HDFS HA

High availability is a characteristic of a system, which aims to ensures an agreed level of operational performance for a higher than normal period. There are three principles of system design in high availability engineering:

• Elimination of single points of failure. This means adding redundancy to the system so that failure of a component does not mean failure of the entire system.
• Reliable crossover. In multithreaded systems, the crossover point itself tends to become a single point of failure. High availability engineering must provide for reliable crossover.
• Detection of failures as they occur. If the two principles above are observed, then a user may never see a failure. But the maintenance activity must.

The most common size for an HA cluster is a two-node cluster, since that is the minimum required to provide redundancy, but many clusters consist of many more, sometimes dozens of nodes. Such configurations can sometimes be categorized into one of the following models:

• Active/active — Traffic intended for the failed node is either passed onto an existing node or load balanced across the remaining nodes. This is usually only possible when the nodes use a homogeneous software configuration.
• Active/passive — Provides a fully redundant instance of each node, which is only brought online when its associated primary node fails.[1] This configuration typically requires the most extra hardware.
• N+1 — Provides a single extra node that is brought online to take over the role of the node that has failed. In the case of heterogeneous software configuration on each primary node, the extra node must be universally capable of assuming any of the roles of the primary nodes it is responsible for. This normally refers to clusters that have multiple services running simultaneously; in the single service case, this degenerates to active/passive.
• N+M — In cases where a single cluster is managing many services, having only one dedicated failover node might not offer sufficient redundancy. In such cases, more than one (M) standby servers are included and available. The number of standby servers is a tradeoff between cost and reliability requirements.
• N-to-1 — Allows the failover standby node to become the active one temporarily, until the original node can be restored or brought back online, at which point the services or instances must be failed-back to it in order to restore high availability.
• N-to-N — A combination of active/active and N+M clusters, N to N clusters redistribute the services, instances or connections from the failed node among the remaining active nodes, thus eliminating (as with active/active) the need for a ‘standby’ node, but introducing a need for extra capacity on all active nodes.

Prior to Hadoop 2.0.0, the NameNode was a single point of failure (SPOF) in an HDFS cluster. Each cluster had a single NameNode, and if that machine or process became unavailable, the cluster as a whole would be unavailable until the NameNode was either restarted or brought up on a separate machine.

This impacted the total availability of the HDFS cluster in two major ways:

• In the case of an unplanned event such as a machine crash, the cluster would be unavailable until an operator restarted the NameNode.
• Planned maintenance events such as software or hardware upgrades on the NameNode machine would result in windows of cluster downtime.

The HDFS High Availability feature addresses the above problems by providing the option of running two redundant NameNodes in the same cluster in an Active/Passive configuration with a hot standby. This allows a fast failover to a new NameNode in the case that a machine crashes, or a graceful administrator-initiated failover for the purpose of planned maintenance.

Architecture

In a typical HA cluster, two separate machines are configured as NameNodes. At any point in time, exactly one of the NameNodes is in an Active state, and the other is in a Standby state. The Active NameNode is responsible for all client operations in the cluster, while the Standby is simply acting as a slave, maintaining enough state to provide a fast failover if necessary.

In order for the Standby node to keep its state synchronized with the Active node, the current implementation requires that the two nodes both have access to a directory on a shared storage device (eg an NFS mount from a NAS). This restriction will likely be relaxed in future versions.

When any namespace modification is performed by the Active node, it durably logs a record of the modification to an edit log file stored in the shared directory. The Standby node is constantly watching this directory for edits, and as it sees the edits, it applies them to its own namespace. In the event of a failover, the Standby will ensure that it has read all of the edits from the shared storage before promoting itself to the Active state. This ensures that the namespace state is fully synchronized before a failover occurs.

In order to provide a fast failover, it is also necessary that the Standby node have up-to-date information regarding the location of blocks in the cluster. In order to achieve this, the DataNodes are configured with the location of both NameNodes, and send block location information and heartbeats to both.

