values for the commons-crypto library, such as which cipher implementations to
use. The config name should be the name of commons-crypto configuration without the
"commons.crypto" prefix.
</td>
</tr>
<tr>
<td><code>spark.authenticate.enableSaslEncryption</code></td>
<td>false</td>
<td>
Enable encrypted communication when authentication is
enabled. This is supported by the block transfer service and the
RPC endpoints.
</td>
</tr>
<tr>
<td><code>spark.network.sasl.serverAlwaysEncrypt</code></td>
<td>false</td>
<td>
Disable unencrypted connections for services that support SASL authentication.
</td>
</tr>
<tr>
<td><code>spark.core.connection.ack.wait.timeout</code></td>
<td><code>spark.network.timeout</code></td>
<td>
How long for the connection to wait for ack to occur before timing
out and giving up. To avoid unwilling timeout caused by long pause like GC,
you can set larger value.
</td>
</tr>
<tr>
<td><code>spark.modify.acls</code></td>
<td>Empty</td>
<td>
Comma separated list of users that have modify access to the Spark job. By default only the
user that started the Spark job has access to modify it (kill it for example). Putting a "*" in
the list means any user can have access to modify it.
</td>
</tr>
<tr>
<td><code>spark.modify.acls.groups</code></td>
<td>Empty</td>
<td>
Comma separated list of groups that have modify access to the Spark job. This can be used if you
have a set of administrators or developers from the same team to have access to control the job.
Putting a "*" in the list means any user in any group has the access to modify the Spark job.
The user groups are obtained from the instance of the groups mapping provider specified by
<code>spark.user.groups.mapping</code>. Check the entry <code>spark.user.groups.mapping</code>
for more details.
</td>
</tr>
<tr>
<td><code>spark.ui.filters</code></td>
<td>None</td>
<td>
Comma separated list of filter class names to apply to the Spark web UI. The filter should be a
standard <a href="http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html">
javax servlet Filter</a>. Parameters to each filter can also be specified by setting a
java system property of: <br />
<code>spark.&lt;class name of filter&gt;.params='param1=value1,param2=value2'</code><br />
For example: <br />
<code>-Dspark.ui.filters=com.test.filter1</code> <br />
<code>-Dspark.com.test.filter1.params='param1=foo,param2=testing'</code>
</td>
</tr>
<tr>
<td><code>spark.ui.view.acls</code></td>
<td>Empty</td>
<td>
Comma separated list of users that have view access to the Spark web ui. By default only the
user that started the Spark job has view access. Putting a "*" in the list means any user can
have view access to this Spark job.
</td>
</tr>
<tr>
<td><code>spark.ui.view.acls.groups</code></td>
<td>Empty</td>
<td>
Comma separated list of groups that have view access to the Spark web ui to view the Spark Job
details. This can be used if you have a set of administrators or developers or users who can
monitor the Spark job submitted. Putting a "*" in the list means any user in any group can view
the Spark job details on the Spark web ui. The user groups are obtained from the instance of the
groups mapping provider specified by <code>spark.user.groups.mapping</code>. Check the entry
<code>spark.user.groups.mapping</code> for more details.
</td>
</tr>
</table>
### TLS / SSL
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.ssl.enabled</code></td>
<td>false</td>
<td>
Whether to enable SSL connections on all supported protocols.
<br />When <code>spark.ssl.enabled</code> is configured, <code>spark.ssl.protocol</code>
is required.
<br />All the SSL settings like <code>spark.ssl.xxx</code> where <code>xxx</code> is a
particular configuration property, denote the global configuration for all the supported
protocols. In order to override the global configuration for the particular protocol,
the properties must be overwritten in the protocol-specific namespace.
<br />Use <code>spark.ssl.YYY.XXX</code> settings to overwrite the global configuration for
particular protocol denoted by <code>YYY</code>. Example values for <code>YYY</code>
include <code>fs</code>, <code>ui</code>, <code>standalone</code>, and
<code>historyServer</code>. See <a href="security.html#ssl-configuration">SSL
Configuration</a> for details on hierarchical SSL configuration for services.
</td>
</tr>
<tr>
<td><code>spark.ssl.[namespace].port</code></td>
<td>None</td>
<td>
The port where the SSL service will listen on.
<br />The port must be defined within a namespace configuration; see
<a href="security.html#ssl-configuration">SSL Configuration</a> for the available
namespaces.
<br />When not set, the SSL port will be derived from the non-SSL port for the
same service. A value of "0" will make the service bind to an ephemeral port.
</td>
</tr>
<tr>
<td><code>spark.ssl.enabledAlgorithms</code></td>
<td>Empty</td>
<td>
A comma separated list of ciphers. The specified ciphers must be supported by JVM.
The reference list of protocols one can find on
<a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">this</a>
page.
Note: If not set, it will use the default cipher suites of JVM.
</td>
</tr>
<tr>
<td><code>spark.ssl.keyPassword</code></td>
<td>None</td>
<td>
A password to the private key in key-store.
</td>
</tr>
<tr>
<td><code>spark.ssl.keyStore</code></td>
<td>None</td>
<td>
A path to a key-store file. The path can be absolute or relative to the directory where
the component is started in.
</td>
</tr>
<tr>
<td><code>spark.ssl.keyStorePassword</code></td>
<td>None</td>
<td>
A password to the key-store.
</td>
</tr>
<tr>
<td><code>spark.ssl.keyStoreType</code></td>
<td>JKS</td>
<td>
The type of the key-store.
</td>
</tr>
<tr>
<td><code>spark.ssl.protocol</code></td>
<td>None</td>
<td>
A protocol name. The protocol must be supported by JVM. The reference list of protocols
one can find on <a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">this</a>
page.
