vitallabs / clojurecast

Clojurecast Scheduler

The cluster maintains a distributed map of jobs (job-id -> job-state). Job state should be a clojure map that contains only serializable values.

By contract, the cluster will maintain a single core.async go-loop that handles calling the job multi-method 'run' on startup and timeout events and message arrival events. When the cluster migrates data to/from a node due to a node outage or controlled lifecycle (e.g. rolling upgrade), the async loop and topic handler is migrated to a successor node.

NOTE: Due to the use of topics, messages to jobs that arrive during cluster migration will be lost.

Implementation

Each cluster member is assigned a subset of values in the map by the HazelCast partitioner. When values are added to the map, they are 'scheduled' which includes being setup to listen to job topic events and are immediately passed to a core.async go process, the "run loop". When they are removed from the map, they are unscheduled and their associated state and run loop is cleaned up.

The run loop will call the scheduler/run method with an initial state and registered a control handler for updating the scheduler run loop.

Jobs that receive events via the job topic bus have a message handler after which a :resume input is sent to the control loop.

Scheduler Subsystem Terminology

• Scheduler
  • Contains local entry listener ID and the set of local Controllers
• Distributed Job Map (each member owns subset of entries according to partitioning strategy)
• Run Loop - a core.async go-loop that executes the workflow
• Listener - a topic listener for this job to receive messages
• Controller ('ctrl') - a control channel that is used to manage the run loop
• LocalEntryListener
  • Called when member when owned key-value pairs are added/removed
  • Registers a listener to the job's topic
  • Starts the local core.async loop
• MigrationListener
  • Called when partitions are being migrated
  • Used to shut down Run Loop and Listener (if source is still up)
  • Creates new Run Loop and Listener on new cluster

clojurecast's Issues

Support for Hazelcast simulator

Make it easier to build test infrastructure for hazelcast based applications.

Support content-based partitioning

We may need to provide wrappers around more complex key types so we can ensure proper key ordering and lookup with a desired partitioning strategy. e.g. workflow jobs are looked up by JobID but we may want to partition by some attribute of the job (e.g. by user). We need a systematic solution to this.

Finish and document distributed component lifecycle

Some support for LB traffic shaping

We may want to shape traffic based on a HZ partitioner to ensure that data and requests line up most of the time.

Some ideas:

• Ring middleware that supports header-based LB?
• API that supports smarter LB's?
• Need a 'best practices' version of this

Ensure that users of Clojurecast can ignore Hazelcast internals

The normal case of building a simple distributed system should not require a deep understanding of the Hazelcast internals. e.g. we shouldn't need to manually implement listeners for normal use cases - e.g. instead we can generate a reify version via a simple API call. We'll use Switchboard and Blackout as test cases for this.

Provide built-in singleton component support

e.g. our slackbot interface should only run on one node, but should be random which node and component should restart on a new node when nodes fail.

Clojure abstraction for distributed queries against data structures

Simple session store model for ring/aleph users

Make it easy to add session middleware to ring stacks. Maybe add support for token-based auth as well?

Cluster lifecycle events reifed as a manifold stream

Make it easy to get a manifold stream model that returns a stream of cluster/member lifecycle events.

Export a distributed function execution API

Focus on distributed and reliable executors

Transaction support

Provide clojure abstractions for leveraging hazelcast 2-phase transactions