The error message indicates that the Dataflow worker instance must have at least 1740 MB of memory, but the f1-micro machine type only has 614 MB of memory.
To fix the error, you need to use a machine type with at least 1740 MB of memory. The default machine type for Dataflow jobs is n1-standard-1, which has 3.75 GB of memory. You can also consider other machine types, such as n1-standard-2 (7.5 GB of memory) or n1-standard-4 (15 GB of memory).
To change the machine type for your Dataflow job, use the --workerMachineType option when submitting your job. For example, to use the n1-standard-1 machine type:
gcloud dataflow jobs run my_job --workerMachineType=n1-standard-1
In Python, you can specify the machine type in your Dataflow pipeline code:
from apache_beam.options.pipeline_options import PipelineOptions
pipeline = beam.Pipeline(options=PipelineOptions(worker_machine_type="n1-standard-1"))
Once you've adjusted the machine type for your Dataflow job, you should be able to submit it successfully.
Additional tips for selecting the right machine type:
- Data size and complexity: If you're processing a large dataset or running a complex pipeline, you may need to use a machine type with more memory.
- Worker count: Dataflow automatically scales the number of workers as needed. However, for a large number of workers, a machine type with more memory might be beneficial.
- Budget: Dataflow charges based on the amount of CPU and memory that your job uses. If you're on a tight budget, you may want to use a smaller machine type.
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