</td>
</tr>
<tr>
<td><code>spark.ssl.needClientAuth</code></td>
<td>false</td>
<td>
Set true if SSL needs client authentication.
</td>
</tr>
<tr>
<td><code>spark.ssl.trustStore</code></td>
<td>None</td>
<td>
A path to a trust-store file. The path can be absolute or relative to the directory
where the component is started in.
</td>
</tr>
<tr>
<td><code>spark.ssl.trustStorePassword</code></td>
<td>None</td>
<td>
A password to the trust-store.
</td>
</tr>
<tr>
<td><code>spark.ssl.trustStoreType</code></td>
<td>JKS</td>
<td>
The type of the trust-store.
</td>
</tr>
</table>
Please refer to the [Security](security.html) page for available options on how to secure different
Spark subsystems.
### Spark SQL

40
docs/monitoring.md
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@ -80,7 +80,10 @@ The history server can be configured as follows:
</tr>
</table>
### Spark configuration options
### Spark History Server Configuration Options
Security options for the Spark History Server are covered more detail in the
[Security](security.html#web-ui) page.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
@ -160,41 +163,6 @@ The history server can be configured as follows:
Location of the kerberos keytab file for the History Server.
</td>
</tr>
<tr>
<td>spark.history.ui.acls.enable</td>
<td>false</td>
<td>
Specifies whether acls should be checked to authorize users viewing the applications.
If enabled, access control checks are made regardless of what the individual application had
set for <code>spark.ui.acls.enable</code> when the application was run. The application owner
will always have authorization to view their own application and any users specified via
<code>spark.ui.view.acls</code> and groups specified via <code>spark.ui.view.acls.groups</code>
when the application was run will also have authorization to view that application.
If disabled, no access control checks are made.
</td>
</tr>
<tr>
<td>spark.history.ui.admin.acls</td>
<td>empty</td>
<td>
Comma separated list of users/administrators that have view access to all the Spark applications in
history server. By default only the users permitted to view the application at run-time could
access the related application history, with this, configured users/administrators could also
have the permission to access it.
Putting a "*" in the list means any user can have the privilege of admin.
</td>
</tr>
<tr>
<td>spark.history.ui.admin.acls.groups</td>
<td>empty</td>
<td>
Comma separated list of groups that have view access to all the Spark applications in
history server. By default only the groups permitted to view the application at run-time could
access the related application history, with this, configured groups could also
have the permission to access it.
Putting a "*" in the list means any group can have the privilege of admin.
</td>
</tr>
<tr>
<td>spark.history.fs.cleaner.enabled</td>
<td>false</td>

205
docs/running-on-yarn.md
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@ -2,6 +2,8 @@
layout: global
title: Running Spark on YARN
---
* This will become a table of contents (this text will be scraped).
{:toc}
Support for running on [YARN (Hadoop
NextGen)](http://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html)
@ -217,8 +219,8 @@ To use a custom metrics.properties for the application master and executors, upd
<td><code>spark.yarn.dist.forceDownloadSchemes</code></td>
<td><code>(none)</code></td>
<td>
Comma-separated list of schemes for which files will be downloaded to the local disk prior to
being added to YARN's distributed cache. For use in cases where the YARN service does not
Comma-separated list of schemes for which files will be downloaded to the local disk prior to
being added to YARN's distributed cache. For use in cases where the YARN service does not
support schemes that are supported by Spark, like http, https and ftp.
</td>
</tr>
@ -265,19 +267,6 @@ To use a custom metrics.properties for the application master and executors, upd
distribution.
</td>
</tr>
<tr>
<td><code>spark.yarn.access.hadoopFileSystems</code></td>
<td>(none)</td>
<td>
A comma-separated list of secure Hadoop filesystems your Spark application is going to access. For
example, <code>spark.yarn.access.hadoopFileSystems=hdfs://nn1.com:8032,hdfs://nn2.com:8032,
webhdfs://nn3.com:50070</code>. The Spark application must have access to the filesystems listed
and Kerberos must be properly configured to be able to access them (either in the same realm
or in a trusted realm). Spark acquires security tokens for each of the filesystems so that
the Spark application can access those remote Hadoop filesystems. <code>spark.yarn.access.namenodes</code>
is deprecated, please use this instead.
</td>
</tr>
<tr>
<td><code>spark.yarn.appMasterEnv.[EnvironmentVariableName]</code></td>
<td>(none)</td>
@ -373,31 +362,6 @@ To use a custom metrics.properties for the application master and executors, upd
in YARN ApplicationReports, which can be used for filtering when querying YARN apps.
</td>
</tr>
<tr>
<td><code>spark.yarn.keytab</code></td>
<td>(none)</td>
<td>
The full path to the file that contains the keytab for the principal specified above.
This keytab will be copied to the node running the YARN Application Master via the Secure Distributed Cache,
for renewing the login tickets and the delegation tokens periodically. (Works also with the "local" master)
</td>
</tr>
<tr>
<td><code>spark.yarn.principal</code></td>
<td>(none)</td>
<td>
Principal to be used to login to KDC, while running on secure HDFS. (Works also with the "local" master)
</td>
</tr>
<tr>
<td><code>spark.yarn.kerberos.relogin.period</code></td>
<td>1m</td>
<td>
How often to check whether the kerberos TGT should be renewed. This should be set to a value
that is shorter than the TGT renewal period (or the TGT lifetime if TGT renewal is not enabled).
The default value should be enough for most deployments.
