gaborgsomogyi commented on a change in pull request #23348: [SPARK-25857][core] Add developer documentation regarding delegation tokens. URL: https://github.com/apache/spark/pull/23348#discussion_r245009244
########## File path: core/src/main/scala/org/apache/spark/deploy/security/README.md ########## @@ -0,0 +1,222 @@ +# Delegation Token Handling In Spark + +This document aims to explain and demistify delegation tokens as they are used by Spark, since +this topic is generally a huge source of confusion. + + +## What are delegation tokens? + +Delegation tokens (DTs from now on) are authentication tokens used by some services to replace +Kerberos service tokens. Many services in the Hadoop ecosystem have support for DTs, since they +have two very desirable advantages over Kerberos tokens: + +* No need to distribute Kerberos credentials + +In a distributed application, distributing Kerberos credentials is tricky. Not all users have +keytabs, and when they do, it's generally frowned upon to distribute them over the network as +part of application data. + +DTs allow for a single place (e.g. the Spark driver) to require Kerberos credentials. That entity +can then distribute the DTs to other parts of the distributed application so they can authenticate +to services. + +* A single token is used for authentication + +If Kerberos authentication were used, each client connection to a server would require a trip +to the KDC and generation of a service ticket. In a distributed system, the number of service +tickets can balloon pretty quickly when you think about the number of client processes (e.g. +Spark executors) vs. the number of service processes (e.g. HDFS DataNodes). That generates +unnecessary extra load on the KDC, and may even run into usage limits set up by the KDC admin. + + +So in short, DTs are *not* Kerberos tokens. They are used to replace Kerberos authentication in +distributed applications, although there is nothing (aside from maybe implementation details) that +ties them to Kerberos. + + +## Lifecycle of DTs + +DTs, unlike Kerberos tokens, are service-specific. There is no centralized location you contact +to create a DT for a service. So, the first step needed to get a DT is being able to authenticate +to the service in question. In the Hadoop ecosystem, that is generally done using Kerberos. + +This requires Kerberos credentials to be available somewhere for the application to use. The user +is generally responsible for providing those credentials, which is most commonly done by logging +in to the KDC (e.g. using "kinit"). That generates a (Kerberos) "token cache" containing a TGT +(ticket granting ticket), which can then be used to request service tickets. + +There are other ways of obtaining TGTs, but, ultimately, you need a TGT to bootstrap the process. + +Once a TGT is available, the target service's client library can then be used to authenticate +to the service using the Kerberos credentials, and request the creation of a delegation token. +This token can now be sent to other processes and used to authenticate to different daemons +belonging to that service. + +And thus the first drawback of DTs becomes apparent: you need service-specific logic to create and +use them. While it would be possible to create a shared API or even a shared service to manage the +creation and use of DTs, that doesn't currently exist, and retrofitting such a system would be a +huge change in a bunch of different services. + +Spark works around this by having a (somewhat) pluggable DT creation API. Support for new +services can be added by implementing a "DT provider" that is then called by Spark when +generating delegation tokens for an application. Spark distributes tokens to executors using +the `UserGroupInformation` Hadoop API, and it's up to the DT provider and the respective +client library to agree on how to use those tokens. + +Once they are created, the semantics of how DTs operate are also service-specific. But, in general, +they try to follow the semantics of Kerberos tokens: + +* A "lifetime" which is for how long the DT is valid before it requires renewal. +* A "renewable life" which is for how long the DT can be renewed. + +Once the token reaches its "renewable life", a new one needs to be created by contacting the +appropriate service, restarting the above process. + + +## DT Renewal, Renewers, and YARN + +This is the most confusing part of DT handling, and part of it is because much of the system was +designed with MapReduce, and later YARN, in mind. + +As seen above, DTs need to be renewed periodically until they finally expire for good. An example +of this is the default configuration of HDFS services: delegation tokens are valid for up to 7 +days, and need to be renewed every 24h. If 24h pass without the token being renewed, the token +cannot be used anymore. And the token cannot be renewed anymore after 7 days. + +This raises the question: who renews tokens? And for a long time the answer was YARN. + +When YARN applications are submitted, a set of DTs is also submitted with them. YARN takes care +of distributing these tokens to containers (using conventions set by the `UserGroupInformation` +API) and, also, keeping them renewed while the app is running. These tokens are used not just +by the application; they are also used by YARN itself to implement features like log collection +and aggregation. + +But this has a few caveats. + + +1. Who renews the tokens? + +This is handled mostly transparently by the Hadoop libraries in the case of YARN. Some services have +the concept of a token "renewer". This "renewer" is the name of the principal