Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/33#discussion_r10141220
--- Diff: core/src/main/scala/org/apache/spark/SecurityManager.scala ---
@@ -0,0 +1,259 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.
+ */
+
+package org.apache.spark
+
+import java.net.{Authenticator, PasswordAuthentication}
+import org.apache.hadoop.io.Text
+import org.apache.hadoop.security.Credentials
+import org.apache.hadoop.security.UserGroupInformation
+import org.apache.spark.deploy.SparkHadoopUtil
+
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * Spark class responsible for security.
+ *
+ * In general this class should be instantiated by the SparkEnv and most
components
+ * 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.ui.acls.enable' and
'spark.ui.view.acls'
+ * control the behavior of the acls. Note that the person who started the
application
+ * always has view access to the UI.
+ *
+ * Spark does not currently support encryption after authentication.
+ *
+ * At this point spark has multiple communication protocols that need to
be secured and
+ * different underlying mechisms are used depending on the protocol:
+ *
+ * - Akka -> The only option here is to use the Akka Remote secure-cookie
functionality.
+ * Akka remoting allows you to specify a secure cookie that
will be exchanged
+ * and ensured to be identical in the connection handshake
between the client
+ * and the server. If they are not identical then the client
will be refused
+ * to connect to the server. There is no control of the
underlying
+ * authentication mechanism so its not clear if the password is
passed in
+ * plaintext or uses DIGEST-MD5 or some other mechanism.
+ * Akka also has an option to turn on SSL, this option is not
currently supported
+ * but we could add a configuration option in the future.
+ *
+ * - HTTP for broadcast and file server (via HttpServer) -> Spark
currently uses Jetty
+ * for the HttpServer. Jetty supports multiple authentication
mechanisms -
+ * Basic, Digest, Form, Spengo, 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 do not support SSL (https), but Jetty can be
configured to use it
+ * so we could add a configuration option for this in the
future.
+ *
+ * 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.
+ *
+ * - ConnectionManager -> The Spark ConnectionManager 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 is not supporting integrity or privacy
protection (encryption)
+ * after authentication. SASL also supports "auth-int" and
"auth-conf" which
+ * SPARK could be 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 connectionManager 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 is 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.
+ * If its acting as a client and trying to send a message to
another ConnectionManager,
+ * it blocks the thread calling sendMessage until the SASL
negotiation has occurred.
+ * The ConnectionManager tracks all the sendingConnections
using the ConnectionId
+ * and waits for the response from the server and does the
handshake.
+ *
+ * - 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 a
+ * companies 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, encypryption, or compression.
+ *
+ * The exact mechanisms used to generate/distributed the shared secret is
deployment specific.
+ *
+ * For Yarn deployments, the secret is automatically generated using the
Akka remote
+ * Crypt.generateSecureCookie() API. 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 should be specified via
the SPARK_SECRET
+ * environment variable. This isn't ideal but it means only the user who
starts the process
+ * has access to view that variable. Note that Spark does try to generate
a secret for
+ * you if the SPARK_SECRET environment variable is not set, but it gets
put into the java
+ * system property which can be viewed by other users, so setting the
SPARK_SECRET environment
+ * variable is recommended.
+ * All the nodes (Master and Workers) need to have the same shared secret
+ * and all the applications running need to have that 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 secured 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.
+ */
+private[spark] class SecurityManager extends Logging {
+
+ // key used to store the spark secret in the Hadoop UGI
+ private val sparkSecretLookupKey = "sparkCookie"
+
+ private val authOn = System.getProperty("spark.authenticate",
"false").toBoolean
+ private val uiAclsOn = System.getProperty("spark.ui.acls.enable",
"false").toBoolean
+
+ // always add the current user and SPARK_USER to the viewAcls
+ private val aclUsers =
ArrayBuffer[String](System.getProperty("user.name", ""),
+ Option(System.getenv("SPARK_USER")).getOrElse(""))
+ aclUsers ++= System.getProperty("spark.ui.view.acls", "").split(',')
+ private val viewAcls = aclUsers.map(_.trim()).filter(!_.isEmpty).toSet
+
+ private val secretKey = generateSecretKey()
+ logDebug("is auth enabled = " + authOn + " is uiAcls enabled = " +
uiAclsOn)
+
+ // Set our own authenticator to properly negotiate user/password for
HTTP connections.
+ // This is needed by the HTTP client fetching from the HttpServer. Put
here so its
+ // only set once.
+ if (authOn) {
+ Authenticator.setDefault(
+ new Authenticator() {
+ override def getPasswordAuthentication(): PasswordAuthentication =
{
+ var passAuth: PasswordAuthentication = null
+ val userInfo = getRequestingURL().getUserInfo()
+ if (userInfo != null) {
+ val parts = userInfo.split(":", 2)
+ passAuth = new PasswordAuthentication(parts(0),
parts(1).toCharArray())
+ }
+ return passAuth
+ }
+ }
+ );
+ }
+
+ /**
+ * Generates or looks up the secret key.
+ *
+ * The way the key is stored depends on the Spark deployment mode. Yarn
+ * uses the Hadoop UGI.
+ *
+ * For non-Yarn deployments, If the environment variable is not set
already
+ * we generate a secret and since we can't set an environment variable
dynamically
+ * we set the java system property SPARK_SECRET. This will allow it to
automatically
+ * work in certain situations. Others this still will not work and this
definitely is
+ * not ideal since other users can see it. We should switch to put it in
+ * a config once Spark supports configs.
+ */
+ private def generateSecretKey(): String = {
+ if (!isAuthenticationEnabled) return null
+ // first check to see if the secret is already set, else generate a
new one
+ if (SparkHadoopUtil.get.isYarnMode) {
+ val secretKey =
SparkHadoopUtil.get.getSecretKeyFromUserCredentials(sparkSecretLookupKey)
+ if (secretKey != null) {
+ logDebug("in yarn mode, getting secret from credentials")
+ return new Text(secretKey).toString
+ } else {
+ logDebug("getSecretKey: yarn mode, secret key from credentials is
null")
+ }
+ }
+ val secret = System.getProperty("SPARK_SECRET",
System.getenv("SPARK_SECRET"))
+ if (secret != null && !secret.isEmpty()) return secret
+ // generate one
+ val sCookie = akka.util.Crypt.generateSecureCookie
+
+ // if we generated the secret then we must be the first so lets set it
so t
+ // gets used by everyone else
+ if (SparkHadoopUtil.get.isYarnMode) {
+
SparkHadoopUtil.get.addSecretKeyToUserCredentials(sparkSecretLookupKey, sCookie)
+ logDebug("adding secret to credentials yarn mode")
--- End diff --
This might be worth logging at INFO level since it happens at most once per
application.
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