</td>
</tr>
<tr>
<td><code>spark.yarn.config.gatewayPath</code></td>
<td>(none)</td>
@ -424,17 +388,6 @@ To use a custom metrics.properties for the application master and executors, upd
See <code>spark.yarn.config.gatewayPath</code>.
</td>
</tr>
<tr>
<td><code>spark.security.credentials.${service}.enabled</code></td>
<td><code>true</code></td>
<td>
Controls whether to obtain credentials for services when security is enabled.
By default, credentials for all supported services are retrieved when those services are
configured, but it's possible to disable that behavior if it somehow conflicts with the
application being run. For further details please see
[Running in a Secure Cluster](running-on-yarn.html#running-in-a-secure-cluster)
</td>
</tr>
<tr>
<td><code>spark.yarn.rolledLog.includePattern</code></td>
<td>(none)</td>
@ -468,48 +421,104 @@ To use a custom metrics.properties for the application master and executors, upd
- The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named `localtest.txt` into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN.
- The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
# Running in a Secure Cluster
# Kerberos
As covered in [security](security.html), Kerberos is used in a secure Hadoop cluster to
authenticate principals associated with services and clients. This allows clients to
make requests of these authenticated services; the services to grant rights
to the authenticated principals.
Standard Kerberos support in Spark is covered in the [Security](security.html#kerberos) page.
Hadoop services issue *hadoop tokens* to grant access to the services and data.
Clients must first acquire tokens for the services they will access and pass them along with their
application as it is launched in the YARN cluster.
In YARN mode, when accessing Hadoop file systems, aside from the service hosting the user's home
directory, Spark will also automatically obtain delegation tokens for the service hosting the
staging directory of the Spark application.
For a Spark application to interact with any of the Hadoop filesystem (for example hdfs, webhdfs, etc), HBase and Hive, it must acquire the relevant tokens
using the Kerberos credentials of the user launching the application
—that is, the principal whose identity will become that of the launched Spark application.
If an application needs to interact with other secure Hadoop filesystems, their URIs need to be
explicitly provided to Spark at launch time. This is done by listing them in the
`spark.yarn.access.hadoopFileSystems` property, described in the configuration section below.
This is normally done at launch time: in a secure cluster Spark will automatically obtain a
token for the cluster's default Hadoop filesystem, and potentially for HBase and Hive.
The YARN integration also supports custom delegation token providers using the Java Services
mechanism (see `java.util.ServiceLoader`). Implementations of
`org.apache.spark.deploy.yarn.security.ServiceCredentialProvider` can be made available to Spark
by listing their names in the corresponding file in the jar's `META-INF/services` directory. These
providers can be disabled individually by setting `spark.security.credentials.{service}.enabled` to
`false`, where `{service}` is the name of the credential provider.
An HBase token will be obtained if HBase is in on classpath, the HBase configuration declares
the application is secure (i.e. `hbase-site.xml` sets `hbase.security.authentication` to `kerberos`),
and `spark.security.credentials.hbase.enabled` is not set to `false`.
## YARN-specific Kerberos Configuration
Similarly, a Hive token will be obtained if Hive is on the classpath, its configuration
includes a URI of the metadata store in `"hive.metastore.uris`, and
`spark.security.credentials.hive.enabled` is not set to `false`.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.yarn.keytab</code></td>
<td>(none)</td>
<td>
The full path to the file that contains the keytab for the principal specified above. This keytab
will be copied to the node running the YARN Application Master via the YARN Distributed Cache, and
will be used for renewing the login tickets and the delegation tokens periodically. Equivalent to
the <code>--keytab</code> command line argument.
If an application needs to interact with other secure Hadoop filesystems, then
the tokens needed to access these clusters must be explicitly requested at
launch time. This is done by listing them in the `spark.yarn.access.hadoopFileSystems` property.
<br /> (Works also with the "local" master.)
</td>
</tr>
<tr>
<td><code>spark.yarn.principal</code></td>
<td>(none)</td>
<td>
Principal to be used to login to KDC, while running on secure clusters. Equivalent to the
<code>--principal</code> command line argument.
```
spark.yarn.access.hadoopFileSystems hdfs://ireland.example.org:8020/,webhdfs://frankfurt.example.org:50070/
<br /> (Works also with the "local" master.)
</td>
</tr>
<tr>
<td><code>spark.yarn.access.hadoopFileSystems</code></td>
<td>(none)</td>
<td>
A comma-separated list of secure Hadoop filesystems your Spark application is going to access. For
example, <code>spark.yarn.access.hadoopFileSystems=hdfs://nn1.com:8032,hdfs://nn2.com:8032,
webhdfs://nn3.com:50070</code>. The Spark application must have access to the filesystems listed
and Kerberos must be properly configured to be able to access them (either in the same realm
or in a trusted realm). Spark acquires security tokens for each of the filesystems so that
the Spark application can access those remote Hadoop filesystems.
</td>
</tr>
<tr>
<td><code>spark.yarn.kerberos.relogin.period</code></td>
<td>1m</td>
<td>
How often to check whether the kerberos TGT should be renewed. This should be set to a value
that is shorter than the TGT renewal period (or the TGT lifetime if TGT renewal is not enabled).
The default value should be enough for most deployments.
</td>
</tr>
</table>
## Troubleshooting Kerberos
Debugging Hadoop/Kerberos problems can be "difficult". One useful technique is to
enable extra logging of Kerberos operations in Hadoop by setting the `HADOOP_JAAS_DEBUG`
environment variable.
