Workers and Parallelization#

Execution Model#

A worker is a thread that is helping execute your dataflow. Workers can be grouped into separate processes, but refer to the individual threads within. A cluster is a set of processes that have been configured to collaborate on running a dataflow.

Bytewax’s execution model uses identical workers. Workers execute all steps (including input and output) in a dataflow and automatically trade data to ensure the semantics of the operators. If a dataflow is run on multiple processes, there will be a slight overhead due to pickling and network communication whenever items must be moved between workers, but it will allow you to paralellize some work for higher throughput. See Stateful Operators and the redistribute operator for more information.

Run Script#

Specifying the Dataflow#

The first argument passed to the script is a dataflow getter string. The string is in the format <dataflow-module>[:<dataflow-getter>].

  • <dataflow-module> points to the Python module containing the dataflow.

  • <dataflow-getter> is either the name of a Python variable with a Dataflow instance, or a function call to a function defined in the module. If missing, this defaults to looking for the variable named flow.

$ python -m examples.simple

For example, if you are at the root of this repository, you can run the “” example by calling the script with the following argument:

$ python -m examples.simple:flow

If instead of a variable, you have a function that returns a dataflow, you can use a string after the : to call the function, possibly with args:

$ python -m "my_dataflow:get_flow('/tmp/file')"

By default this script will run a single worker on a single process.

Single Worker Run#

By default will run your dataflow on a single worker in the current process. This avoids the overhead of setting up communication between workers/processes, but the dataflow will not have any gain from parallelization.

$ python -m examples.simple

Single Process Cluster#

By changing the -w/--workers-per-process argument, you can spawn multiple workers within a single process.

For example you can run the previous dataflow with 3 workers by changing only the command:

$ python -m -w3 examples.simple

Multi-Process Cluster#

If you want to run multiple processes on a single machine, or different machines on the same network, you can use the -i/--process-id,-a/--addresses parameters.

Each invocation of with -i starts up a single process. By executing this command multiple times, you can create a cluster of Bytewax processes on one machine or multiple machines. We recommend you checkout the documentation for waxctl our command line tool which facilitates running a multiple dataflow processes locally, or on Kubernetes.

The -a/--addresses parameter represents a list of addresses for all the processes, separated by a ‘;’. When you run single processes separately, you need to assign a unique id to each process. The -i/--process-id should be a number starting from 0 representing the position of its respective address in the list passed to -a.

For example you want to run 2 processes, with 3 workers each, on two different machines. The machines are known in the network as cluster_one and cluster_two. You should run the first process on cluster_one as follows:

$ python -m simple:flow -w3 -i0 -a "cluster_one:2101;cluster_two:2101"

And on the cluster_two machine as:

$ python -m simple:flow -w3 -i1 -a "cluster_one:2101;cluster_two:2101"
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