that is allowed to +renew the DT. When submitting to YARN, that will be the principal that the YARN service is running +as. Which means that the client application needs to know that information. + +For other resource managers, the renewer mostly does not matter, since there is no service that +is doing the renewal. Except that it sometimes leaks into library code, such as in SPARK-20328. + + +2. What tokens are renewed? + +This is probably the biggest caveat. + +As discussed in the previous section, DTs are service-specific, and require service-specific +libraries for creation *and* renewal. This means that for YARN to be able to renew application +tokens, YARN needs: + +* The client libraries for all the services the application is using +* Information about how to connect to the services the application is using +* Permissions to connect to those services + +In reality, though, most of the time YARN has access to a single HDFS cluster, and that will be +the extent of its DT renewal features. Any other tokens sent to YARN will be distributed to +containers, but will not be renewed. + +This means that those tokens will expire way before their max lifetime, unless some other code +takes care of renewing them. + +And, to make matters worse, not all client libraries even implement token renewal. To use the +example of a service supported by Spark, the `renew()` method of HBase tokens is a no-op. So +the only way to "renew" an HBase token is to create a new one. + + +3. What happens when tokens expire for good? + +The final caveat is that DTs have a maximum life, regardless of renewal. And after that deadline +is met, you need to create new tokens to be able to connect to the services. Which means you need +the ability to connect to the service without a delegation token, which means you need Kerberos +credentials. + +This is especially important for long-running applications that run unsupervised. They must be +able to keep on going without having someone logging into a terminal and typing a password every +few days. + + +## DT Renewal in Spark + +Because of the issues explained above, Spark implements a different way of doing renewal. Spark's +solution is a compromise: it targets the lowest common denominator, which is services like HBase +that do not support actual token renewal. + +In Spark, DT "renewal" is enabled by giving the application a Kerberos keytab. A keytab is +basically your Kerberos password written into a plain text file, which is why it's so sensitive. +By having the keytab, Spark can indefinitely maintain a valid Kerberos TGT. + +With Kerberos credentials available, Spark will create new DTs for the configured services as old +ones expire. So Spark doesn't renew tokens as explained in the previous section: it will create new +tokens at every renewal interval instead, and distribute those tokens to executors. + +This also has another advantage on top of supporting services like HBase: it removes the dependency +on an external renewal service (like YARN). That way, Spark's renewal feature can be used with +resource managers that are not DT-aware, such as Mesos or Kubernetes, as long as the application +has access to a keytab. + + +## DTs and Proxy Users + +"Proxy users" is Hadoop-speak for impersonation. It allows user A to impersonate user B when +connecting to a service, if that service allows it. + +Spark allows impersonation when submitting applications, so that the whole application runs as +user B in the above example. + +Spark does not allow token renewal when impersonation is on. Impersonation was added in Spark +as a means for services (like Hive or Oozie) to start Spark applications on behalf of users. +That means that those services would provide the Spark launcher code with privileged credentials, +to submit applications and, potentially, user code to run as a regular user. + +In that situation, the service credentials should never be made available to the Spark application, +since that would be tantamount to giving your service credentials to unprivileged users. + +The above also implies that running impersonated applications in client mode can be a security +concern, since arbitrary user code would have access to the same local content as the privileged +user. But unlike token renewal, Spark does not prevent that configuration from running. + +When impersonating, the Spark launcher will create DTs for the "proxy" user. In the example +used above, that means that when code authenticates to a service using the DTs, the authenticated +user will be "B", not "A". + +Note that "proxy user" is a very Hadoop-specific concept. It does not apply to OS users (which +is why the client-mode case is an issue) and to services that do not authenticate using Hadoop's +`UserGroupInformation` system. It is generally used in the context of YARN - since an application +submitted as a proxy user will run as that particular user in the YARN cluster, obeying any +Hadoop-to-local-OS-user mapping configured for the service. But the overall support should work +for connecting to other services even when YARN is not being used. + + +## Limitations of DT support in Spark + +There are certain limitations to bear in mind when talking about DTs in Spark. + +The first one is that not all DTs actually expose their renewal period. This is generally a +service configuration that is not generally exposed to clients. For this reason, certain DT Review comment: `For this reason, certain DT providers cannot provide a renewal period to the Spark code` Just for the understanding are these HBase + Hive, right? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