```bash
export HADOOP_JAAS_DEBUG=true
```
Spark supports integrating with other security-aware services through Java Services mechanism (see
`java.util.ServiceLoader`). To do that, implementations of `org.apache.spark.deploy.yarn.security.ServiceCredentialProvider`
should be available to Spark by listing their names in the corresponding file in the jar's
`META-INF/services` directory. These plug-ins can be disabled by setting
`spark.security.credentials.{service}.enabled` to `false`, where `{service}` is the name of
credential provider.
The JDK classes can be configured to enable extra logging of their Kerberos and
SPNEGO/REST authentication via the system properties `sun.security.krb5.debug`
and `sun.security.spnego.debug=true`
## Configuring the External Shuffle Service
```
-Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
```
All these options can be enabled in the Application Master:
```
spark.yarn.appMasterEnv.HADOOP_JAAS_DEBUG true
spark.yarn.am.extraJavaOptions -Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
```
Finally, if the log level for `org.apache.spark.deploy.yarn.Client` is set to `DEBUG`, the log
will include a list of all tokens obtained, and their expiry details
# Configuring the External Shuffle Service
To start the Spark Shuffle Service on each `NodeManager` in your YARN cluster, follow these
instructions:
@ -542,7 +551,7 @@ The following extra configuration options are available when the shuffle service
</tr>
</table>
## Launching your application with Apache Oozie
# Launching your application with Apache Oozie
Apache Oozie can launch Spark applications as part of a workflow.
In a secure cluster, the launched application will need the relevant tokens to access the cluster's
@ -576,35 +585,7 @@ spark.security.credentials.hbase.enabled false
The configuration option `spark.yarn.access.hadoopFileSystems` must be unset.
## Troubleshooting Kerberos
Debugging Hadoop/Kerberos problems can be "difficult". One useful technique is to
enable extra logging of Kerberos operations in Hadoop by setting the `HADOOP_JAAS_DEBUG`
environment variable.
```bash
export HADOOP_JAAS_DEBUG=true
```
The JDK classes can be configured to enable extra logging of their Kerberos and
SPNEGO/REST authentication via the system properties `sun.security.krb5.debug`
and `sun.security.spnego.debug=true`
```
-Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
```
All these options can be enabled in the Application Master:
```
spark.yarn.appMasterEnv.HADOOP_JAAS_DEBUG true
spark.yarn.am.extraJavaOptions -Dsun.security.krb5.debug=true -Dsun.security.spnego.debug=true
```
Finally, if the log level for `org.apache.spark.deploy.yarn.Client` is set to `DEBUG`, the log
will include a list of all tokens obtained, and their expiry details
## Using the Spark History Server to replace the Spark Web UI
# Using the Spark History Server to replace the Spark Web UI
It is possible to use the Spark History Server application page as the tracking URL for running
applications when the application UI is disabled. This may be desirable on secure clusters, or to

631
docs/security.md
View file

@ -3,47 +3,336 @@ layout: global
displayTitle: Spark Security
title: Security
---
* This will become a table of contents (this text will be scraped).
{:toc}
Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the `spark.authenticate` configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate. The shared secret is created as follows:
# Spark RPC
* For Spark on [YARN](running-on-yarn.html) and local deployments, configuring `spark.authenticate` to `true` will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret.
* For other types of Spark deployments, the Spark parameter `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications.
## Authentication
## Web UI
Spark currently supports authentication for RPC channels using a shared secret. Authentication can
be turned on by setting the `spark.authenticate` configuration parameter.
The Spark UI can be secured by using [javax servlet filters](http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html) via the `spark.ui.filters` setting
and by using [https/SSL](http://en.wikipedia.org/wiki/HTTPS) via [SSL settings](security.html#ssl-configuration).
The exact mechanism used to generate and distribute the shared secret is deployment-specific.
### Authentication
For Spark on [YARN](running-on-yarn.html) and local deployments, Spark will automatically handle
generating and distributing the shared secret. Each application will use a unique shared secret. In
the case of YARN, this feature relies on YARN RPC encryption being enabled for the distribution of
secrets to be secure.
A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view ACLs to make sure they are authorized to view the UI. The configs `spark.acls.enable`, `spark.ui.view.acls` and `spark.ui.view.acls.groups` control the behavior of the ACLs. Note that the user who started the application always has view access to the UI. On YARN, the Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters.
For other resource managers, `spark.authenticate.secret` must be configured on each of the nodes.
This secret will be shared by all the daemons and applications, so this deployment configuration is
not as secure as the above, especially when considering multi-tenant clusters.
Spark also supports modify ACLs to control who has access to modify a running Spark application. This includes things like killing the application or a task. This is controlled by the configs `spark.acls.enable`, `spark.modify.acls` and `spark.modify.acls.groups`. Note that if you are authenticating the web UI, in order to use the kill button on the web UI it might be necessary to add the users in the modify acls to the view acls also. On YARN, the modify acls are passed in and control who has modify access via YARN interfaces.
Spark allows for a set of administrators to be specified in the acls who always have view and modify permissions to all the applications. is controlled by the configs `spark.admin.acls` and `spark.admin.acls.groups`. This is useful on a shared cluster where you might have administrators or support staff who help users debug applications.
## Event Logging
If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secured, the permissions should be set to `drwxrwxrwxt` for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.authenticate</code></td>
<td>false</td>
<td>Whether Spark authenticates its internal connections.</td>
</tr>
<tr>
<td><code>spark.authenticate.secret</code></td>
<td>None</td>
<td>
The secret key used authentication. See above for when this configuration should be set.
</td>
</tr>
</table>
## Encryption
Spark supports SSL for HTTP protocols. SASL encryption is supported for the block transfer service
and the RPC endpoints. Shuffle files can also be encrypted if desired.
