doRedis: A parallel backend for foreach using Redis.

Version 3.0.0

Important changes

Version 3 and greater removed default use of L’Ecuyer RNG (repeatable non-L’Ecuyer RNGs are available by default). See the package vignette for details.

Version 2 and greater now depend on the redux package (see for communication with Redis instead of the deprecated rredis package.

Important Redis configuration notes

Set the following parameter in your redis.conf file before using doRedis:

timeout 0

Exercise caution when using doRedis together with doMC or any fork-based R functions like mclapply. If you require a local inner parallel code section, consider using parLapply and makePSOCKcluster or the related doParallel functions instead of fork-based methods. The fork-based functions can work in some cases, but might also lead to trouble because the children share certain resources with the parent process like open socket descriptors. I have in particular run in to trouble with some fast BLAS libraries and fork–in particular the AMD ACML can’t be used in this way at all. Again, exercise caution with fork and doRedis!


Steve Weston’s foreach package is a remarkable parametric evaluation device for the R language. Similarly to lapply-like functions, foreach maps and parameter values expressions to data and aggregates results. Even better, foreach lets you do this in parallel across multiple CPU cores and computers. And even better yet, foreach abstracts the parallel computing details away into modular back-end code. Code written using foreach works sequentially in the absence of a parallel back-end, and works uniformly across a variety of back ends. Think of foreach as the lingua Franca of parallel computing for R.

Redis is a powerful, fast networked database with many innovative features, among them a blocking stack-like data structure (Redis “lists”). This feature makes Redis useful as a lightweight backend for parallel computing. The doRedis package relies on the redux package for communication with a Redis server to define a lightweight parallel backend for foreach using Redis that is elastic and platform-independent.

Here is a quick example procedure for experimenting with doRedis:

  1. Install Redis on your computer.
  2. Install foreach, redux and doRedis packages.
  3. Start the redis server running (see the redis documentation). We assume that the server is running on the host “localhost” and port 6379 (the default Redis port). We assume in the examples below that the worker R processes and the master are running on the same machine. In practice, they can of course run across a network.
  4. Open one or more R sessions that will act as back-end worker processes. Run the following in each session:

(The R session will display status messages but otherwise block for work.) Note: You can add more workers to a work queue at any time. Also note that each back-end worker may advertise for work on multiple queues simultaneously (see the documentation and examples). 5. Open another R session that will act as the master process. Run the following example (a simple sampling approximation of pi):

   foreach(j=1:10,.combine=sum, .multicombine=TRUE) %dopar%
            4*sum((runif(1000000) ^ 2 + runif(1000000) ^ 2) < 1) / 10000000

Let’s define a few terms before we describe how the above example works:

The “jobs” parameter above in the redisWorker and registerDoRedis function names a Redis key used to transfer data between master and worker processes. Think of this name as a reference to a work queue. The master places tasks into the queue, worker R processes pull tasks out of the queue and then return their results to an associated result queue.

The doRedis parallel backend supports dynamic pools of back-end workers. New workers may be added to work queues at any time and can be immediately used by running foreach computations.

The doRedis backend accepts a parameter called chunkSize that sets the number of loop iterations doled out per task, by default one. Optionally set this with the setChunkSize function. Increasing chunkSize can improve performance for quick-running function evaluations by cutting down on the number of tasks. Here is an example that sets chunkSize to 100:

foreach(j=1:500, .options.redis=list(chunkSize=100)) %dopar%  ...

Setting chunkSize too large will adversely impact load-balancing across the workers. For instance, setting chunkSize to the total number of loop iterations will run everything sequentially on one worker!

The redisWorker function is used to manually invoke worker processes that advertise for job tasks on one or more work queues. The function also has parameters for a Redis host, port number and password. For example, if the Redis server is running on a host called “Cazart” with the default Redis port 6379:

redisWorker('jobs', host='Cazart', port=6379)

The registerDoRedis function also contains host and port parameters. Neither the worker nor master R session needs to be running on the same machine as the Redis server.

The startLocalWorkers function invokes one or more background R worker processes on the local machine (internally using the redisWorker function). It’s a convenient way to invoke several workers at once on your local box.

Workers self-terminate when their work queues have been deleted with the removeQueue function.