package rpc_parallel

  1. Overview
  2. Docs
Module type
Class type
  • deprecated [since 2016-11] Use [Rpc_parallel] instead of [Rpc_parallel.Parallel]
module Function : sig ... end
module type Worker = sig ... end
module type Functions = sig ... end
module type Creator = sig ... end
module type Worker_spec = sig ... end
val start_app : ?rpc_max_message_size:int -> ?rpc_handshake_timeout:Core.Time.Span.t -> ?rpc_heartbeat_config:Async.Rpc.Connection.Heartbeat_config.t -> Async.Command.t -> unit

start_app command should be called from the top-level in order to start the parallel application. This function will parse certain environment variables and determine whether to start as a master or a worker.

rpc_max_message_size, rpc_handshake_timeout, rpc_heartbeat_config can be used to alter the rpc defaults. These rpc settings will be used for all connections. This can be useful if you have long async jobs.

module State : sig ... end

Use State.get to query whether the current process has been initialized as an rpc parallel master (start_app or init_master_exn has been called). We return a State.t rather than a bool so that you can require evidence at the type level. If you want to certify, as a precondition, for some function that start_app was used, require a State.t as an argument. If you don't need the State.t anymore, just pattern match on it.

module Expert : sig ... end

If you want more direct control over your executable, you can use the Expert module instead of start_app. If you use Expert, you are responsible for starting the master and worker rpc servers. worker_command_args will be the arguments sent to each spawned worker. Running your executable with these args must follow a code path that calls worker_init_before_async_exn and then start_worker_server_exn. An easy way to do this is to use worker_command.