Spark supports AES-based encryption for RPC connections. For encryption to be enabled, RPC
authentication must also be enabled and properly configured. AES encryption uses the
[Apache Commons Crypto](http://commons.apache.org/proper/commons-crypto/) library, and Spark's
configuration system allows access to that library's configuration for advanced users.
### SSL Configuration
There is also support for SASL-based encryption, although it should be considered deprecated. It
is still required when talking to shuffle services from Spark versions older than 2.2.0.
The following table describes the different options available for configuring this feature.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.network.crypto.enabled</code></td>
<td>false</td>
<td>
Enable AES-based RPC encryption, including the new authentication protocol added in 2.2.0.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.keyLength</code></td>
<td>128</td>
<td>
The length in bits of the encryption key to generate. Valid values are 128, 192 and 256.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.keyFactoryAlgorithm</code></td>
<td>PBKDF2WithHmacSHA1</td>
<td>
The key factory algorithm to use when generating encryption keys. Should be one of the
algorithms supported by the javax.crypto.SecretKeyFactory class in the JRE being used.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.config.*</code></td>
<td>None</td>
<td>
Configuration values for the commons-crypto library, such as which cipher implementations to
use. The config name should be the name of commons-crypto configuration without the
<code>commons.crypto</code> prefix.
</td>
</tr>
<tr>
<td><code>spark.network.crypto.saslFallback</code></td>
<td>true</td>
<td>
Whether to fall back to SASL authentication if authentication fails using Spark's internal
mechanism. This is useful when the application is connecting to old shuffle services that
do not support the internal Spark authentication protocol. On the shuffle service side,
disabling this feature will block older clients from authenticating.
</td>
</tr>
<tr>
<td><code>spark.authenticate.enableSaslEncryption</code></td>
<td>false</td>
<td>
Enable SASL-based encrypted communication.
</td>
</tr>
<tr>
<td><code>spark.network.sasl.serverAlwaysEncrypt</code></td>
<td>false</td>
<td>
Disable unencrypted connections for ports using SASL authentication. This will deny connections
from clients that have authentication enabled, but do not request SASL-based encryption.
</td>
</tr>
</table>
# Local Storage Encryption
Spark supports encrypting temporary data written to local disks. This covers shuffle files, shuffle
spills and data blocks stored on disk (for both caching and broadcast variables). It does not cover
encrypting output data generated by applications with APIs such as `saveAsHadoopFile` or
`saveAsTable`.
The following settings cover enabling encryption for data written to disk:
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.io.encryption.enabled</code></td>
<td>false</td>
<td>
Enable local disk I/O encryption. Currently supported by all modes except Mesos. It's strongly
recommended that RPC encryption be enabled when using this feature.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.keySizeBits</code></td>
<td>128</td>
<td>
IO encryption key size in bits. Supported values are 128, 192 and 256.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.keygen.algorithm</code></td>
<td>HmacSHA1</td>
<td>
The algorithm to use when generating the IO encryption key. The supported algorithms are
described in the KeyGenerator section of the Java Cryptography Architecture Standard Algorithm
Name Documentation.
</td>
</tr>
<tr>
<td><code>spark.io.encryption.commons.config.*</code></td>
<td>None</td>
<td>
Configuration values for the commons-crypto library, such as which cipher implementations to
use. The config name should be the name of commons-crypto configuration without the
<code>commons.crypto</code> prefix.
</td>
</tr>
</table>
# Web UI
## Authentication and Authorization
Enabling authentication for the Web UIs is done using [javax servlet filters](http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html).
You will need a filter that implements the authentication method you want to deploy. Spark does not
provide any built-in authentication filters.
Spark also supports access control to the UI when an authentication filter is present. Each
application can be configured with its own separate access control lists (ACLs). Spark
differentiates between "view" permissions (who is allowed to see the application's UI), and "modify"
permissions (who can do things like kill jobs in a running application).
ACLs can be configured for either users or groups. Configuration entries accept comma-separated
lists as input, meaning multiple users or groups can be given the desired privileges. This can be
used if you run on a shared cluster and have a set of administrators or developers who need to
monitor applications they may not have started themselves. A wildcard (`*`) added to specific ACL
means that all users will have the respective pivilege. By default, only the user submitting the
application is added to the ACLs.
Group membership is established by using a configurable group mapping provider. The mapper is
configured using the <code>spark.user.groups.mapping</code> config option, described in the table
below.
The following options control the authentication of Web UIs:
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.ui.filters</code></td>
<td>None</td>
<td>
See the <a href="configuration.html#spark-ui">Spark UI</a> configuration for how to configure
filters.
</td>
</tr>
<tr>
<td><code>spark.acls.enable</code></td>
<td>false</td>
<td>
Whether UI ACLs should be enabled. If enabled, this checks to see if the user has access
permissions to view or modify the application. Note this requires the user to be authenticated,
so if no authentication filter is installed, this option does not do anything.
</td>
</tr>
<tr>
<td><code>spark.admin.acls</code></td>
<td>None</td>
<td>
Comma-separated list of users that have view and modify access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.admin.acls.groups</code></td>
<td>None</td>
<td>
Comma-separated list of groups that have view and modify access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.modify.acls</code></td>
<td>None</td>
<td>
Comma-separated list of users that have modify access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.modify.acls.groups</code></td>
<td>None</td>
<td>
Comma-separated list of groups that have modify access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.ui.view.acls</code></td>
<td>None</td>
<td>
Comma-separated list of users that have view access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.ui.view.acls.groups</code></td>
<td>None</td>
<td>
Comma-separated list of groups that have view access to the Spark application.
