DEPRECATED: An Apache Mesos framework for Riak KV. Do not run in production.
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README.md

Build Status

Riak Mesos Framework [in beta]

An Apache Mesos framework for Riak KV, a distributed NoSQL key-value data store that offers high availability, fault tolerance, operational simplicity, and scalability.

Note: This project is an early proof of concept. The code is a beta release and there may be bugs, incomplete features, incorrect documentation or other discrepancies.

Installation

Please refer to the documentation in riak-mesos-tools for information about installation and usage of the Riak Mesos Framework.

Build

For build and testing information, visit docs/DEVELOPMENT.md.

Architecture

The Riak Mesos Framework is typically deployed as a marathon app via a CLI tool such as riak-mesos or dcos riak. Once deployed, it can accept commands which result in the creation of Riak nodes as additional tasks on other Mesos agents.

Architecture

Scheduler

The Riak Mesos Framework scheduler is currently written in Golang due to mesos-go's usage of HTTP API calls accessible in the Mesos Master. The alternative language bindings mostly rely on libmesos.so which is more difficult to debug and work with in general.

Resourcing

The scheduler will attempt to spread Riak nodes across as many different mesos agents as possible to increase fault tolerance. If there are more nodes requested than there are agents available, the scheduler will then start adding more Riak nodes to existing agents. Following is a flowchart describing the basic logic followed by the scheduler to reserve resources, create persistent volumes, launch Riak nodes, and handle status updates for those nodes:

Flow Chart

Executor

The executor manages a few processes including the Riak process itself and an EPMD replacement, and the Riak node process itself. We chose to have the executor run natively on the host machine using the Mesos containerizer in order to avoid usage of Docker due to concerns about its stability in certain Mesos environments. This creates a slightly more complicated build process since Erlang packages need to be built per platform, but it increases the reliability of the Mesos tasks.

Inter-node Communication

In normal environments, distributed erlang applications communicate with each other by attempting to connect on EPMD's default port, which then communicates the necessary connection information between the two applications. In a Mesos environment however, it is not always possible to assume that a port (such as EPMD's) will be available for binding, so we wrote a replacement called cEPMD to deal with this issue. cEPMD listens on a random port available on the Mesos agent, and coordinates which ports each node can talk on by storing that information in Zookeeper.

Fault Tolerance

What happens when the Riak Mesos Scheduler fails?

In order to survive a scheduler failure without affecting a running set of tasks (Riak nodes in this case), the scheduler needs to implement a few things. The DCOS Service Specification is a great place to start when trying to find ways to make a scheduler more fault tolerant.

The Riak Mesos Scheduler does the following to deal with potential failures:

  • Registers a failover timeout: Doing this allows the scheduler to reconnect to the Mesos master within a certain amount of time.
  • Persists the Framework ID for failover: The scheduler persists the Framework ID to Zookeeper so that it can reregister itself with the Mesos Master, allowing access to tasks previously launched.
  • Persists task IDs for failover: The scheduler stores metadata about each of the tasks (Riak nodes) that it launches including the task ID in Zookeeper.
  • Reconciles tasks upon failover: In the event of a scheduler failover, the scheduler reconciles each of the task IDs which were previously persisted to Zookeeper so that the most up to date task status can be provided by Mesos.

With the above features implemented, the workflow for the scheduler startup process looks like this (with or without a failure):

  1. Check Zookeeper to see if a Framework ID has been persisted for this named instance of the Riak Mesos Scheduler.
    1. If it has, attempt to reregister the same Framework ID with the Mesos master
    2. If it has not, perform a normal Framework registration, and then persist the assigned Framework ID in Zookeeper for future failover.
  2. Check Zookeeper to see if any nodes have already been launched for this instance of the framework.
    1. If some tasks exist and had previously been launched, perform task reconciliation on each of the task IDs previously persisted to Zookeeper, and react to the status updates once the Mesos master sends them
    2. If there are no tasks from previous runs, reconciliation can be skipped
  3. At this point, the current state of each running task should be known, and the scheduler can continue normal operation by responding to resource offers and API commands from users.

What happens when a Riak Mesos Executor or Riak node fails?

The executor also needs to employ some features for fault tolerance, much like the scheduler. The Riak Mesos Executor does the following:

  • Enables checkpointing: Checkpointing tells Mesos to persist status updates for tasks to disk, allowing those tasks to reconnect to the mesos agent for certain failure modes.
  • Uses resource reservations: Performing a RESERVE operation before launching tasks allows a task to be launched on the same Mesos agent again after a failure without the possibility of another framework taking those resources before a failover can take place.
  • Uses persistent volumes: Performing a CREATE opertaion before launching tasks (and after a RESERVE operation) instructs Mesos agents to create a volume for stateful data (such as the Riak data directory) which exists outside of the tasks container (which is normally deleted with garbage collection if a task fails).

Given those features, the following is what a node launch workflow looks like (from the point of view of the scheduler):

  1. Receive a createNode operation from the API (user initiated).
  2. Wait for resources from the Mesos master.
  3. Check offers to ensure that there is enough capacity on the Mesos agent for the Riak node.
  4. Perform a RESERVE operation to reserve the required resources on the Mesos agent.
  5. Perform a CREATE operation to create a persistent volume on the Mesos agent for the Riak data.
  6. Perform a LAUNCH operation to launch the Riak Mesos Executor / Riak node on the given Mesos agent.
  7. Wait for the executor to send a TASK_RUNNING update back to the scheduler through Mesos.
    1. If the Riak node is already part of a cluster, the node is now successfully running.
    2. If the Riak node is not part of a cluster, and there are other nodes in the named cluster, attempt to perform a cluster join from the new node to existing nodes.

The node failure workflow looks like the following:

  1. Scheduler receives a failure status update such as a lost executor, or a task status update such as TASK_ERROR, TASK_LOST, TASK_FAILED, or TASK_KILLED.
  2. Determine whether or not the task failure was intentional (such as a node restart, or removal of the node)
    1. If the failure was intentional, remove the node from the Riak cluster, and continue normal operation.
    2. If the failure was unintentional, attempt to relaunch the task with a LAUNCH operation on the same Mesos agent and the same persistence id by looking up metadata for that Riak node previously stored in Zookeeper.
  3. If the executor is relaunched, the executor and Riak software will be redeployed to the same Mesos agent. The Riak software will get extracted (from a tarball) into the already existing persistent volume.
    1. If there is already Riak data in the persistent volume, the extraction will overwrite all of the old Riak software files while preserving the Riak data directory from the previous instance of Riak. This mechanism also allows us to perform upgrades for the version of Riak desired.
    2. The executor will then ask the scheduler for the current riak.conf and advanced.config templates which could have been updated via the scheduler's API.
    3. The executor can then attempt to start the Riak node, and send a TASK_RUNNING upate to the scheduler through Mesos. The scheduler can then perform cluster logic operations as described above.

Director

Due to the nature of Apache Mesos and the potential for Riak nodes to come and go on a regular basis, client applications using a Mesos based cluster must be kept up to date on the cluster's current state. Instead of requiring this intelligence to be built into Riak client libraries, a smart proxy application named Director has been created which can run alongside client applications.

Director

For more information related to the Riak Mesos Director, please read docs/DIRECTOR.md