It is vital for the correct operation of an HA cluster that only one of the NameNodes be Active at a time. Otherwise, the namespace state would quickly diverge between the two, risking data loss or other incorrect results. In order to ensure this property and prevent the so-called “split-brain scenario,” the administrator must configure at least one fencing method for the shared storage. During a failover, if it cannot be verified that the previous Active node has relinquished its Active state, the fencing process is responsible for cutting off the previous Active’s access to the shared edits storage. This prevents it from making any further edits to the namespace, allowing the new Active to safely proceed with failover.

Hardware Resources

In order to deploy an HA cluster, you should prepare the following:

• NameNode machines – the machines on which you run the Active and Standby NameNodes should have equivalent hardware to each other, and equivalent hardware to what would be used in a non-HA cluster.
• Shared storage – you will need to have a shared directory which both NameNode machines can have read/write access to. Typically this is a remote filer which supports NFS and is mounted on each of the NameNode machines. Currently only a single shared edits directory is supported. Thus, the availability of the system is limited by the availability of this shared edits directory, and therefore in order to remove all single points of failure there needs to be redundancy for the shared edits directory. Specifically, multiple network paths to the storage, and redundancy in the storage itself (disk, network, and power). Because of this, it is recommended that the shared storage server be a high-quality dedicated NAS appliance rather than a simple Linux server.

In an HA cluster, the Standby NameNode also performs checkpoints of the namespace state, and thus it is not necessary to run a Secondary NameNode, CheckpointNode, or BackupNode in an HA cluster. In fact, to do so would be an error. This also allows one who is reconfiguring a non-HA-enabled HDFS cluster to be HA-enabled to reuse the hardware which they had previously dedicated to the Secondary NameNode.

HA clusters reuse the nameservice ID to identify a single HDFS instance that may in fact consist of multiple HA NameNodes. In addition, a new abstraction called NameNode ID is added with HA. Each distinct NameNode in the cluster has a different NameNode ID to distinguish it. To support a single configuration file for all of the NameNodes, the relevant configuration parameters are suffixed with the nameservice ID as well as the NameNode ID.

Automatic Failover

Automatic failover adds two new components to an HDFS deployment: a ZooKeeper quorum, and the ZKFailoverController process (abbreviated as ZKFC).

Apache ZooKeeper is a highly available service for maintaining small amounts of coordination data, notifying clients of changes in that data, and monitoring clients for failures. The implementation of automatic HDFS failover relies on ZooKeeper for the following things:

• Failure detection – each of the NameNode machines in the cluster maintains a persistent session in ZooKeeper. If the machine crashes, the ZooKeeper session will expire, notifying the other NameNode that a failover should be triggered.
• Active NameNode election – ZooKeeper provides a simple mechanism to exclusively elect a node as active. If the current active NameNode crashes, another node may take a special exclusive lock in ZooKeeper indicating that it should become the next active.

The ZKFailoverController (ZKFC) is a new component which is a ZooKeeper client which also monitors and manages the state of the NameNode. Each of the machines which runs a NameNode also runs a ZKFC, and that ZKFC is responsible for:

• Health monitoring – the ZKFC pings its local NameNode on a periodic basis with a health-check command. So long as the NameNode responds in a timely fashion with a healthy status, the ZKFC considers the node healthy. If the node has crashed, frozen, or otherwise entered an unhealthy state, the health monitor will mark it as unhealthy.
• ZooKeeper session management – when the local NameNode is healthy, the ZKFC holds a session open in ZooKeeper. If the local NameNode is active, it also holds a special “lock” znode. This lock uses ZooKeeper’s support for “ephemeral” nodes; if the session expires, the lock node will be automatically deleted.
• ZooKeeper-based election – if the local NameNode is healthy, and the ZKFC sees that no other node currently holds the lock znode, it will itself try to acquire the lock. If it succeeds, then it has “won the election”, and is responsible for running a failover to make its local NameNode active. The failover process is similar to the manual failover described above: first, the previous active is fenced if necessary, and then the local NameNode transitions to active state.