</td>
</tr>
<tr>
<td><code>spark.user.groups.mapping</code></td>
<td><code>org.apache.spark.security.ShellBasedGroupsMappingProvider</code></td>
<td>
The list of groups for a user is determined by a group mapping service defined by the trait
<code>org.apache.spark.security.GroupMappingServiceProvider</code>, which can be configured by
this property.
<br />By default, a Unix shell-based implementation is used, which collects this information
from the host OS.
<br /><em>Note:</em> This implementation supports only Unix/Linux-based environments.
Windows environment is currently <b>not</b> supported. However, a new platform/protocol can
be supported by implementing the trait mentioned above.
</td>
</tr>
</table>
On YARN, the view and modify ACLs are provided to the YARN service when submitting applications, and
control who has the respective privileges via YARN interfaces.
## Spark History Server ACLs
Authentication for the SHS Web UI is enabled the same way as for regular applications, using
servlet filters.
To enable authorization in the SHS, a few extra options are used:
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td>spark.history.ui.acls.enable</td>
<td>false</td>
<td>
Specifies whether ACLs should be checked to authorize users viewing the applications in
the history server. If enabled, access control checks are performed regardless of what the
individual applications had set for <code>spark.ui.acls.enable</code>. The application owner
will always have authorization to view their own application and any users specified via
<code>spark.ui.view.acls</code> and groups specified via <code>spark.ui.view.acls.groups</code>
when the application was run will also have authorization to view that application.
If disabled, no access control checks are made for any application UIs available through
the history server.
</td>
</tr>
<tr>
<td>spark.history.ui.admin.acls</td>
<td>None</td>
<td>
Comma separated list of users that have view access to all the Spark applications in history
server.
</td>
</tr>
<tr>
<td>spark.history.ui.admin.acls.groups</td>
<td>None</td>
<td>
Comma separated list of groups that have view access to all the Spark applications in history
server.
</td>
</tr>
</table>
The SHS uses the same options to configure the group mapping provider as regular applications.
In this case, the group mapping provider will apply to all UIs server by the SHS, and individual
application configurations will be ignored.
## SSL Configuration
Configuration for SSL is organized hierarchically. The user can configure the default SSL settings
which will be used for all the supported communication protocols unless they are overwritten by
protocol-specific settings. This way the user can easily provide the common settings for all the
protocols without disabling the ability to configure each one individually. The common SSL settings
are at `spark.ssl` namespace in Spark configuration. The following table describes the
component-specific configuration namespaces used to override the default settings:
protocols without disabling the ability to configure each one individually. The following table
describes the the SSL configuration namespaces:
<table class="table">
<tr>
<th>Config Namespace</th>
<th>Component</th>
</tr>
<tr>
<td><code>spark.ssl</code></td>
<td>
The default SSL configuration. These values will apply to all namespaces below, unless
explicitly overridden at the namespace level.
</td>
</tr>
<tr>
<td><code>spark.ssl.ui</code></td>
<td>Spark application Web UI</td>
@ -58,49 +347,205 @@ component-specific configuration namespaces used to override the default setting
</tr>
</table>
The full breakdown of available SSL options can be found on the [configuration page](configuration.html).
SSL must be configured on each node and configured for each component involved in communication using the particular protocol.
The full breakdown of available SSL options can be found below. The `${ns}` placeholder should be
replaced with one of the above namespaces.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>${ns}.enabled</code></td>
<td>false</td>
<td>Enables SSL. When enabled, <code>${ns}.ssl.protocol</code> is required.</td>
</tr>
<tr>
<td><code>${ns}.port</code></td>
<td>None</td>
<td>
The port where the SSL service will listen on.
<br />The port must be defined within a specific namespace configuration. The default
namespace is ignored when reading this configuration.
<br />When not set, the SSL port will be derived from the non-SSL port for the
same service. A value of "0" will make the service bind to an ephemeral port.
</td>
</tr>
<tr>
<td><code>${ns}.enabledAlgorithms</code></td>
<td>None</td>
<td>
A comma separated list of ciphers. The specified ciphers must be supported by JVM.
<br />The reference list of protocols can be found in the "JSSE Cipher Suite Names" section
of the Java security guide. The list for Java 8 can be found at
<a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/StandardNames.html#ciphersuites">this</a>
page.
<br />Note: If not set, the default cipher suite for the JRE will be used.
</td>
</tr>
<tr>
<td><code>${ns}.keyPassword</code></td>
<td>None</td>
<td>
The password to the private key in the key store.
</td>
</tr>
<tr>
<td><code>${ns}.keyStore</code></td>
<td>None</td>
<td>
Path to the key store file. The path can be absolute or relative to the directory in which the
process is started.
</td>
</tr>
<tr>
<td><code>${ns}.keyStorePassword</code></td>
<td>None</td>
<td>Password to the key store.</td>
</tr>
<tr>
<td><code>${ns}.keyStoreType</code></td>
<td>JKS</td>
<td>The type of the key store.</td>
</tr>
<tr>
<td><code>${ns}.protocol</code></td>
<td>None</td>
<td>
TLS protocol to use. The protocol must be supported by JVM.
<br />The reference list of protocols can be found in the "Additional JSSE Standard Names"
section of the Java security guide. For Java 8, the list can be found at
<a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/StandardNames.html#jssenames">this</a>
page.
</td>
</tr>
<tr>
<td><code>${ns}.needClientAuth</code></td>
<td>false</td>
<td>Whether to require client authentication.</td>
</tr>
<tr>
<td><code>${ns}.trustStore</code></td>
<td>None</td>
<td>
Path to the trust store file. The path can be absolute or relative to the directory in which
the process is started.
