High Availability
By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.
Standby Masters with ZooKeeper
Overview
Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected “leader” and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master’s state, and then resume scheduling. The entire recovery process (from the time the the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling new applications – applications that were already running during Master failover are unaffected.
Learn more about getting started with ZooKeeper here.
Configuration
In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:
System property Meaning
spark.deploy.recoveryMode Set to ZOOKEEPER to enable standby Master recovery mode (default: NONE).
spark.deploy.zookeeper.url The ZooKeeper cluster url (e.g., 192.168.1.100:2181,192.168.1.101:2181).
spark.deploy.zookeeper.dir The directory in ZooKeeper to store recovery state (default: /spark).
Possible gotcha: If you have multiple Masters in your cluster but fail to correctly configure the Masters to use ZooKeeper, the Masters will fail to discover each other and think they’re all leaders. This will not lead to a healthy cluster state (as all Masters will schedule independently).
Details
After you have a ZooKeeper cluster set up, enabling high availability is straightforward. Simply start multiple Master processes on different nodes with the same ZooKeeper configuration (ZooKeeper URL and directory). Masters can be added and removed at any time.
In order to schedule new applications or add Workers to the cluster, they need to know the IP address of the current leader. This can be accomplished by simply passing in a list of Masters where you used to pass in a single one. For example, you might start your SparkContext pointing to spark://host1:port1,host2:port2. This would cause your SparkContext to try registering with both Masters – if host1 goes down, this configuration would still be correct as we’d find the new leader, host2.
There’s an important distinction to be made between “registering with a Master” and normal operation. When starting up, an application or Worker needs to be able to find and register with the current lead Master. Once it successfully registers, though, it is “in the system” (i.e., stored in ZooKeeper). If failover occurs, the new leader will contact all previously registered applications and Workers to inform them of the change in leadership, so they need not even have known of the existence of the new Master at startup.
Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that new applications and Workers can find it to register with in case it becomes the leader. Once registered, you’re taken care of.
Single-Node Recovery with Local File System
Overview
ZooKeeper is the best way to go for production-level high availability, but if you just want to be able to restart the Master if it goes down, FILESYSTEM mode can take care of it. When applications and Workers register, they have enough state written to the provided directory so that they can be recovered upon a restart of the Master process.
Configuration
In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:
System property Meaning
spark.deploy.recoveryMode Set to FILESYSTEM to enable single-node recovery mode (default: NONE).
spark.deploy.recoveryDirectory The directory in which Spark will store recovery state, accessible from the Master’s perspective.
Details
This solution can be used in tandem with a process monitor/manager like monit, or just to enable manual recovery via restart.
While filesystem recovery seems straightforwardly better than not doing any recovery at all, this mode may be suboptimal for certain development or experimental purposes. In particular, killing a master via stop-master.sh does not clean up its recovery state, so whenever you start a new Master, it will enter recovery mode. This could increase the startup time by up to 1 minute if it needs to wait for all previously-registered Workers/clients to timeout.
While it’s not officially supported, you could mount an NFS directory as the recovery directory. If the original Master node dies completely, you could then start a Master on a different node, which would correctly recover all previously registered Workers/applications (equivalent to ZooKeeper recovery). Future applications will have to be able to find the new Master, however, in order to register.
摘自官网，但是国内应用环境一般都应用到yarn上，所以可以自己研究使用。