rrq_controller | R Documentation |
Create a new controller. This is the new interface that will
replace rrq_controller soon, at which point it will rename back
to rrq_controller
.
rrq_controller(
queue_id,
con = redux::hiredis(),
timeout_task_wait = NULL,
follow = NULL,
check_version = TRUE,
offload_path = NULL,
offload_threshold_size = Inf
)
queue_id |
An identifier for the queue. This will prefix all
keys in redis, so a prefix might be useful here depending on
your use case (e.g. |
con |
A redis connection. The default tries to create a redis
connection using default ports, or environment variables set as in
|
timeout_task_wait |
An optional default timeout to use when
waiting for tasks with rrq_task_wait. If not given, then we
fall back on the global option |
follow |
An optional default logical to use for tasks
that may (or may not) be retried. If not given we fall back
on the global option |
check_version |
Logical, indicating if we should check the
schema version. You can pass |
offload_path |
The path to create an offload store at (passed
to |
offload_threshold_size |
The maximum object size, in bytes,
before being moved to the offload store. If given, then larger
data will be saved in |
An rrq_controller
object, which is opaque.
A task is queued with $enqueue()
, at which point it becomes PENDING
Once a worker selects the task to run, it becomes RUNNING
If the task completes successfully without error it becomes COMPLETE
If the task throws an error, it becomes ERROR
If the task was cancelled (e.g., via $task_cancel()
) it becomes
CANCELLED
If the task is killed by an external process, crashes or the worker
dies (and is running a heartbeat) then the task becomes DIED
.
The status of an unknown task is MISSING
Tasks in any terminal state (except IMPOSSIBLE
) may be retried
with task_retry
at which point they become MOVED
, see
vignette("fault-tolerance")
for details
A worker appears and is IDLE
When running a task it is BUSY
If it receives a PAUSE
message it becomes PAUSED
until it
receives a RESUME
message
If it exits cleanly (e.g., via a STOP
message or a timeout) it
becomes EXITED
If it crashes and was running a heartbeat, it becomes LOST
Most of the time workers process tasks, but you can also send them "messages". Messages take priority over tasks, so if a worker becomes idle (by coming online or by finishing a task) it will consume all available messages before starting on a new task, even if both are available.
Each message has a "command" and may have "arguments" to that command. The supported messages are:
PING
(no args): "ping" the worker, if alive it will respond
with "PONG"
ECHO
(accepts an argument of a string): Print a string to the
terminal and log of the worker. Will respond with OK
once the
message has been printed.
EVAL
(accepts a string or a quoted expression): Evaluate an
arbitrary R expression on the worker. Responds with the value of
this expression.
STOP
(accepts a string to print as the worker exits, defaults
to "BYE"): Tells the worker to stop.
INFO
(no args): Returns information about the worker (versions
of packages, hostname, pid, etc).
PAUSE
(no args): Tells the worker to stop accepting tasks
(until it receives a RESUME
message). Messages are processed
as normal.
RESUME
(no args): Tells a paused worker to resume accepting
tasks.
REFRESH
(no args): Tells the worker to rebuild their
environment with the create
method.
TIMEOUT_SET
(accepts a number, representing seconds): Updates
the worker timeout - the length of time after which it will exit
if it has not processed a task.
TIMEOUT_GET
(no args): Tells the worker to respond with its
current timeout.
Every time a task is saved, or a task is completed, results are
saved into the Redis database. Because Redis is an in-memory
database, it's not a great idea to save very large objects into
it (if you ran 100 jobs in parallel and each saved a 2GB object
you'd likely take down your redis server). In addition, redux
does not support directly saving objects larger than 2^31 - 1
bytes into Redis. So, for some use cases we need to consider
where to store larger objects.
The strategy here is to "offload" the larger objects - bigger than
some user-given size - onto some other storage system. Currently
the only alternative supported is a disk store
(object_store_offload_disk
) but we hope to expand this
later. So if your task returns a 3GB object then we will spill
that to disk rather than failing to save that into
Redis.
The storage directory for offloading must be shared amoung all users of the queue. Depending on the usecase, this could be a directory on the local filesystems or, if using a queue across machines, it can be a network file system mounted on all machines.
How big is an object? We serialise the object
(redux::object_to_bin
just wraps serialize
) which creates
a vector of bytes and that is saved into the database. To get an
idea of how large things are you can do:
length(redux::object_to_bin(your_object))
. At the time this
documentation was written, mtcars
was 3807
bytes, and a
million random numbers was 8,000,031
bytes. It's unlikely that
a offload_threshold_size
of less than 1MB will be sensible.
# Create a new controller; the id will be specific to your
# application. Here, we use 'rrq:example'
obj <- rrq_controller("rrq:example")
# Create a task for this controller to work on:
t <- rrq_task_create_expr(runif(10), controller = obj)
# Wait for the task to complete
rrq_task_wait(t, controller = obj)
# Fetch the task's result
rrq_task_result(t, controller = obj)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.