</td>
</tr>
<tr>
<td><code>${ns}.trustStorePassword</code></td>
<td>None</td>
<td>Password for the trust store.</td>
</tr>
<tr>
<td><code>${ns}.trustStoreType</code></td>
<td>JKS</td>
<td>The type of the trust store.</td>
</tr>
</table>
## Preparing the key stores
Key stores can be generated by `keytool` program. The reference documentation for this tool for
Java 8 is [here](https://docs.oracle.com/javase/8/docs/technotes/tools/unix/keytool.html).
The most basic steps to configure the key stores and the trust store for a Spark Standalone
deployment mode is as follows:
* Generate a key pair for each node
* Export the public key of the key pair to a file on each node
* Import all exported public keys into a single trust store
* Distribute the trust store to the cluster nodes
### YARN mode
The key-store can be prepared on the client side and then distributed and used by the executors as the part of the application. It is possible because the user is able to deploy files before the application is started in YARN by using `spark.yarn.dist.files` or `spark.yarn.dist.archives` configuration settings. The responsibility for encryption of transferring these files is on YARN side and has nothing to do with Spark.
For long-running apps like Spark Streaming apps to be able to write to HDFS, it is possible to pass a principal and keytab to `spark-submit` via the `--principal` and `--keytab` parameters respectively. The keytab passed in will be copied over to the machine running the Application Master via the Hadoop Distributed Cache (securely - if YARN is configured with SSL and HDFS encryption is enabled). The Kerberos login will be periodically renewed using this principal and keytab and the delegation tokens required for HDFS will be generated periodically so the application can continue writing to HDFS.
To provide a local trust store or key store file to drivers running in cluster mode, they can be
distributed with the application using the `--files` command line argument (or the equivalent
`spark.files` configuration). The files will be placed on the driver's working directory, so the TLS
configuration should just reference the file name with no absolute path.
Distributing local key stores this way may require the files to be staged in HDFS (or other similar
distributed file system used by the cluster), so it's recommended that the undelying file system be
configured with security in mind (e.g. by enabling authentication and wire encryption).
### Standalone mode
The user needs to provide key-stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in `SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`. In this mode, the user may allow the executors to use the SSL settings inherited from the worker which spawned that executor. It can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. If that parameter is set, the settings provided by user on the client side, are not used by the executors.
The user needs to provide key stores and configuration options for master and workers. They have to
be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in
`SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`.
The user may allow the executors to use the SSL settings inherited from the worker process. That
can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. In that case, the settings
provided by the user on the client side are not used.
### Mesos mode
Mesos 1.3.0 and newer supports `Secrets` primitives as both file-based and environment based secrets. Spark allows the specification of file-based and environment variable based secrets with the `spark.mesos.driver.secret.filenames` and `spark.mesos.driver.secret.envkeys`, respectively. Depending on the secret store backend secrets can be passed by reference or by value with the `spark.mesos.driver.secret.names` and `spark.mesos.driver.secret.values` configuration properties, respectively. Reference type secrets are served by the secret store and referred to by name, for example `/mysecret`. Value type secrets are passed on the command line and translated into their appropriate files or environment variables.
Mesos 1.3.0 and newer supports `Secrets` primitives as both file-based and environment based
secrets. Spark allows the specification of file-based and environment variable based secrets with
`spark.mesos.driver.secret.filenames` and `spark.mesos.driver.secret.envkeys`, respectively.
### Preparing the key-stores
Key-stores can be generated by `keytool` program. The reference documentation for this tool is
[here](https://docs.oracle.com/javase/7/docs/technotes/tools/solaris/keytool.html). The most basic
steps to configure the key-stores and the trust-store for the standalone deployment mode is as
follows:
Depending on the secret store backend secrets can be passed by reference or by value with the
`spark.mesos.driver.secret.names` and `spark.mesos.driver.secret.values` configuration properties,
respectively.
* Generate a keys pair for each node
* Export the public key of the key pair to a file on each node
* Import all exported public keys into a single trust-store
* Distribute the trust-store over the nodes
Reference type secrets are served by the secret store and referred to by name, for example
`/mysecret`. Value type secrets are passed on the command line and translated into their
appropriate files or environment variables.
### Configuring SASL Encryption
## HTTP Security Headers
SASL encryption is currently supported for the block transfer service when authentication
(`spark.authenticate`) is enabled. To enable SASL encryption for an application, set
`spark.authenticate.enableSaslEncryption` to `true` in the application's configuration.
Apache Spark can be configured to include HTTP headers to aid in preventing Cross Site Scripting
(XSS), Cross-Frame Scripting (XFS), MIME-Sniffing, and also to enforce HTTP Strict Transport
Security.
When using an external shuffle service, it's possible to disable unencrypted connections by setting
`spark.network.sasl.serverAlwaysEncrypt` to `true` in the shuffle service's configuration. If that
option is enabled, applications that are not set up to use SASL encryption will fail to connect to
the shuffle service.
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.ui.xXssProtection</code></td>
<td><code>1; mode=block</code></td>
<td>
Value for HTTP X-XSS-Protection response header. You can choose appropriate value
from below:
<ul>
<li><code>0</code> (Disables XSS filtering)</li>
<li><code>1</code> (Enables XSS filtering. If a cross-site scripting attack is detected,
the browser will sanitize the page.)</li>
<li><code>1; mode=block</code> (Enables XSS filtering. The browser will prevent rendering
of the page if an attack is detected.)</li>
</ul>
</td>
</tr>
<tr>
<td><code>spark.ui.xContentTypeOptions.enabled</code></td>
<td><code>true</code></td>
<td>
When enabled, X-Content-Type-Options HTTP response header will be set to "nosniff".
