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Distributed Computing

Distributing your computations across multiple workers is a fundamental part of any big data processing. When you build production-scale pipelines, you need to adjust the number of workers and resources that are allocated to each job to optimize throughput.

A Pachyderm worker is an identical Kubernetes pod that runs the Docker image that you specified in the pipeline spec. Your analysis code does not affect how Pachyderm distributes the workload among workers. Instead, Pachyderm spreads out the data that needs to be processed across the various workers and makes that data available for your code.

When you create a pipeline, Pachyderm spins up worker pods that continuously run in the cluster waiting for new data to be available for processing. You can change this behavior by setting "standby" :true. Therefore, you do not need to recreate and schedule workers for every new job.

For each job, all the datums are queued up and then distributed across the available workers. When a worker finishes processing its datum, it grabs a new datum from the queue until all the datums complete processing. If a worker pod crashes, its datums are redistributed to other workers for maximum fault tolerance.

The following animation shows how distributed computing works:

Distributed computing basics

In the diagram above, you have three Pachyderm worker pods that process your data. When a pod finishes processing a datum, it automatically takes another datum from the queue to process it. Datums might be different in size and, therefore, some of them might be processed faster than others.

Each datum goes through the following processing phases inside a Pachyderm worker pod:

Phase Description
Downloading The Pachyderm worker pod downloads the datum contents
into Pachyderm.
Processing The Pachyderm worker pod runs the contents of the datum
against your code.
Uploading The Pachyderm worker pod uploads the results of processing
into an output repository.

When a datum completes a phase, the Pachyderm worker moves it to the next one while another datum from the queue takes its place in the processing sequence.

The following animation displays what happens inside a pod during the datum processing:

Distributed processing internals

You can control the number of worker pods that Pachyderm runs in a pipeline by defining the parallelism parameter in the pipeline specification.


"parallelism_spec": {
   // Exactly one of these two fields should be set
   "constant": int
   "coefficient": double

Pachyderm has the following parallelism strategies that you can set in the pipeline spec:

Strategy Description
constant Pachyderm starts the specified number of workers. For example,
if you set "constant":10, Pachyderm spreads the computation workload among ten workers.
coefficient Pachyderm starts a number of workers that is a multiple of
your Kubernetes cluster size. For example, if your Kubernetes cluster has ten nodes,
and you set "coefficient": 0.5, Pachyderm starts five workers. If you set parallelism to "coefficient": 2.0, Pachyderm starts twenty workers.

By default, Pachyderm sets parallelism to “constant": 1, which means that it spawns one worker per Kubernetes node for this pipeline.

See Also:

Last update: July 16, 2020