</td>
</tr>
<tr>
<td><code>spark.ui.strictTransportSecurity</code></td>
<td>None</td>
<td>
Value for HTTP Strict Transport Security (HSTS) Response Header. You can choose appropriate
value from below and set <code>expire-time</code> accordingly. This option is only used when
SSL/TLS is enabled.
<ul>
<li><code>max-age=&lt;expire-time&gt;</code></li>
<li><code>max-age=&lt;expire-time&gt;; includeSubDomains</code></li>
<li><code>max-age=&lt;expire-time&gt;; preload</code></li>
</ul>
</td>
</tr>
</table>
## Configuring Ports for Network Security
# Configuring Ports for Network Security
Spark makes heavy use of the network, and some environments have strict requirements for using tight
firewall settings. Below are the primary ports that Spark uses for its communication and how to
configure those ports.
### Standalone mode only
## Standalone mode only
<table class="table">
<tr>
@ -141,7 +586,7 @@ configure those ports.
</tr>
</table>
### All cluster managers
## All cluster managers
<table class="table">
<tr>
@ -182,54 +627,70 @@ configure those ports.
</tr>
</table>
### HTTP Security Headers
Apache Spark can be configured to include HTTP Headers which aids in preventing Cross
Site Scripting (XSS), Cross-Frame Scripting (XFS), MIME-Sniffing and also enforces HTTP
Strict Transport Security.
# Kerberos
Spark supports submitting applications in environments that use Kerberos for authentication.
In most cases, Spark relies on the credentials of the current logged in user when authenticating
to Kerberos-aware services. Such credentials can be obtained by logging in to the configured KDC
with tools like `kinit`.
When talking to Hadoop-based services, Spark needs to obtain delegation tokens so that non-local
processes can authenticate. Spark ships with support for HDFS and other Hadoop file systems, Hive
and HBase.
When using a Hadoop filesystem (such HDFS or WebHDFS), Spark will acquire the relevant tokens
for the service hosting the user's home directory.
An HBase token will be obtained if HBase is in the application's classpath, and the HBase
configuration has Kerberos authentication turned (`hbase.security.authentication=kerberos`).
Similarly, a Hive token will be obtained if Hive is in the classpath, and the configuration includes
URIs for remote metastore services (`hive.metastore.uris` is not empty).
Delegation token support is currently only supported in YARN and Mesos modes. Consult the
deployment-specific page for more information.
The following options provides finer-grained control for this feature:
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.ui.xXssProtection</code></td>
<td><code>1; mode=block</code></td>
<td>
Value for HTTP X-XSS-Protection response header. You can choose appropriate value
from below:
<ul>
<li><code>0</code> (Disables XSS filtering)</li>
<li><code>1</code> (Enables XSS filtering. If a cross-site scripting attack is detected,
the browser will sanitize the page.)</li>
<li><code>1; mode=block</code> (Enables XSS filtering. The browser will prevent rendering
of the page if an attack is detected.)</li>
</ul>
</td>
</tr>
<tr>
<td><code>spark.ui.xContentTypeOptions.enabled</code></td>
<td><code>spark.security.credentials.${service}.enabled</code></td>
<td><code>true</code></td>
<td>
When value is set to "true", X-Content-Type-Options HTTP response header will be set
to "nosniff". Set "false" to disable.
</td>
</tr>
<tr>
<td><code>spark.ui.strictTransportSecurity</code></td>
<td>None</td>
<td>
Value for HTTP Strict Transport Security (HSTS) Response Header. You can choose appropriate
value from below and set <code>expire-time</code> accordingly, when Spark is SSL/TLS enabled.
<ul>
<li><code>max-age=&lt;expire-time&gt;</code></li>
<li><code>max-age=&lt;expire-time&gt;; includeSubDomains</code></li>
<li><code>max-age=&lt;expire-time&gt;; preload</code></li>
</ul>
Controls whether to obtain credentials for services when security is enabled.
By default, credentials for all supported services are retrieved when those services are
configured, but it's possible to disable that behavior if it somehow conflicts with the
application being run.
</td>
</tr>
</table>
See the [configuration page](configuration.html) for more details on the security configuration
parameters, and <a href="{{site.SPARK_GITHUB_URL}}/tree/master/core/src/main/scala/org/apache/spark/SecurityManager.scala">
<code>org.apache.spark.SecurityManager</code></a> for implementation details about security.
## Long-Running Applications
Long-running applications may run into issues if their run time exceeds the maximum delegation
token lifetime configured in services it needs to access.
Spark supports automatically creating new tokens for these applications when running in YARN mode.
Kerberos credentials need to be provided to the Spark application via the `spark-submit` command,
using the `--principal` and `--keytab` parameters.
The provided keytab will be copied over to the machine running the Application Master via the Hadoop
Distributed Cache. For this reason, it's strongly recommended that both YARN and HDFS be secured
with encryption, at least.
The Kerberos login will be periodically renewed using the provided credentials, and new delegation
tokens for supported will be created.
# Event Logging
If your applications are using event logging, the directory where the event logs go
(`spark.eventLog.dir`) should be manually created with proper permissions. To secure the log files,
the directory permissions should be set to `drwxrwxrwxt`. The owner and group of the directory
should correspond to the super user who is running the Spark History Server.
This will allow all users to write to the directory but will prevent unprivileged users from
reading, removing or renaming a file unless they own it. The event log files will be created by
Spark with permissions such that only the user and group have read and write access.